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Rapid floods usually occur in an uncertain and unpredictable time, and it will cause major disaster to environment and humanity especially at recreational areas. An IoT early warning system using Arduino technology is proposed to cater this problem. The system includes sensors for temperature, humidity, water flow, and ultrasonic measurements used for further analysis. This research aims to develop a system to monitor and detect water flow activity, such as water level and velocity, and notify of potential rapid floods earlier than estimated occurring time. The current temperature and humidity of the areas also can be recorded with the proposed device The System Development Life Cycle (SDLC) methodology was adapted for implementation of the project. Field testing at Puncak Janing Waterfall in Kedah State was chosen as test site for sensor functionality evaluation. Data is stored in a Firebase database, with an ESP32 Lo-Ra used for connectivity suitable for remote area and coding was done in Arduino IDE tool. The project successfully monitors and stores water level and velocity data and the data can be use as benchmarking the time rapid flood will occur.

Badminton is a thrilling and competitive sport that has gained popularity across the world, attracting players of all ages. The sport fosters social interaction among players which helps create friendships and engagement within a community. Although many local sport centres provide badminton courts, some facilities lack a systematic way to handle bookings. The booking process can be frustrating and time-consuming. This may result in delayed replies, miscommunication and conflicting booking information. In this paper, we present iSmashON, a mobile application developed to simplify the booking process by providing systematic way of booking badminton courts, chat with other players and manage bookings. It features real-time availability updates and personalized notifications. The development encompasses four phases which are requirement analysis, design and development, testing and refinement. The evaluation of the developed mobile application is done using Technology Acceptance Model (TAM) that considers four components which are Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude (ATT) and Behavioural Intention (BI). Questionnaire with 13 questions is prepared and distributed over 30 respondents with at least one-year experience of playing badminton. Results from the TAM evaluation shows a favourable attitude among respondents towards using the mobile application as a medium for booking badminton courts. This is drawn by the highest mean score obtained from ATT component. It is believed that the iSmashON application is expected to enhance the booking experience, providing more efficient and user-friendly process that will be benefited by badminton community.

The use of smartphone and digital platform increased skyrocketed over the past decade. Due to generation gap between parent and children, youth internet addiction among early adolescents seems to be increasing. Internet addiction is more likely in children who feel unsupervised, have their privacy violated, or have poor parent-child relationships. Parental supervision, involvement, and meaningful connections reduce these risks and protect children from excessive internet use. Thus, this is the reason why a mobile app that can protect early adolescents is needed. By using this mobile app, parents can track their children’s online activity, detect unwanted keywords in Google searches, and receive pop-up alerts. The app's search history lets parents address concerns. SQLite manages keyword lists and search history efficiently. Usability testing and user feedback showed app effectiveness. Participating in their children's online activities teach responsible digital behaviour and creates a safe and supportive online environment. Future work entails enhancing cross-platform compatibility, refining content detection capabilities, and implementing cloud-based synchronization. Further, integrating advanced parental controls and comprehensive reporting mechanisms will fortify the app's ability to protect children's online experiences, positioning the Child-CyberCare app as a robust tool for ensuring digital safety for children.

Living in a world of hectic schedule between work and personal life has made many individuals difficult to find time for exercising and keep track their fitness progress. Memberships for gym nowadays is costly and even hiring a personal trainer is not everyone can afford. Due to these challenges, this paper introduces a fitness tracker mobile application named iCalFit that assists users in managing their calisthenics exercises routine and encourage individuals to engage in exercises despite their constrained time and resources. The application provides guides for calisthenics exercises, tracking users’ exercises, its calorie loss and reminders. Development methodology involved three main phases which are requirement identification, design and development and testing. The assessment of the mobile application is based on technology acceptance model, that considers three main components: Intention of Use (IU), Perceive Usefulness (PU) and Attitude (A). The questionnaire covers 15 questions distributed over to 31 randomly selected participants. PU had gained highest score out of three components, proven that the mobile application developed is efficient and capable of assisting user during exercise routine. The development of iCalFit is believed that it can solve individuals with limited fitness opportunities and drives for a healthy lifestyle.

Monitoring a plant's health and looking for signs of infection are two highly important aspects of sustainable agriculture. Monitoring plant diseases by manually is an extremely time-consuming and tedious task. It takes a significant amount of time, a substantial amount of labor, as well as knowledge in plant diseases to achieve. Image processing is thus used in the process of detecting plant diseases. This project mainly focuses on corn leaves disease recognition using convolutional neural network. The Xception model, which is a part of a convolutional neural network capable of classifying images into broad object categories, would be the model of choice for this image classification. Using Convolutional Neural Network (CNN), this study aims to build and test an image classification system for identifying corn leaf diseases recognition. This research dataset is trained by analyzing a big dataset that contains pictures of various diseases that might affect corn leaves as well as pictures of corn leaves that are healthy in order to precisely identify them. The data were then analysed using a methodology known as the Agile model, which included phases for planning, requirement analysis, design, development, testing, and documentation. The findings from the study provide evidence on the precision with which the Xception model performs when applied to the datasets that have been gathered. Strongly, the results of the study will emphasize the need for developing a thorough image classification system in detecting plant diseases without human intervention.