http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/80 SEET Sun, 14 Jun 2026 01:46:52 GMT 2026-06-14T01:46:52Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31631 Title: Development of a Hand-Held Device for Women Assault Reporting. In 2025 4th International Conference on Computing and Information Technology Authors: Isah, Omeiza Rabiu Abstract: The issue of insecurity is a major concern for women and society. The World Health Organisation’s statistics show that about one in three women worldwide experience physical or sexual violence in their intimate or non-partner relationships. This is a disturbing figure. Various crimes against women such as physical violence, kidnapping, rape, sexual assault, and sexual harassment occur at different places at any given time of the day, especially in isolated places and mostly during late hours. These crimes contribute to the local and global crime indexes, as evidenced by the increasing criminality score. Governments have tried to address these security challenges by implementing stricter laws, but crime rates remain high. Unfortunately, related works exist but are limited as they lack critical features such as a secured and exclusive fingerprint verification for users, a subsystem to prevent a potentially detrimental false alarm from occurring, and an effective alerting mechanism to alert relatives. To overcome these shortcomings, this research proposes a hand-held device for women assault reporting that incorporates: a secured fingerprint verification subsystem, a vibration-based alert subsystem for prompting the user to prevent false alarms, an emergency text along with a phone call established to the predefined contacts as a more urgent alert mechanism, and a built-in microphone feature for environmental audio surveillance established via phone call connection. The system's response time was an average of 4 seconds, the False Acceptance Rate (FAR) was 6%, and the False Rejection Rate (FRR) was 5%. These promising results indicate that the system can effectively reduce crime against women, improve the sense of safety in women anywhere they go, and mitigate the overall crime rate. Tue, 22 Apr 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31631 2025-04-22T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31475 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. Mon, 22 Apr 2024 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31475 2024-04-22T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31474 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 Mon, 22 Apr 2024 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31474 2024-04-22T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31473 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. Sun, 01 Jun 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31473 2025-06-01T00:00:00Z