Open Access

Article

Article ID: 4181

Predictive model for students’ academic performance using machine learning approach

by Wadzani Aduwamai Gadzama, Ogah Stephen Ugbowu, Lucy Bulus Dalhatu

Computing and Artificial Intelligence, Vol.4, No.1, 2026;

The early prediction of students' academic performance using machine learning has emerged as a valuable approach for identifying at-risk learners and enabling timely intervention. Many factors, such as students' academic background, prior performance, institutional policies, and learning environment, influence educational outcomes; their complex interplay remains inadequately understood in many contexts. This study aimed to explore the effectiveness of machine learning algorithms in predicting students' academic performance at Adamawa State University, Mubi, Adamawa State, Nigeria. This study used 1,730 datasets from the academic records of first-year students from the Faculty of Science for the 2024/2025 academic session. The study split the datasets into 80% training and 20% for testing. Data were analysed using the Waikato Environment for Knowledge Analysis (Weka) and Python. The model was evaluated using students' cumulative grade point averages (CGPAs) from the academic session results. The machine learning algorithms used were Logistic Regression (LR), Random Forest (RF), Decision Trees (DT), Naïve Bayesian (NB), and Support Vector Machines (SVM). Experimental results based on various performance metrics indicate that the SVM model achieved the best result with an accuracy of 0.92, precision of 0.92, recall of 0.93, and F1-score of 0.93. The results revealed that the SVM approach outperforms individual benchmark methods and provides robust insight into factors that determine academic success. The findings offer evidence-based guidance for educators, departments, faculties, institutional management, and policymakers to design targeted interventions to improve learning outcomes.

Open Access

Article

Article ID: 4104

Performance evaluation of B-Tree and hash indexing under varying data sizes in relational database systems

by Hassan Bediar Hashim

Computing and Artificial Intelligence, Vol.4, No.1, 2026;

This study investigates query performance optimization in relational database management systems (RDBMSs) by evaluating two common indexing techniques, B-Tree and Hash indexing, under varying dataset sizes. With the rapid growth of data generated by IoT systems, enterprise applications, and digital services, efficient query execution has become essential for maintaining scalability and system performance. The research compares three database configurations: no indexing, B-Tree indexing, and Hash indexing, while applying a Cost-Based Optimization (CBO) strategy to improve query plan selection. Experimental results reveal that query response time increases significantly with larger datasets, especially when no indexing is used. Both indexing methods substantially enhance performance compared to full-table scans, achieving improvements ranging from 35% to 60% depending on dataset size and query workload. The measured speedup factors reached up to 2.60×, confirming the effectiveness of indexing in reducing execution time. Further analysis indicates that B-Tree indexing consistently performs better than Hash indexing in large-scale and mixed-query environments due to its logarithmic search efficiency and support for range queries. B-Tree indexing reduced execution time to nearly 40–45% of the baseline, whereas Hash indexing achieved approximately 55–60% under similar conditions. The findings emphasize that selecting an appropriate indexing strategy is critical for optimizing database query performance, and that the effectiveness of each method depends largely on workload characteristics and dataset scale.

Open Access

Review

Article ID: 3991

Image processing techniques for detection of objects in blurry pictures: A comprehensive review

by Ghassan Abdullah Abdulwasea Al-Maamari, Mubarak Mohammed Al-Ezzi Sufyan, Ramzi Hamid Abdo Al-Jaberi, Mokhtar H. Al-Sarori, Mahfoudh Al-Asaly, Asma’a Khalil Alkershi

Computing and Artificial Intelligence, Vol.4, No.1, 2026;

Detecting objects in blurry and degraded images remains a critical unsolved challenge in computer vision, affecting applications from medical diagnostics and autonomous navigation to remote sensing and surveillance. Image degradation caused by motion, defocus, poor lighting, or environmental factors severely compromises feature visibility and limits the performance of conventional detection algorithms. This paper presents a comprehensive, systematic review of state-of-the-art techniques designed to address this problem. We first categorize common image degradations and analyze classical and deep learning-based solutions for image deblurring and enhancement, including CNN (Convolutional Neural Networks), GAN (Generative Adversarial Networks), and transformer architectures. The review then critically examines object detection models, particularly YOLO (You Only Look Once) and CNN-based networks, adapted for low-quality inputs. A key focus is on integrated pipelines that jointly optimize restoration and detection. We synthesize findings from over 200 studies, highlighting performance across diverse domains such as UAV (Unmanned Aerial Vehicle) imagery, underwater exploration, and medical analysis. Furthermore, we discuss standard datasets and evaluation metrics, identify persistent challenges including real-time processing, multi-degradation handling, and domain adaptation, and outline promising research directions. This review serves as a foundational resource for researchers and practitioners aiming to build robust vision systems for real-world, blur-prone environments.

