One of the most commonly problem in the field of network intrusion detection system is the tremendous number of redundant and irrelevant information used to build an intrusion detection system. In order to overcome this problem, we have used and compared two dimensionality reduction methods namely PCA and Fuzzy PCA which allows us to keeping just the most relevant information from the network traffic data. Then, we have applied K nearest Neighbour algorithm in order to classify the test samples of connections into a normal or attack category. The conducted experiments were made by using KDDcup99 dataset. The results obtained reveal that Fuzzy PCA method outperforms PCA in detecting U2R and DoS (Denial of Service) attacks.

Principal Component Analysis (PCA) is one of the key methods for solving data analysis problems with a large number of dimensions like face recognition. Nevertheless, classical PCA is based on L2-Norm which is very sensitive to noises. Recently, a new robust PCA approach has been proposed by replacing L2-Norm with L1-Norm (PCA-L1). However, PCA-L1 requires a lot of time to calculate the projection bases. To solve this problem, we propose to use a wavelets feature extraction method as pre-processing step to face recognition. Extensive experiments on two well-known face image datasets namely ORL and Georgia Tech Face-Database(GTFD), show that the proposed approach minimizes execution time and has a recognition rate up to 96.7% for ORL and 85% for GTFD.

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring.

As Android malware grows increasingly sophisticated, traditional detection methods struggle to keep pace, creating an urgent need for robust, interpretable, and real-time solutions to safeguard mobile ecosystems. This study introduces YoloMal-XAI, a novel deep learning framework that transforms Android application files into RGB image representations by mapping DEX (Dalvik Executable), Manifest.xml, and Resources.arsc files to distinct color channels. Evaluated on the CICMalDroid2020 dataset using YOLO11 pretrained classification models, YoloMal-XAI achieves 99.87% accuracy in binary classification and 99.56% in multi-class classification (Adware, Banking, Riskware, SMS, and Benign). Compared to ResNet-50, GoogLeNet, and MobileNetV2, YOLO11 offers competitive accuracy with at least 7× faster training over 100 epochs. Against YOLOv8, YOLO11 achieves comparable or superior accuracy while reducing training time by up to 3.5×. Cross-corpus validation using Drebin and CICAndMal2017 further confirms the model’s generalization capability on previously unseen malware. An ablation study highlights the value of integrating DEX, Manifest, and Resources components, with the full RGB configuration consistently delivering the best performance. Explainable AI (XAI) techniques—Grad-CAM, Grad-CAM++, Eigen-CAM, and HiRes-CAM—are employed to interpret model decisions, revealing the DEX segment as the most influential component. These results establish YoloMal-XAI as a scalable, efficient, and interpretable framework for Android malware detection, with strong potential for future deployment on resource-constrained mobile devices.