International Journal of Synergy in Engineering and Technology

An Exploration of Visual Communication in Educating Safe and Unsafe Touch Awareness Through Education Books and Board Games

Amarah A Karim — 2026-10-31

The concept of safe and unsafe touch is vital for child protection, empowering children to understand personal boundaries and recognize inappropriate behavior. This study, titled "My Voice, My Boundaries", aims to raise awareness among children, parents, and the community through a combination of digital and printed media, supported by visual communication and interactive learning tools. Using a quantitative research approach, data was collected via questionnaires from parents and educators, revealing a strong preference for visual stories, attractive scenarios and game-based activities learning as effective teaching methods. A visual analysis of existing educational materials highlighted the effectiveness of combining digital and printed formats with interactive elements to enhance engagement. The project culminated in the development of an interactive book and educational game board. Following the implementation of these tools, Year 3 and 4 students at Sultan Omar Primary School, Dungun, demonstrated strong understanding of safe and unsafe touch. They could identify relevant body parts and appropriate responses—such as telling a trusted adult, running away, or firmly saying “Stop!”. The students responded positively to the vibrant visuals, cartoon characters, and engaging activities, reporting increased confidence and comfort in addressing inappropriate behavior. This project highlights the importance and effectiveness of using visual and interactive approaches in educating children about personal safety.

Design of an IoT-Based Flood Monitoring and Alert System for UC TATI Residential College

Siti Norwahidayah Wahab — 2026-10-31

Flood incidents pose a significant threat to residential areas, particularly in low-lying regions and campus environments where water level changes can occur rapidly and disrupt daily activities. At Universiti College TATI (UC TATI), the residential college area is vulnerable to localized flooding during heavy rainfall, which highlights the need for a reliable and real-time monitoring solution. This paper presents the design and development of an IoT-based real-time flood monitoring and alert system for UC TATI Residential College. The proposed system utilizes water level sensors integrated with a microcontroller platform and wireless communication technology to continuously monitor water level conditions and transmit data to a cloud-based platform for real-time visualization and monitoring. When predefined threshold levels are exceeded, the system automatically triggers alert notifications to relevant users to enable early warning and timely action. The system architecture, including hardware components, software framework, communication process, and data flow, is described in detail. The methodology covers sensor data acquisition, threshold-based decision logic, and alert dissemination workflow. This work focuses on the conceptual design and system development aspects and serves as a foundation for future implementation, testing, and performance evaluation in the UC TATI campus environment.

A Recent Systematic Review of Cloud-Assisted Web Filtering Framework using Software-Defined Networking

Lukmanulhakim Ngah — 2026-10-31

The increasing complexity of cloud and network environments has intensified the need for more advanced web filtering mechanisms, particularly those leveraging Software-Defined Networking (SDN). This systematic literature review examines the current state of research on "Cloud-Assisted Web Filtering Framework using SDN" to address existing gaps and emerging challenges. The problem lies in the evolving security threats, resource management inefficiencies, and scalability limitations faced by traditional network architectures. To systematically explore these issues, the PRISMA protocol was applied, with data collection conducted across two leading scientific databases, Web of Science and Scopus. A total of 24 primary studies were identified and analysed. The findings were organized into three thematic categories: (1) Security and Attack Mitigation in SDN and IoT Systems, where advanced frameworks for intrusion detection and DDoS mitigation were prominent; (2) Intelligent Resource Management and Optimization in SDN, Fog, and Edge Systems, focusing on strategies such as reinforcement learning and blockchain-based task offloading to enhance resource utilization; and (3) Network Management, Scalability, and Architectural Innovations, highlighting developments in hierarchical control models, federated authentication systems, and SD-WAN traffic prediction. The results demonstrate significant advancements in applying SDN to support more adaptive, resilient, and efficient cloud-assisted web filtering solutions. However, challenges remain, particularly concerning integration complexity, latency management, and ensuring robust multi-domain coordination. In conclusion, this review provides a comprehensive synthesis of current approaches and identifies critical areas for future research, emphasizing the importance of intelligent, secure, and scalable frameworks in the evolving landscape of cloud and SDN-based web filtering systems.

