We have to get back to an economy that grows from the bottom up" Joe Biden, Fri, July 9, 2021. Oklahoma International Publishing (OkIP), such as Elsevier, Wiley, IGI Global, and others, is a start-up conference, journal & publishing organization incorporated in Oklahoma, United States of America.
Adaptive Zero Trust Framework for Detection and Mitigation of Data Poisoning Attacks in Collaborative Edge Intelligence Systems
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: The decentralized architecture of collaborative edge intelligence systems, offers scalability, resilience but also introduces a critical security vulnerability - insider threats. Data Poisoning attack through a single compromised or malicious node can inject false information and has the potential to disrupt the collective intelligence and compromise the integrity of the entire swarm. Traditional security systems rely on a centralized node for learning and detection, and are not suitable for decentralized, dynamic edge networks & environments. Building on Zero Trust principles, we introduce a decentralized, peer-driven reputation system with an adaptive consensus mechanism, such that each node continually evaluates the credibility of data from its peers and keeps a local reputation score. The thresholds for data anomaly and reputation score required for isolation are not static; but are made to dynamically adjust to the network’s real-time state. This approach was evaluated with a simulation of 200 nodes, where 10% of the nodes acted as malicious actors injecting sophisticated, statistically similar false data. Our framework achieved a 95.6% detection rate for malicious nodes, achieving complete threat isolation within an average of 12 communication rounds - a significant reduction in threat propagation time compared to traditional systems in similar scenarios. Furthermore, the adaptive nature of the framework reduced false positives of benign, noisy nodes by over 75% compared to non-adaptive anomaly detection models. This high efficacy was achieved while imposing a 5% additional computational overhead and a marginal increase in network traffic of 9% on individual edge nodes. Our findings demonstrate that an adaptive, peer-validated, decentralized approach can achieve high detection accuracy and rapid isolation with minimal computational overhead, offering a robust and practical solution for securing collaborative edge intelligence systems against insider threats. About this Paper Cite this paper as: Josh-Falade O. (2025). Adaptive Zero Trust Framework for Detection and Mitigation of Data Poisoning Attacks in Collaborative Edge Intelligence Systems. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIS25#13. https://doi.org/10.55432/978-1-6692-0011-6_13 Presented at: The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025 Contact: Olabisi Josh-Falade josh-faladeoa@mcu.edu.ng
Data driven Intelligent approach for intrusion detection in IoT environments
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: This paper contributes to the development of intelligent data-driven approaches to improve intrusion detection in IoT environments. The proposed detection model combines a hybrid AutoEncoder-LSTM with a dense supervised classifier designed to capture the temporal structure of network traffic and transform latent representations into explicit predictions. The model architecture relies on a dual reconstruction (MSE) and classification (CrossEntropy) loss function allowing more accurate intrusion prediction for binary and multiclass classification. The model training is performed on the IoT-23 dataset and improved using regularization techniques to capture realistic attack scenarios specific to connected devices. The results show that the model achieves relatively high performance with an F1 score of 99.97 outperforming the scores obtained by similar models trained on NSL-KDD (F1 score of 98.88). Our model is suitable for intrusion detection in modern IoT networks, combining robustness and generalization capabilities. About this Paper Cite this paper as: Eric Fotsing E., Nguendap S., Hamdane M.A., Nzeukou A., Lienou J.P. (2025). Data driven Intelligent approach for intrusion detection in IoT environments. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIS25#09. https://doi.org/10.55432/978-1-6692-0011-6_12 Presented at: The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025 Contact: Severin Nguendap snguendap@gmail.com
YOLO algorithms for Solar Panel and Boiler Detection from Aerial Imagery
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: The integration of solar panels and boilers, considered key renewable energy technologies, remains crucial for addressing global sustainable energy challenges. Aerial imagery from drones and satellites plays an important role in monitoring and assessing these infrastructures. This study presents a comparative evaluation of three You Only Look Once (YOLO) model architectures, specifically YOLOv8-Large, YOLOv10-Large and YOLOv11-Large for the detection of solar panels and boilers. Through Exploratory Data Analysis (EDA), the patterns and characteristics of the dataset were identified, providing a strong basis for developing a robust object detection model. YOLOv10-Large demonstrated the best performance, achieving a precision of 89.2%, a recall of 81.3%, mAP (50) of 83.1%, and an F1 score of 85.0%. This advancement prioritizes efficiency and reliability as the backbone, contributing to more sustainable and resource-efficient practices in industries that rely on aerial imagery analysis. About this Paper Cite this paper as: Koomson D., Abugre J., Moore S. (2025). YOLO algorithms for Solar Panel and Boiler Detection from Aerial Imagery. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIS25#06. https://doi.org/10.55432/978-1-6692-0011-6_11 Presented at: The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025 Contact: Douglas Koomson douglaskojokoomson@gmail.com
The Impact of Generative AI on Critical Thinking and Academic Integrity in Higher Education: A Mixed-Methods Study
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: Accessibility and integration of generative artificial intelligence (GenAI) tools like ChatGPT, Claude, and Gemini into higher education present both unprecedented opportunities and significant challenges. This study addresses the growing concern that overreliance on GenAI may compromise students' independent reasoning and the authenticity of academic work. Through an explanatory sequential mixed-methods design, this research examines the impact of GenAI on critical thinking and academic integrity. The quantitative phase surveyed 450 undergraduate students across three Nigerian institutions and three disciplines, using the California Critical Thinking Skills Test (CCTST) and the SAMR model to assess integration levels. The qualitative phase utilized semi-structured interviews and focus groups to explain the statistical results. Key findings reveal a stark dichotomy: students integrating AI at the 'Modification/Redefinition' level demonstrated significant gains in critical thinking, effectively using AI as a Vygotskian "More Knowledgeable Other." Conversely, those at the 'Substitution' level exhibited stagnation in reasoning skills and higher rates of plagiarism flags, utilizing AI merely as a "Cognitive Shortcut." The study further identifies an "Ethical Gray Zone" caused by institutional policy lags and instructor ambivalence. Ultimately, this paper argues that GenAI is not inherently detrimental; rather, its impact relies on the pedagogical framework—specifically, shifting from shortcut-seeking behaviors to partnership-oriented workflows. About this Paper Cite this paper as: Tadema J., Oluwagbemi J, Mesioye A., Josh-Falade O.(2025). The Impact of Generative AI on Critical Thinking and Academic Integrity in Higher Education: A Mixed-Methods Study. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIS25#12. https://doi.org/10.55432/978-1-6692-0011-6_10 Presented at: The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025 Contact: Ayobami Mesioye ayobamimesioye@gmail.com
Intelligent Opportunistic Routing in VANETs using Neural Network
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: Several research areas attract researchers in the field of Vehicular Ad Hoc Networks (VANETs), a network recognized for its constraints and challenges, particularly in routing. Ensuring efficient routing is challenging due to network changes and frequent link interruptions. These days, Artificial Intelligence (AI) is the technology that is growing the fastest and changing the most. As a result, their technological advancements have had a significant impact on the development of routing protocols. Our goal in this paper is to incorporate AI-based methods to improve the performance of routing in VANET, especially Opportunistic Routing (OR), which is very intriguing. For this, we chose the Fuzzy Logic Opportunistic Routing protocol (FLOR), which uses fuzzy logic. After conducting a thorough analysis of this protocol, we have identified specific drawbacks and limitations. To address these issues, we will integrate AI techniques to create an adaptive and intelligent routing protocol that leverages a neural network. This combination of these two approaches will ensure better performance in terms of packet delivery and throughput and will reduce packet loss. About this Paper Cite this paper as: Slimani S., Boulfekhar S., Adela K., Tiako P. F. (2025). Intelligent Opportunistic Routing in VANETs using Neural Network. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIS25#11. https://doi.org/10.55432/978-1-6692-0011-6_9 Presented at: The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025 Contact: Souad Slimani souad.slimani@univ-bejaia.dz
Artificial Intelligence-Driven Digital Transformation of Supply Chain Management
This is a Chapter in: Book: Business Solutions and Environment Print ISBN 978-1-6692-0010-9 Online ISBN 978-1-6692-0009-3 Series: Chronicle of Business Chapter Abstract: Traditional Supply Chain Management (SCM) approaches are increasingly inadequate for modern global challenges due to their linear and disconnected nature. Digital transformation is crucial for addressing the challenges of the conventional supply chain. This paper examines the role of artificial intelligence in enhancing supply chain management. After presenting the literature review, the article analyzes the use cases of companies. The findings indicate that the main artificial intelligence applications used in supply chain are demand forecasting, inventory management, and logistics optimization. The used artificial intelligence technologies are machine learning and predictive analytics techniques. Integrating artificial intelligence into the supply chain enhances cost reduction, improves inventory management, mitigates disruptions, and increases resilience, while also reducing environmental impact. Despite its benefits, digitalization presents several risks across multiple dimensions. Key concerns include inaccuracies within data sets, as well as issues related to data privacy and security. Forecasting inaccuracies also pose significant challenges. Additionally, a critical risk associated with increased automation is the potential for job displacement, a pressing issue that remains a concern in the field. About this Paper Cite this paper as: Zaoui H., Kamsu-Foguem B., Tchuente D., Foguem C. (2025) Artificial Intelligence-Driven Digital Transformation of Supply Chain Management. In: Tiako P.F. (ed) Business Solutions and Environment. Chronicle of Business. OkIP. CABR25#11. https://doi.org/10.55432/978-1-6692-0009-3_5 Presented at: The 2025 OkIP International Conference on Advances in Business Research (CABR) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025. Contact: Hassane Zaoui zaoui2584@gmail.com
America’s Utility-Scale Electricity Generation Landscape in Transition: A 2025 Analysis of Capacity Growth, Regional Trends, and Energy Resource Dynamics
This is a Chapter in: Book: Business Solutions and Environment Print ISBN 978-1-6692-0010-9 Online ISBN 978-1-6692-0009-3 Series: Chronicle of Business Chapter Abstract: America’s power mix is rapidly shifting. Using over 1.3 TW of utility-scale capacity data (Hitachi Energy Velocity Suite; APPA), this study synthesizes development by fuel, ownership, geography, and status (including cancellations, retirements, and storage). Solar leads: 55% of capacity under development and over 80% of 2024 additions. Wind development fell 22%, while natural-gas projects rose 58%. Grid-scale storage surged 181% in two years, with over 165 GW in development. Coal is phasing out (83 GW retired since 2017, another 56 GW planned by 2029). Non-utility generators hold 88% of new capacity, limiting public-power participation despite serving 9% of load. Texas RE and WECC dominate pipelines, yet ~336 GW of planned projects since 2017 were canceled. Findings inform energy decision-makers. About this Paper Cite this paper as: Mbasso W.F., Harrison A., Dagal I., Jangir J., Tiako P.F. (2025). America’s Utility-Scale Electricity Generation Landscape in Transition: A 2025 Analysis of Capacity Growth, Regional Trends, and Energy Resource Dynamics. In: Tiako P.F. (ed) Business Solutions and Environment. Chronicle of Business. OkIP. CABR25#10. https://doi.org/10.55432/978-1-6692-0009-3_4 Presented at: The 2025 OkIP International Conference on Advances in Business Research (CABR) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025. Contact: Wulfran Fendzi Mbasso fendzi.wulfran@yahoo.fr
Alertness to AI Algorithmic Biases in the Healthcare System
This is a Chapter in: Book: Business Solutions and Environment Print ISBN 978-1-6692-0010-9 Online ISBN 978-1-6692-0009-3 Series: Chronicle of Business Chapter Abstract: Artificial intelligence (AI) is assisting practitioners in performing certain tedious tasks with computer algorithms, particularly those involving large volumes of data, bringing recent progress in medicine. Technical and social biases can undermine confidence in these algorithms, so the algorithms still require human validation. Especially when the approach by which AI systems are made can cause ethical concern. This work aims to review how the concepts of biases are integrated in different countries. Particularly, the approach of the ethical dimension and regulation that France, the United States (US), and China have when developing AI should be studied to understand better how it can lead to biases and ethical interrogations. The purpose is to assess the basis on which algorithms are created in the present healthcare system. Results show (i) a necessity to enrich AI systems, increase their explainability, and recommendations. (ii) Countries’ approaches to AI’s opportunities align with their beliefs. About this Paper Cite this paper as: Sime R., Foguem C., Fosso Wamba S., Kamsu-Foguem B.(2025) Alertness to AI Algorithmic Biases in the Healthcare System. In: Tiako P.F. (ed) Business Solutions and Environment. Chronicle of Business. OkIP. CAIS25#10. https://doi.org/10.55432/978-1-6692-0009-3_3 Presented at: The 2025 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025. Contact: Rufin Sime rufinsime@gmail.com
Bibliometric Review on Digital Platforms in Africa
This is a Chapter in: Book: Business Solutions and Environment Print ISBN 978-1-6692-0010-9 Online ISBN 978-1-6692-0009-3 Series: Chronicle of Business Chapter Abstract: A bibliometric study is performed on digital platforms in Africa, systematically mapping the principal contributors, prevailing thematic areas, and prospective avenues for future investigation. The analysis underscores the significant role played by South Africa, alongside a specific group of other English-speaking nations—including Nigeria, Kenya, Ghana, Ethiopia, Uganda, and Zimbabwe. Among the core topics most frequently addressed are the digital and gig economies, online learning environments, social media applications, digital health, and mental health—domains that continue to hold considerable relevance in light of accelerating advancements in artificial intelligence and machine learning. The continent’s combination of rapid demographic growth and substantial investments in digital infrastructure constitutes a uniquely favorable context for the proliferation of digital platforms. Moreover, the literature also identifies a range of peripheral yet noteworthy themes, such as mobile money, financial inclusion, innovation and entrepreneurship, agriculture, youth engagement, and sustainable development. Collectively, these insights contribute to a more comprehensive understanding of both the current state and the evolving trajectory of digital platform research across the African continent. About this Paper Cite this paper as: Tchuente D., Pandit D., Zejnilovic L. (2025). Bibliometric Review on Digital Platforms in Africa. In: Tiako P.F. (ed) Business Solutions and Environment. Chronicle of Business. OkIP. CABR25#7. https://doi.org/10.55432/978-1-6692-0009-3_2 Presented at: The 2025 OkIP International Conference on Advances in Business Research (CABR) in Oklahoma City, Oklahoma, USA, and Online, on October 1, 2025. Contact: Dieudonne Tchuente dtchuente@gmail.com
Comparison between both Methods of Explanations: LIME and SHAP
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: Structural engineers can greatly benefit from understanding the reasons behind the behavior of machine learning algorithms in several key areas, such as feature engineering, model selection, confidence in predictions, taking action based on those predictions, and the development of more user-friendly interfaces. As a result, interpretability has become a central issue in deep learning, and research into interpretable models has gained significant attention from both industry and academia. Due to their transparency, these models are often preferred because they can achieve similar accuracy to non-interpretable models in certain applications. When interpretability is crucial, they may still be chosen even if their accuracy is slightly lower. However, limiting machine learning to models that are interpretable can often be a significant drawback. In this paper, we present a case study focused on predicting crack types using model-agnostic methods to explain deep learning predictions. These methods provide considerable flexibility in model selection, explanations, and representations by treating deep learning models as black-box functions. About this Paper Cite this paper as: Lahna T., Kamsu-Foguem B.(2025). Comparison between both Methods of Explanations: LIME and SHAP. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#11. https://doi.org/10.55432/978-1-6692-0011-6_8 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Tarik Lahna lahnatk@ucla.edu
Close-Combat Weapon Detection in Crisis Zones using YOLOv8
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: Enhancing security measures in the crisis-affected regions of Cameroon, which have faced prolonged unrest over the years, is critically important. Detecting close-combat weapon within dense crowds can significantly improve surveillance and safety amidst ongoing challenges. These regions require advanced security solutions to address persistent threats faced by the local population. This study develops a detection model based on the You Only Look Once (YOLOv8) architecture to accurately identify and segment sticks and machetes in these crisis-affected areas. By assembling a diverse dataset that captures various scenarios, orientations, and lighting conditions, the model learns to recognize the distinctive features of these objects. By enhancing security measures through advanced technology, this study aims to contribute to ongoing efforts to safeguard communities and restore stability in these troubled areas. About this Paper Cite this paper as: Nkamgan L., Onanena Guelan R., Mbous Ikong J., Videme Bossou O., Essimbi Zobo B.(2025) Close-Combat Weapon Detection in Crisis Zones using YOLOv8. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#10. https://doi.org/10.55432/978-1-6692-0011-6_7 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Laurent Nkamgan nkamgans@gmail.com
Leveraging AI to Enhance Human-Driven Software Development: A Comparative Study Across Diverse Applications
