| 1 |
Author(s):
Ikhioya Emmanuel, Ajaegbu C. (Prof.).
Page No : 1-14
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Load Balancing for Virtual Machines in Heterogeneous Data Center Networks Using Software Defined Networking Integrated with Multi Criteria Decision Making: A Systematic Review.
Abstract
Efficient load balancing is vital in heterogeneous data center networks to ensure optimal resource use and service performance. Traditional methods struggle with dynamic VM workloads and diverse resource demands. This systematic review explores the integration of Software-Defined Networking (SDN) and Multi-Criteria Decision Making (MCDM) as a solution. SDN offers centralized, programmable control, while MCDM enables intelligent decision-making across multiple performance metrics. Reviewing recent literature, this study highlights key techniques—such as the Weighted Sum Model (WSM)—and identifies gaps in real-time adaptability and metric integration. The review serves as a foundation for developing scalable, QoS-aware load balancing systems in cloud environments.
| 2 |
Author(s):
Ayankoya F. Y., Kuyoro S. O., Ikhioya Emmanuel.
Page No : 15-31
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Security Threat Mitigation in SDN.
Abstract
Software-Defined Networking (SDN) has transformed network management by decoupling the control and data planes, enabling centralized control and programmability. While SDN enhances flexibility and scalability, its centralized architecture introduces critical security challenges, including Distributed Denial of Service (DDoS) attacks, API exploits, and controller compromises. This study provides a comprehensive review of SDN security vulnerabilities and evaluates mitigation techniques such as authentication protocols, anomaly detection systems, resilient architectures, and secure communication protocols. The findings highlight the importance of multi-layered defense strategies to safeguard SDN environments and address evolving cyber threats. Gaps in scalability, real-time adaptation, and integration with emerging technologies are also identified, paving the way for future research.
| 3 |
Author(s):
Kuyoro S. O., Ayankoya F. Y., Ikhioya Emmanuel, Adeyemi John.
Page No : 32-47
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Network Performance Optimization with Intent‑Based Networking: A Review.
Abstract
Intent-Based Networking (IBN) is transforming network management by shifting from manual, device-oriented configurations to high-level, intent-driven automation. In an IBN system, network operators express high-level objectives that are automatically translated into detailed network policies and configurations through artificial intelligence (AI), natural language processing (NLP), and closed-loop control mechanisms. This paper reviews recent advances in IBN, with a focus on its architecture, automation techniques, security frameworks, and AI-driven policy generation. Moreover, we discuss intent negotiation frameworks and voice-enabled interfaces for industrial automation. By integrating these technologies with Software-Defined Networking (SDN) and Network Function Virtualization (NFV), IBN optimizes network performance by enhancing resource utilization, reducing latency, and improving overall reliability. This paper also outlines the challenges and future research directions necessary for the deployment of IBN in next-generation networks.
| 4 |
Author(s):
Ibrahim Abdul Sa'ad, Collins Nnalue Udanor, Modesta E. Ezema, Caleb Markus, Mathew Akwu Adaji.
Page No : 48-63
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A Systematic Review of Federated Deep Learning Models for Intrusion Detection in Distributed Satellite Data Centres.
Abstract
This systematic review evaluates existing federated and deep learning intrusion detection approaches with a focus on their suitability for distributed satellite and critical infrastructure environments. Following PRISMA procedures, 13,213 records were identified, 11,265 duplicates removed, 1,948 screened, and 10 studies met full eligibility criteria. Across these studies, federated learning models achieved competitive detection outcomes, with reported accuracy ranging from eighty-eight percent to ninety-seven percent and F1 scores between eighty-five percent and ninety-six percent, often differing from centralised models by less than three percent. Communication efficiency improved substantially, with several studies demonstrating reductions of thirty to sixty percent in update bandwidth due to parameter rather than data transmission. Privacy preservation scored consistently high across all federated implementations, while centralised systems showed significant exposure risk. Weaknesses emerged in handling non-independent data, where performance dropped by up to ten percent in some studies, and in susceptibility to gradient poisoning, which degraded accuracy by seven percent in controlled experiments. Mathematical formulations remained underdeveloped, with limited convergence proofs and insufficient modelling of secure aggregation. The findings indicate that federated deep learning is methodologically superior for satellite data centres but requires realistic satellite traffic datasets, hybrid optimisation such as blockchain-assisted aggregation, and improved mathematical modelling to ensure robustness.
