Electroencephalography (EEG) serves as a crucial neurodiagnostic tool by recording the electrical brain activity via attached electrodes on the patient’s head. While artificial intelligence (AI) exhibited considerable promise in medical diagnostics, its potential in the realm of neurodiagnostics remains underexplored. This research addresses this gap by proposing an innovative approach employing time-series classification of EEG data, leveraging long-short-term memory (LSTM) neural networks for the identification of abnormal brain activity, particularly seizures. To enhance the performance of the proposed model, metaheuristic algorithms were employed for optimizing hyperparameter collection. Additionally, a tailored modification of the variable neighborhood search (VNS) is introduced, specifically tailored for this neurodiagnostic application. The effectiveness of this methodology is evaluated using a carefully curated dataset comprising real-world EEG recordings from both healthy individuals and those affected by epilepsy. This software-based approach demonstrates noteworthy results, showcasing its efficacy in anomaly and seizure detection, even when working with relatively modest sample sizes. This research contributes to the field by illuminating the potential of AI in neurodiagnostics, presenting a methodology that enhances accuracy in identifying abnormal brain activities, with implications for improved patient care and diagnostic precision.

Maintaining trust among employees, employers, and institutions is fundamental to business and research, yet the rise of always-online digital systems and expanding workforces poses new risks to operational integrity. Fluctuating work environments, evolving motivations, and gaps in training can leave organizations vulnerable to insider threats, including inadvertent data leaks and intentional exfiltration. In this study, an investigation of natural language processing (NLP) methods applied to HTTP activity logs to identify potential insider threats through behavioral patterns is conducted. Two experiments were conducted using TF-IDF and Word2Vec text representations, each combined with an XGBoost classifier whose hyperparameters were optimized using a newly proposed iteration stagnation-aware variable neighborhood search (ISAVNS) metaheuristic. The ISAVNS introduces a stagnation-detection mechanism that enables adaptive recovery during optimization, improving exploration and convergence stability. Evaluation on publicly available insider-threat datasets confirmed the high effectiveness of the proposed framework, with the TF-IDF-based model reaching an accuracy of 97.63% and the Word2Vec-based counterpart attaining 97.71%.

Benzene is a toxic airborne contaminant and a recognized cancer-causing agent that presents substantial health hazards even at minimal concentrations. The precise prediction of benzene concentrations is crucial for reducing exposure, guiding public health strategies, and ensuring adherence to environmental regulations. Because of benzene's high volatility and prevalence in metropolitan and industrial areas, its atmospheric levels can vary swiftly influenced by factors like vehicular exhaust, weather patterns, and manufacturing processes. Predictive models, especially those driven by machine learning algorithms and real-time data streams, serve as effective instruments for estimating benzene concentrations with notable precision. This research emphasizes the use of recurrent neural networks (RNNs) for this objective, acknowledging that careful selection and calibration of model hyperparameters are critical for optimal performance. Accordingly, this paper introduces a customized version of the elk herd optimization algorithm, employed to fine-tune RNNs and improve their overall efficiency. The proposed system was tested using real-world air quality datasets and demonstrated promising results for predicting benzene levels in the atmosphere.