This is a Chapter in: Book: Intelligent and Sustainable Solutions https://www.okipublishing.com/book/index.php/okip/catalog/book/60 Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: This study explores how Generative AI, including AI-Powered tools like ChatGPT, can enhance the Software Development Life Cycle (SDLC). In a software engineering course, students worked in teams on 12 use case studies spanning web development, mobile apps, and game development. These use case studies covered domains such as education, sports, healthcare, and entertainment. Teams adopted dual roles as stakeholders and developers. Each team first defined a use case study and outlined project requirements. Use case studies were then randomly assigned, and teams worked as developers, specifying requirements and architectural designs. Scrum-style meetings facilitated collaboration. The paper compared developer-created and AI-generated user stories, functional and non-functional requirements, and architectural designs, including UML diagrams. Results showed ChatGPT excelled in structured web and app development domains but struggled significantly in game development and faced considerable difficulty in generating UML diagrams across all applications. This research highlights the strengths and limitations of Generative AI in enhancing software development processes. About this Paper Cite this paper as: Fellah A. (2025) Leveraging AI to Enhance Human-Driven Software Development: A Comparative Study Across Diverse Applications. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#8. https://doi.org/10.55432/978-1-6692-0011-6_6 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Aziz Fellah afellah@nwmissouri.edu
Predictive Maintenance of Pavement Cracks in Airport Facilities Based on Drones and Computer Vision
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract [Under Revision]: Manual inspection of damages in airport facilities such as pavement cracks is difficult due to the reliability objective and the high demands of time and costs. This can be automated using a system of unmanned aerial vehicles (UAVs) for aerial imagery of damages. Many computer vision-based approaches have been applied by several researchers to address the limitations of crack detection but they have their limitations. The purpose of this paper is to describe how the limitations can be overcome by using various hybrid methods based on artificial intelligence (AI) and deep learning (DL) techniques. Also, it shows how convolutional neural networks may be introduced in drones to automate the detection of pavement cracks in airport facilities. The outline of the proposed system is composed of three modules which are: Image acquisition, Crack detection, and Image-based 3D modeling. In addition, this paper has shown that this proposed system can participate in building a 3D digital representation of pavement cracks in airport facilities automatically from a DSLR camera within the context of the digital twin. About this Paper Cite this paper as: Lahna T.(2025). Predictive Maintenance of Pavement Cracks in Airport Facilities Based on Drones and Computer Vision. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#7. https://doi.org/10.55432/978-1-6692-0011-6_5 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Tarik Lahna lahnatk@ucla.edu
Real-Time Crack Detection and Segmentation in Metallic Surfaces Using YOLO v11-Seg Model Integrated with HMI for Industrial Applications
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: Cracks in metallic surfaces fail industrial systems, compromising systems' safety and productivity. It is therefore imperative to detect such cracks in their initial stage, so preventive measures can be taken to avoid downtime and associated risks. In this paper, we report the novel adaptation and application of the YOLO v11-seg model for precise crack segmentation in metallic surfaces. The specialized configuration optimizes both detection accuracy and inference speed, making it suitable for industrial applications. The deep learning model is integrated into a Human-Machine Interface (HMI) using the Open Neural Network Exchange (ONNX) format, to enable seamless real-time visualization and interaction. This enhances usability for industrial operators, bridging the gap between advanced AI models and practical deployment. We report a fully functional and versatile inspection tool by combining the deep learning model with hardware and real-time video processing via Python. A custom dataset comprising 1,111 training and 246 test images was curated, annotated with segmentation masks, and augmented using the Albumentations library to improve generalization. The model showed detection and segmentation precisions of 96% and 94%, respectively. About this Paper Cite this paper as: Qureshia S., Fatimaa A., Daudpotoa J., Shaikha A., Qayooma A. (2025). Real-Time Crack Detection and Segmentation in Metallic Surfaces Using YOLO v11-Seg Model Integrated with HMI for Industrial Applications. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#9. https://doi.org/10.55432/978-1-6692-0011-6_4 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Jawaid Daudpoto jawaid.daudpoto@faculty.muet.edu.pk
Revolutionizing Renewable Energy: Advanced Synthetic Climatic Models for Enhanced Energy Predictability and Optimization
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: The efficacy of renewable energy systems rests heavily on solar irradiance and temperature. In reality, however, these data tend to be under or unavailable in certain areas, making accurate design, modeling, and analysis of renewable energy systems unfathomably problematic. This paper discusses the creation and evaluation of novel synthetic climatic models aimed at providing region-specific realistic climatic data. These models utilize highly advanced statistical and machine learning algorithms combined with solar irradiance and temperature models to capture and integrate solar irradiance and temperature data with relative high temporal and spatial resolution. Models’ extensive validation against real climatic datasets considerably increased their reliability and robustness under various conditions including extreme and less plausible scenarios. These results highlight the ability of the models to close the gap posed by the utter absence of reliable, region accurate data in supplementing renewable energies. Such findings are greatly beneficial for improving energy yield forecast, system design, and performance monitoring and evaluation for areas with scarce data resources. About this Paper Cite this paper as: Fendzi Mbasso W., Dagal I., Ambe H., Pradeep J., Fendzi Donfack E., Pierre Tiako P.F. (2025). Revolutionizing Renewable Energy: Advanced Synthetic Climatic Models for Enhanced Energy Predictability and Optimization. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CEST25#6. https://doi.org/10.55432/978-1-6692-0011-6_3 Presented at: The 2025 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Wulfran Fendzi Mbasso fendzi.wulfran@yahoo.fr
