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.
Power System Resilience Metrics – A Practical Analysis and Review
This is a Chapter in: Book: Energy Applications and Smart Perspectives Print ISBN 978-1-6692-0018-5 Online ISBN 978-1-6692-0017-8 Series: Chronicle of Energy Chapter Abstract: Power system resilience has emerged as a key research topic, due to the increasing threats on grid posed by extreme events, e.g. natural disasters, severe weather, and cyberattacks. Enhancing power system resilience to withstand disruptive natural or human-caused events is crucial for ensuring the safety and sustainability of the communities they serve. The ability of power systems to maintain operations during disruptions is the benefit of increased energy resilience. Although considerable research, over the last few decades, has been conducted to define, model, and quantify resilience from various aspects, the lack of universally accepted evaluation methods and resilience metrics hinders the assessment and comparison of resilience across different power systems, as is typically done in power system reliability studies. About this Paper Cite this paper as: Belu R. (2026). Power System Resilience Metrics – A Practical Analysis and Review. In: Tiako P.F. (ed) Energy Applications and Smart Perspectives. Chronicle of Energy. OkIP. CEST26#10. https://doi.org/10.55432/978-1-6692-0017-8_4 Presented at: The 2026 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026 Contact: Radian Belu radian_belu@subr.edu
A Course in AI Power and Energy Applications
This is a Chapter in: Book: Energy Applications and Smart Perspectives Print ISBN 978-1-6692-0018-5 Online ISBN 978-1-6692-0017-8 Series: Chronicle of Energy Chapter Abstract: Artificial intelligence (AI) applications are becoming increasingly important in all socio-economic and industry domains, making AI education a must. However, AI education is a complex undertaking as the field can be positioned from multiple perspectives, system theory, statistics, computer sciences, and human-centered design. Proposed course serves as a gateway to the intersection of AI and energy engineering, offering comprehensive technology and applications exploration. Course objectives include understanding AI concepts, theories, tools, models, and engineering applications About this Paper Cite this paper as: Belu R., Lacy F., Yasser Ismail Y. (2026). A Course in AI Power and Energy Applications. In: Tiako P.F. (ed) Energy Applications and Smart Perspectives. Chronicle of Energy. OkIP. CEST26#11. https://doi.org/10.55432/978-1-6692-0017-8_1 Presented at: The 2026 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026 Contact: Radian Belu radian_belu@subr.edu
Decentralized Edge Intelligence Framework for Low-Latency, Privacy-Preserving Fraud Detection in Distributed Financial Ecosystems
This is a Chapter in: Book: Theory and Practice for Business Support Print ISBN 978-1-6692-0014-7 Online ISBN 978-1-6692-0013-0 Series: Chronicle of Business Chapter Abstract: Centralized Intelligence based fraud detection systems require all transactional data to be transmitted to remote servers, which introduces latency, increases bandwidth and infrastructure costs, and aggregates sensitive financial data in a single location. Federated Learning (FL) and Edge AI however, offer a promising alternative, by enabling collaborative model training across distributed clients while keeping raw data localized. This paper presents DeFRAD, a federated learning fraud detection framework evaluated in a simulated edge oriented setting using the widely adopted Kaggle Credit Card Fraud Detection dataset partitioned into non IID client distributions. By training a lightweight Multi Layer Perceptron (MLP) using the FedAvg algorithm over 100 clients, the framework’s performance is compared with two baselines which include a centralized MLP trained on pooled data and isolated local models trained independently on each client. Results indicate that DeFRAD achieves near centralized predictive performance with a trade-off in latency and privacy, and substantially outperforms isolated local models. A latency and communication analysis using an edge oriented simulation reflects that on device inference can be performed in less than 20ms, which is critical for financial transaction