http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/93 2026-06-17T00:49:38Z 2026-06-17T00:49:38Z EBENEBE, Chika. F. Usman, Abraham Usman David, Michael Adejo, Achonu . O. Audu, Moses Waheed http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31475 2026-05-22T19:33:18Z 2024-04-22T00:00:00Z

Title: Towards Green Future Cellular Network in Nigeria: Artificial Intelligence Approach Authors: EBENEBE, Chika. F.; Usman, Abraham Usman; David, Michael; Adejo, Achonu . O.; Audu, Moses Waheed Abstract: For the purpose of lowering energy costs and minimizing the use of fossil fuels, the information sector has traditionally targeted green communications. Without a doubt, the number of linked terminals and the amount of network equipment will continue to grow dramatically in the current 5G and next 6G eras, driving up energy costs. It is becoming more and more crucial to promote the advancement of green communications. But there is no denying that 6G will come with a host of new and more demanding specifications for intelligence, security, flexibility, and Quality of Service (QoS), all of which will make it harder to increase energy efficiency. Additionally, the dynamic energy harvesting process which is expected to be widely used in 6G makes network administration and power control even more difficult. Artificial Intelligence (AI) has been widely regarded as the sole way to handle these issues and minimize the need for human intervention. In order to reduce energy consumption, increase energy efficiency, and control energy harvesting in many communication settings, academia and industry have undertaken a great deal of research. The primary factors for green communications are discussed in this study, along with a review of relevant studies on AI-based green communications. This work focus is on the application of AI approaches to network management and energy efficiency enhancement as we move toward a greener future. The ways in which advanced Deep Learning (DL) and other Machine Learning (ML) approaches can work in tandem with traditional AI techniques and mathematical models to lower algorithm complexity and increase accuracy rates in future communication beyond 5G was examined. Lastly, the current concerns and unresolved research questions related to AI Techniques for future green communication was presented. Relative research towards green cellular network communication (CNC) was presented and it shows that Heuristic algorithms are widely used. Both flexibility and efficiency can be increased, using Heuristic algorithms and machine learning (ML) together. Also, Reinforcement Learning (RL) and Deep reinforcement learning (DRL) approaches helps to achieve the best policy for resource allocation and power control. However, the training process is challenged by the extraordinarily large action space resulted from nature of the metrics taken into consideration.

2024-04-22T00:00:00Z John, Adikwu David, Michael Chika, Innocent Achimugu, Sunday http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31474 2026-05-22T19:28:13Z 2024-04-22T00:00:00Z

Title: Smart Traffic Management System for Smart Cities with Emergency Prioritization Authors: John, Adikwu; David, Michael; Chika, Innocent; Achimugu, Sunday Abstract: As urban populations continue to grow, the demand for efficient traffic management in smart cities becomes increasingly pressing. Traditional traffic lights, with fixed timing sequences, often lead to unnecessary delays for motorists and fail to accommodate vehicle priorities. This paper proposes a Smart Traffic Management System (STMS) tailored for smart cities, focusing on emergency prioritization. The system integrates a microcontroller, Radio Frequency Identification (RFID), and infrared (IR) sensors, and responds dynamically based on the nature of the vehicle and lane congestion. Emergency Vehicles (EVs) such as Ambulances and Fire trucks are given priority through the RFID setup while sensors track the number of vehicles on each lane. Leveraging sensor networks and intelligent algorithms, the STMS dynamically adjusts traffic signals and communicates with emergency services to ensure swift response times during critical situations. Through simulation studies and real-world implementation, the effectiveness and scalability of the proposed STMS are evaluated, demonstrating its potential to enhance urban mobility, reduce congestion, and improve emergency response capabilities in smart cities

2024-04-22T00:00:00Z Oluwatuyi, Abiola A. David, Michael Usman, Abraham Usman Onwuka, Elizabeth N. Ishaku, Shedrack http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31473 2026-05-22T19:17:36Z 2025-06-01T00:00:00Z

Title: Realtime Stress Monitoring And Data Acquisition System Using Electroencephalogram Sensor. Authors: Oluwatuyi, Abiola A.; David, Michael; Usman, Abraham Usman; Onwuka, Elizabeth N.; Ishaku, Shedrack Abstract: Mental stress negatively impacts human health, necessitating early detection for timely intervention. With the advent of wearable stress monitoring devices, real-time remote patient monitoring has become increasingly feasible. This research presents a real-time EEG-based stress monitoring system designed for short-range, localized use. The system captures EEG signals, processes them, and transmits data via Bluetooth to a mobile application, providing immediate feedback within the monitoring range. Unlike cloud-based remote monitoring solutions, this system operates in a proximity environment and does not store data on external servers or facilitate remote physician accessibility. The proposed methodology involves EEG signal acquisition using a ThinkGear ASIC Module (TGAM), signal preprocessing for artifact removal, feature extraction based on frequency domain analysis, and classification of stress levels using threshold-based metrics. The system was tested on seven individuals under various conditions, with EEG parameters analyzed to determine stress levels. Sensitivity analysis was performed to assess the sensor's accuracy in detecting brainwave activity. Results indicate a correlation between stress levels and EEG signal variations, confirming the system's viability for mental health applications. The study contributes to remote health monitoring and lays the groundwork for future advancements in stress assessment tools.

2025-06-01T00:00:00Z David, Michael Chikezie, Chekwas Ifeanyi Usman, Abraham Usman Zubair, Sulieman Ohize, Henry Ohiani Ojeniyi, Joseph http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31379 2026-05-20T16:08:59Z 2026-04-09T00:00:00Z

Title: Conf-Gate XGBoost-RF Hybrid Model for Multi-Class Anomaly Classification in 5G-Enabled eMTC IoT Networks. Authors: David, Michael; Chikezie, Chekwas Ifeanyi; Usman, Abraham Usman; Zubair, Sulieman; Ohize, Henry Ohiani; Ojeniyi, Joseph Abstract: The rapid growth of Internet of Things (IoT) deployments in 5G Enhanced Machine-Type Communication (eMTC) networks has significantly increased the network attack surface. A major challenge for Network Anomaly Detection Systems (NADS) in this environment is severe class imbalance, where dominant benign traffic obscures rare but high-impact attacks, leading to poor minority-class detection. This paper presents Conf-Gate XGBoost-RF, a two-stage hybrid anomaly detection architecture designed to address this limitation without compromising real-time performance. The framework employs a high-speed XGBoost classifier for initial screening and a confidence-gated mechanism that selectively routes low-confidence predictions to a specialist Random Forest trained on synthetically balanced data. Evaluation on the large-scale CICIoT2023 dataset shows that the proposed model achieves 99.32% accuracy and a Macro F1-score of 0.80, substantially outperforming single-stage baselines. Notably, recall for critical low-volume attacks, such as Command Injection, improves by over 34%. With an average inference latency of 0.87 ms, the proposed approach remains compatible with the stringent low-latency requirements of 5G eMTC control signaling, demonstrating a practical balance between computational efficiency and rare-attack sensitivity.

2026-04-09T00:00:00Z