Research summaries

Publication summaries &
key findings

Every publication selected for this site — journal articles, conference papers, book chapters and edited books — is summarised below in the same structure: the problem it addresses, the methodology, the core finding (or, for an edited book, its core contribution), why it matters, and the exact APA citation. Short syntheses of each research area are in the research focus areas. Each also has its own page with a citable summary, its key findings (for an edited book, what it covers), an at-a-glance table of methods and data, the full abstract or publisher description, practical implications, related publications, and the citation in five formats. Where the source gives them, the page adds definitions, limitations and open questions. Papers marked open access are free to read in full. This is a curated selection, not a complete bibliography. The evidence overviews below synthesise each main theme against the wider literature. The same works are listed as plain references in all selected publications, and as BibTeX, RIS and CSL-JSON.

Research synthesis
Machine learning & data science
Research synthesis
Cybersecurity
Research synthesis
Healthcare informatics
Research synthesis
ESG & sustainability measurement
Research synthesis
Sustainable software engineering
Research synthesis
Project management & project success
Research synthesis
Computing education
Evidence overview
Can ESG compliance be measured reliably? Evidence from ESG ratings, project-level instruments, machine learning and supply chain records
Evidence overview
What predicts IT project failure and success? Evidence on project managers, bias and team transparency from machine learning and experiments
Evidence overview
What is holding back Nigerian agriculture and farm technology adoption? Evidence from extension workers and farmer surveys
Evidence overview
What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS
Evidence overview
How does national culture shape knowledge sharing and collaboration in transition economies? Evidence from Albania

Theme

ESG and sustainability measurement

How Environmental, Social, and Governance (ESG) compliance can be measured and verified — at project level in manufacturing and software engineering, in supply chains under regulatory volatility, and in the ESG ratings of large financial firms.

Read the research synthesis: ESG & sustainability measurement →

Read the research synthesis: Sustainable software engineering →

Read the evidence overview for this theme →

2026

Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes

Journal article · Strang & Vajjhala (2026) · Sustainability, 18(18), Article 9359 · Scopus Q1 (CiteScore 8.9) · Web of Science Q2 · Impact Factor 4.1 (JCR 2025)
The Problem
Supply chain decision-makers must manage fast-changing trade, labor and environmental regulation, yet it is unknown whether their perceptions of these regulatory-volatility risks form a coherent construct or predict whether a supply chain engagement succeeds.
The Methodology
Quantitative retrospective analysis of archival organisational records (N = 1,988): descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics.
The Core Finding
Perceived regulatory-volatility risks do not form a single construct (KMO = 0.470), and labor and environmental risks were rated lowest of all (medians 0 and 1 on 0–5 scales) during a period of record forced-labor enforcement. A perfect in-sample classifier (AUC = 1.000) was an artefact of label leakage from near-duplicate items (r = 0.915), not genuine predictive skill.
Why it matters
The perception gap exposes firms to "sustainability leakage": sudden enforcement pushes them to exit suppliers rather than remediate. For anyone building AI decision support from organizational records, the perfect-accuracy result is a concrete warning about label leakage. The authors recommend multidimensional (not composite) risk dashboards and provenance-aware data governance for supply chain analytics.
The Citation
Strang, K. D., & Vajjhala, N. R. (2026). Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes. Sustainability, 18(18), Article 9359. https://doi.org/10.3390/su18189359
Key findings & abstract →DOI 10.3390/su18189359Free PDFOpen access
2026

Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis

Journal article · Strang & Vajjhala (2026) · Systems, 14(8), Article 990 · Scopus Q1 (CiteScore 5.4) · Web of Science SSCI Q1 · Impact Factor 3.8 (JCR 2025)
The Problem
Organizations increasingly want to measure Environmental, Social, and Governance (ESG) compliance at the level of individual software engineering projects, but it is unclear what machine learning can learn from the evaluation records firms actually produce.
The Methodology
Single-case socio-technical systems study of archival project records (N = 207) using an open-source data science workflow: correlation analysis, principal component analysis, common-method-bias diagnostics, and kNN and SVM classification.
The Core Finding
In 207 financial software project records, stakeholder-rated social and governance factors tracked the overall project score almost perfectly (r = +0.995 and +0.966) while the environmental factor was unrelated to it, and machine learning classifiers performed weakly (kNN AUC = 0.497; SVM accuracy 61.8%). ESG measurement from such records reflects the firm’s rating practice as much as the algorithm — a socio-technical systems result.
Why it matters
The study shows that ESG measurement is shaped as much by the human rating process and organizational templates as by the algorithm — a socio-technical systems view. The near-unity correlations describe how the firm scores projects rather than distinct ESG constructs, and the weak classifiers show the limits of learning from such records. It offers a proof of concept and a research agenda for AI-enabled, project-level ESG measurement.
The Citation
Strang, K. D., & Vajjhala, N. R. (2026). Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis. Systems, 14(8), Article 990. https://doi.org/10.3390/systems14080990
Key findings & abstract →DOI 10.3390/systems14080990Free PDFOpen access
2026

Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors

Journal article · Strang & Vajjhala (2026) · Information, 17(4), Article 311 · Scopus Q1 · Web of Science ESCI Q2 · Impact Factor 4.3 (JCR 2025)
The Problem
ESG frameworks measure firms and the SDGs measure nations, but no validated instrument existed for measuring ESG compliance in the individual projects where sustainability commitments are actually carried out.
The Methodology
Quantitative instrument development and validation with survey data from 2,231 project sponsors and decision-makers in North American manufacturing (NAICS 31–33): principal component analysis, exploratory factor analysis and structural equation modelling.
The Core Finding
A survey of 2,231 North American manufacturing project sponsors produced a validated seven-item, two-factor instrument — ESG planning and ESG monitoring and controlling — with excellent fit (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035). The instrument gives project offices a standardized, process-based benchmark for project-level ESG compliance.
Why it matters
The SDGs work at national level and ESG ratings at company level, but until now there was no validated instrument for measuring ESG integration inside projects. The instrument gives project managers, sustainability officers, and policy-makers a standardized benchmark, and the complete survey is shared so it can be replicated in other industries and countries.
The Citation
Strang, K. D., & Vajjhala, N. R. (2026). Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors. Information, 17(4), Article 311. https://doi.org/10.3390/info17040311
Key findings & abstract →DOI 10.3390/info17040311Free PDFOpen access
2024

Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms

Journal article · Strang & Vajjhala (2024) · Sustainability, 16(23), Article 10240 · Scopus · Web of Science Q2 · Impact Factor 4.1 (JCR 2025)
The Problem
Investors rely on ESG ratings to judge corporate governance, but it is untested whether those ratings reflect the anti-corruption record — misconduct and arbitration cases — of the largest asset management firms.
The Methodology
Quantitative archival study of 14 large asset management firms combining AI-assisted collection of legal, misconduct and arbitration records with ESG ratings from three providers, analysed with Bayesian correlation and bootstrapping.
The Core Finding
For 14 asset managers each holding more than USD 1 trillion, ESG scores from MSCI, Sustainalytics and CSRhub were unrelated to misconduct or arbitration case counts (all Bayes factors below 1), and the providers disagreed with each other (MSCI versus Sustainalytics r = −0.069). The ratings studied do not capture this governance evidence.
Why it matters
Regulators ask financial firms to report CO₂ emissions, yet for firms that mainly rent offices, governance issues such as money laundering and corruption are far more material. The study shows that ESG indices can miss the governance risks that matter most in finance, which matters for investors, regulators, and anyone relying on ESG scores as a proxy for ethical conduct.
The Citation
Strang, K. D., & Vajjhala, N. R. (2024). Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms. Sustainability, 16(23), Article 10240. https://doi.org/10.3390/su162310240
Key findings & abstract →DOI 10.3390/su162310240Free PDFOpen access

Theme

Project management and project success

Empirical studies of what predicts project success and failure — project manager experience, bias, commitment, risk decisions in a crisis, and team transparency — often using machine learning on project big data.

Read the research synthesis: Project management & project success →

Read the evidence overview for this theme →

2025

Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success?

Journal article · Strang & Vajjhala (2025) · Information, 16(11), Article 955 · Scopus Q1 · Web of Science ESCI Q2 · Impact Factor 4.3 (JCR 2025)
The Problem
Projects still fail roughly half the time, and established predictors such as budget, end-user community size and certification explain little of the difference between success and failure.
The Methodology
Post-positivist quantitative hypothesis testing on Fintech project data: correlation, conditioned correlation, t-tests and ordinary least squares regression with budget, end-user community size and certification as controls.
The Core Finding
Team members’ willingness to disclose their past performance evaluations during procurement significantly improved project success after controlling for budget, end-user community size and certification, roughly doubling the explained variance in project success from 9.6% to 18.8%.
Why it matters
Projects still fail roughly half the time. The results suggest that transparency factors — specifically, willingness to share past performance — can outweigh traditional resource-allocation variables in predicting Fintech project outcomes, giving project sponsors and procurement teams a practical, low-cost signal to use when staffing projects.
The Citation
Strang, K. D., & Vajjhala, N. R. (2025). Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success? Information, 16(11), Article 955. https://doi.org/10.3390/info16110955
Key findings & abstract →DOI 10.3390/info16110955Free PDFOpen access
2025

Exploring project manager commitment using machine learning on fuzzy big data

Journal article · Strang & Vajjhala (2025) · International Journal of Project Organisation and Management, 17(2), pp. 135–152 · Scopus · Q4
The Problem
Project manager organizational commitment is usually measured with self-report surveys, and little is known about which project attributes predict it in real project data.
The Methodology
Exploratory machine learning study of retrospective secondary big data (439 successful US government IT projects, 1,230 features): multidimensional scaling, silhouette scores, and linear regression, random forest and SVM regression with ten-fold cross-validation.
The Core Finding
Across 439 successful US government IT projects with 1,230 features, linear regression outperformed random forest and SVM in predicting project manager commitment tenure (R² = 0.238 versus 0.108 and 0.047). Line of business, years of experience, team network collaboration type, cross-industry subject matter and contract status were the strongest predictors.
Why it matters
The study shows that secondary, unstructured big data about actual behaviour can be turned into predictive factors of project manager commitment, bypassing speculative survey-based perceptions. The authors argue this offers organisational decision-makers robust, data-driven strategies to improve talent retention and project success rates.
The Citation
Strang, K. D., & Vajjhala, N. R. (2025). Exploring project manager commitment using machine learning on fuzzy big data. International Journal of Project Organisation and Management, 17(2), 135–152. https://doi.org/10.1504/IJPOM.2025.146727
Key findings & abstract →DOI 10.1504/IJPOM.2025.146727
2024

An Exploratory Big Data Approach to Understanding Commitment in Projects

Conference paper · Vajjhala & Strang (2024) · WorldCIST 2024 (World Conference on Information Systems and Technologies), Lecture Notes in Networks and Systems, vol. 989, pp. 66–75, Springer Nature Switzerland AG · Scopus · Web of Science
The Problem
Project manager commitment is usually gauged through biased self-reports, and it is unknown whether retrospective project big data can serve as an objective indicator.
The Methodology
Exploratory pragmatic machine learning study of a declassified U.S. government IT project repository (439 successful projects, 1,230 features) using radial analysis, multidimensional scaling and supervised regression (linear, random forest, SVM) with relief-based feature ranking.
The Core Finding
Supervised machine learning on 439 successful US government IT projects showed linear regression best predicted project manager commitment tenure (r² = 0.238), with line of business, experience, network collaboration tools and cross-industry team membership as the strongest features — supporting an association between higher commitment and project success.
Why it matters
The study offers a more objective, big data-driven way to understand project manager commitment based on actual behavioral evidence rather than self-reported surveys, which suffer from bias and small effect sizes. Its insights are aimed at global stakeholders in projects and programs concerned with retaining experienced talent and reducing high project failure rates.
The Citation
Vajjhala, N. R., & Strang, K. D. (2024). An Exploratory Big Data Approach to Understanding Commitment in Projects. In Á. Rocha (Ed.), WorldCIST 2024 (World Conference on Information Systems and Technologies) (pp. 66–75). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-60227-6_6
Key findings & abstract →DOI 10.1007/978-3-031-60227-6_6
2023

Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods

Journal article · Strang & Vajjhala (2023) · International Journal of Information Technology Project Management, 14(1), pp. 1–24 · Scopus · Web of Science · Q3
The Problem
Thousands of IT projects fail every year, yet the indicators most strongly associated with failure have not been mined systematically from large project datasets.
The Methodology
Pragmatic recursive mixed-methods design on U.S. Government Accountability Office big data (about 17,430 IT projects): structured data cleaning, random forest classification of project breach/failure, and post-hoc logistic regression in SPSS.
The Core Finding
Random forest analysis of about 17,430 US government IT projects identified seven failure indicators with 79.9% precision, 81% recall and a ROC area of 0.849. Post-hoc logistic regression confirmed project manager experience, project budget and outsourced versus in-house project managers as significant predictors of failure.
Why it matters
Roughly half of IT-related projects fail and earlier studies of success factors left 88–98% of the variation unexplained. The study shows that a pragmatic mixed-methods sequence — machine learning first, followed by logistic regression — can pull useful failure indicators out of messy project big data, and the authors urge researchers to use actual organizational project metrics rather than surveys of opinion.
The Citation
Strang, K. D., & Vajjhala, N. R. (2023). Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods. International Journal of Information Technology Project Management, 14(1), 1–24. https://doi.org/10.4018/IJITPM.317221
Key findings & abstract →DOI 10.4018/IJITPM.317221Open access
2022

How Bias Impacted the Project Manager Decision to Not Terminate a Failing Project

Journal article · Strang & Vajjhala (2022) · International Journal of Information Technology Project Management, 13(1), pp. 1–17 · Scopus · Web of Science · Q3
The Problem
Competent project managers sometimes keep failing projects alive after a risk event, and the role of hidden value-prospect bias in that decision had not been measured experimentally.
The Methodology
Online repeated-measures controlled experiment with 16 experienced U.S. IT project managers in a simulated military software project, using a PERT competency test, basic and incentive-biased decision conditions, paired t-test and repeated-measures ANOVA.
The Core Finding
A 400% bonus incentive significantly lowered 16 experienced IT project managers’ likelihood of cancelling a failing project (from 3.438 to 2.438 on a 1–5 scale; p = .005, Cohen’s d = 0.826). Risk competency, not certification, was the significant protective factor (η² = 0.218).
Why it matters
Value prospect incentives (money and ego) can push even certified project managers to continue a doomed project against policy, which may explain some of the roughly 50% global project failure rate. The authors recommend that sponsors require both verified certification and tested competency when hiring project managers, and give existing project managers periodic refresher training in ethical decision making and risk estimation.
The Citation
Strang, K. D., & Vajjhala, N. R. (2022). How Bias Impacted the Project Manager Decision to Not Terminate a Failing Project. International Journal of Information Technology Project Management, 13(1), 1–17. https://doi.org/10.4018/IJITPM.304059
Key findings & abstract →DOI 10.4018/IJITPM.304059
2022

Global Risk and Contingency Management Research in Times of Crisis

Edited book · Vajjhala & Strang (2022) · IGI Global, Advances in Logistics, Operations, and Management Science series, 388 pp. · Edited book · Scopus
The Problem
Organizations and governments faced COVID-19, economic, climate and terrorism crises with contingency plans that often failed.
The Approach
Edited volume of 16 chapters; chapter methods include inferential statistics with auditing theory, maturity-model development, financial time-series contagion analysis, Lean Six Sigma case study, conceptual and sociological analysis, and regional empirical studies.
The Core Contribution
The volume compiles evidence on risk and contingency management across nations and industries — from petroleum and automotive risk models to cybersecurity, healthcare informatics and educational inequality — offering business leaders lessons for future crises.
Why it matters
The pandemic tested organisational risk and contingency plans worldwide. Collecting studies of decisions made during real crises gives business leaders and policymakers examples of what to do and what not to do when the next crisis arrives.
The Citation
Vajjhala, N. R., & Strang, K. D. (Eds.). (2022). Global Risk and Contingency Management Research in Times of Crisis. IGI Global. https://doi.org/10.4018/978-1-6684-5279-0
Contents, description & citation →DOI 10.4018/978-1-6684-5279-0
2022

Testing Risk Management Decision Making Competency of Project Managers in a Crisis

Journal article · Strang & Vajjhala (2022) · The Journal of Modern Project Management, 10(1), pp. 52–71 · Scopus (at publication)
The Problem
Failing projects are often not cancelled in a crisis, and it is unclear how much cognitive bias and individual factors such as certification, competency and experience drive project managers’ risk decisions.
The Methodology
Repeated-measures controlled quasi-experiment with 24 experienced U.S. project managers (competency test, basic decision, biased decision) analysed with Spearman correlation, MANOVA, repeated-measures ANOVA, Friedman test and post-hoc t-tests.
The Core Finding
In a controlled experiment with 24 experienced US project managers, a biasing incentive significantly lowered decision quality, and certification and risk competency were the only individual factors significantly related to good decisions (rho = 0.549 to 0.712). Competent, certified project managers made the best decisions under bias, whereas a certified but incompetent project manager failed to cancel.
Why it matters
The authors argue that bias such as money and ego can override ethical risk management judgment even among certified project managers, which could be a problem for recruiters who rank certification highly without seeking evidence of competency. They suggest certified PMs receive additional risk management training, that certification courses include experiential exercises on quantifying and mitigating risks, and that employers offer short ethics or organizational refresher courses.
The Citation
Strang, K. D., & Vajjhala, N. R. (2022). Testing Risk Management Decision Making Competency of Project Managers in a Crisis. The Journal of Modern Project Management, 10(1), 52–71. https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02904
Key findings & abstract →Free PDFOpen access

Theme

Machine learning and data science applications

Reproducible, statistically validated machine learning and data analytics applied to real problems — weed classification, weather prediction, SME performance, recommender systems and open big data — and a systematic review of deep generative models for talking face generation, their evaluation and their governance.

Read the research synthesis: Machine learning & data science →

2026

Talking Face Generation in Socio-Technical Systems: A Systematic Review of Deep Learning Architectures, Deployment Contexts, and Governance Frameworks

Systematic review · Salahudeen et al. (2026) · Systems, 14(10), Article 1198 · Scopus Q1 (CiteScore 5.4) · Web of Science SSCI Q1 · Impact Factor 3.8 (JCR 2025)
The Problem
Talking face generation can now synthesise photorealistic, real-time speaking video from a single image and an audio clip and is entering healthcare, education, identity management and public communication, yet no prior review was at once systematic, current to 2024–2025, and comprehensive across its technical, ethical and socio-technical dimensions.
The Methodology
Systematic literature review (Kitchenham and Charters; reported to PRISMA 2020) of 92 primary studies published 2016–2025, screened from 18,343 records in six repositories over four search iterations, with a five-criterion quality assessment and qualitative narrative synthesis across six research questions.
The Core Finding
Across 92 primary studies (2016–2025), Diffusion Transformers and Gaussian Splatting account for about 36% of studies dated 2024 or later while GAN use has fallen below 3%; training data are demographically skewed and under-documented, and the dominant metrics (PSNR, SSIM, SyncNet) do not measure what deployment requires. Only one of the 92 systems embeds any technical safeguard — a non-compliant post hoc watermark — and none implements consent or provenance, even though EU AI Act Article 50 transparency obligations have applied since 2 August 2026.
Why it matters
Talking face generation now produces photorealistic, real-time speaking video from a single image and an audio clip and is entering healthcare, education, identity management and public communication, yet the reviewed state of the art satisfies none of the EU AI Act Article 50 transparency obligations that have applied since 2 August 2026. The authors argue that the gap is structural rather than a failure of individual researchers — publication incentives reward benchmark optimisation alone — so venues, funders and regulators need to make provenance, consent and demographic auditing conditions of publication and deployment. The RTFG-STS Framework and three proposed deployment-fitness metrics give researchers and deployers a starting point that still requires empirical validation.
The Citation
Salahudeen, R., Fonkam, M., Vajjhala, N. R., Junaidu, S. B., & Garba, A. (2026). Talking Face Generation in Socio-Technical Systems: A Systematic Review of Deep Learning Architectures, Deployment Contexts, and Governance Frameworks. Systems, 14(10), Article 1198. https://doi.org/10.3390/systems14101198
Key findings & abstract →DOI 10.3390/systems14101198Free PDFOpen access
2026

A Comparative Evaluation of Deep Learning Architectures for Weed Classification, with an Exploratory Out-of-Distribution Analysis of Albanian Field Images

Journal article · Kumar et al. (2026) · Information, 17(8), Article 751 · Scopus Q1 · Web of Science ESCI Q2 · Impact Factor 4.3 (JCR 2025)
The Problem
Weed-classification studies usually rank deep learning architectures from single training runs, which can overstate differences between models and says nothing about transfer to new farming regions.
The Methodology
Comparative deep learning experiment on the DeepWeeds benchmark (nine classes) with leakage-checked splits, repeated multi-seed training of six architectures, statistical model comparison, and an exploratory out-of-distribution probe on unlabelled Albanian field images.
The Core Finding
Fine-tuned DINOv2 and EfficientNet-B0 performed best on nine-class DeepWeeds data, but differences among several high-performing models were not consistently statistically significant across random seeds. An exploratory test on Albanian field images revealed a clear geographic domain shift, so locally collected and labelled data are needed before deployment.
Why it matters
Single-run leaderboard comparisons can overstate differences between models; this study shows why multi-seed evaluation, leakage-checked splits, and statistical testing matter for agricultural computer vision. For site-specific weed management in new regions such as the Western Balkans, locally collected and labelled data are needed before deployment.
The Citation
Kumar, R., Vajjhala, N. R., Ramollari, E., & Joca, E. (2026). A Comparative Evaluation of Deep Learning Architectures for Weed Classification, with an Exploratory Out-of-Distribution Analysis of Albanian Field Images. Information, 17(8), Article 751. https://doi.org/10.3390/info17080751
Key findings & abstract →DOI 10.3390/info17080751Free PDFOpen access
2025

Hyperlocal Temperature and Humidity Prediction Using Supervised Machine Learning

Conference paper · Gigov et al. (2025) · 2025 2nd Global AI Summit – International Conference on Artificial Intelligence and Emerging Technology (AI Summit), pp. 940–945, IEEE · Scopus
The Problem
City-level (hyperlocal) weather prediction needs models that balance accuracy against computational cost, but simple and complex supervised regressors are rarely compared on long daily records.
The Methodology
Comparative supervised regression experiment (linear, polynomial, decision tree, random forest) on ten years of daily Open-Meteo data for Sofia, Berlin and Tokyo with a chronological 7:3 split, grid-search tuning, and R², MAE, RMSE and execution-time evaluation.
The Core Finding
On ten years of daily data for Sofia, Berlin and Tokyo, linear regression matched random forest for temperature (R² > 0.994) at a fraction of the run time and clearly outperformed it for humidity (R² 0.940–0.964), while decision trees performed worst. Simple linear models are the efficient choice for hyperlocal forecasting.
Why it matters
The results challenge the assumption that complex ensemble methods are necessary for accurate weather prediction: model selection should prioritize the relationship structure in the data rather than algorithmic complexity. Fast, accurate linear models suit precision agriculture, smart city infrastructure, event planning and resource-constrained IoT deployments where real-time predictions are essential.
The Citation
Gigov, L., Vajjhala, N. R., & Stoilov, A. (2025). Hyperlocal Temperature and Humidity Prediction Using Supervised Machine Learning. In 2025 2nd Global AI Summit – International Conference on Artificial Intelligence and Emerging Technology (AI Summit) (pp. 940–945). IEEE. https://doi.org/10.1109/AISummit66170.2025.11411099
Key findings & abstract →DOI 10.1109/AISummit66170.2025.11411099
2025

Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings

Conference paper · Ajibesin et al. (2025) · Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023), Lecture Notes in Networks and Systems, vol. 1072, pp. 221–231, Springer Nature Singapore · Scopus
The Problem
Conventional web prefetching assumes servers have idle time, an assumption that breaks down in high-traffic, congested network environments.
The Methodology
Design and evaluation of a web prefetching model: preprocessing of server log files, construction of an online navigation graph, clustering with the WEKA machine learning toolkit, and comparison against conventional prefetching techniques on domain client-group logs.
The Core Finding
A prefetching approach that builds an online navigation graph from server logs and clusters navigation behaviour with WEKA outperformed conventional prefetching techniques on domain client-group logs, particularly once server idle time allowed prefetching across all user predictions.
Why it matters
Network congestion reduces the usefulness of information because pages load slowly. A prefetching method that works when server idle time is scarce, and that combines with web caching, is directly applicable to busy web services and IoT-connected environments.
The Citation
Ajibesin, A. A., Vajjhala, N. R., Joel, E., & Rakshit, S. (2025). Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings. In F. Lin, D. Pastor, N. Kesswani, A. Patel, S. Bordoloi, & C. Koley (Eds.), Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023) (pp. 221–231). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5786-2_17
Key findings & abstract →DOI 10.1007/978-981-97-5786-2_17
2024

Profitability, effectiveness, operational efficiency, and market growth of SMEs in Albania after piloting data analytics

Journal article · Vajjhala & Strang (2024) · International Journal of Services and Standards, 14(1), pp. 51–64 · Scopus · Q4
The Problem
Small and medium-sized enterprises in transition economies such as Albania are adopting data analytics, but evidence on whether it improves their performance is scarce.
The Methodology
Quantitative post-positivist survey of 41 Albanian SMEs: descriptive statistics, Pearson and Spearman correlation, generalised linear model, MANOVA, and random forest, SVM, linear regression and neural network models.
The Core Finding
Among 41 Albanian SMEs, data-analytics use correlated positively with operational efficiency, process optimisation, profitability and market growth (r = 0.540 to 0.769), with a significant multivariate effect (MANOVA F(1, 38) = 20.029, p < 0.001); a random forest model explained the outcomes best (R² = 0.661).
Why it matters
The authors conclude that the strong positive associations between data analytics and key performance indicators show SMEs should integrate data-driven mechanisms into their business strategies. For SMEs and policymakers, the study sets a baseline for further research and calls for attention to data analytics within business strategy.
The Citation
Vajjhala, N. R., & Strang, K. D. (2024). Profitability, effectiveness, operational efficiency, and market growth of SMEs in Albania after piloting data analytics. International Journal of Services and Standards, 14(1), 51–64. https://doi.org/10.1504/IJSS.2024.140078
Key findings & abstract →DOI 10.1504/IJSS.2024.140078
2023

Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons

Conference paper · Imoh et al. (2023) · Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021, Lecture Notes in Networks and Systems, vol. 404, pp. 3–11, Springer Nature Singapore · Scopus
The Problem
More than 23,000 people were reported missing in Nigeria in 2020, and existing search methods — word of mouth, media announcements and social media — are slow and ineffective.
The Methodology
Experimental system design: face recognition with a convolutional neural network, facial calibration and modelling for feature extraction, and comparison of face encodings against a query image.
The Core Finding
The authors built an experimental face-recognition system that combines a convolutional neural network with facial calibration and modelling to extract face encodings and match them against a query image, as a faster tool for identifying missing persons.
Why it matters
Current approaches to finding missing persons in Nigeria are described as slow and ineffective where time is critical. An automated, deep-learning-based face matching tool could speed up search and identification for law enforcement, human rights organisations and families.
The Citation
Imoh, N., Vajjhala, N. R., & Rakshit, S. (2023). Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons. In S. Basu, D. K. Kole, A. K. Maji, D. Plewczynski, & D. Bhattacharjee (Eds.), Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021 (pp. 3–11). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0105-8_1
Key findings & abstract →DOI 10.1007/978-981-19-0105-8_1
2021

An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product

Conference paper · Saheed et al. (2021) · Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India, April 23–24, 2021, Revised Selected Papers, Part I, Communications in Computer and Information Science, vol. 1440, pp. 317–326, Springer International Publishing · Scopus · Web of Science (CPCI)
The Problem
Software defect prediction models are usually judged on accuracy alone, which is insufficient for the imbalanced data typical of defect datasets.
The Methodology
Experimental machine learning comparison: seven ensemble models (CatBoost, LightGBM, XGBoost and boosted/bagged variants) plus a logistic regression baseline evaluated on six software defect datasets (NASA repository) with AUC, precision, recall, F-measure and Matthews correlation coefficient.
The Core Finding
A seven-model ensemble of boosted and bagged learners, evaluated with AUC, precision, recall, F-measure and Matthews correlation coefficient on NASA defect datasets, outperformed a logistic regression baseline, and the ensemble CatBoost model gave outstanding performance on all three datasets reported.
Why it matters
Accurate defect prediction helps developers target testing and produce more reliable software. Many earlier studies judged models on accuracy only; using AUC, F-measure and MCC gives a fuller picture, especially on the imbalanced datasets typical of defect prediction.
The Citation
Saheed, Y. K., Longe, O., Baba, U. A., Rakshit, S., & Vajjhala, N. R. (2021). An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product. In M. Singh, V. Tyagi, P. K. Gupta, J. Flusser, T. Ören, & V. R. Sonawane (Eds.), Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India, April 23–24, 2021, Revised Selected Papers, Part I (pp. 317–326). Springer International Publishing. https://doi.org/10.1007/978-3-030-81462-5_29
Key findings & abstract →DOI 10.1007/978-3-030-81462-5_29
2021

Novel User Preference Recommender System Based on Twitter Profile Analysis

Conference paper · Vajjhala et al. (2021) · Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020), Advances in Intelligent Systems and Computing, vol. 1248, pp. 85–93, Springer Singapore · Scopus
The Problem
Conventional recommender systems lack interactive ways to adjust recommendation weights and ignore the interests users express on social media.
The Methodology
System design and experimental evaluation: mining and analysis of Twitter profiles and timelines on the IBM Watson platform to predict product and service categories, with a reported correlation between consumed categories and tweet content.
The Core Finding
A recommender built on IBM Watson that mines a user’s Twitter profile and timeline predicted the category of goods and services the user is most likely to consume, and the authors report a strong correlation between consumed categories and tweet content.
Why it matters
Recommender systems help consumers make informed, personalised decisions, but most lack interactive control over algorithm weights. Using a person’s own public social media activity as the preference signal offers e-commerce services a cold-start-friendly source of personalisation.
The Citation
Vajjhala, N. R., Rakshit, S., Oshogbunu, M., & Salisu, S. (2021). Novel User Preference Recommender System Based on Twitter Profile Analysis. In S. Borah, R. Pradhan, N. Dey, & P. Gupta (Eds.), Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020) (pp. 85–93). Springer Singapore. https://doi.org/10.1007/978-981-15-7394-1_7
Key findings & abstract →DOI 10.1007/978-981-15-7394-1_7
2017

Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey

Book chapter · Biba et al. (2017) · In Collaborative Filtering Using Data Mining and Analysis, pp. 217–235, IGI Global · Scopus
The Problem
Collaborative filtering recommender systems suffer from sparsity and opacity, and the potential of visual data mining to address these shortcomings had not been surveyed.
The Methodology
State-of-the-art literature survey of visual data mining taxonomies and tools, memory-based, model-based and hybrid collaborative filtering, and visual collaborative filtering systems evaluated on EachMovie, MovieLens and Netflix data.
The Core Finding
The survey organizes visual data mining techniques, tools and collaborative filtering approaches, and concludes that combining visual data mining with collaborative filtering has led to substantial improvement and can alleviate the shortcomings of traditional collaborative filtering.
Why it matters
Visual data mining augments, rather than replaces, traditional data mining by bringing the data analyst into the exploration process through visual interpretation of large datasets. For recommender systems, the authors conclude that visual techniques help make sense of what is happening between users in collaborative filtering systems and address problems such as sparse data, scalability and new users or items.
The Citation
Biba, M., Vajjhala, N. R., & Nishani, L. (2017). Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey. In Collaborative Filtering Using Data Mining and Analysis (pp. 217–235). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch012
Key findings & abstract →DOI 10.4018/978-1-5225-0489-4.ch012
2015

Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study

Conference paper · Vajjhala et al. (2015) · 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy, pp. 489–496, IEEE · Scopus · Web of Science
The Problem
Decision-makers need to find hidden relationships in large qualitative open datasets without specialist big data infrastructure.
The Methodology
Positivist exploratory case study applying simple correspondence analysis in SPSS 21 to an open big dataset of 125,087 global terrorism records (1970–2013), relating nine attack methods to 13 world regions.
The Core Finding
Simple correspondence analysis in standard statistical software, applied to 125,087 global terrorism records (1970–2013), related nine attack methods to 13 world regions with two dimensions capturing 94.5% of inertia — demonstrating an accessible method for visualizing open big data.
Why it matters
The study shows that qualitative big data can be analyzed with desktop statistical software to reveal hidden relationships that business managers could use when selecting marketing partners and supply chain providers or deciding to scale down operations in risky areas. The authors aim to generalize the methodology rather than the specific results, and call for more empirical big data analytics papers and a methodology guidebook for practitioners and researchers.
The Citation
Vajjhala, N. R., Strang, K. D., & Sun, Z. (2015). Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study. In 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy (pp. 489–496). IEEE. https://doi.org/10.1109/FiCloud.2015.15
Key findings & abstract →DOI 10.1109/FiCloud.2015.15

Theme

Healthcare informatics

Deep learning for medical imaging and analytical methods such as data envelopment analysis for healthcare management.

Read the research synthesis: Healthcare informatics →

2026

Enhancing Healthcare Informatics with Transparent and Explainable AI

Edited book · Eappen et al. (2026) · Auerbach Publications (CRC Press), 280 pp. · Edited book · Scopus
The Problem
Clinicians, patients and health workers will not rely on AI in healthcare informatics unless its decisions are explainable and transparent.
The Approach
Edited volume of 11 chapters by international contributors; chapters range from ethical and conceptual analysis to systematic review (EEG analysis) and applied explainable-AI system descriptions.
The Core Contribution
The volume applies explainable and transparent AI across clinical decision-making, telehealth, electronic health records, EEG analysis, wearable monitoring and mental health, pairing ethical analysis with applied systems and strategies for building trust.
Why it matters
Healthcare differs from other domains where AI has succeeded: a recommendation about treatment must be explainable to the clinician and the patient. The book focuses specifically on transparency and explainability as the condition for safe adoption.
The Citation
Eappen, P., Vajjhala, N. R., Guo, R., Shinners, L., & Gunn, V. (Eds.). (2026). Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press). https://doi.org/10.1201/9781003604440
Contents, description & citation →DOI 10.1201/9781003604440
2026

Deep Learning for Medical Image Analysis: CNN-based Pneumonia Detection on Chest X-Rays