processing, and FL model training requires less data transfer than centralized training. Strong privacy guarantees beyond standard FL are not included in this study. Overall, DeFRAD demonstrates that FL based fraud detection offers a practical balance between predictive performance, communication cost, and deployment feasibility for edge oriented financial ecosystems. About this Paper Cite this paper as: Akinola K., Josh-Falade O. (2026). Decentralized Edge Intelligence Framework for Low-Latency, Privacy-Preserving Fraud Detection in Distributed Financial Ecosystems. In: Tiako P.F. (ed) Theory and Practice for Business Support. Chronicle of Business. OkIP. APDC26#8. https://doi.org/10.55432/978-1-6692-0013-0_1 Presented at: The 2025 OkIP International Conference on Advances in Parallel and Distributed Computing (APDC) in Oklahoma City, OK, USA, and Online, on April 1, 2026. Contact: Olabisi Josh-Falade josh-faladeoa@mcu.edu.ng
Leading Path Layer (LPL): Governing Autonomous Authority Under Perceptual Uncertainty
This is a Chapter in: Book: Smart Computing and Emerging Applications Print ISBN 978-1-6692-0016-1 Online ISBN 978-1-6692-0015-4 Series: Chronicle of Computing Chapter Abstract: As artificial intelligence expands into embodied robots, vehicles and autonomous machines, failures increasingly arise from systems continuing to act when their understanding is no longer sufficient to justify continued participation. Existing robotic safety standards address known hazards via downstream mechanisms such as torque limits, emergency stops, and collision thresholds. These approaches assume sufficiently reliable perception and are activated only after a potentially dangerous state is detected, implicitly preserving autonomous authority even as underlying understanding degrades. In unstructured, human-centered environments, uncertainty is inherently structural and may lead to a previously under-specified failure mode: “safe-but-uncertain operation”, where a system complies with all hard constraints while continuing to exercise autonomous authority under insufficient understanding. This paper introduces the Leading Path Layer (LPL), an upstream governance layer that conditions whether autonomous authority is permitted based on system understanding, enforcing relinquishment of authority before traditional limits are threatened. This work reframes safety from improving behavior under uncertainty to defining when behavior should cease entirely. About this Paper Cite this paper as: Whitfield II, K. (2026). Leading Path Layer (LPL): Governing Autonomous Authority Under Perceptual Uncertainty. In: Tiako P.F. (ed) Smart Computing and Emerging Applications. Chronicle of Computing. OkIP. CAIF26#1. https://doi.org/10.55432/978-1-6692-0015-4_5 Presented at: The 2026 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026 Contact: Keyonn Whitfield II mike.williams8034@pm.me
Hybrid Inverse-Document-Frequency-Weighted Scoring for Bilingual Medical Diagnosis: A Multi-Agent Approach
This is a Chapter in: Book: Smart Computing and Emerging Applications Print ISBN 978-1-6692-0016-1 Online ISBN 978-1-6692-0015-4 Series: Chronicle of Computing Chapter Abstract: Accurate disease ranking from free-text symptom descriptions is a core challenge in virtual medical consultation, especially for multilingual populations. We propose a hybrid scoring engine that combines Inverse-Document-Frequency (IDF)-weighted rule-based inference with a soft-voting ensemble classifier, deployed within a lightweight multi-agent architecture. Three targeted mechanisms drive performance: an IDF-inspired specificity factor amplifying rare, pathognomonic symptoms up to a factor of two; a graduated negation penalty preventing premature hypothesis rejection; and a uniqueness bonus resolving ties between symptom-overlapping diseases. A 500-pattern bilingual (English/French) Natural Language Processing (NLP) pipeline feeds the engine with normalized symptom concepts. Evaluated on 100 bilingual test cases spanning 41 diseases in 10 clinical categories, the system achieves 72% top-1 accuracy, 93% top3, and 100% top-5, with an English–French gap of only 4 percentage points. About this Paper Cite this paper as: Kuete-Menou D.R., Akong O. M., Lienou J-P. (2026). Hybrid IDF-Weighted Scoring for Bilingual Medical Diagnosis: A Multi-Agent Approach. In: Tiako P.F. (ed) Smart Computing and Emerging Applications. Chronicle of Computing. OkIP. CAIF26#10. https://doi.org/10.55432/978-1-6692-0015-4_4 Presented at: The 2026 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026 Contact: Donald Kuete Menou kuetedonald@gmail.com
