Speech segment detection based on gated recurrent unit (GRU) recurrent neural networks for the Kurdish language was investigated in the present study. The novelties of the current research are the utilization of a GRU in Kurdish speech segment detection, creation of a unique database from the Kurdish language, and optimization of processing parameters for Kurdish speech segmentation. This study is the first attempt to find the optimal feature parameters of the model and to form a large Kurdish vocabulary dataset for a speech segment detection based on consonant, vowel, and silence (C/V/S) discrimination. For this purpose, four window sizes and three window types with three hybrid feature vector techniques were used to describe the phoneme boundaries. Identification of the phoneme boundaries using a GRU recurrent neural network was performed with six different classification algorithms for the C/V/S discrimination. We have demonstrated that the GRU model has achieved outstanding speech segmentation performance for characterizing Kurdish acoustic signals. The experimental findings of the present study show the significance of the segment detection of speech signals by effectively utilizing hybrid features, window sizes, window types, and classification models for Kurdish speech.

In today's market conditions, the importance of competition is obvious. Organizations must direct the right resources to the right investment to increase their competitive power and stay in the market. In this respect, the Human Resource Management (HRM) unit has also entered the digitalization phase. The digitalization phase in Human Resources (HR) has made significant progress, particularly in the recruitment process, with the help of Artificial Intelligence (AI). During this phase that creates a loss of value for the organization, searching for candidates among hundreds or even thousands of applications, selecting the most suitable one for the job, and placing the suitable ones in open positions within the institution; As a result of the evaluations made with artificial intelligence technology, it can be carried out without loss of cost and time. Thus, the AI approach ensures that interviews are managed quickly and with less cost in the recruitment process. Furthermore, AI enables the efficient fulfillment of various activities of the HRM unit, such as training, orientation, and career planning. The present study attempts to explain the impact of Industry 4.0 and AI on human resource management processes as a result of a literature review. This study, examined the role of Industry 4.0 and artificial intelligence in human resources management by making a literature review.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in critical infrastructure, defense, and civilian applications, and face new cybersecurity threats. In this work, we present a novel hybrid deep learning architecture that combines Mamba, Kolmogorov-Arnold Networks (KAN), and Liquid Neural Networks for real-time cyberattack detection in UAV systems. The proposed Mamba-KAN-Liquid (MKL) model integrates Mamba’s selective state-space mechanism for temporal dependency modeling, KAN’s learnable activation functions for feature representation, and Liquid networks’ dynamic adaptation capabilities for real-time anomaly detection. Extensive evaluations on CIC-IDS2017, CSE-CIC-IDS2018, and synthetic UAV telemetry datasets demonstrate that our model achieves detection rates exceeding 95% across six different attack scenarios, including GPS spoofing (97.3%), network jamming (95.8%), man-in-the-middle attacks (96.2%), sensor manipulation (94.7%), DDoS (98.1%), and zero-day attacks (89.4%). The model meets real-time processing requirements with an average inference time of 47.3 ms for a sample batch size of 32, making it suitable for practical deployment on resource-constrained UAV platforms.

Electric Vehicle Charging Stations (EVCSs) are critical components of modern transportation infrastructure. However, smart grid integrations create new security vulnerabilities. Cyber attacks can damage EVCSs, cause financial losses, and compromise user security. Traditional security measures are not enough. By analyzing the large data sets produced by EVCSs, machine learning (ML) can detect anomalies and provide predictive maintenance. The increasing importance of EVCSs in smart grid infrastructure necessitates taking advanced security measures. Machine learning-based intrusion detection systems represent a promising solution to the dynamic and complex cyber threats facing these critical infrastructures. Through continuous learning and adaptation, ML can provide a robust defense mechanism that ensures the security and reliability of EVCSs in the face of evolving cyber threats.

This article discusses the innovative application of artificial intelligence (AI) to develop a predictive model that aims to evaluate the condition of the machine by focusing on the probability of failure. The research uses a synthetic dataset prepared to simulate real-world situations where machines are equipped with sensors that monitor various health indicators and record the occurrence of faults. This data set consists of 10,000 inputs, each containing five numerical measurements: air temperature, process temperature, rotation speed, torque, and machine wear, in addition to the type of product produced, for a total of six input variables. The output of the model is the fault state of the machine, displayed as true or false. A hybrid artificial neural network integrating a GRU (Gated Recurrent Unit)-based model with the Transformer Encoder block was used for prediction. This combination highlights the superior predictive capabilities of the model. This approach represents a shift from traditional maintenance programs, which are often time-based and often result in unnecessary resource use, to a more efficient, condition-based maintenance strategy. This new strategy aims to ensure that maintenance activities are carried out only when necessary, thus optimizing resource use and minimizing downtime.