Electrical systems keep things running in modern life, but they often run into problems like imbalances, short circuits, ground faults, and overloading, which can cause equipment to break down, fires to break out, and even large-scale blackouts. To make matters worse, acts of sabotage, physical damage, or cyberattacks on systems like SCADA can mess up operations, throw grids off balance, and set off cascading failures. To avoid these risks, there is a growing need for smarter tools that can keep track of system performance and flag potential issues before they get out of hand. In this paper, we suggest a deep learning model built on the inception architecture, designed to monitor electrical systems, call out potential security faults, and spot malicious actions. Taking advantage of deep learning, our approach helps increase fault prediction accuracy and keep operations on track.

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.

CAPTCHAs, used to be an important element of security, are now facing more challenges due to the great advancements in artificial intelligence. In this paper, we investigate whether CAPTCHAs are still effective in protecting websites from automated threats. A deep learning model is suggested to automatically recognize CAPTCHA embedded characters, with performance metrics achieving an accuracy of 99.46%, an AUC of 99.98%, a precision of 99.46% and a recall of 99.43%. These findings highlight the increasing susceptibility of CAPTCHAs to sophisticated AI driven attacks and seek to emphasize the pressing importance of reevaluating CAPTCHA technologies to guarantee sustainable security.

In forensic investigations, facial recognition techniques serve as critical tools for identifying and apprehending suspects. In this study, we investigate the impact of Convolutional Neural Networks (CNNs) parallelization on the performance of facial recognition models within forensic contexts. Through experiments, we demonstrate the potential benefits of parallelization in enhancing model accuracy and robustness. Leveraging a reduced dataset, we employ augmentation techniques to expand the diversity of training samples. Our findings highlight the advantages of CNN parallelization in achieving superior recognition outcomes. Nevertheless, we identify constraints linked to excessive parallelization, which may induce model overfitting.

The pervasive issue of online sexism continues to pose significant challenges, fostering environments characterized by toxicity and perpetuating harmful societal norms. In response, this paper presents an approach for the discovery of sexist statements employing convolutional neural networks (CNNs), Word Embeddings, and data augmentation techniques. Through the fusion of CNNs’ capacity for hierarchical feature extraction with the semantic representations afforded by Word Embeddings, our method achieves exemplary discrimination performance. Additionally, the incorporation of data augmentation enriches the training dataset, thereby augmenting model generalization and resilience. Empirical evaluation on a larger dataset of statements demonstrates the efficacy of our approach, surpassing many baseline approaches in terms of discovery accuracy, precision, recall and F1-score.

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments.

The Tamazight civilization stands as a significant cultural entity, marked by its linguistic diversity, historical legacy, and scriptural traditions, which collectively enrich the cultural tapestry of North Africa. Among these traditions, the Tamazight handwritten script assumes particular importance, embodying centuries of cultural identity and artistic expression. Recognizing the imperative of safeguarding this cultural heritage, our study focuses on Tamazight handwritten character recognition. Leveraging the strategic application of Transfer Learning, we explore its efficacy in this domain. Transfer Learning presents a robust framework wherein pre-existing models are adapted for specific tasks despite limited data availability. Our research employs three prominent Transfer Learning architectures: VGG, ResNet, and MobileNet. Through a rigorous comparative analysis, we discern the efficacy of these methodologies in the context of Tamazight handwritten character recognition. Our findings underscore the potential of Transfer Learning to significantly augment the accuracy and efficiency of script recognition systems, thereby advancing the overarching objective of preserving and propagating the Tamazight cultural heritage.