Conference paper · Haveri & Vajjhala (2026) · 2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN), pp. 156–161, IEEE · Scopus · IEEE Xplore
The Problem
Deep learning models for chest X-ray pneumonia detection are often reported without confidence intervals, calibration or reporting-standard compliance, limiting their clinical credibility.
The Methodology
Binary medical image classification with an EfficientNetV2B0 convolutional neural network and ImageNet transfer learning on the Kermany paediatric chest X-ray dataset, following CLAIM and TRIPOD-AI reporting, with bootstrapped confidence intervals, DeLong test, calibration metrics and Grad-CAM.
The Core Finding
A lightweight EfficientNetV2B0 model trained under CLAIM and TRIPOD-AI reporting standards achieved an AUC of 0.967 (95% CI 0.953–0.979) and pneumonia recall of 0.98 on paediatric chest X-rays, missing only about 2% of true cases. Lower normal-class recall (0.752) reflects a false-positive tendency that is a clinically preferable trade-off in triage.
Why it matters
The results show that a compact model of about 7.1 million parameters can deliver clinically meaningful pneumonia detection without the heavy computation of larger networks, making it suited to edge devices, telehealth networks and low-resource healthcare settings. Its high sensitivity favours triage workflows, where missing pneumonia is costlier than an unnecessary follow-up, and the authors outline integration with PACS and CMMS platforms.
The Citation
Haveri, K., & Vajjhala, N. R. (2026). Deep Learning for Medical Image Analysis: CNN-based Pneumonia Detection on Chest X-Rays. In 2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN) (pp. 156–161). IEEE. https://doi.org/10.1109/ICPCSN68523.2026.11543623
Key findings & abstract →DOI 10.1109/ICPCSN68523.2026.11543623
2026

The Rise of AI in Healthcare: A Transparent Approach to Implementation

Book chapter · Eappen et al. (2026) · In AI in Healthcare: A Transparent Approach to Informatics, pp. 3–16, Chapman and Hall/CRC · Scopus
The Problem
Artificial intelligence is entering clinical care faster than the transparency, accountability and governance arrangements needed to use it safely and equitably.
The Methodology
Conceptual and review chapter on transparency and accountability in healthcare AI implementation: barriers to adoption, regulatory compliance (GDPR, HIPAA), multi-actor engagement, and governance recommendations.
The Core Finding
The chapter shows that successful healthcare AI depends on transparency, accountability and collaboration among patients, providers, developers, regulators and insurers, and identifies liability, algorithmic opacity and unclear accountability for AI-influenced outcomes as the key barriers to adoption.
Why it matters
Opaque or unaccountable AI can harm patients and widen health disparities. The chapter provides a structured account of the ethical, legal and social conditions under which AI can be adopted safely, useful to health system leaders, developers and regulators.
The Citation
Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (2026). The Rise of AI in Healthcare: A Transparent Approach to Implementation. In P. Eappen, V. Gunn, D. Zikos, & N. R. Vajjhala (Eds.), AI in Healthcare: A Transparent Approach to Informatics (pp. 3–16). Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875-2
Key findings & abstract →DOI 10.1201/9781003624875-2
2026

AI in Healthcare: A Transparent Approach to Informatics

Edited book · Eappen et al. (2026) · Chapman and Hall/CRC, 222 pp. · Edited book · Scopus
The Problem
Health systems are adopting AI without shared practice for making it transparent and accountable across informatics, patient-centred care and clinical applications.
The Approach
Edited volume of 11 chapters (4 parts) by international contributors; chapter methods range from conceptual and ethical analysis to case studies and technical deep-learning evaluations.
The Core Contribution
The volume combines conceptual, ethical, case-based and technical chapters — from transparent implementation and algorithmic scheduling to deep learning for abdominal trauma diagnosis and transparent drug-class prediction — into a framework for transparent healthcare AI.
Why it matters
AI adoption in healthcare is accelerating after the pandemic, and the editors set out to give academics, practitioners and policymakers evidence-informed guidance that covers the technology, its ethical and regulatory constraints, and its human impact in one place.
The Citation
Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (Eds.). (2026). AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875
Contents, description & citation →DOI 10.1201/9781003624875
2024

Smart Health: Advancements in Machine Learning and the Internet of Things Solutions

Book chapter · Vajjhala & Eappen (2024) · In Machine Learning and IoT Applications for Health Informatics, pp. 31–51, CRC Press · Scopus
The Problem
Machine learning and the Internet of Things promise better healthcare, but their integration with healthcare analytics raises unresolved technical, ethical and security problems.
The Methodology
Review chapter on the convergence of healthcare analytics, machine learning and IoT: benefits, integration challenges and recommended solutions, ethical and security considerations, and emerging advances.
The Core Finding
The chapter shows that combining machine learning and IoT supports enhanced patient care, early disease detection, operational efficiency and personalised treatment, and that data privacy, model interpretability, bias mitigation and secure connectivity are prerequisites for deployment.
Why it matters
ML and IoT are being deployed in healthcare faster than the governance around them matures. The chapter combines the technical promise with the practical and ethical conditions for safe, equitable deployment.
The Citation
Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In P. Samui, S. S. Roy, W. Zhang, & Y.-H. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3
Key findings & abstract →DOI 10.1201/9781003424987-3
2024

Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends

Book chapter · Vajjhala & Eappen (2024) · In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency, Advances in Business Information Systems and Analytics, pp. 245–260, IGI Global · Scopus
The Problem
Healthcare managers need robust ways to measure and compare efficiency, and the contribution and limits of data envelopment analysis (DEA) in healthcare had not been consolidated.
The Methodology
Narrative review chapter consolidating published applications of data envelopment analysis (DEA), a non-parametric efficiency method, in hospital management, nursing and outpatient services.
The Core Finding
The review shows that DEA is widely applied in hospital management, nursing and outpatient services for efficiency measurement, benchmarking, resource allocation and performance evaluation, and identifies six limitations, including input/output selection, outlier sensitivity, inability to handle statistical noise and the static nature of traditional models.
Why it matters
The limitations and challenges identified in the chapter underscore the need for further research and methodological advancements in applying DEA in healthcare management.
The Citation
Vajjhala, N. R., & Eappen, P. (2024). Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends. In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency (pp. 245–260). IGI Global. https://doi.org/10.4018/979-8-3693-0255-2.ch011
Key findings & abstract →DOI 10.4018/979-8-3693-0255-2.ch011
2023

The Role of 5G Networks in Healthcare Applications

Book chapter · Vajjhala & Eappen (2023) · In Applications of 5G and Beyond in Smart Cities, pp. 87–98, CRC Press · Scopus
The Problem
COVID-19 exposed overcrowded hospitals and unequal access to medical resources, and the potential of 5G networks to support smarter healthcare remained unclear to policymakers.
The Methodology
Review chapter of 5G technologies and their application to smart healthcare services, with a discussion of deployment challenges and leadership and policy recommendations.
The Core Finding
The chapter maps 5G technologies to smart healthcare applications, sets out the challenges of deploying them, and gives leadership and policy recommendations for designing 5G-enabled healthcare strategies.
Why it matters
A growing global population and overcrowded hospitals make smarter healthcare management a necessity. 5G’s bandwidth, latency and connectivity characteristics underpin many smart-city health services, and leaders need a clear view of both the applications and the deployment barriers.
The Citation
Vajjhala, N. R., & Eappen, P. (2023). The Role of 5G Networks in Healthcare Applications. In A. Bajpai & A. Balodi (Eds.), Applications of 5G and Beyond in Smart Cities (pp. 87–98). CRC Press. https://doi.org/10.1201/9781003227861-5
Key findings & abstract →DOI 10.1201/9781003227861-5
2022

Health Informatics and Patient Safety in Times of Crisis

Edited book · Vajjhala & Eappen (2022) · IGI Global, Advances in Healthcare Information Systems and Administration series, 286 pp. · Edited book · Scopus
The Problem
The COVID-19 pandemic exposed gaps in how health informatics and information technology protect patient safety during crises.
The Approach
Edited volume of 13 chapters; chapter methods include machine-learning prediction of coronavirus spread, conceptual design principles for clinical decision support, big data analytics review, registry implementation case study, and national and regional case studies of telemedicine and emergency response.
The Core Contribution
The volume documents how health informatics performed during the pandemic — machine learning for predicting spread, telemedicine, clinical decision support design, big data analytics, registries and national emergency responses — and shows how improved informatics can protect patient safety in future crises.
Why it matters
The pandemic showed how much health data, technology and access to informatics matter. Reassessing what worked, and what could have worked better, prepares health systems for future pandemics and other crises.
The Citation
Vajjhala, N. R., & Eappen, P. (Eds.). (2022). Health Informatics and Patient Safety in Times of Crisis. IGI Global. https://doi.org/10.4018/978-1-6684-5499-2
Contents, description & citation →DOI 10.4018/978-1-6684-5499-2

Theme

Cybersecurity

Cyber risk in higher education and society, and how organizations and decision-makers can mitigate it.

Read the research synthesis: Cybersecurity →

2025

Cybersecurity in Knowledge Management: Cyberthreats and Solutions

Edited book · Vajjhala & Strang (2025) · CRC Press, 240 pp. · Edited book · Scopus
The Problem
Organizations must protect their knowledge assets against increasingly sophisticated cyber threats without losing the agility that innovation requires.
The Approach
Edited volume of 14 chapters; chapter methods include AI/ML system proposals (LSTM–XGBoost fake-news detection, hate-speech detection), comparative technique reviews (malware classification), case studies of breaches and insider threats, and conceptual models (a radial best-practice model for supply chain cyber resilience).
The Core Contribution
The volume connects knowledge management and cybersecurity through AI-based detection (including hybrid LSTM–XGBoost fake-news detection), malware classification, breach and insider-threat cases, supply chain resilience, and the human and organizational factors of social engineering.
Why it matters
Knowledge systems concentrate an organisation’s most valuable information, yet cybersecurity and knowledge management are usually studied apart. The volume treats them together, with sector cases (supply chains, higher education, digital marketing) and regional perspectives (Africa, Kazakhstan).
The Citation
Vajjhala, N. R., & Strang, K. D. (Eds.). (2025). Cybersecurity in Knowledge Management: Cyberthreats and Solutions. CRC Press. https://doi.org/10.1201/9781003498094
Contents, description & citation →DOI 10.1201/9781003498094
2025

AI-Enabled Threat Intelligence and Cyber Risk Assessment

Edited book · Martiri et al. (2025) · CRC Press, 202 pp. · Edited book · Scopus
The Problem
Artificial intelligence is reshaping cyber threat intelligence and risk assessment, but adoption barriers, workforce gaps and ethical and legal questions remain unresolved.
The Approach
Edited volume of 10 chapters; chapters combine literature-based trend analyses, thematic and trend analysis of workforce upskilling, sector cases (digital marketing, healthcare, Kazakh businesses), and framework development.
The Core Contribution
The volume maps current trends in AI-enabled threat intelligence and assembles sector and regional evidence — digital marketing fraud, healthcare compliance, workforce upskilling and Kazakh businesses — into guidance for building resilient AI-enabled cybersecurity frameworks.
Why it matters
AI is changing both attack and defence. The volume combines technical, organisational, ethical and regional perspectives so that researchers, policymakers and practitioners can assess where AI-based threat intelligence helps, where it raises new risks, and how to govern it.
The Citation
Martiri, E., Vajjhala, N. R., & Dalipi, F. (Eds.). (2025). AI-Enabled Threat Intelligence and Cyber Risk Assessment. CRC Press. https://doi.org/10.1201/9781003504979
Contents, description & citation →DOI 10.1201/9781003504979
2024

Exploring Cybersecurity Risks in Higher Education Environments with Machine Learning

