Mulugeta Tilahun Bekele

https://doi.org/10.64823/ijeee.2601006

Abstract: Conventional smart-grid systems often rely on fixed control strategies that have limited ability to respond dynamically to changing electricity demand, renewable-energy variability, equipment degradation, and unexpected faults. This study proposes an AI-Driven Adaptive Smart Grid (AI-ASG) for real-time energy optimization and automated fault detection. The framework integrates machine learning, real-time sensing, adaptive demand-response control, and intelligent fault classification to improve grid efficiency, reliability, and operational responsiveness. A mixed-methods quantitative and qualitative research design was adopted. Smart-grid operational data, including load demand, voltage, current, frequency, power factor, renewable-energy generation, and equipment-fault indicators, were processed through preprocessing, feature extraction, and AI-based prediction. Adaptive optimization was evaluated using energy consumption reduction, peak-load reduction, renewable-energy utilization, power-loss reduction, voltage stability, fault-detection accuracy, precision, recall, F1-score, false-alarm rate, detection latency, computational overhead, and system scalability. Qualitative assessment considered interpretability, operator usability, interoperability, adaptability, and decision-support effectiveness. Performance was compared with conventional rule-based control, non-adaptive machine-learning control, and the proposed adaptive AI approach. The proposed AI-ASG demonstrated improved operational performance across energy-management and fault-detection measures. Quantitative evaluation indicated reductions in unnecessary energy consumption and peak demand, improved renewable-energy utilization and voltage stability, and faster fault identification. The adaptive model also achieved higher fault-classification accuracy, precision, recall, and F1-score while reducing false alarms and detection latency compared with conventional approaches. Qualitative findings indicated improved system interpretability, operator confidence, adaptability, and real-time decision support. The proposed AI-driven adaptive architecture provides an integrated approach for intelligent energy optimization and real-time fault management, supporting more responsive, reliable, efficient, and scalable smart-grid operation.

https://doi.org/10.64823/ijcsa.2601008

Abstract: The rapid expansion of distance education has increased access to learning but has also intensified challenges related to assessment security, academic integrity, quality assurance, and student trust. Conventional Learning Management Systems (LMS) often rely on centralized architectures that are vulnerable to data tampering, unauthorized access, delayed verification, and limited transparency in grading and credential management. This study proposes a Blockchain–Artificial Intelligence (AI) Hybrid Framework designed to enhance secure assessment, institutional quality assurance, and student trust in distance education through decentralized verification and intelligent analytics. A mixed-methods research design integrating quantitative and qualitative approaches was employed. Quantitative evaluation was conducted using assessment records, blockchain transaction logs, AI prediction outputs, and system performance metrics collected from a distance learning environment. Qualitative data were obtained through interviews and structured questionnaires involving students, instructors, and quality assurance experts. The proposed framework integrates blockchain-based immutable assessment records with AI-driven automated grading, anomaly detection, plagiarism identification, and predictive quality analytics. Performance was compared with conventional cloud-based e-learning systems using assessment integrity, grading accuracy, fraud detection rate, transaction verification time, system latency, user trust, and overall quality assurance effectiveness as evaluation parameters. Experimental results demonstrated that the proposed framework achieved 99.8% assessment data integrity, 98.6% AI grading accuracy, 97.9% academic misconduct detection accuracy, 95.8% quality assurance compliance, and 96.7% student trust satisfaction, while reducing verification time by 64% and assessment processing latency by 42% compared with traditional centralized systems. Qualitative findings further confirmed improved transparency, fairness, accountability, and confidence in online assessment processes. The study concludes that integrating blockchain and artificial intelligence provides a scalable, secure, and trustworthy solution for sustainable distance education, strengthening assessment reliability, institutional quality assurance, and learner confidence while supporting evidence-based educational decision-making.

https://doi.org/10.64823/ijcsa.2601007

Abstract: High-quality genomic DNA is fundamental for molecular diagnostics, precision medicine, agricultural biotechnology, microbial genomics, and biodiversity conservation. However, existing DNA extraction protocols are generally optimized for specific organisms, resulting in inconsistent DNA yield, purity, processing time, and cost when applied across different biological kingdoms. Furthermore, conventional laboratory methods lack intelligent mechanisms for predicting DNA quality before downstream genomic analyses. This study aimed to develop and evaluate an AI-Driven Universal DNA Extraction and Quality Prediction Framework capable of extracting high-quality genomic DNA from bacterial, plant, and mammalian samples while accurately predicting DNA quality using artificial intelligence. A mixed-methods research design was employed using representative bacterial (Escherichia coli), plant (Arabidopsis thaliana), and mammalian blood samples. An eco-friendly universal DNA extraction protocol was integrated with supervised machine learning algorithms. The proposed framework was compared with CTAB, phenol–chloroform, and commercial silica column methods using DNA yield (ng/µL), purity (A260/A280 and A260/A230), DNA integrity, extraction time, reagent cost, reproducibility, PCR amplification success, and AI prediction accuracy. The proposed framework achieved an average DNA purity of 1.87–1.95 (A260/A280), increased DNA yield by 22.8%, reduced extraction time by 34.5%, lowered reagent cost by 41.2%, and achieved a 97.1% PCR amplification success rate across bacterial, plant, and mammalian samples. The AI-based quality prediction model attained 96.8% prediction accuracy, providing reliable and interpretable assessments of DNA quality for genomic applications. The proposed AI-driven universal framework provides a scalable, environmentally sustainable, and cost-effective solution for cross-kingdom genomic DNA extraction and intelligent quality prediction. Its superior performance across multiple biological sample types demonstrates its potential to standardize molecular biology workflows and support clinical genomics, agricultural biotechnology, environmental DNA research, and biodiversity conservation.

https://doi.org/10.64823/ijcsa.2601005

Abstract: The rapid adoption of digital higher education has transformed teaching, learning, and assessment by increasing accessibility and flexibility. However, it has also introduced challenges related to academic integrity, equitable access to learning opportunities, and effective quality assurance. Although artificial intelligence (AI) is increasingly used to address these challenges, many existing AI systems lack transparency, limiting stakeholder trust and accountability. This study aimed to develop and validate an Explainable Artificial Intelligence (XAI) framework that enhances academic integrity, supports inclusive learning, and strengthens quality assurance in digital higher education through transparent and interpretable AI-driven decision-making. A convergent mixed-methods research design was employed. Quantitative data were collected from 12,500 learner records, online assessment logs, learning management system interactions, and institutional quality assurance indicators from multiple higher education institutions. Qualitative data were obtained through semi-structured interviews and focus group discussions involving students, instructors, instructional designers, and quality assurance experts. Explainable machine learning algorithms and fairness assessment techniques were integrated into the proposed framework to detect academic misconduct, identify at-risk learners, evaluate educational quality, and generate interpretable recommendations. The proposed framework achieved an accuracy of 95.1%, precision of 94.3%, recall of 93.8%, an F1-score of 94.0%, and an Area Under the Curve (AUC) of 0.97, while reducing false-positive academic misconduct alerts by 27% compared with conventional AI models. Qualitative findings demonstrated that explainable AI significantly improved stakeholder trust, perceived fairness, transparency, and confidence in AI-supported educational decision-making. The developed XAI framework provides a transparent, ethical, and scalable solution for improving academic integrity, promoting inclusive learning, and strengthening quality assurance in digital higher education. The framework contributes to trustworthy AI adoption and supports evidence-based institutional decision-making, thereby advancing sustainable digital transformation in higher education.