Voice authentication systems are a comfortable way of protection since users do not need to remember passwords or carry identification cards. As a unique identifier for all individuals, voice is a practical tool to authenticate people into security services. In this paper, recent single-modal and multimodal voice authentication studies are reviewed with an explanation of underlying feature extraction and classification methods. This paper also discusses security attacks on voice authentication systems, including random attacks, mimicry attacks, replay attacks, voice synthesizing attacks, counterfeit attacks, and hidden voice command attacks.

This paper investigates how people's finger and nail appearance helps diagnose various diseases, such as Darier's disease, Muehrcke's lines, alopecia areata, beau's lines, bluish nails, and clubbing, by image processing and deep learning techniques. We used a public dataset consisting of 17 different classes with 655 samples. We divided the dataset into three folds based on a widely used rule, the 0.7:0.2:0.1, for training, validation, and testing purposes. We tested the EfficientNet-B2 model for performance evaluation purposes by using Noisy-Student weights by setting the batch size and epochs as 32 and 1000. The model achieves a 72% accuracy score and 91% AUC score for test samples to detect fingernail diseases. The empirical findings in this study provide a new understanding that the EfficientNet-B2 model can categorize nail disease types through numerous classes.

With the novel developments in Wireless Sensor Network (WSN) technologies, environmental data collection and processing services are applied in diverse industrial and scientific areas. However, energy limitations and vulnerabilities of WSN nodes are still the main drawbacks of technological developments in the area. Understanding the energy utilization patterns of nodes helps to detect abnormal node behaviors and prevent malicious nodes. In this study, we observe the energy utilization behaviors of nodes and found that nodes have distinctive activity patterns based on their types. We also found that source, sink, and relay nodes on the data propagation path have higher energy consumption patterns compared to other nodes