Conference paper · Strang & Vajjhala (2024) · 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN), pp. 1–6, IEEE · Scopus · IEEE Xplore · Web of Science
The Problem
Universities hold sensitive data on large, decentralized websites but lack a practical way to screen those sites for cybersecurity breach risk.
The Methodology
Exploratory comparative study applying unsupervised machine learning (t-SNE, radial analysis, correspondence analysis) in Python to hypertext data scraped from about 848 U.S. higher education website records.
The Core Finding
Unsupervised machine learning (t-SNE with radial and correspondence analysis) applied to hypertext from about 848 US higher-education website records, containing over 4,000 potential indicators, scored breach likelihood on a 1–3 scale and flagged concentrations of higher-risk components; a combined control feature was the most useful risk signal.
Why it matters
Universities run open, complex and decentralized IT infrastructures that make them targets for phishing, ransomware, DDoS attacks and insider threats. The authors argue that website-based machine learning risk scoring lets decision-makers in educational institutions spot anomalies that may indicate cybercrime or impending attacks, while method and data triangulation are needed to verify the reliability and validity of machine learning outputs.
The Citation
Strang, K. D., & Vajjhala, N. R. (2024). Exploring Cybersecurity Risks in Higher Education Environments with Machine Learning. In 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) (pp. 1–6). IEEE. https://doi.org/10.1109/ICPCSN62568.2024.00008
Key findings & abstract →DOI 10.1109/ICPCSN62568.2024.00008
2023

Why Cyberattacks Disrupt Society and How to Mitigate Risk

Book chapter · Strang & Vajjhala (2023) · In Cybersecurity for Decision Makers, pp. 1–28, CRC Press / Taylor & Francis · Scopus
The Problem
Industry 4.0 organizations are increasingly exposed to cyberattacks, yet decision-makers lack an evidence-based picture of why attacks succeed and how to mitigate them.
The Methodology
Narrative review and descriptive case analysis of 25 major publicly reported cyberattacks (2020–2022), classified by attack type, source and motive, with a review of Industry 4.0 security issues.
The Core Finding
An analysis of 25 major cyberattacks (2020–2022) found extortion to be the most common motive (44%), followed by service disruption (32%) and data theft (24%), with 36% involving bitcoin ransom. Most incidents traced to missed warnings, unpatched systems and insecure contractors, which log audits, prompt patching, update scanning, awareness training and contractual security clauses can address.
Why it matters
Cyberattacks have crippled governments and companies, disrupting supply chains, fuel and food supply, healthcare and public services, and the FBI reported losses exceeding $4.2 billion from internet crime in 2020. The chapter gives decision makers concrete lessons from real incidents and argues that organizations adopting Industry 4.0 technologies must assess their vulnerabilities and put benchmark-based cybersecurity policies in place, or users will lose trust.
The Citation
Strang, K. D., & Vajjhala, N. R. (2023). Why Cyberattacks Disrupt Society and How to Mitigate Risk. In N. R. Vajjhala & K. D. Strang (Eds.), Cybersecurity for Decision Makers (pp. 1–28). CRC Press / Taylor & Francis. https://doi.org/10.1201/9781003319887-1
Key findings & abstract →DOI 10.1201/9781003319887-1
2023

Cybersecurity for Decision Makers

Edited book · Vajjhala & Strang (2023) · CRC Press, 424 pp. · Edited book · Scopus
The Problem
Managers, policymakers and practitioners make cybersecurity decisions with little access to evidence that connects theory to practice.
The Approach
Edited volume of 23 chapters by 46 contributors from 11 countries; chapter methods include case-study analysis, systematic literature review, empirical surveys of psychological factors, comparative regional analysis, and conceptual and policy frameworks.
The Core Contribution
Contributors from 11 countries integrate theory with evidence-based practice on cyberattack causes, human factors, awareness, cybercrime trends, emerging domains such as space and cyber-physical systems, and business continuity, giving decision-makers actionable mitigation strategies.
Why it matters
Cybersecurity literature is often written for technical specialists. This volume is aimed explicitly at decision makers at all levels and at policymakers, combining case evidence, statistics and frameworks that non-technical leaders can act on.
The Citation
Vajjhala, N. R., & Strang, K. D. (Eds.). (2023). Cybersecurity for Decision Makers. CRC Press. https://doi.org/10.1201/9781003319887
Contents, description & citation →DOI 10.1201/9781003319887
2023

Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management

Conference paper · Olaniyan et al. (2023) · Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022, Lecture Notes in Electrical Engineering, vol. 984, pp. 419–426, Springer Nature Singapore · Scopus
The Problem
Organizations that depend on open networks and cloud services face a growing volume of cyber threats that rule-based defences struggle to detect.
The Methodology
Conceptual and applied overview (short conference paper) of AI-based user and entity behavioural analytics for cyber threat detection and vulnerability management; no empirical dataset is reported in the abstract.
The Core Finding
The paper positions user and entity behavioural analytics (UEBA) as an AI approach that scans large volumes of system and network data to discover where attacks originate and to recommend responses to organizational decision-makers.
Why it matters
As organisations store sensitive and personal data on open networks and cloud infrastructure, the number of cyber risks grows with Internet use. UEBA offers decision-makers a data-driven way to detect anomalous behaviour by users and entities rather than relying only on signature-based defences.
The Citation
Olaniyan, R., Rakshit, S., & Vajjhala, N. R. (2023). Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management. In P. Chatterjee, D. Pamucar, M. Yazdani, & D. Panchal (Eds.), Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022 (pp. 419–426). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8493-8_32
Key findings & abstract →DOI 10.1007/978-981-19-8493-8_32
2020

Evaluating Information Security Awareness and Compliance in Sub-Saharan Africa: An Interpretivist Perspective

Conference paper · Nasir & Vajjhala (2020) · Proceedings of the 13th IADIS International Conference Information Systems 2020 (IS 2020), pp. 187–190, IADIS Press · Scopus
The Problem
Information security awareness and compliance are low in sub-Saharan Africa, and the behavioural, social, cultural and organizational causes in African SMEs are largely ignored by a literature centred on developed countries.
The Methodology
Research design paper for an interpretivist, qualitative, multisite exploratory case study: audio-recorded in-depth personal interviews with 50 managers across 20 medium-sized companies in Nigeria (two per sector across ten key economic sectors).
The Core Finding
The paper establishes an interpretivist, multisite case-study design — in-depth interviews with 50 managers in 20 medium-sized Nigerian companies across ten economic sectors — for identifying the non-technical drivers of information security compliance in SMEs.
Why it matters
Most information security awareness and compliance literature comes from developed countries or from Asia and the Middle East; evidence on African SMEs is scarce. Understanding the human and organisational factors behind non-compliance could help leaders avoid the loss of brand equity and revenue that follows security breaches.
The Citation
Nasir, S., & Vajjhala, N. R. (2020). Evaluating Information Security Awareness and Compliance in Sub-Saharan Africa: An Interpretivist Perspective. In M. B. Nunes, P. Isaías, P. Powell, & B. Bontchev (Eds.), Proceedings of the 13th IADIS International Conference Information Systems 2020 (IS 2020) (pp. 187–190). IADIS Press. https://www.iadisportal.org/digital-library/evaluating-information-security-awareness-and-compliance-in-sub-saharan-africa-an-interpretivist-perspective
Key findings & abstract →

Theme

Digital transformation and AI in business

Artificial intelligence and machine learning in SMEs, supply chains, retail and finance — the applications, benefits and implementation challenges of digital business technologies.

2025

Digital Retail Innovations: The Role of Artificial Intelligence (AI) and Machine Learning (ML)

Book chapter · Vajjhala et al. (2025) · In Digital Transformation: A Business Imperative, Advances in Emerging Markets and Business Operations, pp. 75–86, Routledge India · Scopus
The Problem
Retail and digital businesses face a crowded field of technologies — AI, machine learning, deep learning, blockchain, IoT, NLP and big data — with little guidance on how they interact.
The Methodology
Overview chapter: descriptive analysis of seven imperative technologies for digital business (AI, ML, DL, blockchain, IoT, NLP, big data), their applications, benefits, challenges and interplay.
The Core Finding
The chapter identifies seven technologies as imperative for digital business, explains each one’s applications, benefits and challenges, and shows how their interplay affects business strategy, operations and innovation.
Why it matters
Retail and other digital businesses are adopting several transformative technologies at once. A single overview that treats them together, including how they interact, helps managers and researchers plan implementation rather than evaluate each technology in isolation.
The Citation
Vajjhala, N. R., Rakshit, S., & Denga, E. M. (2025). Digital Retail Innovations: The Role of Artificial Intelligence (AI) and Machine Learning (ML). In A. Behl & B. Sampat (Eds.), Digital Transformation: A Business Imperative (pp. 75–86). Routledge India. https://doi.org/10.4324/9781003626640-8
Key findings & abstract →DOI 10.4324/9781003626640-8
2025

Artificial Intelligence for Supply Chain Optimization: Benefits, Challenges, and Potential Solutions

Book chapter · Bole & Vajjhala (2025) · In Artificial Intelligence-Enabled Businesses: How to Develop Strategies for Innovation, pp. 81–93, Wiley · Scopus
The Problem
Firms need resilient supply chains in volatile markets, but integrating artificial intelligence into supply chain operations brings its own implementation challenges.
The Methodology
Conceptual review chapter synthesising earlier studies on the benefits, implementation challenges and potential solutions of AI in supply chain management.
The Core Finding
The chapter assesses AI’s advantages for organizational supply chains, sets out methods for integrating AI into supply chain processes, and pairs each implementation challenge with potential solutions, arguing that AI adoption is a critical step toward competitiveness.
Why it matters
Firms have lost competitive advantage through inadequate supply chain management, and resilient supply chains are essential in uncertain markets. The chapter gives managers a structured view of AI’s benefits, implementation barriers and remedies.
The Citation
Bole, D. K., & Vajjhala, N. R. (2025). Artificial Intelligence for Supply Chain Optimization: Benefits, Challenges, and Potential Solutions. In S. Dixit, M. Maurya, V. Jain, & G. Subramaniam (Eds.), Artificial Intelligence-Enabled Businesses: How to Develop Strategies for Innovation (pp. 81–93). Wiley. https://doi.org/10.1002/9781394234028.ch5
Key findings & abstract →DOI 10.1002/9781394234028.ch5
2022

Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs)

Conference paper · Borah et al. (2022) · Cognitive Informatics and Soft Computing: Proceeding of CISC 2021, Lecture Notes in Networks and Systems, vol. 375, pp. 717–726, Springer Nature Singapore · Scopus
The Problem
Small and medium-sized enterprises need artificial intelligence to stay competitive, but the cost, duration and failure risk of AI projects make many hesitate.
The Methodology
Discussion and review paper on AI applications in SMEs, covering implementation challenges, solutions and advantages; no empirical study is reported in the abstract.
The Core Finding
The paper sets out the AI applications relevant to SMEs together with the challenges, solutions and advantages of implementation, concluding that SMEs depend on AI and cloud-based solutions for growth despite the risks.
Why it matters
Most AI adoption literature focuses on large firms. This paper addresses the resource-constrained SME context, where cost, implementation time and failure risk shape whether and how AI — often through cloud services — is adopted.
The Citation
Borah, S., Kama, C., Rakshit, S., & Vajjhala, N. R. (2022). Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs). In P. K. Mallick, A. K. Bhoi, P. Barsocchi, & V. H. C. de Albuquerque (Eds.), Cognitive Informatics and Soft Computing: Proceeding of CISC 2021 (pp. 717–726). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-8763-1_59
Key findings & abstract →DOI 10.1007/978-981-16-8763-1_59
2022

Exploratory Review of Applications of Machine Learning in the Finance Sector

Book chapter · Rakshit et al. (2022) · In Advances in Data Science and Management: Proceedings of ICDSM 2021, Lecture Notes on Data Engineering and Communications Technologies, pp. 119–125, Springer Nature Singapore · Scopus · EI Compendex
The Problem
Financial institutions process large volumes of heterogeneous data, and an overview of which machine learning algorithms and techniques the sector applies was lacking.
The Methodology
Exploratory literature review of state-of-the-art machine learning applications, algorithms and techniques in the finance sector.
The Core Finding
The exploratory review surveys state-of-the-art machine learning applications, algorithms and techniques used in finance and sets out how machine learning can maximise productivity in financial institutions.
Why it matters
The finance sector is a pillar of national economies and increasingly processes heterogeneous big data with machine learning. A concise review of the algorithms in use helps researchers and practitioners orient themselves before deeper study.
The Citation
Rakshit, S., Clement, N., & Vajjhala, N. R. (2022). Exploratory Review of Applications of Machine Learning in the Finance Sector. In S. Borah, S. K. Mishra, B. K. Mishra, V. E. Balas, & Z. Polkowski (Eds.), Advances in Data Science and Management: Proceedings of ICDSM 2021 (pp. 119–125). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-5685-9_12
Key findings & abstract →DOI 10.1007/978-981-16-5685-9_12

Theme

Internet of Things and smart industries

Sustainable Internet of Things design for smart industries, agriculture, manufacturing and education.