Single-Source Heterogeneous Programming Approach for Power System Transient Stability Analysis and Alternating Current Power Flow
This is a Chapter in: Book: Smart Computing and Emerging Applications Print ISBN 978-1-6692-0016-1 Online ISBN 978-1-6692-0015-4 Series: Chronicle of Computing Chapter Abstract: The rise of heterogeneous computing requires that parallel applications be executed on various hardware from different vendors. Thus, efficient solutions emerge using compiler directives, communication protocols, high-level libraries, and execution policies. This paper presents a portable heterogeneous programming approach for scientific application development in Python. The implementation realizes the parallel architecture through Message Passing Interface (MPI) with CPU-based and GPU-based array computing at a high level. Two numerical algorithms in the computational power system area, transient stability analysis (TSA) and alternating current power flow (ACPF), are used to test the fitness of the design. The performance result on the largest 36864-bus-12288-generator system indicates that a 30-second TSA program run with pure CPU-based parallelism finishes the computation in 9.89 seconds, and the program’s CPU+GPU acceleration (hybrid switch) reaches 2.33 seconds on a cluster node, making it 12.9x faster than real-time simulation. The ACPF solution can be calculated to converge in 9.69 seconds on the CPU and 2.38 seconds in hybrid mode, respectively. The proposed method significantly improves computational performance with high portability and adaptiveness to various hardware environments. About this Paper Cite this paper as: Yang X., Wang C., Jin S. (2026). Single-Source Heterogeneous Programming Approach for Power System Transient Stability Analysis and Alternating Current Power Flow. In: Tiako P.F. (ed) Smart Computing and Emerging Applications. Chronicle of Computing. OkIP. APDT26#6. https://doi.org/10.55432/978-1-6692-0015-4_3 Presented at: The 2026 OkIP International Conference on Advances in Parallel and Distributed Computing (APDC) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026. Contact: Xin Yang xin.yang2@utsouthwestern.edu
Decentralized Computing Framework for Real-time Energy Management in Sustainable Smart Cities
This is a Chapter in: Book: Smart Computing and Emerging Applications Print ISBN 978-1-6692-0016-1 Online ISBN 978-1-6692-0015-4 Series: Chronicle of Computing Chapter Abstract: Centralized power grids are increasingly incapable of handling the bidirectional and intermittent nature of renewable energy sources (solar and wind). This lack of real-time responsiveness leads to grid instability, energy leakage during transmission, and frequent blackouts during peak periods. Centralized cloud processing introduces high latency, making it slow for the split-second decisions required in micro-grid management. From existing studies, Edge computing provides the technical blueprint for the next generation of smart and time-sensitive infrastructure. This study therefore proposes a Decentralized Edge Computing Framework, that involves deploying lightweight Machine Learning models at the power edge layer – the transformer and household levels. Leveraging the Pecan Street Dataport Dataset, the world’s largest high-resolution energy dataset, the methodology involves local data processing such that edge nodes negotiate energy distribution between electric vehicle (EV) charging stations and residential solar units in real-time, without sending data back to a central server. Experimental results suggest that the framework can significantly mitigate grid instability, demonstrating a 38% reduction in grid peak-load stress under the modeled conditions. Comparative analysis indicates that localized decision-making (Edge) has the potential to outperform centralized systems (Cloud) in stabilizing voltage levels and reducing transmission losses. For the larger society, this translates to lower energy bills, a more reliable power grid, and the democratization of