Open Access

Article

Article ID: 3958

From calculators to artificial intelligence: A multi-level framework for technology adoption and resistance in education and organisations

by Michael Mncedisi Willie

Computing and Artificial Intelligence, Vol.4, No.1, 2026;

Technological innovations are frequently associated with enhanced efficiency and improved decision-making; however, their initial adoption has often been characterised by notable resistance. This study examined the persistence of this phenomenon by synthesising empirical evidence from the historical adoption of calculators, spreadsheets, and statistical software, and by comparing these insights with contemporary developments in artificial intelligence (AI). The findings indicated that resistance extended beyond technical limitations and reflected a complex, multi-dimensional process shaped by individual factors such as self-efficacy, perceived usefulness, and anxieties related to skill displacement as well as organisational culture and broader systemic conditions. The evidence further demonstrated that successful adoption was contingent upon structured support mechanisms, incremental exposure to new technologies, and the clear articulation of value propositions. At the organisational level, effective leadership, adequate resource allocation, and coherent policy alignment emerged as critical enablers of sustained integration. Drawing on these insights, the study proposed a multi-level conceptual framework that integrates individual, organisational, and systemic determinants to guide future technology adoption. The framework underscores that meaningful and sustainable integration depends on coordinated, cross-level interventions rather than isolated initiatives. The framework also highlights the need for further empirical research into AI adoption in under-resourced contexts. It calls for critical engagement with the ethical implications of algorithmic decision-making in evolving socio-technical systems.

Open Access

Article

Article ID: 3893

Verifying artificial intelligence-generated images: Socio-technical approaches to authenticity

by Michael Mncedisi Willie

Computing and Artificial Intelligence, Vol.3, No.4, 2025;

The rapid proliferation of artificial intelligence (AI) has transformed visual media, enabled highly realistic AI-generated images, and raised ethical, social, and security concerns. Generative artificial intelligence (Generative AI) architectures, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, allow content creation that is increasingly indistinguishable from human-made visuals, facilitating creativity, education, and communication. However, these capabilities also introduce risks of manipulation, identity fraud, misinformation, and deepfake attacks across social, political, corporate, academic, and humanitarian domains. This study investigates AI image verification as a socio-technical response to synthetic visuals, focusing on social media, artistic, and forensic contexts. It employed a qualitative design combining thematic literature review and case study analysis. Thematic analysis identified patterns in verification approaches, including pixel-level analysis, metadata forensics, machine learning classifiers, watermarking, and blockchain-enabled methods. Case studies explored real-world applications, highlighting perceptual biases, strategic use of synthetic content, and governance and digital literacy challenges. Findings reveal that human perception alone is insufficient for reliably discerning authenticity, with individuals frequently misclassifying AI-generated images as real. Integrating machine learning, metadata analysis, and blockchain verification, hybrid technical approaches significantly improve detection accuracy. Socio-technical factors, including platform policies, ethical norms, organisational governance, and user literacy, shape the effectiveness of verification methods. The study presents a conceptual framework linking technological, organisational, and societal dimensions, emphasising the need for coordinated strategies that combine algorithmic innovation, regulatory oversight, and public engagement. Practical implications include deploying hybrid verification systems, strengthening governance and ethical standards, enhancing digital literacy, and fostering cross-disciplinary collaboration to safeguard trust, authenticity, and integrity in digital media.

Open Access

Article

Article ID: 4243

The algorithmic guardians: AI and computer vision for global faunal welfare, conservation, and future policy trajectories in the Indian subcontinent

by Ayan Paul

Computing and Artificial Intelligence, Vol.3, No.3, 2025;

Artificial Intelligence (AI) and Computer Vision (CV) are rapidly transforming animal welfare, conservation, and ecosystem management by enabling scalable, real-time analysis of large multimodal datasets. Traditional monitoring approaches are increasingly inadequate due to the exponential growth of visual and sensor data across farms, urban ecosystems, and wildlife habitats. This paper presents a structured review of AI/CV methodologies—including convolutional neural networks, You Only Look Once (YOLO)-based detection, and pose estimation—for quantitative faunal assessment. A systematic synthesis is provided across key domains such as precision livestock farming, urban animal welfare, wildlife conservation, and marine ecosystem monitoring. The study adopts a structured literature review methodology, outlining database selection, inclusion criteria, and comparative evaluation of state-of-the-art techniques. Key findings indicate that AI-driven systems significantly enhance early disease detection, behavioral analysis, and conservation efficiency, though challenges persist in terms of data scarcity, algorithmic bias, and deployment constraints in low-resource environments. A comparative analysis highlights trade-offs between accuracy, computational efficiency, and scalability across different AI architectures. The paper also identifies critical research gaps, including the lack of standardized datasets, limited cross-species generalization, and insufficient integration with policy frameworks. Finally, the study proposes a conceptual framework integrating AI, edge computing, and ethical governance for sustainable faunal management. The findings underscore the need for interdisciplinary collaboration and responsible AI deployment to ensure equitable and scalable benefits across diverse ecological and socio-economic contexts.