2024

Designing Sustainable Internet of Things Solutions for Smart Industries

Edited book · Thandekkattu & Vajjhala (2024) · IGI Global, Advances in Business Information Systems and Analytics series, 496 pp. · Edited book · Scopus
The Problem
Industrial Internet of Things deployments improve efficiency but can add to environmental impact unless sustainability is designed in from the start.
The Approach
Edited volume of 13 chapters; chapters include technical frameworks (smart farming with AIoT, IoT data fusion for disease surveillance), a meta-analysis of IoT integration in education, and sector reviews (manufacturing, dairy, mobility, SMEs).
The Core Contribution
The volume shows how IoT can be designed for sustainability across education, waste reduction, agriculture and smart farming, disease surveillance, autonomous vehicles, Industry 5.0 manufacturing and SMEs, combining technical frameworks with sector reviews.
Why it matters
IoT deployments often overlook environmental considerations. The volume frames sustainability — renewable energy integration, waste reduction and ethics — as a design requirement for industrial IoT rather than an afterthought.
The Citation
Thandekkattu, S. G., & Vajjhala, N. R. (Eds.). (2024). Designing Sustainable Internet of Things Solutions for Smart Industries. IGI Global. https://doi.org/10.4018/979-8-3693-5498-8
Contents, description & citation →DOI 10.4018/979-8-3693-5498-8

Theme

Agriculture, food security and farm information systems

Food security, agricultural policy, and the adoption of farm management information systems in Nigeria and West Africa — from farmers, extension workers, and policy data.

Read the evidence overview for this theme →

2022

Thematic Analysis of Agricultural Government Policy and Operational Problems

Journal article · Strang et al. (2022) · Agricultural Research, 11(3), pp. 549–556 · Scopus · Web of Science · Q3
The Problem
Nigerian agricultural policy is designed far from the farm, and the ground-truth governance and operational problems facing rural farmers are rarely documented.
The Methodology
Qualitative constructivist grounded theory study: one-day focus group with 16 agricultural extension workers in Northeast Nigeria, member checking, and discourse analysis coded into thematic concept maps in NVivo.
The Core Finding
Extension workers representing about 900 rural farmers in Northeast Nigeria identified six core failures of government support: input quality and dissemination, fair subsidization, training, market facilitation, corruption and local security. Fake farm inputs, poor roads, insurgency-driven insecurity, and limited credit and land compound these problems.
Why it matters
The authors argue the government is the ‘keystone’ stakeholder: if the six core deficiencies were fixed—for example making good-quality inputs fairly available with modern-methods training—other operational problems might be mitigated through natural market growth. They recommend funding farmers’ cooperative associations, strengthening security, eliminating corruption, prioritizing the agricultural extension system, and greater involvement of private businesses and NGOs.
The Citation
Strang, K. D., Che, F., & Vajjhala, N. R. (2022). Thematic Analysis of Agricultural Government Policy and Operational Problems. Agricultural Research, 11(3), 549–556. https://doi.org/10.1007/s40003-021-00588-2
Key findings & abstract →DOI 10.1007/s40003-021-00588-2
2021

Contemporary Usage of Farm Management Information Systems in Nigeria

Book chapter · Vajjhala et al. (2021) · In Recent Developments in Individual and Organizational Adoption of ICTs, pp. 82–95, IGI Global · Scopus
The Problem
Farm management information systems could raise productivity in Nigeria, but the demographic factors that predict farmers’ adoption were unknown.
The Methodology
Positivist predictive correlational survey of 105 farm owners, managers and labourers on FMIS-using farms in the Jos Plateau, Nigeria (stratified random sampling), analysed with Pearson and Spearman correlation and discriminant analysis in SPSS 25.
The Core Finding
In a survey of 105 farm owners, managers and labourers on the Jos Plateau, only marital status, gender and language correlated with FMIS e-adoption; age, education, land size and software experience did not, and a discriminant model using the three significant factors was statistically significant (p = .014).
Why it matters
Agriculture contributes over 24% of Nigeria’s GDP and employs 68% of the labor force, yet farmers are not adopting FMIS to improve productivity. The authors state that the results should generalize to other rural farm decision makers in Nigeria and suggest males were more influential in FMIS use on farms, while widowed women left to run farms after Boko Haram violence were less likely to adopt FMIS.
The Citation
Vajjhala, N. R., Strang, K. D., & Bitrus, N. S. (2021). Contemporary Usage of Farm Management Information Systems in Nigeria. In O. Yildiz (Ed.), Recent Developments in Individual and Organizational Adoption of ICTs (pp. 82–95). IGI Global. https://doi.org/10.4018/978-1-7998-3045-0.ch005
Key findings & abstract →DOI 10.4018/978-1-7998-3045-0.ch005
2021

Agriculture Business Problems: Analysis of Research and Probable Solutions in Africa

Book chapter · Strang et al. (2021) · In Opportunities and Strategic Use of Agribusiness Information Systems, Advances in Business Information Systems and Analytics, pp. 33–58, IGI Global · Scopus
The Problem
Agriculture in West Africa, and Nigeria in particular, is held back by problems that the fragmented empirical literature has not synthesized.
The Methodology
Retrospective critical literature review of empirical agriculture studies (2015–2020) with keyword Pareto analysis, contingency analysis by level of analysis and subject, and cross-case synthesis; no new data collected.
The Core Finding
A critical review of 2015–2020 empirical studies identifies lack of modern-method training, corrupt input dissemination, poor infrastructure, terrorism-driven insecurity, lack of credit and weak strategic planning as the key problems, and finds only weak evidence of significant information-system use in agriculture.
Why it matters
The authors propose integrating training, knowledge sharing and attitude change – including via social media – and combining infrastructure, technology provision and anti-corruption efforts led by government as probable solutions. Their keyword themes and conceptual models are intended for agriculture software developers, practitioners, extension professionals and researchers, and they recommend mixed-methods studies that pair empirical evidence with problem-solving by local subject matter experts.
The Citation
Strang, K. D., Che, F. N., & Vajjhala, N. R. (2021). Agriculture Business Problems: Analysis of Research and Probable Solutions in Africa. In Opportunities and Strategic Use of Agribusiness Information Systems (pp. 33–58). IGI Global. https://doi.org/10.4018/978-1-7998-4849-3.ch003
Key findings & abstract →DOI 10.4018/978-1-7998-4849-3.ch003
2020

Urgently strategic insights to resolve the Nigerian food security crisis

Journal article · Strang et al. (2020) · Outlook on Agriculture, 49(1), pp. 77–85 · Scopus · Web of Science · Q2
The Problem
Nigeria’s food security crisis needs localized, prioritized solutions, but policy rarely draws on the practical knowledge of the extension workers who mentor rural farmers.
The Methodology
Pragmatic inductive study using consensual qualitative research with embedded nominal brainstorming in a six-hour focus group of 16 extension workers, prioritised with multiple correspondence analysis in SPSS 25.
The Core Finding
A focus group of extension workers representing about 900 farmers produced a significant two-dimension model of solutions, ranking affordable seeds and fertilizer, modern farming training, market information, roads and transport, affordable credit and agricultural mentors as the most strategically urgent.
Why it matters
The authors argue that food security solutions in Nigeria fail unless they are data-driven and customized by local experts to local sociocultural conditions. Their prioritized model is intended to generalize to rural farmers in Nigeria and to government policymakers in developing countries, pointing to fixes such as restoring input distribution, supporting extension workers and creating farmers’ cooperative unions.
The Citation
Strang, K. D., Che, F., & Vajjhala, N. R. (2020). Urgently strategic insights to resolve the Nigerian food security crisis. Outlook on Agriculture, 49(1), 77–85. https://doi.org/10.1177/0030727019873012
Key findings & abstract →DOI 10.1177/0030727019873012
2020

Voice of farmers in the agriculture crisis in North-East Nigeria: Focus group insights from extension workers

Journal article · Che et al. (2020) · International Journal of Development Issues, 19(1), pp. 43–61 · Outstanding Paper · 2021 Emerald Literati Awards · Scopus · Q2
The Problem
The voices of rural farmers in conflict-affected North-East Nigeria are largely missing from research on the region’s agriculture crisis.
The Methodology
Sequential mixed-methods constructivist design: grounded theory literature review followed by a consensual qualitative research focus group with 16 extension workers (nominal group technique, discourse analysis, thematic concept mapping in NVivo 12).
The Core Finding
Extension workers from three local government areas of Adamawa State confirmed themes from the literature and added new ones, showing that rural farmers face significant problems with government support in six areas: farm input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity.
Why it matters
The authors argue that if these six core areas were addressed, other problems might be indirectly mitigated through natural market growth, and that the government is the keystone stakeholder for all solutions. Policymakers should recognize the realities rural farmers face and urgently review the current process of farm input dissemination.
The Citation
Che, F. N., Strang, K. D., & Vajjhala, N. R. (2020). Voice of farmers in the agriculture crisis in North-East Nigeria: Focus group insights from extension workers. International Journal of Development Issues, 19(1), 43–61. https://doi.org/10.1108/IJDI-08-2019-0136
Key findings & abstract →DOI 10.1108/IJDI-08-2019-0136
2019

Factors impacting farm management decision making software adoption

Journal article · Strang et al. (2019) · International Journal of Sustainable Agricultural Management and Informatics, 5(1), pp. 1–14 · Scopus · Q3 · Web of Science
The Problem
Agricultural information systems can improve farm decisions in Nigeria, but little is known about why farmers continue or stop using such software.
The Methodology
Quantitative positivist survey grounded in expectation confirmation theory with 97 valid responses from farm owners, managers and labourers on the Jos Plateau, Nigeria, analysed with confirmatory factor analysis and structural equation modelling.
The Core Finding
Structural equation modelling of 97 Jos Plateau farm owners, managers and labourers showed that confirmation experience significantly drove satisfaction (path = 0.524) and perceived usefulness (path = 0.383), yet no factor significantly predicted continuance intention, which was uniformly very high.
Why it matters
The results raise controversial issues about the AIS education given to Nigerian farmers and the effectiveness of government agriculture technology funding. The authors suggest policy makers provide more educational seminars on alternative AIS products, including local vendor demonstrations, and recommend replication in other developing countries in Western Africa and elsewhere.
The Citation
Strang, K. D., Bitrus, S. N., & Vajjhala, N. R. (2019). Factors impacting farm management decision making software adoption. International Journal of Sustainable Agricultural Management and Informatics, 5(1), 1–14. https://doi.org/10.1504/IJSAMI.2019.10019819
Key findings & abstract →DOI 10.1504/IJSAMI.2019.10019819

Theme

Technology adoption and digital consumers

How firms, students, and consumers in emerging economies adopt — or resist — web technologies, e-services, learning systems, and online purchasing.