energy, where neighborhoods can become self-sustaining energy islands, reducing reliance on fossil-fuel-dependent centralized utilities. About this Paper Cite this paper as: Oluwagbemi J.B., Tadema J., Josh-Falade O. and Mesioye A. (2026). Decentralized Computing Framework for Real-time Energy Management in Sustainable Smart Cities. In: Tiako P.F. (ed) Smart Computing and Emerging Applications. Chronicle of Computing. OkIP. CEST26#6 https://doi.org/10.55432/978-1-6692-0015-4_2 Presented at: The 2026 OkIP International Conference on Energy and Sustainable Technologies (CEST) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026 Contact: Olabisi Josh-Falade josh-faladeoa@mcu.edu.ng
Retrieval Augmented Generation, Semantic Retrieval, Software Update Monitoring, Vulnerability Intelligence
This is a Chapter in: Book: Smart Computing and Emerging Applications Print ISBN 978-1-6692-0016-1 Online ISBN 978-1-6692-0015-4 Series: Chronicle of Computing Chapter Abstract: Software update information is often fragmented across multiple sources such as vendor release notes, vulnerability disclosures, and community discussions. This fragmentation makes it difficult for maintainers and system administrators to identify and relate important updates and security issues in a timely manner. This paper presents a vendor-centered semantic retrieval framework that consolidates update information using similarity-based search within a Retrieval-Augmented Generation (RAG) architecture. The system integrates release notes, vulnerability records from the Common Vulnerabilities and Exposures (CVE) and National Vulnerability Database (NVD), and community feedback from Reddit discussions, embedding these artifacts into a shared vector space using sentence-transformer models and Facebook AI Similarity Search (FAISS) indexing. The framework was evaluated on a dataset of 1,200 software version updates, 248 Reddit discussions, and 312 vulnerability records collected over two years. Results show that semantic retrieval improves related update detection by 41 percent compared to keyword baselines, enables 36 percent earlier detection of critical security updates, and reduces manual inspection effort by about 30 percent. These findings indicate that vendor-centered semantic similarity can effectively connect fragmented software update information and support more timely update decisions. About this Paper Cite this paper as: Fahad F.M., Berhe S. (2026). RAG-Based Semantic Retrieval for Software Update Triage. In: Tiako P.F. (ed) Smart Computing and Emerging Applications. Chronicle of Computing. OkIP. CAIF26#8 https://doi.org/10.55432/978-1-6692-0015-4_1 Presented at: The 2026 OkIP International Conference on Artificial Intelligence Frontiers (CAIF) in Oklahoma City, Oklahoma, USA, and Online, on April 1, 2026 Contact: Fnu Mohammed Fahad m_lnu18@u.pacific.edu
Energy Applications and Smart Perspectives
This book introduces a collection of selected papers from the 2026 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on April 01 and October 01. 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 Energy Applications with Smart Perspectives. * 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 Online ISBN 978-1-6692-0013-4 Print ISBN 978-1-6692-0013-5 Preface Table of Contents (coming soon) Detailed Table of Contents (coming soon) IN-PROGRESS BOOK TABLE OF CONTENTS The tentative list of chapters below is subject to change. New chapters will be added upon acceptance by the TPC; some will be removed upon withdrawal. A chapter will be assigned a DOI and published when the camera-ready is received and accepted. >> A Course in AI Power and Energy Applications Radian Belu, Fred Lacy, Yasser Ismail >> Investigation and Prediction of the Ageing Process of Energy Storage Systems Operating in Tropical Sub-Saharan Zones [Under Revision] Raoul Watsop Sonkoue, Jacques Mbous Ikong, Raissa Onanena >> Remote Management of a Micro Hydroelectric Power Plant [Under Revision] Michel Neussie, Bernard Kamsu-Foguem, Joseph Voufo >> Power System Resilience Metrics – A Practical Analysis and Review Radian Belu >> Study and Control of a Micro Energy Production System in an Isolated Site via a Virtual Power Plant (VPP) [Under Revision] Justin Bande Ngwa, Raissa Onanena, Emmanuel Tchomgo Fenelou >> More titles coming soon...
