Intentionally mutilated fingerprints pose a significant challenge in forensic identification. Such deliberate actions typically stem from individuals seeking to evade detection or association with past or prospective criminal activities. The detection of damaged fingerprints presents a formidable obstacle for most of current forensic systems, often leading to a pronounced incidence of false negatives. The ramifications of a false negative are profound, as they preclude the establishment of links between suspects and crime scenes, impeding the acquisition of vital evidence and potentially stalling investigative progress. In response to this critical issue, this paper delves into the development of a deep learning based model expressly designed to accurately discern and capture patterns present in damaged fingerprints.

High-performance Machine Learning (ML) models are indispensable in cybersecurity due to the need for real-time threat detection, scalability in handling large datasets, and the ability to recognize complex patterns and evolving threats. These models should reduce false positives and negatives, adapt to dynamic environments, and enable automated response mechanisms. This paper introduces an innovative methodology aimed at improving the performance and interpretability of ML models in binary classification, with a distinct emphasis on network intrusion detection. The proposed approach centers on an in-depth analysis of the confusion matrix, utilizing its insights to enhance model performance. We test our methodology on the UNSW-NB15 network intrusion dataset. We managed to improve almost all metrics with an increase in accuracy from 81.92% to 89.6%, recall from 76.42% to 89.22%, and F1 score from 82.51% to 89.76%, with the potential to obtain more improvements.

This paper introduces a novel ensemble learning methodology that leverages the confusion matrix and statistical metrics, particularly the mean, to intelligently select the most suitable model from a pool of candidates for predictive tasks on test inputs. The approach has been tested on a well-known Network Intrusion Detection Dataset (UNSW-NB15) and compared against its underlying models and other ensembling methods where it yielded superior results in three performance metrics (accuracy, recall, and F1). Out approach is model-agnostic, easily adaptable, and scalable to accommodate any number of models. Additionnally, the use of interpretable metrics such as the confusion matrix and mean enhances explainability, providing a comprehensive understanding of the model’s decision-making processes and improvement directions.

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.

This paper investigates the application of GradCAM, an explainable AI (XAI) technique, to enhance the transparency and precision of fingerprint authentication systems in forensics, particularly in detecting fingerprint mutilation—a common method used to evade biometric security measures. Employing the SOCOfing dataset, which contains both unaltered and synthetically altered fingerprint images, we apply GradCAM to visualize and understand the decision-making process of a convolutional neural network (CNN) model trained to recognize and classify these alterations. Our study not only demonstrates the model’s effectiveness in identifying different types of fingerprint modifications but also identifies areas where the model’s performance can be enhanced. Through detailed visual analysis, we uncover the model’s focus points and assess its reliability across various alteration types and difficulty levels. The insights gained underline the potential of XAI in improving the robustness and reliability of biometric verification systems, paving the way for more secure and equitable AI applications in high-stakes environments.