Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

Current lifestyles promote the development and advancement in wireless technologies, especially in Wireless Sensor Networks (WSN) due to its several benefits. WSN offers a low cost, low data rate, flexible routing, longer lifetime, and low-energy consumption suitable for unmanned and long term monitoring. Among huge WSN applications, some key applications are smart houses, environmental monitoring, military applications, and other monitoring applications. As a result, ubiquitous increase in the number of wireless devices occupying the 2.4GHz frequency band. This causes a dense wireless connection followed by interference problem to WSN in the 2.4GHz frequency band. WSN is most affected by the interference issue because it has a lower data rate and transmission power compared to WLAN. Despite efforts made by researchers, to the author's knowledge, the interference issue is still a major problem in wireless networks. This paper aims to review the coexistence and interference issues of existing wireless technologies in the 2.4GHz Industrial, Scientific and Medical (ISM) band.

The next stage performance evaluation of an athlete can be predicted by implementing Artificial Intelligence technique. In track cycling event, coach and sports physician are concerned with the performance of the cyclist. The performance prediction may help to fine-tune the cyclist training intensities and strategies planning. This study was conducted to fulfil the prediction requirement by adopting a Fuzzy Inference System to classify the cyclist current cycling performance state. The six levels of output classification by a Fuzzy Inference System are to indicate the athlete’s current state performance using the body temperature, heart rate variability and speed as input parameters. An Adaptive Neuro-Fuzzy Inference System was applied to predict the cycling speed that can be achieved in the next lap. Using Adaptive Neuro-Fuzzy Inference System method, the average speed for the next laps can be predicted and compared with the actual speed. The regression value with r = 0.9029 indicates the Adaptive Neuro-Fuzzy Inference System is an adequate prediction algorithm to evaluate the cyclist performance. The predicted time to complete compared favourably with the actual finishing time with a ± 13.6% average error. Hence, the developed system is reliable and suitable for sports events that deal with speed and time.

Urban flooding is increasingly exacerbated by the accumulation of floating debris in rivers, which obstructs water flow, degrades water quality, and poses significant risks to human safety and environmental sustainability. Effective monitoring of floating debris is therefore critical for early flood warning and long-term water resource management. This study presents a real-time monitoring framework that integrates deep learning-based object detection models, You Only Look Once (YOLO) with video surveillance for the identification and quantification of floating debris in urban rivers. Field deployments were conducted in flood-prone sites in Shah Alam, Malaysia, to evaluate the system under real-world environmental conditions. Results show that YOLOv7 achieved higher accuracy and robustness across diverse debris classes and lighting conditions compared to YOLOv9, with precision, recall, and F1-scores demonstrating strong detection reliability. Beyond technical accuracy, the system provides timely and actionable information for flood risk assessment, river management, and environmental monitoring. By automating debris detection and quantification, this study contributes to Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action), offering a scalable monitoring solution for flood-prone regions.

Object tracking is an important application in which applicable in many areas, included but not limited to tracking the items in shopping malls, or instrument tracking in factories/industrial areas. The application of Radio Frequency Identification (RFID) for object tracking has expanded not to only object tracking but also cover human monitoring (e.g. workers monitoring, marathon monitoring) and livestock monitoring. Advantages of using RFID compared to other wireless technologies for monitoring applications is that multiple objects can be detected simultaneously. The aim of this paper is to characterize the RFID readability in relation to operational factors, orientation and range. The outcome of this research is useful as a reference for future works. This study will provide a feasibility study on the use of Ultra High Frequency (UHF) solutions for future studies focusing on animal tracking. This work used ThingMagic M6e RFID reader from JADAK as the RFID reader and the maximum detection range for different RFID tags were identified. In addition, this work also investigates different GEN-2 setting such as Tari. Despite using the commercialized tags, this study was conducted to investigate the characteristic of the tags in the real-deployment environment (for goat monitoring). The eating behaviour of each goat is then monitored through the Received Signal Strength Indicator (RSSI) between tag and reader.