Open Access

Article

Article ID: 4398

YOLOv26-based automated crater detection and geometric measurement using Chandrayaan-2 Orbiter High Resolution Camera (OHRC) imagery

by Meenakshi Sarkar, Prit Gajjar

Computing and Artificial Intelligence, Vol.4, No.2, 2026;

Accurate detection and measurement of lunar craters are crucial for a better understanding of the geological history and evolution of the Moon’s surface. Manually detecting and measuring craters using high-resolution images is not only tedious but may also involve a certain amount of subjectivity. What makes this proposal innovative is the combination of the You Only Look Once (YOLO) algorithm with the Segment Anything Model for accurate segmentation of craters’ pixels and measurement of their geometrical properties. In this framework, the locations of the craters are automatically detected using the YOLOv26 algorithm, followed by accurate determination of the crater regions using the Segment Anything Model (SAM). Finally, the accurate determination of the diameter, radius, and surface area of the craters is carried out using the pixel measurement and scale factor conversion technique. This approach will be trained and tested using a dataset comprised of about 12,000 images obtained by dividing the Orbiter High Resolution Camera (OHRC) lunar imagery into image patches of 640 × 640 dimensions and divided in the ratio of training set: validation set: test set as 70:20:10. This model gave a precision value of 91.5%, a recall of 88.6%, mAP@50 of 95.6%, and mAP@50–95 of 88.6%, thereby outperforming all other versions of YOLO that were analysed and showing strong competency.

Open Access

Article

Article ID: 4439

Static vs. dynamic cloud scheduling: A benchmark driven performance and energy study

by Saba Naz, Altaf Hussain

Computing and Artificial Intelligence, Vol.4, No.2, 2026;

Major multinational corporations, including Amazon, Microsoft, IBM, and Google, continue to advance network and computational infrastructures through the deployment of highly efficient data centers worldwide to strengthen their cloud computing services. Despite these technological advancements, cloud computing environments still face several critical challenges, such as optimal resource utilization, cost efficiency, security, fault tolerance, scalability, and energy consumption. For organizations operating private clouds or delivering cloud-based services, the primary objectives include maximizing resource utilization, minimizing response and waiting times, increasing throughput and profitability, and enhancing the overall user experience, with energy efficiency being a particularly important concern. This study presents an empirical evaluation of various static and dynamic cloud job scheduling algorithms using the CloudSimPlus simulation framework. The algorithms are assessed using two well-established scientific benchmark datasets: HCSP and GoCJ instances. Experimental results reveal that the Resource-Aware Load Balancing Algorithm (RALBA) emerges as the most energy-efficient static scheduling approach, achieving reduced makespan, improved resource utilization, and lower energy consumption. In contrast, the Max–Min algorithm exhibits the highest makespan, the lowest throughput, inefficient resource utilization, and the greatest energy consumption. Among the dynamic scheduling techniques, DE-RALBA demonstrates superior resource utilization and competitive makespan performance while maintaining relatively efficient energy usage. However, the results also indicate that dynamic scheduling approaches may incur higher energy consumption depending on workload characteristics and rescheduling overhead. Unlike prior studies that evaluate scheduling strategies in isolation, this work provides a unified, benchmark-driven, and energy-aware comparison of static and dynamic scheduling paradigms, enabling deeper insights into performance–energy trade-offs.

Open Access

Article

Article ID: 4448

Epigenetic regulation for dynamic UAV-swarm optimization—EpiSwarm

by Jordi Vallverdú

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

Evolutionary algorithms adapt mainly through selection, recombination, and mutation, which in changing environments forces costly re-optimization and slow recovery after disruption. We present EpiSwarm, an evolutionary algorithm for dynamic industrial UAV-swarm inspection in which each candidate solution carries a persistent genotype and a fast-changing, partially heritable epigenotype. The genotype encodes mission structure; the epigenotype is a decaying memory of recent environmental stress that modulates task activation, route repair, operator intensity, and energy-aware reassignment. On a UAV-swarm inspection benchmark (8 UAVs, 45 tasks, three volatility regimes, n = 10 replications) EpiSwarm reduces mean replanning churn by about 60% (from 12–15 to 5–6 changes per epoch) and improves mission utility by 10.7–14.2% over the best of four baselines—including a memory-archive GA—with this advantage robust across four objective-weight specifications (p between 0.009 and 0.052). A matched paired ablation (n = 20) shows that the benefit of transgenerational inheritance is regime-dependent: it is significant at low volatility (p = 0.025) and vanishes as disruptions accelerate. We show that this crossover is not incidental but follows a closed-form memory-horizon condition, τepi ≲ τenv/g, that predicts a priori the regime in which inherited memory ceases to be informative—a computational instance of the established evolutionary-biology result that the value of transgenerational information is set by environmental autocorrelation. The stress-driven update is directionally positive but not statistically separable at this sample size, and is reported as such. EpiSwarm therefore contributes a quantitatively predictable, low-churn regulatory mechanism for dynamic swarm optimization rather than a universally beneficial add-on.

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.