| 5 |
Author(s):
Ibrahim Abdul Sa'ad, Collins Nnalue Udanor, Modesta E. Ezema, Caleb Markus, Mathew Akwu Adaji.
Page No : 64-75
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Deep Learning and Explainable AI Models for Intrusion Detection in Space-Ground Communication Networks: A Review.
Abstract
This review critically evaluates the suitability of deep learning and explainable artificial intelligence approaches for intrusion detection in satellite ground-station environments, addressing the escalating cybersecurity risks facing the National Space Research and Development Agency (NASRDA) and broader space communication networks. Using a systematic narrative review across IEEE Xplore, ACM, Scopus and arXiv, the analysis compares CNN, LSTM, GRU, autoencoder and transformer-based IDS models, revealing that while reported accuracies frequently exceed 92% on benchmark datasets, performance declines by 20% to 35% under domain shift, demonstrating poor transferability to space–ground telemetry. XAI methods such as SHAP, LIME and Integrated Gradients appear in more than 80% of reviewed studies, yet empirical results show a 30% to 60% increase in inference latency, raising concerns about operational feasibility in real-time satellite control systems. A mathematical hybrid model combining CNN, LSTM and transformer components with a structured anomaly-scoring function and explanation regulariser is formulated to address these limitations. Findings indicate that multi-model fusion enhances anomaly sensitivity, domain-specific feature engineering improves robustness, and integrated XAI pathways strengthen analyst trust while exposing computational bottlenecks. The proposed conceptual architecture for NASRDA advances the field by aligning detection workflows, interpretability mechanisms and feedback loops with the constraints of aerospace communication systems. The review concludes by identifying key research priorities, including the development of satellite-specific datasets, real-traffic validation of hybrid IDS models, and deployment of low-latency XAI dashboards for operational security.
| 6 |
Author(s):
Authority O. A. U..
Page No : 76-90
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Musical Engineering as Epistemic Bridge: Integrating Acoustical Physics, Cognitive Neuroscience, and Decolonial Musicology.
Abstract
This article introduces Musical Engineering as a framework that bridges acoustical physics, cognitive neuroscience, and decolonial musicology. Using Critical Realism as its lens, the study examines how sound’s physical properties, frequency, resonance, and timbre, interact with human perception and culturally situated practices of music-making. The research addresses the fragmentation of knowledge across science and culture, which has limited a holistic understanding of music as both a phenomenon and a practice. A mixed qualitative design was employed, combining acoustical modeling of indigenous instruments (Dundun, Oja) and Western instruments (violin, bagpipes), interpretive analysis of neuroscientific data on auditory perception, and ethnographic case studies. Fieldwork was conducted in Lagos State, Nsukka (Nigeria), Maryland (USA), and Scotland (UK), involving 54 musicians, instrument makers, and educators from both African and Western classical traditions. Findings show that sound principles are culturally mediated rather than neutral. Musical Engineering emerges as an epistemic bridge, offering pathways for inclusive learning, curriculum reform, and future research in areas such as AI-driven sound studies and global acoustical databases.
| 7 |
Author(s):
Ifekanandu Chukwudi Christian (Ph.D.).
Page No : 91-107
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Warehouse Automation and Operational Performance of Freight Forwarding Firms in Rivers State, Nigeria.