Design and Development of a Secure Fingerprint-Based Verification System for Examination Attendance

Siti Norwahidayah Wahab — 2026-10-31

xamination attendance management plays a critical role in ensuring academic integrity, yet conventional methods such as manual sign-in sheets, identity cards, and QR-based systems remain vulnerable to impersonation and fraudulent practices [7]. Biometric authentication, particularly fingerprint verification, offers a reliable solution due to its uniqueness and permanence [8]. This paper presents the design and development of a secure fingerprint-based verification system for examination attendance. The proposed system focuses on a structured framework that integrates fingerprint enrollment, verification, and secure attendance recording to mitigate identity related misconduct during examinations. The system architecture is designed to include key components such as a fingerprint sensor, processing unit, secure database, and user management modules for administrators and invigilators. A detailed methodology is presented to describe the enrollment and verification workflows, authentication procedures, and security considerations, including template protection and access control mechanisms. Unlike existing studies that primarily emphasize implementation performance, this work concentrates on the conceptual design and system development aspects, highlighting security requirements specific to examination environments. The proposed framework aims to enhance the reliability, security, and integrity of examination attendance processes and serves as a foundation for future implementation, testing, and performance evaluation of biometric-based examination management systems.

Tomato Leaf Disease Classification Using a Mobile-Integrated Deep Learning Framework Based on Custom CNN and ResNet50 Models

Olayiwola Charles Adesoba — 2026-10-31

Tomato leaf diseases pose a significant threat to crop productivity and require timely diagnosis to minimize yield losses and improve disease management. This study developed and validated a mobile-integrated deep learning framework for automated tomato leaf disease classification using a custom convolutional neural network (TDD-Net) and a fine-tuned ResNet50 model. A heterogeneous dataset comprising 15,549 publicly available and field-acquired tomato leaf images representing bacterial spot, early blight, grey leaf spot, late blight, and healthy leaves was used for model development. Both models were trained under identical optimization settings and evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed TDD-Net achieved superior performance, attaining a validation accuracy of 98.94%, precision of 0.9882, recall of 0.9863, F1-score of 0.9871, and an AUC of 0.9944, outperforming the fine-tuned ResNet50 across all evaluation metrics. The trained TDD-Net model was subsequently integrated into a FlutterFlow-based mobile application with Firebase support to enable real-time disease diagnosis using smartphone images. The results show that a task-specific convolutional neural network provides highly accurate tomato leaf disease classification while supporting practical deployment through an accessible mobile decision-support system for precision agriculture.

The Brief review of Nafion Composite Nanofillers Membranes

SUHAILA ABDULLAH — 2026-10-31

Proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolysers (PEMWESs) play central role in expanding clean hydrogen energy system. Perfluorosulfonic acid ionomers, particlarly Nafion, serve as the benchmark solid polymer electrolyte duet o their high proton conductivity and structural stability under fully hydrated confditions. However, pristine Nafion experiences severe performance loss under elevated temperatures (> 80^OC) and low relative humidity duet to membrane dehydration, while also remaining susceptible to fuel crossover and radical induced chemical degradation. Incorporating nanofillers into the nafion matrix offers a versatile strategy to overcome these operational limitations. This review systematically evaluates six principal filler strategies: metal oxides, solid acids, carbon based nanomaterials, clays and zeolites, metal oragnic and covalent oragnoc frameworks (MOFs/COFs) and polymeric/ biobased additives. We examine the mechanistic roles of these additives in mantaining hydration, creating additional proton conducting pathways, forming barrier network againts fuel crossover, and reinforcing mechanical and radical statbility. Finally, key research directions are highlighted, emphasizing the rational design of hybrid nanofillers.