Emotion recognition from audio signals is essential in forensic applications, offering insight into emotional states during interrogations, threat assessments, and crime scene analysis. This paper proposes an Inception-based deep learning model tailored for forensic arabic audio emotion recognition. The Inception architecture, with its multiscale feature extraction capabilities, efficiently captures subtle emotional details from complex audio signals. The model was evaluated on a dataset that represents a diverse range of emotional expressions, achieving superior performance in accuracy, robustness, and adaptability compared to traditional approaches. Its precision and ability to handle real-world variability make it particularly suited for forensic investigations. This work underscores the potential of advanced neural architectures in enhancing forensic decision-making and analysis.

In the current landscape of IT, ransomware attacks pose a major threat to cybersecurity resulting in significant monetary losses and data breaches. The detection of ransomware in time presents a challenge due to its constant evolution and complex strategies for escaping detection. This study introduces a deep learning tool —named RansFighter—based on Gated Recurrent Unit (GRU) specifically developed for ransomware detection. Our model shows, at test time, an Accuracy of 96.67%, a Precision of 97.01%, a Recall of 96.37%, an F1-Score of 96.69% and an Area Under the Curve (AUC) of 96.67%. It showcases the potential of GRUs as valuable assets to safeguard systems against ransomware threats.