AI Application Potential and Prospects in Materials Science: A Focus on Polymers
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: The integration of artificial intelligence (AI) into materials science is profoundly transforming polymer discovery, manufacturing and quality control. This study explores the potential of AI-based approaches in three key areas: (1) prediction of polymer properties using advanced models such as PolymerGNN and PolyBERT, (2) optimization of industrial processes via reinforcement learning to improve energy efficiency and material quality, and (3) automatic defect detection using computer vision models such as YOLOv8 and Faster R-CNN. Experimental results show significant improvements in terms of prediction accuracy, energy consumption reduction (10-25%) and defect identification efficiency. Despite these advances, challenges remain, notably data quality, model interpretability and integration into industrial processes. This study highlights the transformative impact of AI on polymer science, and provides an analysis of the performance of applied models. About this Paper Cite this paper as: Kpoghomou R., Nassiet V., Kamsu-Foguem B. (2025). AI Application Potential and Prospects in Materials Science: A Focus on Polymers. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#12. https://doi.org/10.55432/978-1-6692-0011-6_2 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Raphael Kpoghomou raphaelkpogomou@gmail.com
Integrating AI into Scrum: Enhancing and Refining Agile Software Development Practices
This is a Chapter in: Book: Intelligent and Sustainable Solutions Print ISBN 978-1-6692-0012-3 Online ISBN 978-1-6692-0011-6 Series: Chronicle of Computing Chapter Abstract: The integration of Artificial Intelligence (AI) into software development methodologies has sparked significant interest nowadays, driving innovation and transforming agile practices by refining existing frameworks to boost efficiency, productivity, and decision-making. This paper aims to enhance the Scrum process by leveraging AI tools to streamline tasks such as sprint planning, feature prioritization, backlog refinement, Scrum Master duties, and progress tracking. AI can assist Scrum teams by improving predictive analytics, simplifying decision-making, and automating processes, while also fostering better team collaboration and supporting diverse experiences and skill levels. The paper outlines a conceptual framework for integrating AI-powered tools into Scrum, drawing on real-world use case studies of its application in software development teams. Additionally, it examines the challenges and opportunities of adopting AI in agile practices, highlighting its potential to enhance efficiency and adaptability in software development workflows. About this Paper Cite this paper as: Fellah A. (2025) Integrating AI into Scrum: Enhancing and Refining Agile Software Development Practices. In: Tiako P.F. (ed) Intelligent and Sustainable Solutions. Chronicle of Computing. OkIP. CAIF25#6. https://doi.org/10.55432/978-1-6692-0011-6_1 Presented at: The 2025 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 2, 2025 Contact: Aziz Fellah afellah@nwmissouri.edu
This book introduces a collection of selected papers from the 2025 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on April 02 and October 01, 2025. One of the missions of the Academia & Industry Convention series is to promote quality research and development in a diverse and inclusive environment. Authors and participants from around the world with diverse origins, experiences, and backgrounds participated in these events. The International Technical Program Committees (TPC) thank those who submitted papers for consideration. Two to three experts in the field peer-reviewed each submitted paper for originality, relevance, contribution, structure, clarity, appropriateness, discussion, and conclusion. The document includes articles addressing various challenges and perspectives on Intelligent and Sustainable Solutions. New Book Chapter Publication Model OkIP is pleased to introduce its first Continuous Chapter Publishing (CCP) model. CCP means that each paper will be published as a chapter of the book after peer review, acceptance, and revision. The internal pagination of each chapter begins on page 1 rather than a continuous pagination of chapters throughout the book. In addition, each chapter has a unique Chapter Citation ID number, which replaces the page numbers in the citation. This change allows each Chapter to be published online immediately in its final form, reducing the overall publication time. ABOUT THIS BOOK Online ISBN 978-1-6692-0011-6 Print ISBN 978-1-6692-0012-3 Preface Table of Contents Detailed Table of Contents BOOK TABLE OF CONTENTS Part I. Artificial Intelligence for Software Development and Education >> Integrating AI into Scrum: Enhancing and Refining Agile Software Development Practices Aziz Fellah >> Leveraging AI to Enhance Human-Driven Software Development: A Comparative Study Across Diverse Applications Aziz Fellah >> The Impact of Generative AI on Critical Thinking and Academic Integrity in Higher Education: A Mixed-Methods Study John Tadema, Johnson Oluwagbemi, Ayobami Mesioye, Olabisi Josh-Falade Part II. Explanation and Prediction in Artificial Intelligence >> Revolutionizing Renewable Energy: Advanced Synthetic Climatic Models for Enhanced Energy Predictability and Optimization Wulfran Fendzi Mbasso, Idriss Dagal, Harrison Ambe, Jangir Pradeep, Emmanuel Fendzi Donfack, Pierre Tiako >> Comparison between both Methods of Explanations: LIME and SHAP Tarik Lahna, Bernard Kamsu-Foguem >> Predictive Maintenance of Pavement Cracks in Airport Facilities based on Drones and Computer Vision Tarik Lahna Part III. Intelligent Detection and Routing Mechanisms >> Data-driven Intelligent Approach for Intrusion Detection in IoT Environments Eric Fotsing, Severin Nguendap, Moussa Allamine Hamdane, Armand Nzeukou, Jean Pierre Lienou >> Adaptive Zero Trust Framework for Detection and Mitigation of Data Poisoning Attacks in Collaborative Edge Intelligence Systems Olabisi Josh-Falade >> Intelligent Opportunistic Routing in VANETs using Neural Network Souad Slimani, Samra Boulfekhar, Karima Adela, Pierre F Tiako Part IV. Artificial Intelligence Applications in the Industry and Military >> Real-Time Crack Detection and Segmentation in Metallic Surfaces Using YOLO v11-Seg Model Integrated with HMI for Industrial Applications Shahmir Qureshia, Asfia Fatimaa, Jawaid Daudpotoa, Aamir Shaikha, Abdul Qayooma >> AI Application Potential and Prospects in Materials Science: A Focus on Polymers Raphael Kpoghomou, Valerie Nassiet, Bernard Kamsu-Foguem >> YOLO algorithms for Solar Panel and Boiler Detection from Aerial Imagery Douglas Koomson, James Abugre, Stephen Moore >> Close-Combat Weapon Detection in Crisis Zones using YOLOv8 Laurent Nkamgam, Raïssa Onanena Guelan, Jacques Mbous Ikong, Olivier Videme Bossou, Bernard Essimbi Zobo Author Index
