Author (s) : DOI : 10.32692/IJDI-ERET/14.2.2025.2501

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1). Attia Hussien Gomaa, Faculty of Engineering, Benha University, Cairo, Egypt

Abstract :

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Failure Mode and Effects Analysis (FMEA) is a foundational technique for identifying, prioritizing, and mitigating potential failure modes in manufacturing systems. However, traditional FMEA methods—being manual, static, and retrospective—are increasingly inadequate in today’s complex, data-driven industrial landscape. This paper introduces FMEA 4.0, a digital framework that integrates core Industry 4.0 technologies—including the Internet of Things (IoT), artificial intelligence (AI), digital twins, big data analytics, and cloud computing—to transform FMEA into a real-time, predictive, and adaptive risk management system. The study critically examines the limitations of conventional FMEA and outlines the evolution toward a more intelligent, automated, and continuous approach. FMEA 4.0 facilitates dynamic risk assessment, early failure detection, optimized maintenance planning, improved asset utilization, and enhanced overall equipment effectiveness (OEE). A structured implementation methodology is proposed, based on the DMAIC (Define, Measure, Analyze, Improve, Control) framework, to ensure systematic integration with existing quality management systems. The framework also incorporates key performance indicators (KPIs) aligned with strategic organizational goals, enabling continuous monitoring, data-driven decision-making, and sustained improvement in reliability, safety, and operational performance. By unifying digital technologies with proven quality principles, FMEA 4.0 emerges as a strategic enabler of resilience, agility, and competitiveness in smart manufacturing. The paper concludes with practical implementation guidance and insights for researchers and industry professionals advancing digital transformation in reliability and risk management.

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Author (s) : DOI : 10.32692/IJDI-ERET/14.2.2025.2502

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1). Abraham Danlami, Federal University, Wukari, Taraba State, Nigeria
2). E. J. Garba, Modibbo Adama University, Yola, Adamawa State, Nigeria
3). Y. M. Malgwi, Modibbo Adama University, Yola, Adamawa State, Nigeria
4). S. E. Dogo, Taraba State University, Jalingo, Taraba State, Nigeria

Abstract :

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A bank network is an interconnected system designed to facilitate financial transactions, minimize customer waiting times, and reduce the risk of errors caused by automated systems or bots. However, over the past three decades, cyberattacks have emerged as a significant threat to the security of these networks. The tools like penetration testing, machine learning classifiers, multi-factor authentication, network traffic analysis, and bot detection systems are reactive rather than proactive. As a result, these measures may fail to detect or prevent sophisticated attacks in real time. Such vulnerabilities can be exploited by attackers to gain unauthorized access to banking networks, posing serious risks to data integrity and customer privacy. In this study, we aim to enhance the performance of an anomaly-based Intrusion Detection System (IDS) within a banking environment by developing a Stacked Hybrid Classifier. To achieve this, the study employed the CICIDS2017 dataset, which contains realistic traffic data simulating various types of attacks and benign behaviors. The dataset's inherent class imbalance common in intrusion detection scenarios was addressed using the Synthetic Minority Oversampling Technique (SMOTE), ensuring more balanced training and improved sensitivity to minority attack classes. The model was implemented using Python, stacking multiple base classifiers including Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbors. The predictions from these models were combined to form a meta-model, resulting in a more robust and generalized detection capability. The study was evaluated using stratified k-fold cross-validation to ensure consistent and unbiased performance assessment across different data partitions. The results show that the stacked Hybrid classifier achieved the best accuracy of 99.76%, along with superior precision, recall, and F1 scores when compared to the individual classifiers. This demonstrates the effectiveness of Hybrid learning, data balancing, and rigorous validation in improving IDS performance in banking networks.

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Author (s) :

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1). Gayathri Jeevanandham, Ariel University, Ariel, ISRAEL

Abstract :

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The global rise in diabetes prevalence has intensified the demand for glucose monitoring technologies that are accurate, cost-effective, and stable. Although enzyme-based glucose sensors are widely used, they face significant limitations, including poor stability, temperature sensitivity, and high manufacturing costs. As a promising alternative, enzyme-free glucose sensors based on transition metal oxide (TMO) nanostructures such as NiO, Co3O4, CuO and ZnO offer intrinsic electrocatalytic activity, chemical robustness, and tunable physicochemical properties. This review examines recent advances in the design, synthesis, and application of TMO based nanomaterials for non-enzymatic glucose detection. We highlight how nanoengineering strategies including morphology control, doping, and composite formation enhance sensor performance. The sensors discussed demonstrate high sensitivity, low detection limits, rapid response times, and excellent selectivity in complex biological matrices. These advancements underscore the potential of TMO nanostructures to enable reliable, scalable, and wearable glucose biosensors for real-time diabetes monitoring.

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Author (s) :

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1). Lee Moyo, Harare Institute of Technology, Harare, Zimbabwe
2). Ngonidzashe A. Musiwedzingo, Harare Institute of Technology, Harare, Zimbabwe
3). Elizabeth Ticharwa, Harare Institute of Technology, Harare, Zimbabwe

Abstract :

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Marula trees are native to the southern Africa and are known for their edible fruits and nuts. Marula nutshells which are the hard marula fruit seed, a result of sclerocarya birrea (marula) fruit processing are an abundant underused biomass in Rutenga, Zimbabwe. They have a high carbon content and porous nature, making them suitable for activated carbon synthesis. This review investigates the potential of value adding sclerocarya birrea (marula) nutshells through activated carbon production, for use in the marula oil refining. The use of sclerocarya birrea biomass/nutshells for synthesis of activated carbon addresses the issue of circular economy by using waste generated at the oil manufacturing factory to produce the oil refining agent and also addresses the issues of sustainability by using waste as a raw material. The review explored physical and chemical activated carbon synthesis methods and identified pyrolysis temperature and chemical type as the key critical parameters. The review further examines the potential of marula-derived activated carbon in oil refining, specifically in the removal of contaminants, pigments and oxidation products.

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