SAFETYNET: A COLLABORATIVE AND PRIVACY-PRESERVING MOBILE SAFETY APPLICATION SYSTEM

ABSTRACT Personal safety concerns continue to rise globally, with increasing incidents of assault, domestic violence, and kidnappings highlighting the limitations and inefficiencies of current safety applications. This paper presents a collaborative, robust and privacy-preserving mobile safety application system. The system explores a community-driven approach to emergency response. Developed using Agile development methodology with the Flutter

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ADVANCING HEALTHCARE THROUGH EMERGING TECHNOLOGIES: A SYSTEMATIC REVIEW OF BENEFITS, CHALLENGES, METHODOLOGIES, AND RESEARCH DIRECTIONS

 ABSTRACT Healthcare is rapidly evolving with advanced technology improving operations, early diagnosis, and medical procedures. Integrating these innovations enhances accuracy, efficiency, and speed, ultimately transforming patient care and medical practices. These advancements contribute to a safer, more effective, and accessible healthcare system for both patients and professionals. A comprehensive review of 1,008 research articles on

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EXPLORING CO-DESIGN CHALLENGES AND INSIGHTS FOR CO-DESIGNING AN MHEALTH INTERVENTION FOR MALARIA PREVENTION AND CONTROL IN MARGINALIZED COMMUNITIES IN NIGERIA

ABSTRACT Our research investigated the issues and benefits inherent with conducting co-design research in marginalized/ hard-to-reach communities in Nigeria, using Ebonyi State as a case study. The study concentrated on creating a design framework for health intervention creators to enable them to create culturally relevant and community -oriented interventions system that will help them to

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SOFTWARE ENGINEERING FOR THE INDEPENDENT NATIONAL ELECTORAL COMMISSION’S (INEC) ELECTION VIEWING PORTAL (IREV)

ABSTRACT Free and fair elections are central to democratic governance. Nonetheless, many developing democracies continue to struggle with perfecting their election processes to enhance fairness and transparency. Even if elections are fair, the processes must be seen by citizens as transparent. Election technologies have been introduced to improve effectiveness and transparency in many countries. In

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A SURVEY ON TRADITIONAL AND DEEP LEARNING TECHNIQUES FOR CYBER ATTACK DETECTION IN INTERNET OF MEDICAL THINGS

ABSTRACT Apparently, connected devices on Internet has grown astronomically to about 18.8 billion globally in the year 2024. IoT is the network of physical devices equipped with sensors and applications to generate and transmit data over the internet. Internet of Medical Things (IoMT) is an emerging subset of IoT and it involves integration of health

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DISTRIBUTED LEDGER TECHNOLOGIES (DLTS): RECONCILING THE TRADE-OFF BETWEEN TRANSACTION EFFICIENCY AND FINANCIAL REGULATIONS

ABSTRACT One aspect of human society that has set us apart is our unwillingness to trust others. This difficulty has led to a situation known as the prisoner’s dilemma, in which trust can only be earned by frequent exchanges, a good reputation, or third-party enforcement. The requirement for more effectively enabled societies that can coordinate

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CROSS-PARTITION ATTENTION (CPA): A PROPOSED SCALABLE TRANSFORMER FRAMEWORK FOR SMART CONTRACT VULNERABILITY DETECTION

ABSTRACT Blockchain’s decentralized trust model relies heavily on secure smart contracts. However, vulnerabilities such as reentrancy and integer overflow persist due to the limitations of existing detection tools. While transformer-based models (e.g., CodeBERT) offer improvements over traditional rule-based methods, they struggle with context truncation and token-level bias, often failing to capture cross-function vulnerabilities in lengthy

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ENSEMBLE TECHNIQUES FOR DEEPFAKE DETECTION: COMPARING STACKING, WEIGHTED VOTING AND AVERAGING APPROACHES

ABSTRACT Over the years, deepfake detection has been an emerging challenge because of the increase in highly realistic computer-generated synthetic media. Although the study examined deep learning and conventional machine learning models separately, the ideal ensemble technique for integrating CNN-extracted features with SVM and XGBoost classifiers is relatively obscure. Our study shows the comparative analysis

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CLASSIFICATION OF SICKLE CELL ANAEMIA SEVERITY USING HAEMATOLOGICAL PARAMETERS: A MACHINE LEARNING APPROACH

ABSTRACT Sickle Cell Anaemia (SCA) is a genetic blood disorder that caused abnormal haemoglobin and severe health complications. In Nigeria, nearly 150,000 infants, 2% of newborns, were diagnosed annually, highlighting its public health impact. SCA severity varied and was linked to complications like vaso-occlusive and other critical case. Traditional severity assessments were subjective, but machine

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