Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.

This study presents a prediction model based on Logistic Growth Curve (LGC) to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depends on partial data from the COVID-19 cases in Malaysia. The model is studied on the effectiveness of the three phases of MCO implemented in Malaysia, where the model perfectly fits with the R2 value 0.989. Evidence from this study suggests that results of the prediction model match with the progress and effectiveness of the MCO to flatten the curve, and thus is helpful to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

Supply chains in the modern era have developed into very complex value networks and have become a critical source of competitive advantage. Verifying the origin of raw materials and maintaining visibility of goods and merchandise as they move through the value chain network is difficult. The Internet of Things (IoT) application may assist businesses in observing, tracking, and monitoring items, activities, and processes inside their value chain networks. Both blockchain and Internet of Things technologies have the potential to accelerate the implementation of Industry 4.0 and transform foreign trade procedures. Integrated technology utilization may be a component of the answer to this future. This paper’s contribution is to demonstrate how the marriage of Blockchain technology with IoT infrastructure may simplify and enhance contemporary supply chains in a variety of ways. Such as increasing scalability, security, immutability, and auditing, as well as information flows, traceability, and interoperability, and quality, using Ping Asset devices and Chainlink, and so setting the groundwork for future research projects.

The increasing adoption of cryptocurrency has underscored the critical need for robust security measures to protect digital assets stored in cryptocurrency wallets. Traditional security approaches have often proven inadequate in addressing the rapidly evolving threats in the digital landscape. In response, cloud-based security solutions have emerged as a promising method to enhance wallet protection, leveraging scalability, flexibility, and advanced security features. This study investigates the security challenges faced by cryptocurrency wallets and explores the potential of cloud-based solutions, focusing on multi-factor authentication, encryption protocols, real-time monitoring, and secure backup and recovery. The research assesses the effectiveness of these solutions in mitigating risks such as unauthorized access, data breaches, and digital asset theft. Findings reveal that cloud-based security solutions significantly improve protection by offering scalable, adaptable frameworks. However, challenges remain, including privacy concerns, regulatory compliance, and the cost of implementation. The research introduces a cost-efficient approach that integrates cloud-based technologies to optimize the total cost of ownership while maintaining robust security. This study also discusses the regulatory and privacy implications of cloud security in cryptocurrency ecosystems. In conclusion, this research provides novel insights into the integration of cloud-based security solutions, offering a comprehensive framework for safeguarding digital assets in cryptocurrency wallets. It contributes to the growing body of knowledge on the feasibility and impact of cloud technologies in enhancing the security of cryptocurrency systems.