Read the evidence overview for this theme →

2020

Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample

Journal article · Strang & Vajjhala (2020) · International Journal of E-Services and Mobile Applications, 12(1), pp. 39–56 · Scopus · Q3
The Problem
Online retailers targeting fast-growing BRICS markets such as India know little about which social, demographic and emotional factors drive young consumers’ e-service purchase intentions.
The Methodology
Exploratory post-positivist quantitative survey of 63 young educated consumers in Andhra Pradesh and Telangana, India: chi-square tests, Spearman correlation, binary logistic regression and discriminant analysis in SPSS.
The Core Finding
Among 63 young educated Indian consumers, satisfaction, happiness, positive feelings and pleasant feelings predicted online purchase intention while excitement did not. Satisfaction alone correctly classified 90.5% of consumers, and satisfied consumers were almost 28 times more likely to intend to buy (odds ratio 27.91).
Why it matters
BRICS countries account for 40% of the world’s population and about a third of world GDP, and India has a rapidly growing young online population whose culture differs from the Western samples most e-commerce studies use. The findings give marketing managers a simple go/no-go predictor — consumer satisfaction with e-services — for forecasting online purchases among emerging technology-literate consumers in India.
The Citation
Strang, K. D., & Vajjhala, N. R. (2020). Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample. International Journal of E-Services and Mobile Applications, 12(1), 39–56. https://doi.org/10.4018/IJESMA.2020010103
Key findings & abstract →DOI 10.4018/IJESMA.2020010103
2019

Impact of Psycho-Demographic Factors on Smartphone Purchase Decisions

Conference paper · Vajjhala & Strang (2019) · Proceedings of the 2019 International Conference on Information System and System Management (ISSM 2019), Rabat, Morocco, pp. 5–10, ACM · Scopus
The Problem
E-commerce marketers often segment young consumers by gender, age and income, but it is unclear whether these factors or trust actually drive online purchase decisions across cultures.
The Methodology
Post-positivist cross-cultural structured-questionnaire study of 89 young technology-literate consumers in New York State and Andhra Pradesh/Telangana, analysed with chi-square, Spearman correlation, cluster analysis, logistic regression and discriminant analysis in SPSS 22.
The Core Finding
Among 89 young technology-literate consumers in the USA and India, gender, age and income did not influence online smartphone purchase decisions. Trust in government and social media and feeling controlled by the e-commerce system predicted decisions significantly (Wilks’ λ = 0.595, p < .001), explaining about 41% of the variance.
Why it matters
The findings suggest marketing managers can increase online purchases of digital devices by designing e-commerce systems that reduce consumers’ feeling of being controlled — for example by making email and phone details optional when valid payment data are provided. The authors expect the results to generalize to young, technology-literate urban consumers who buy moderately to highly priced smartphones online.
The Citation
Vajjhala, N. R., & Strang, K. D. (2019). Impact of Psycho-Demographic Factors on Smartphone Purchase Decisions. In Proceedings of the 2019 International Conference on Information System and System Management (ISSM 2019), Rabat, Morocco (pp. 5–10). ACM. https://doi.org/10.1145/3394788.3394790
Key findings & abstract →DOI 10.1145/3394788.3394790
2018

A Study on Application of Web 3.0 Technologies in Small and Medium Enterprises of India

Journal article · Potluri & Vajjhala (2018) · The Journal of Asian Finance, Economics and Business, 5(2), pp. 73–79 · Scopus and Web of Science (indexed at publication)
The Problem
Indian small and medium enterprises are under pressure to adopt Web 3.0 technologies, but the opportunities and obstacles their managers perceive had not been documented.
The Methodology
Qualitative study of managers from 40 Indian SMEs in five economic sectors using semi-structured questionnaires with open-ended questions, content analysis and thematic coding in NVivo 11.
The Core Finding
Interviews with managers from 40 Indian SMEs across five sectors produced 12 subthemes: advantages centred on service integration and new functionalities, while challenges centred on privacy and security, financial and technological constraints, and organizational barriers.
Why it matters
SMEs drive much of India’s economy but have limited technology budgets. The themes give SME leaders a practical map for short- and long-term information systems strategy, helping them make better use of technology assets to improve productivity and competitiveness.
The Citation
Potluri, R. M., & Vajjhala, N. R. (2018). A Study on Application of Web 3.0 Technologies in Small and Medium Enterprises of India. The Journal of Asian Finance, Economics and Business, 5(2), 73–79. https://doi.org/10.13106/jafeb.2018.vol5.no2.73
Key findings & abstract →DOI 10.13106/jafeb.2018.vol5.no2.73Free PDFOpen access
2018

Examining Internet Behavior of Young Technology-Literate Consumers in India

Conference paper · Vajjhala & Strang (2018) · Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans, Association for Information Systems · Scopus · Web of Science
The Problem
Online retailers in India lack evidence on which demographic and emotional factors predict young technology-literate consumers’ Internet purchase intention.
The Methodology
Within-group correlational survey of 63 online shoppers in Andhra Pradesh and Telangana testing eight hypotheses with chi-square tests, Spearman correlation, binary logistic regression and discriminant analysis in Minitab 18.
The Core Finding
Among 63 Indian online shoppers, age and income were related to purchase intention, and happiness, satisfaction, positive and pleasant feelings — but not excitement — predicted it. Satisfaction alone correctly classified 90.5% of respondents.
Why it matters
Because most online consumer behavior studies come from Western countries, the findings add evidence from India’s distinct culture of high power distance and low uncertainty avoidance, where consumers are likely to be willing to adopt new technology. The authors argue the results can be generalized to the upcoming generation of young consumers in India, while recommending replication with larger samples and other cultures.
The Citation
Vajjhala, N. R., & Strang, K. D. (2018). Examining Internet Behavior of Young Technology-Literate Consumers in India. In Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans. Association for Information Systems. https://aisel.aisnet.org/amcis2018/
Key findings & abstract →
2017

Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses

Journal article · Strang & Vajjhala (2017) · Journal of Organizational and End User Computing, 29(3), pp. 49–67 · Scopus · Q2 · Web of Science · SSCI/SCIE
The Problem
Universities impose mandatory learning management systems on students, yet it is unclear which Technology Acceptance Model 3 factors explain resistance and whether they predict actual performance.
The Methodology
Post-positivist within-group survey of 65 senior supply chain management students at SUNY using the TAM3 instrument, with hierarchical, surface response and multiple regression on behavioural intention and actual course grade.
The Core Finding
Among 65 senior supply chain students using a new mandatory LMS, perceived usefulness, enjoyment, voluntariness and results demonstrability explained 52.3% of behavioral intention, and behavioral intention predicted actual course grade — confirming a key TAM3 tenet with a performance outcome rather than self-report alone.
Why it matters
The authors argue that management students’ technology acceptance motivations will generalize to the future supply chain workforce, so understanding why they resist new mandatory software can help decision makers address critical success factors and improve organizational performance. They also contribute parsimonious models with very few factors that capture 56% of the variance in behavioral intention and 49% in actual performance.
The Citation
Strang, K. D., & Vajjhala, N. R. (2017). Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses. Journal of Organizational and End User Computing, 29(3), 49–67. https://doi.org/10.4018/JOEUC.2017070103
Key findings & abstract →DOI 10.4018/JOEUC.2017070103

Theme

Culture, knowledge management and transition economies

National and organizational culture, knowledge sharing, and communities of practice in the SMEs of transition economies such as Albania.

Read the evidence overview for this theme →

2018

Sociotechnical Challenges of Transition Economy SMEs During EU Integration

Book chapter · Vajjhala & Strang (2018) · In Doing Business in Europe: Economic Integration Processes, Policies, and the Business Environment, Contributions to Management Science, pp. 295–313, Springer International Publishing · Scopus · Web of Science (BKCI)
The Problem
SMEs in transition economies such as Albania face social and technical barriers to technological innovation as their countries integrate with the EU.
The Methodology
Qualitative case study of Albania extending prior quantitative research: in-depth interviews with 20 managers in 10 medium-sized enterprises, analysed to identify critical success factors.
The Core Finding
Interviews with 20 managers in ten Albanian medium-sized enterprises identified five critical success factors: investment in ICT for work processes, investment in employee training, perceived usefulness of the technology, employee self-efficacy, and openness toward new technology.
Why it matters
SMEs are the backbone of transition and emerging economies but have limited financial, human and technological resources for innovation. Identifying the factors that let them meet EU-integration demands is useful to SME executives, organisational researchers and socio-economic practitioners in transition and developing countries.
The Citation
Vajjhala, N. R., & Strang, K. D. (2018). Sociotechnical Challenges of Transition Economy SMEs During EU Integration. In A. M. Dima (Ed.), Doing Business in Europe: Economic Integration Processes, Policies, and the Business Environment (pp. 295–313). Springer International Publishing. https://doi.org/10.1007/978-3-319-72239-9_14
Key findings & abstract →DOI 10.1007/978-3-319-72239-9_14
2017

Measuring Organizational-Fit Through Socio-Cultural Big Data

Journal article · Vajjhala & Strang (2017) · New Mathematics and Natural Computation, 13(2), pp. 145–158 · Scopus · Q3 · Web of Science · ESCI
The Problem
Organizations make hiring and partnering decisions on cultural fit, but no published studies had used big data to measure the relationship between culture and organizational fit.
The Methodology
Conceptual paper based on a literature review of big data and national and organisational culture models, proposing a socio-cultural viability dimension and a two-phase sampling, data-reduction and hypothesis-testing approach.
The Core Finding
The paper proposes “viability” as a socio-cultural sixth V of big data and a two-phase method — sampling and data reduction, followed by hypothesis testing — for turning socio-cultural big data into generalizable organizational-fit evidence.
Why it matters
Collecting and analyzing big data requires large economic and time investments, so the authors argue it makes no sense to do so if the results will not be socio-culturally meaningful for the population of interest. Publicly available social networking data could let researchers test cultural dimensions such as uncertainty avoidance and individualism–collectivism, informing decisions on hiring, partnering and technology acceptance, though researchers must be cautious about big data quality.
The Citation
Vajjhala, N. R., & Strang, K. D. (2017). Measuring Organizational-Fit Through Socio-Cultural Big Data. New Mathematics and Natural Computation, 13(2), 145–158. https://doi.org/10.1142/S179300571740004X
Key findings & abstract →DOI 10.1142/S179300571740004X
2016

Communities of Practice in Transition Economies: Innovation in Small- and Medium-Sized Enterprises

Book chapter · Vajjhala (2016) · In Organizational Knowledge Facilitation through Communities of Practice in Emerging Markets, Advances in Knowledge Acquisition, Transfer, and Management, pp. 31–44, IGI Global · Scopus
The Problem
SMEs in transition economies have limited resources for innovation, and the conditions under which communities of practice succeed there had not been theorized.
The Methodology
Conceptual chapter developing a framework for communities of practice in SMEs of transition economies from the knowledge management and culture literature.
The Core Finding
The chapter shows that the success of communities of practice in transition-economy SMEs depends partly on social and cultural factors that differ from those in developed and developing countries, and develops a framework for applying communities of practice in that context.
Why it matters
According to the chapter, CoPs can help SMEs with limited financial and human resources improve efficiency and productivity by leveraging the organization's knowledge resources. The proposed framework and the factors identified as limiting CoPs are aimed at supporting innovation in SMEs in transition economies.
The Citation
Vajjhala, N. R. (2016). Communities of Practice in Transition Economies: Innovation in Small- and Medium-Sized Enterprises. In Organizational Knowledge Facilitation through Communities of Practice in Emerging Markets (pp. 31–44). IGI Global. https://doi.org/10.4018/978-1-5225-0013-1.ch002
Key findings & abstract →DOI 10.4018/978-1-5225-0013-1.ch002
2014