Management Practices for Transitioning to a Circular Economy: Challenges and Opportunities
This is a Chapter in: Book: Business Solutions and Environment Print ISBN 978-1-6692-0010-9 Online ISBN 978-1-6692-0009-3 Series: Chronicle of Business Chapter Abstract: Sustainable resource use and biodiversity preservation are critical. While sustainable business models have become deeply ingrained, obstacles remain to transitioning to a circular economy (CE). This paper investigates the complexities of modern environments as resources, exploring sustainable business models by connecting technologies and their semiotic contexts. We analyze the challenges businesses face in shifting to CE, focusing on management practices, technological barriers, and stakeholder collaboration. We propose a framework for stakeholder collaboration and practical managerial actions to facilitate CE adoption, thereby enhancing sustainable economic transformation. About this Paper Cite this paper as: Dagal I., Mbasso W.F., Harrison A., Jangir J., Fendzi-Donfack E., Tiako P.F. (2025). Management Practices for Transitioning to a Circular Economy: Challenges and Opportunities. In: Tiako P.F. (ed) Business Solutions and Environment. Chronicle of Business. OkIP. ICBF25#12. https://doi.org/10.55432/978-1-6692-0009-3_1 Presented at: The 2025 OkIP International Conference on Business Frontiers (ICBF) in Oklahoma City, OK, USA, and Online, on April 2, 2025 Contact: Wulfran Fendzi Mbasso fendzi.wulfran@yahoo.fr
This book introduces a collection of selected papers from the 2025 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on April 02 and October 01, 2025. Some unpublished papers from last year's event might also be considered for inclusion in this volume. One of the missions of the Academia & Industry Convention series is to promote quality research and development in a diverse and inclusive environment. Authors and participants from around the world with diverse origins, experiences, and backgrounds participated in these events. The International Technical Program Committees (TPC) thank those who submitted papers for consideration. Two to three experts in the field peer-reviewed each submitted paper for originality, relevance, contribution, structure, clarity, appropriateness, discussion, and conclusion. The document includes articles addressing various challenges and perspectives on business solutions and environment. New Book Chapter Publication Model OkIP is pleased to introduce its first Continuous Chapter Publishing (CCP) model. CCP means that each paper will be published as a chapter of the book after peer review, acceptance, and revision. The internal pagination of each chapter begins on page 1 rather than a continuous pagination of chapters throughout the book. In addition, each chapter has a unique Chapter Citation ID number, which replaces the page numbers in the citation. This change allows each Chapter to be published online immediately in its final form, and the whole publication time becomes shorter. ABOUT THIS BOOK Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Preface Table of Contents Detailed Table of Contents BOOK TABLE OF CONTENTS Part I Sustainable Economic Transformation and Growth >> America’s Utility-Scale Electricity Generation Landscape in Transition: A 2025 Analysis of Capacity Growth, Regional Trends, and Energy Resource Dynamics Wulfran Fendzi Mbasso, Harrison Ambe, Idriss Dagal, Jangir Pradeep, Pierre Tiako >> Bibliometric Review on Digital Platforms in Africa Dieudonne Tchuente, Dhruv Pandit, Leid Zejnilovic >> Management Practices for Transitioning to a Circular Economy: Challenges and Opportunities Idriss Dagal, Wulfran Fendzi Mbasso, Ambe Harrison, Pradeep Jangir, Emmanuel Fendzi-Donfack, Pierre Tiako Part II Artificial Intelligence in Business and Society >> Artificial Intelligence-Driven Digital Transformation of Supply Chain Management Hassane Zaoui, Bernard Kamsu-Foguem, Dieudonne Tchuente, Clovis Foguem >> Alertness to AI Algorithmic Biases in the Healthcare System Rufin Sime, Clovis Foguem, Samuel Fosso Wamba, Bernard Kamsu-Foguem Author Index
Simulating Disease Transmission Models Via Agent Interaction Using Large Language Models
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing https://www.okipublishing.com/book/index.php/okip/catalog/series/1 Chapter Abstract: Traditional epidemic models often fall short in capturing the complexity of human behaviors, a gap highlighted by the COVID-19 pandemic. This research explores the integration of large language models (LLMs) and agent-based modeling (ABM) with the Susceptible-Exposed-Infectious-Recovered (SEIR) framework to enhance epidemiological simulations. By leveraging LLMs, this study aims to develop a more dynamic and realistic model of disease transmission that reflects individual and community-level interactions. Our methodology utilizes the HPC-powered Mesa-Geo framework to incorporate geographically informed human agents (GeoAgents) and simulates disease spread through a detailed SEIR model, enriched by the advanced capabilities of the GPT4-XL Flan Alpaca model for generating nuanced human interactions. Preliminary results indicate that such an integration can effectively simulate complex social behaviors and adherence to public health measures, suggesting a promising direction for future epidemiological modeling. This approach not only addresses critical gaps in traditional modeling but also sets the stage for further research that could enhance public health strategies through more accurate and adaptive simulations. Cite this paper as: Chen J., Alhajjar E. (2024). Simulating Disease Transmission Models Via Agent Interaction Using Large Language Models. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#23. https://doi.org/10.55432/978-1-6692-0007-9_18 Presented at: The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024 Contact: Elie Alhajjar eliealhajjar@gmail.com
Towards Machine Learning Maintenance in Airport Infrastructure
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: The total operating costs of airport infrastructures include substantial upkeep expenses. Maintenance management methods are becoming increasingly important because any damage can lead to significant consequences at the airport. Many classic maintenance methods, like "run to failure" or preventive methods, are lacking in different fields because they're expensive and show a non-maintenance management approach. Many methods related to BIM will detect all possible failures and resolve them before they occur, a type of management known as "predictive maintenance." Predictive maintenance enables you to maximize the availability of engineering systems, prevent downtime, reduce downtime, and increase safety. Cite this paper as: Lahna T. (2024). Towards Machine Learning Maintenance in Airport Infrastructure. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#21. https://doi.org/10.55432/978-1-6692-0007-9_17 Presented at: The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024 Contact: Tarik Lahna lahnatk@ucla.edu
Prospect for Blockchain Applications & Perspectives: a Vision from French Occitanie
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: A blockchain is a technology for storing and transmitting information without a control body. This document presents the point of view of digital players in Occitanie, a French region bordering Spain, on blockchain development projects in different sectors of production of goods and services, including energy and health care. The prospects and challenges of blockchain applications are also mentioned. Cite this paper as: Cheukap Djampou E. J., Kamsu-Foguem B. (2024). Prospect for Blockchain Applications & Perspectives: a Vision from French Occitanie In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#19. https://doi.org/10.55432/978-1-6692-0007-9_16 Presented at: The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024 Contact: Bernard Kamsu-Foguem bkamsufo@yahoo.fr
Towards Performance in Aeronautics: The Role of Artificial Intelligence
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: AI integration is growing interest in the aeronautics sector. Common AI technologies include autonomous intelligent systems, predictive analytics and machine learning. The benefits of AI integration include cost reduction, improved customer satisfaction, time savings, enhanced safety and security, better decision-making, problem-solving, and operational efficiency. By implementing new AI technologies aeronautics companies enhance their performance. Cite this paper as: Zaoui A., Kamsu-Foguem B., Fossa-Wamba S., Tchuente D. (2024). Towards Performance in Aeronautics: the Role of Artificial Intelligence. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAIS24#17. https://doi.org/10.55432/978-1-6692-0007-9_15 Presented at: The 2024 OkIP International Conference on Automated and Intelligent Systems (CAIS) in Oklahoma City, Oklahoma, USA, and Online on October 2, 2024 Contact: Amina Zaoui amina.zaoui17@gmail.com
Sustainability: The Main Pillar of Industry 5.0
The implementation of sustainability principles into Industry 5.0 is crucial in the face of climate change and rising global environmental issues. This study aims to analyze the interaction between Industry 5.0 and sustainability. The review of 464 papers extracted from the Scopus database demonstrates the increasing annual scientific production and illustrates the growing interest in the topic by researchers and scholars. The analysis of clusters provides scope on the dimensions of Industry 5.0 and sustainability with the domination of the environmental aspects like climate change, energy efficiency, environmental impact and sustainable development. The importance of the digital technology is also revealed.
Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: Developing an effective and accurate battery management system that can predict the state of charge of electric vehicles is essential to enhance the safety and efficiency of electric vehicles. The assessment of the state of charge of the battery is important not only for determining the amount of energy that is available from the battery but also for determining how long the battery will last. This paper provides a brief understanding of how the state of charge estimation was predicted before and after the era of Machine Learning (ML). In addition, it proposes an accurate and fast state of charge estimation for lithium-ion batteries in electric vehicle applications using machine learning. The proposed model is designed to be generalizable across various inputs, applicable to both new and old batteries, and robust under different charging and discharging scenarios. Model performance and accuracy were evaluated using predefined metrics such as Root Mean Square Error and Mean Absolute Error. Among the four machine learning algorithms that achieved an approximate error of 0.75, while Multiple Linear Regression (MLR) model was chosen for its lightness and speed in training and testing. The findings of this research contribute to the advancement of Battery Management System (BMS) design and implementation for enhancing the efficiency and safety of Lithium-Ion Batteries (LIBs) in real-world driving scenarios. In the future, further research might be conducted to study the implementation of a variety of deep learning algorithms, as well as the estimation of battery health and the remaining useful life. Cite this paper as: Eid A. M., Nassif A. B., Ghenai C., Youssef H. Y., Alkhaja L. A., Almazrouei H. H. (2024). Machine Learning-Based SOC Prediction for Lithium-ion Batteries in Electric Vehicles. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CAPE24#11. https://doi.org/10.55432/978-1-6692-0007-9_12 Presented at: The 2024 OkIP International Conference on Advances in Power and Energy (CAPE) in Oklahoma City, Oklahoma, USA, and Online, on October 3, 2024 Contact: Ali Bou Nassif anassif@sharjah.ac.ae
Experimental Study of Parallel-Connected DC-DC Buck-Boost Converters FPGA Chaos Controlled
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: Chaos control is used to design a controller that is able to eliminate the chaotic behavior of nonlinear dynamic systems that experience such phenomena. This paper discuss the use of the FPGA as a controller of a parallel-connected DC-DC buck-boost converter, the goal of this paper is to build a controller that is capable of controlling the output current of a photovoltaic cells and minimize the effect of the module buck-boost converter chaotic behavior on the output voltage. To achieve this goal this paper presents two different methods, FPGA control the duty cycle and the frequency of the output controlling signal, this technique is done through software (FPGA code), and a delayed feedback control scheme in a module converter in the continuous-current conduction mode (CCM) using MATLAB/SIMULINK simulation. Thus, this paper shows the FPGA capabilities in the power industry and it’s specifies a guideline to overcome some of the obstacles when dealing with an FPGA as a buck-boost converter controller, and MATLAB/SIMULINK simulation results show the effectiveness and robustness of the scheme. Cite this paper as: Natsheh A., Nguyen T. H., Sreekumar P. (2024). Experimental Study of Parallel-Connected DC-DC Buck-Boost Converters FPGA Chaos Controlled. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CEST24#21. https://doi.org/10.55432/978-1-6692-0007-9_11 Accepted for: The 2024 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024 Contact: Ammar Natsheh ammar_natsheh@yahoo.com