Abstract
This study examined warehouse automation and operational performance of freight forwarding firms in Rivers State. The study adopted the correlational research design and the quantitative research approach. The population of the study consisted of 96 registered freight forwarding firms in Rivers State. Thirty-four (34) freight forwarding firms were selected for the study using purposive sampling technique. The sampling unit consisted of operational managers, warehouse managers, warehouse supervisors and store keepers of the 34 selected freight forwarding firms in Rivers State. A sample size of 136 staff was drawn from the 34 selected freight forwarding firms in Rivers State. Data were collected from the respondents using a structured questionnaire. The data collected were analyzed statistically while the Spearman Rank Order Correlation Coefficient (rho) and the SPSS version 24 were used to test the hypotheses. The findings revealed that automated picking system has significant relationship with operational efficiency of freight forwarding firms. Automated picking system was also found to have significant relationship with operational excellence of freight forwarding firms. The study also found a significant relationship between automated inventory management system and operational efficiency of freight forwarding firms. The study equally revealed that automated inventory management system is significantly related to operational excellence of freight forwarding firms. From the findings, it was concluded that warehouse automation such as automated picking system and automated inventory management system significantly improve operational performance of freight forwarding firms in Rivers State. Therefore, it was recommended that freight forwarding firms in Nigeria should adopt warehouse automation as it would improve their operational performance.
| 8 |
Author(s):
Oladipo Sunday Oluwadare, Kuyoro Afolashade, Amanze Ruth, Orimogunje Hope Tolulope, Akinwunmi Damilare.
Page No : 108-122
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Large Language Model Enabled Multilingual Chatbot for Inclusive Banking and Financial Accessibility.
Abstract
In the world today financial inclusion drive is on the rise and Nigeria is making efforts to achieve it. However, having over 500 indigenous languages, linguistic diversity remains a critical problem, among other factors, in achieving Financial Inclusion. Digital platforms, especially Artificial intelligence enabled chatbots are gaining traction as avenues for inclusion but the English centric characteristics observable in them is limiting financial inclusion thereby excluding millions of citizens who need financial services. Hence, there is need for the design and implementation of a multilingual chatbot that includes capability for interaction in four of the major Nigerian languages (Hausa, Igbo, Yoruba and Pidgin) using Gemini Large Language Model (LLM) as the driver of the needed natural and context-aware conversations across these languages. The system’s architectural components are client, middleware, Artificial Intelligence and data layers which interacting together enables scalable support for banking services that could be common or core. The result of evaluation show that the system improves accessibility and user satisfaction among the linguistically diverged users. Therefore, this study contributed to enhancing the strategy of Nigeria’s National Financial Inclusion while incentivizing the necessity of adopting localized AI solutions as instruments of bridging socio-economic divides.
| 9 |
Author(s):
Tolulope Elizabeth Adenekan (Ph.D.), Ibrahim Sunday Oyekola.
Page No : 123-136
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Data Handling Stages and Information Security Practice among Non-Academic Staff of State-Owned Polytechnics, Oyo State, Nigeria.
Abstract
Information security serves as the strategic effort to shield data from threats. This study investigated the influence of data handling stages and information security practices among Non-Academic staff of State–owned polytechnics, Oyo State, Nigeria. The study was guided by Traid CIA model and Record Continuum Model and used a descriptive survey design, for the population of 1109. The sample size for the study is 285 which was derived using Krejcie and Morgan sample size table and stratified sampling techniques was employed. Tools for data collection was a structured questionnaires which was administered across these three polytechnics (The Polytechnic, Ibadan; Adeseun Ogundoyin Polytechnic, Eruwa; The Oke-Ogun Polytechnic, Saki); after which 272 valid responses were analyzed using SPSS. Descriptive results showed a high level of information security practice, and a high implementation of data handling stages with a smaller significant contribution. The findings indicate that strong role and permission management substantially enhance the confidentiality, integrity, and availability of institutional records. In contrast, gaps in systematic classification, archiving, monitoring of system use, and control of unauthorized software weaken overall resilience. The study concludes that institutionalizing clear data-handling procedures, instituting regular training and periodic user-access reviews will substantially improve information security among registry staff.
