DeepBiteNet: A Lightweight Ensemble Framework for Multiclass Bug Bite Classification Using Image-Based Deep Learning.

: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. : For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks-DenseNet121, EfficientNet-B0, and MobileNetV3-Small-using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output.

Joint Driver State Classification Approach: Face Classification Model Development and Facial Feature Analysis Improvement.

Driver drowsiness remains a critical factor in road safety, necessitating the development of robust detection methodologies. This study presents a dual-framework approach that integrates a convolutional neural network (CNN) and a facial landmark analysis model to enhance drowsiness detection. The CNN model classifies driver states into "Awake" and "Drowsy", achieving a classification accuracy of 92.

ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem.