The significance of fire in human society encompasses essential functions like illumination, warmth, and cooking but also poses immense risk when uncontrolled, leading to catastrophic damage and loss of life. Traditional firefighting responses are often hindered by geographical and logistical challenges, resulting in delays that exacerbate the severity of fires. This research introduces an innovative solution through the use of an autonomous firefighting drone, designed for round-the-clock surveillance and rapid response to fire scenes. Utilizing image processing and neural networks, the drone can efficiently detect fire and smoke, serving as the first responder, and is equipped with fire extinguishing balls to initiate suppression. The work extends to explore the application of AI edge aerial drones in disaster response, not only to fires but also floods and landslides, particularly in Malaysia and Southeast Asia. By focusing on various urban, peri-urban, and rural contexts, the research delineates potential implementation strategies aimed at enhancing situational awareness for first responders and reducing response time to reach victims, thereby facilitating more effective disaster response operations. The studys findings point to a considerable advancement in firefighting technology that could lead to decreased fire damage and saved lives, filling a critical gap in the disaster response playbook. This advancement in firefighting technology enhances response times, decreases fire damage, and ultimately, saves lives.

This article introduces an efficient analysis of indoor 4.5 GHz radio wave propagation by using a proposed three-dimensional (3-D) ray-tracing (RT) modeling and measurement. The attractive facilities of this frequency band have significantly increased in indoor radio wave communication systems. Radio propagation predictions by simulation method based on a site-specific model, such as RT is widely used to categorize radio wave channels. Although practical measurement provides accurate results, it still needs a considerable amount of resources. Hence, a computerized simulation tool would be a good solution to categorize the wireless channels. The simulation has been performed with an in-house developed software tool. Here, the 3-D shooting bouncing ray tracing (SBRT) and the proposed 3-D ray tracing simulation have been performed separately on a specific layout where the measurement is done. Several comparisons have been performed on the results of the measurement: the proposed method, and the existing SBRT method simulation with respect to received signal strength indication (RSSI) and path loss (PL). The comparative results demonstrate that the RSSI and the PL of proposed RT have better agreements with measurement than with those from the conventional SBRT outputs.

Recent technological developments have led to the emergence of new trends in agriculture to ensure increased production through sustainable utilization of resources. One of the new trends is Precision Agriculture (PA). However, like many new emerging trends, PA is faced with a number of uncertainties and hurdles. One of the major issues and hurdles in precision agriculture is the uncertainty of optimum application of resources necessary for optimum yield production while minimizing resource wastage for sustainable agricultural production. Therefore, this paper presents a fuzzy based Decision Support System (DSS) to intelligently allocate water and fertilizer used in crop production based on the age of the plant and data collected from the soil and surrounding environment with the aid of a network of sensors. An embedded hardware system consisting of a Cartesian robot has also been implemented to ensure effective application of the fuzzy derived decisions. Two Fuzzy Inference Systems (FIS) consisting of linear, non-linear Membership Functions and a 149-rule base for the PA system have been developed in MATLAB, implemented using Arduino microcontrollers and tested on spinach plants whereby results show that the linear FIS is able to achieve more accurate results in terms of the quality of yield realized and resource utilllisation. Resource utilllisation comparison with other systems in literature reviewed is also done whereby the DSS is able to save up to 2031 ml and 524 ml of water and fertilizer respectively.

This research proposes an automated sensory system that uses the Blynk IoT platform to monitor the production of palm tree oil. The system incorporates temperat This research proposes an automated sensory system that uses the Blynk IoT platform to monitor the production of palm tree oil. The system incorporates temperat

The purpose of the study is to evaluate how well two different edge detection techniques differentiate between different guava leaf disease kinds. More specific The purpose of the study is to evaluate how well two different edge detection techniques differentiate between different guava leaf disease kinds. More specific