An EEG signal is used for capturing the signals from the brain, which helps in localization of epileptogenic region, thereby which plays a vital role for a successful surgery. The focal and non-focal signals are obtained from the epileptogenic region and normal region respectively. The localization of epileptic seizure with the help of focal signal is necessary while detecting seizures. Hence, the present article provides detailed analysis of EEG signals. The Focal and Non-focal signals are decomposed using EMD-DWT. A combination of EMD-DWT decomposition method in accordance with log-energy entropy gives an efficient accuracy in comparison to other entropy in differentiating the Focal from Non-focal signals. The extracted features are subjected to SVM and KNN classifiers whose performance will be calculated and verified with respect to accuracy, sensitivity and specificity. At the end, it will be shown that KNN produces the highest accuracy when compared to SVM classifier.

Melanoma is highly dangerous and can spread rapidly to other parts of the body. It has an increasing fatality rate among different types of cancer. Timely detection of skin malignancies can reduce overall mortality. Therefore, clinical screening methods require more time and accuracy for diagnosis. An automated, computer-aided system would facilitate earlier melanoma detection, thereby increasing patient survival rates. This paper identifies melanoma images using a Convolutional Neural Network. Skin images are preprocessed using Histogram Equalization and Gabor transforms. A Gabor filter-based Convolutional Neural Network (CNN) classifier trains and classifies the extracted features. We adopt Gabor filters because they are bandpass filters that transform a pixel into a multi-resolution kernel matrix, providing detailed information about the image. This study suggests a method with accuracy, sensitivity, and specificity of 98.58%, 98.66%, and 98.75%, respectively. This research supports SDGs 3 and 4 by facilitating early melanoma detection and enhancing AI-driven medical education.

The primary cause of accidents in recent years has been driver fatigue, which has resulted in serious injuries, fatalities, and financial losses. This paper proposes a smart accident management system that combines various technologies to enhance road safety significantly. Three features are implemented in this work. First, a mechanism is developed in such a way that if the driver is found to be an alcoholic, it is sensed using the MQ3 sensor; then the engine will not start. Second, in case the driver is drowsy or yawning, it is detected using eye aspect ratio and mouth aspect ratio, which are calculated using a live video feed of the driver while driving the vehicle. The facial features or patterns of eye and mouth movements that are associated with drowsiness and yawning are detected by using a residual neural network classifier. The results are experimentally validated and could successfully detect drowsiness/yawning. If the driver is found to be drowsy or yawning, a wake-up message is generated to alert the driver. Third, in case of accidents, it will be sensed by a vibration sensor; intimation will be sent to the concerned people using GPS (Global Positioning System) and GSM (Global System for Mobile Communications) modules. The concerned people get to know the exact location so that they can rush to the place. Overall integration of these features will help in minimizing and managing road accidents. In most of the existing works, data available is used, whereas in this work, an own data set is prepared from real-time data. With this real-time data, the model accuracy is found to be 98%, which is better than the existing results. The novelty of this work is that the driver’s state is continuously monitored; using a live video feed, drowsiness is detected, and simultaneously, alcohol consumption is also detected. The alarm signals are generated even if one condition is true, and all three safety features are integrated as a single unit in the prototype developed. Cite this article as: D. Ch, J. K, P. B, R. MR, H. P. Lee and G. Kasilingam, “Residual neural network–based smart accident management system,” Electrica, 26, 132, 2026, doi:10.5152/electrica.2026.25132.