Power Quality Improvement of a Standalone Power Supply System Powered by PV, Wind, and Batteries via LSTM-based ANN Controllers
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: Standalone electric power supply systems that integrate wind and photovoltaic (PV) technologies are widely employed in various applications. These systems require a battery storage system to ensure a continuous power supply, regardless of changes in loads, wind speed, and solar irradiance. To ensure a reliable and uniform power supply, several modifications need to be implemented in these hybrid systems. Power quality is an essential parameter in autonomous power systems, especially those that depend on hybrid energy sources. The battery should undergo the process of charging when there is an excess of energy and discharging when there is a demand from the loads. To achieve this, a bidirectional power flow circuit is utilized to establish a connection between the battery and the network, incorporating an appropriate controlling mechanism. Even in scenarios where there is only partial shading, a Modified Invasive Weed Optimization technique is employed to achieve the most efficient power operation of the photovoltaic (PV) system. Additionally, a Sliding Mode Control strategy is utilized for the wind turbine. This research investigates the application of multiple photovoltaic (PV) systems and wind turbines, each configured with different arrangements of parallel and series combinations of PV panels. The goal is to determine the appropriate rating for the power supply system. The control units in this hybrid standalone power system utilize artificial neural network (ANN) controllers that are based on long short-term memory (LSTM). The aforementioned controllers, when combined with the proposed control methodologies, improve power quality in various scenarios. The results of this investigation are presented using Hardware-in-the-Loop (HIL) on the OPAL-RT platform to evaluate the efficacy of the proposed methodology. Cite this paper as: Molu R., Mbasso W.F., Naoussi S., Wira P., Ambe H., Saatong K. T. (2024) Power Quality Improvement of a Standalone Power Supply System Powered by PV, Wind, and Batteries via LSTM-based ANN Controllers. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CEST24#20. https://doi.org/10.55432/978-1-6692-0007-9_10 Presented at: The 2024 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024 Contact: Reagan J. J. Molu molureagan@yahoo.fr
Strategic Energy Management System for Improving the Power Quality of PV-Wind-Battery-based Standalone Microgrids
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: The global community is currently engaged in the exploration of various techniques to utilize renewable energy sources with the objective of mitigating the effects of global warming and decreasing dependence on fossil fuels. The deployment of solar and wind energy has experienced substantial adoption in various geographical areas worldwide. However, the predictability and controllability of solar irradiance and wind speed are constrained. Therefore, it is crucial to incorporate an energy storage system to optimize the utilization of these energy sources by converting them into electrical energy. Within the domain of medium power applications, batteries are recognized as a prominent selection. However, batteries require significant maintenance and are susceptible to self-discharge, resulting in a gradual decrease in storage capacity over time. Hydrogen storage is a feasible alternative for high-power applications, offering a cost-efficient solution in comparison to batteries. The stored hydrogen can be utilized for various applications, including transportation and electricity generation. An electrolyzer is an electrochemical device that employs electrical energy to efficiently perform the process of water electrolysis, which involves the decomposition of water into its elemental components, namely oxygen and hydrogen. Nevertheless, the slow heat transfer dynamics hinder the rapid generation of hydrogen, necessitating the implementation of a novel control technique to improve production efficiency in response to changes in solar irradiance and wind speed. The deployment of boost, buck, and DC to DC bidirectional converters is utilized to maintain a consistent voltage at the DC-link across different operational scenarios. A novel control algorithm has been developed to ensure power quality at the 3-phase AC load bus and efficiently manage energy in the hybrid standalone system. The outcomes of this inquiry, conducted utilizing MATLAB/Simulink, are exhibited to evaluate the efficiency in various scenarios. Cite this paper as: Mbasso W.F., Molu R. J., Naoussi S.R.D., Ambe H., Donfack E. F., Saatong K. T. (2024) Strategic Energy Management System for Improving the Power Quality of PV-Wind-Battery-based Standalone Microgrids In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. CEST24#17. https://doi.org/10.55432/978-1-6692-0007-9_9 Presented at: The 2024 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 3, 2024 Contact: Wulfran Fendzi Mbasso fendzi.wulfran@yahoo.fr
Hybrid Parallelization for Accelerating Visibility-Graph Construction and Community Detection on Temporal Data
This is a Chapter in: Book: Competitive Tools, Techniques, and Methods Print ISBN 978-1-6692-0008-6 Online ISBN 978-1-6692-0007-9 Series: Chronicle of Computing Chapter Abstract: In the present era, vast amounts of time series data, particularly in biology, require efficient analysis due to high-dimensional datasets from advanced technologies. Transforming time series into networks and applying community detection methods can uncover dynamic patterns as temporal network communities. However, the large size of these datasets often extends analysis time. High Performance Computing (HPC) addresses this by accelerating traditional applications. This article presents an HPC-optimized version of the visibility-graph-based temporal community detection method. Enhancing the original algorithm with parallel processing, including shared memory and message passing models, improves adaptability. Experiments using an artificial sine series and two real-world datasets—biological time series and power consumption patterns—on Clemson's Palmetto Cluster demonstrate significant performance improvements and scalability over the original approach. Cite this paper as: Jia X., Zheng M., Wang L., Jin S. (2024) Hybrid Parallelization for Accelerating Visibility-Graph Construction and Community Detection on Temporal Data. In: Tiako P.F. (ed) Competitive Tools, Techniques, and Methods. Chronicle of Computing. OkIP. AHPC24#06. https://doi.org/10.55432/978-1-6692-0007-9_8 Presentated at: The 2024 OkIP International Conference on Advances in High-Performance Computing (AHPC) in Oklahoma City, Oklahoma, USA, and Online on October 3, 2024. Contact: Xun Jia xunj@g.clemson.edu