Collaboration strategies for a transition economy: measuring culture in Albania

Journal article · Vajjhala & Strang (2014) · Cross Cultural Management, 21(1), pp. 78–103 · Web of Science · SSCI · Q1 · ABDC · ERA
The Problem
Foreign organizations collaborating with Albania had no empirical measure of its national culture, because Albania was absent from Hofstede’s cultural-dimension data.
The Methodology
Non-experimental post-positivist survey of 73 business professionals in Tirana validating a five-factor Hofstede-based instrument through exploratory and confirmatory factor analysis, with national culture indexes computed and benchmarked against Hofstede data.
The Core Finding
A validated five-factor survey of 73 Tirana business professionals produced the first Hofstede-style indexes for Albania (power distance 78.7, individualism 42.7, masculinity 72.9, uncertainty avoidance 64.6, long-term orientation 52.2). Albania’s profile is closest to its Balkan and Turkish neighbours rather than to Western cultures such as the USA.
Why it matters
The Albanian indexes give foreign governments, corporations and nonprofits general indicators of national beliefs, norms and values to benchmark against their own culture profile when collaborating in this emerging transition economy. Large gaps between two nations' indexes suggest the approaching entity must customize its approach, and the validated instrument can be reused to profile other countries.
The Citation
Vajjhala, N. R., & Strang, K. D. (2014). Collaboration strategies for a transition economy: measuring culture in Albania. Cross Cultural Management, 21(1), 78–103. https://doi.org/10.1108/CCM-02-2013-0023
Key findings & abstract →DOI 10.1108/CCM-02-2013-0023
2014

Influence of cultural factors on knowledge sharing in medium-sized enterprises within transition economies

Journal article · Vajjhala & Baghurst (2014) · International Journal of Knowledge Management Studies, 5(3/4), pp. 304–321 · Scopus · Q3
The Problem
Knowledge-sharing initiatives in transition-economy firms often stall, and the cultural factors behind employee participation in Albanian enterprises were not understood.
The Methodology
Qualitative multisite exploratory case study: 27-question open-ended interviews with 20 managers from ten medium-sized enterprises across five Albanian sectors, coded in NVivo 9 following Creswell’s procedure until six themes emerged.
The Core Finding
Interviews with 20 managers in ten Albanian medium-sized enterprises showed that national and organizational culture shape knowledge sharing: 85% said national culture influences employee behavior, 70% named lack of trust as a key barrier, and 90% said top management support is essential.
Why it matters
Medium-sized firms in transition economies often adopt generic, Western-developed knowledge-sharing models without considering culture. The authors advise Albanian SME leaders to account for national culture, build a supportive organizational culture led by top management, introduce incentives and training, address fears and clustering, and adapt information systems to national and organizational cultural requirements rather than buying generic knowledge management systems.
The Citation
Vajjhala, N. R., & Baghurst, T. (2014). Influence of cultural factors on knowledge sharing in medium-sized enterprises within transition economies. International Journal of Knowledge Management Studies, 5(3/4), 304–321. https://doi.org/10.1504/IJKMS.2014.067235
Key findings & abstract →DOI 10.1504/IJKMS.2014.067235

Theme

Computing education

Teaching blockchain, cybersecurity, and functional programming in computer science and management curricula.

Read the research synthesis: Computing education →

2026

A Spotlight on the Role of Functional Programming & Haskell in Computing & Software Development

Conference paper · Fonkam & Vajjhala (2026) · Computational Science and Computational Intelligence (CSCE 2025), Communications in Computer and Information Science (CCIS 2939), vol. 2939, pp. 56–63, Springer Nature Switzerland AG · Scopus
The Problem
Computing educators debate whether teaching functional programming with Haskell improves learning and transfers to the mainstream languages students later use.
The Methodology
Systematic literature review of 28 empirical studies (2015–2025) on functional programming and Haskell in education, drawn from ACM DL, IEEE Xplore, Google Scholar and computing-education venues, with thematic synthesis.
The Core Finding
A systematic review of 28 empirical studies (2015–2025) found mixed but promising outcomes: integrated approaches outperform purely functional ones, benefits for mathematical reasoning and abstract thinking are consistent, and transfer to mainstream object-oriented languages is selective.
Why it matters
The authors argue that as AI tools increasingly generate code, functional programming and abstract math and logic become more rather than less critical, because they build the reasoning needed to model, design and validate software and to supervise AI tools. They recommend integrating mathematical and logical concepts across computing courses and using declarative languages such as Haskell or Prolog as introductory vehicles before or alongside industry imperative languages.
The Citation
Fonkam, M., & Vajjhala, N. R. (2026). A Spotlight on the Role of Functional Programming & Haskell in Computing & Software Development. In H. R. Arabnia (Ed.), Computational Science and Computational Intelligence (CSCE 2025) (pp. 56–63). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-032-22202-2_5
Key findings & abstract →DOI 10.1007/978-3-032-22202-2_5
2024

Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving

Edited book · Fonkam & Vajjhala (2024) · IGI Global, Advances in Systems Analysis, Software Engineering, and High Performance Computing series, 245 pp. · Edited book · Scopus
The Problem
Traditional, siloed curricula leave graduates without the computational thinking, logic and problem-solving skills the future workforce needs.
The Approach
Edited volume of 11 chapters; chapters include conceptual frameworks, curriculum and lesson-plan proposals, case studies, a survey-based consumer study, and a review of computational thinking tools.
The Core Contribution
The volume reframes computational thinking as a discipline-independent skill set and provides lesson plans, case studies, tools and strategies for embedding it in fields well beyond computing, from educational robotics to digital marketing and literature.
Why it matters
Educators must prepare students for professions that do not yet exist using tools that are still emerging. Presenting computational thinking as a way of reasoning rather than a coding skill makes it applicable in every subject, and the volume pairs that argument with lesson plans, case studies and strategies.
The Citation
Fonkam, M. M., & Vajjhala, N. R. (Eds.). (2024). Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving. IGI Global. https://doi.org/10.4018/979-8-3693-1974-1
Contents, description & citation →DOI 10.4018/979-8-3693-1974-1
2020

Ideologies and Issues for Teaching Blockchain Cybersecurity in Management and Computer Science

Book chapter · Strang et al. (2020) · In Innovations in Cybersecurity Education, pp. 109–126, Springer · Scopus
The Problem
Universities are beginning to teach blockchain as part of cybersecurity, but there was no framework for how to teach it to management and computer science students.
The Methodology
Conceptual chapter combining a literature search (ProQuest Central and EBSCO) with the authors’ teaching experience and cybersecurity curriculum frameworks (ACM CSEC, ABET, CAE) to develop a two-dimensional teaching typology.
The Core Finding
A literature search found only five relevant peer-reviewed papers, which — with the authors’ teaching experience — led to a two-dimensional teaching typology (stakeholder role from programmer to managerial decision-maker; teaching ideology from theoretical to hands-on). The chapter concludes that blockchain is an essential component of modern cybersecurity education.
Why it matters
The typology can help university decision-makers and academic administrators design and deliver cybersecurity and risk management degrees, and help students decide what to study. The authors argue that cybersecurity curricula, especially in emerging economies, should balance core security knowledge with practical skills and include blockchain, given workforce shortages and the growth of distributed technologies.
The Citation
Strang, K. D., Che, F., & Vajjhala, N. R. (2020). Ideologies and Issues for Teaching Blockchain Cybersecurity in Management and Computer Science. In K. Daimi & G. Francia III (Eds.), Innovations in Cybersecurity Education (pp. 109–126). Springer. https://doi.org/10.1007/978-3-030-50244-7_7
Key findings & abstract →DOI 10.1007/978-3-030-50244-7_7

Theme

Algorithms and bioinformatics

String-matching algorithms and statistical relational learning for biological sequence and genomics data.

2023

Comparative Analysis of Bit-Parallel String Pattern Matching Algorithms for Biological Sequences

Journal article · Muhammad et al. (2023) · Operational Research in Engineering Sciences: Theory and Applications, 6(1), pp. 322–331 · Scopus · Q2
The Problem
Bioinformatics needs fast pattern matching over DNA, RNA and protein sequences, but the many bit-parallel string-matching algorithms have not been compared side by side for biological data.
The Methodology
Comparative literature review of bit-parallel exact and approximate string matching algorithms (Shift-Or/Shift-And, BNDM, TNDM, SBNDM and parameterized variants) with a tabulated comparison of 11 algorithms by complexity, drawbacks and application area.
The Core Finding
A comparison of 11 bit-parallel algorithms shows that bit-parallelism is versatile for biological sequence analysis but is limited mainly by the requirement that pattern length not exceed the computer word length. The BNDM family is most efficient within that limit, and optimal performance requires designing algorithms around the nature of the target sequence.
Why it matters
Because biological sequence data is generated rapidly and matching involves large amounts of computation, choosing an appropriate algorithm is difficult; the comparison is intended to help researchers select the most appropriate method for a particular application area. The authors note that benchmark algorithms applied to RNA without modification perform poorly, pointing to structure-aware bit-parallel methods as future work.
The Citation
Muhammad, M. Y., Fonkam, M., Thandekatu, S. G., Rakshit, S., & Vajjhala, R. N. (2023). Comparative Analysis of Bit-Parallel String Pattern Matching Algorithms for Biological Sequences. Operational Research in Engineering Sciences: Theory and Applications, 6(1), 322–331. https://oresta.org/article-view/?id=554
Key findings & abstract →Open access
2022

Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review

Book chapter · Biba & Vajjhala (2022) · In Handbook of Machine Learning Applications for Genomics, Studies in Big Data, pp. 31–42, Springer Nature Singapore Pte Ltd. · Scopus · EI Compendex
The Problem
Genomics data are relational, noisy and incomplete, violating the independence assumptions of most machine learning methods.
The Methodology
State-of-the-art literature review (66 references) of probabilistic graphical models and statistical relational learning approaches — probabilistic relational models, relational dependency networks, relational Markov networks — and their genomics applications.
The Core Finding
The review shows that statistical relational learning — probabilistic relational models, relational dependency networks and relational Markov networks — suits genomics tasks from sequence annotation to GWAS, and identifies the computational complexity of inference, followed by graph size, as the main limitation shared by most methods.
Why it matters
Genomic data is being generated faster than existing methods can analyze it, and annotating all sequences manually is unfeasible and costly, so the authors argue SRL methods will be essential to automatically annotate sequences and handle large genomic data sets. They expect demand for machine learning methods that can adapt to these big data sets to increase over the next decade.
The Citation
Biba, M., & Vajjhala, N. R. (2022). Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review. In S. S. Roy & Y.-H. Taguchi (Eds.), Handbook of Machine Learning Applications for Genomics (pp. 31–42). Springer Nature Singapore Pte Ltd. https://doi.org/10.1007/978-981-16-9158-4_3
Key findings & abstract →DOI 10.1007/978-981-16-9158-4_3
2022

Efficient Structural Matching for RNA Secondary Structure Using Bit-Parallelism

Conference paper · Muhammad et al. (2022) · High Performance Computing and Networking: Select Proceedings of CHSN 2021, Lecture Notes in Electrical Engineering, vol. 853, pp. 399–409, Springer Nature Singapore · Scopus
The Problem
Annotating the function of newly discovered RNA requires finding exactly and structurally similar RNA secondary structures, which existing exact-matching algorithms do not handle efficiently.
The Methodology
Algorithm design: a bit-parallel exact structural matching (s-matching) algorithm for RNA secondary structure that advances the Shift-Or algorithm with encoding techniques for s-strings; average-time optimality is stated as an assumption.
The Core Finding
The authors developed a bit-parallel structural matching (s-matching) algorithm that extends Shift-Or with encoding techniques for s-strings, solving exact s-matching on RNA secondary structure with assumed average-time optimality.
Why it matters
Structural similarity between RNA sequences helps infer their biological function. An efficient exact structural matching algorithm gives bioinformatics pipelines a fast way to find structurally similar RNA, complementing sequence-level matching.
The Citation
Muhammad, M. Y., Thandekkattu, S. G., Rakshit, S., & Vajjhala, N. R. (2022). Efficient Structural Matching for RNA Secondary Structure Using Bit-Parallelism. In Ch. Satyanarayana, D. Samanta, X.-Z. Gao, & R. K. Kapoor (Eds.), High Performance Computing and Networking: Select Proceedings of CHSN 2021 (pp. 399–409). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9885-9_33
Key findings & abstract →DOI 10.1007/978-981-16-9885-9_33