Theory and Practice for Business Support
This book introduces a collection of selected papers from the 2026 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on April 01 and October 01, 2026. 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 Theories and Practices for Business Support. * 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 using continuous chapter numbering 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, thereby shortening the overall publication time ABOUT THIS BOOK Print ISBN 978-1-6692-0014-7 Online ISBN 978-1-6692-0013-0 Preface (coming soon) Table of Contents (coming soon) Detailed Table of Contents (coming soon) IN-PROGRESS BOOK TABLE OF CONTENTS The tentative list of chapters below is subject to change. New chapters will be added upon acceptance by the TPC; some will be removed upon withdrawal. A chapter will be assigned a DOI and published when the camera-ready is received and accepted. >> Decentralized Edge Intelligence Framework for Low-Latency, Privacy-Preserving Fraud Detection in Distributed Financial Ecosystems Kayode Akinola, Olabisi Josh-Falade DOI: 10.55432/978-1-6692-0013-0_1 >> Explainable Artificial Intelligence in Industry 5.0 [Under Revision] Michel Neussie, Bernard Kamsu-Foguem DOI: 10.55432/978-1-6692-0013-0_2 >> Dynamic Prediction of Energy Consumption in Rural Healthcare Centers in Cameroon [Under Revision] Jules Nyonga, Raissa Onanena, Stephane Atock DOI: 10.55432/978-1-6692-0013-0_3 >> More titles coming soon... Author Index (In Progress)
Smart Computing and Emerging Applications
This book introduces a collection of selected papers from the 2026 OkIP Academia & Industry Convention conferences held Online and in Oklahoma City, Oklahoma, USA, on April 01 and October 01, 2026. 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 using continuous chapter numbering 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, thereby shortening the overall publication time. ABOUT THIS BOOK Online ISBN 978-1-6692-0015-4 Print ISBN 978-1-6692-0016-1 Preface Table of Contents (coming soon) Detailed Table of Contents (coming soon) IN-PROGRESS BOOK TABLE OF CONTENTS The tentative list of chapters below is subject to change. New chapters will be added upon acceptance by the TPC; some will be removed upon withdrawal. A chapter will be assigned a DOI and published when the camera-ready is received and accepted. >> RAG-Based Semantic Retrieval for Software Update Triage Fnu Mohammed Fahad, Solomon Berhe Link: https://www.okipublishing.com/book/index.php/okip/catalog/book/81 DOI: 10.55432/978-1-6692-0015-4_1 >> Decentralized Computing Framework for Real-time Energy Management in Sustainable Smart Cities Johnson Oluwagbemi, John Tadema, Olabisi Josh-Falade, Ayobami Mesioye Link: https://www.okipublishing.com/book/index.php/okip/catalog/book/82 DOI: 10.55432/978-1-6692-0015-4_2 >> Single-Source Heterogeneous Programming Approach for Power System Transient Stability Analysis and Alternating Current Power Flow Xin Yang, Shuangshuang Jin, Cong Wang DOI: 10.55432/978-1-6692-0015-4_3 >> A Situation-Aware Multi-Agent Architecture for Intelligent Virtual Medical Consultation Donald Richie Kuete Menou, Onabib Mathias Akong, Jean-Pierre Lienou DOI: 10.55432/978-1-6692-0015-4_4 >> Leading Path Layer (LPL): Governing Autonomous Authority Under Perceptual Uncertainty Keyonn Whitfield II DOI: 10.55432/978-1-6692-0015-4_5 >> More titles coming soon... Author Index (In Progress)
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 data-driven intelligent approaches aimed at enhancing intrusion detection within IoT environments. The proposed detection model integrates a hybrid AutoEncoder-LSTM architecture with a supervised dense classifier, specifically designed to capture the temporal structure of network traffic and transform latent representations into explicit predictions. The model’s architecture leverages a dual loss function, combining Mean Squared Error (MSE) for reconstruction and Cross-Entropy for classification, thereby enabling high-precision intrusion detection for both binary and multi-class scenarios. The model was trained on the IoT-23 dataset and optimized using regularization techniques to capture the complex patterns of modern cyberattacks. Experimental results demonstrate the robustness of the proposed approach, achieving an F1-score of 99.97% on the IoT-23 dataset and outperforming state-of-the-art methods on the NSL-KDD benchmark. Furthermore, the model maintains a low computational footprint, making it highly suitable for resource-constrained IoT devices. 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