DSpace Community: SEET

Design And Implementation of a Secure Remote Patient Monitoring System Using Advanced Encryption Standard and Meta Mask Authentication

2026-07-13T22:47:38Z

Title: Design And Implementation of a Secure Remote Patient Monitoring System Using Advanced Encryption Standard and Meta Mask Authentication Authors: Nuhu, Kontagora Bello; James, Agajo; Soempit, C. E; Inalegwu, A. E; Jack, K. E; Eustace, Manayi Dogo Abstract: Remote Patient Monitoring (RPM) systems enable continuous tracking of patient vital signs outside clinical settings, improving outcomes and reducing hospital visits. However, privacy and security concerns remain a major barrier to their adoption. In this paper, we present the design and implementation of a secure RPM system that uses Advanced Encryption Standard (AES) to encrypt sensor data and MetaMask (an Ethereum wallet/extension) to authenticate users. The system integrates biomedical sensors (ECG, pulse oximeter, and temperature) with an Arduino microcontroller and Wi-Fi module. Encrypted data are transmitted to a cloud database, while physicians' access and decrypt data via a web interface protected by MetaMask login. A prototype was developed and tested, it successfully collected and transmitted patient vitals (heart rate, SpO₂, temperature) to the cloud and permitted only authenticated users to view the decrypted data. Performance evaluation of the system showed that it achieved an average 981.1ms delay in rendering the collected vitals to the doctor. The AES encryption on the Arduino recorded minimal latency of <50ms per block, while network transmission delay was within 500ms per upload. The proposed approach ensured confidentiality of health data in transit and at rest, addressing data privacy challenges in RPM.

Development of a Hand-held Device for Women Assault Reporting

2026-07-13T22:31:13Z

Title: Development of a Hand-held Device for Women Assault Reporting Authors: Nuhu, Kontagora Bello; Paul, Omagbemi; Eustace, Manayi Dogo; 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.

Intelligent Evaporative Cooling Systems for Post-Harvest Fruit and Vegetable Preservation: A Systematic Literature Review

2026-07-13T22:25:03Z

Title: Intelligent Evaporative Cooling Systems for Post-Harvest Fruit and Vegetable Preservation: A Systematic Literature Review Authors: Isah, Omeiza Rabiu; Segun, Emmanuel Adebayo; Nuhu, Kontagora Bello; Eustace, Manayi Dogo; Buhari, Ugbede Umar; Danlami, Maliki Abstract: Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R2 of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy– Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS.

Development of an Intelligent Meat Spoilage Detection and Grading System Using Particle Swarm Optimization-based Convolutional Neural Network

2026-07-13T22:19:34Z

Title: Development of an Intelligent Meat Spoilage Detection and Grading System Using Particle Swarm Optimization-based Convolutional Neural Network Authors: Isah, Omeiza Rabiu; Adegoke, Israel Adedolapo; Nuhu, B. K Abstract: This research developed an intelligent meat spoilage and quality grading system using a particle swarm optimization-based convolutional neural network. It addressed the problems associated with the subjective manual assessment of meat quality and inefficient and expensive meat quality grading systems as well as the lack of a comprehensive dataset for meat quality detection. This research created a new dataset for meat spoilage and quality detection. Furthermore, a PSO-based convolutional neural network was trained with the new dataset for the classification and the grading of the meat. The Python code is then integrated into the Raspberry Pi 4 to make it a stand-alone system. Comparative analysis indicated that the PSO-based CNN performed better compared to the baseline CNN by 2.91% for accuracy, 2.49% for precision, 0.99% for F1-score, 1.87% for recall, 2.74% for specificity and 1.14% for sensitivity. The obtained results implied improved food safety in the food processing industry and retail environments. In addition, the intelligent system provides support to human experts for accurate assessment of meat quality