Nowadays pedestrian detection plays an important role in security and driving assistance. Detecting moving object is complex, and some of the detection methods are comparatively ineffective and slow. In relation to human detection it is very useful to combine independent information sources, such as appearance and motion. To achieve acceptable detection performance, we propose inter-frames differencing image to compute the region of interest, and MB-BIF to extract features. The MB-BIF approach combines two well-known methods, the Multi-Block Local Binary Pattern and Biologically Inspired Method. We evaluate the performance of different features descriptors on different databases, and our method shows good efficiency.

Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability.

Food shortages are some of the direct consequences of plant diseases in Developing countries. Artificial intelligence combined with image processing can help respond to these threats. In this paper, Inception v3 and MobileNet v2 are presented for plant disease detection using smartphones. We implemented the proposed methods and conducted the detection experiments carried out on an open dataset of 54305 images of leaves containing 14 species, spread over 38 distinct classes of combinations [plants, diseases], including healthy plants. The models were also run on the improved version of this dataset including 87867 images, and where there was no imbalance between classes. The results show that the Inception v3 method outperforms MobileNet v2, a state-of-the art model in terms of precision both on the original dataset and its augmented version, which makes it a very useful advisory or early warning tool. It should be noted that the presented method could be further improved by integrating several other varieties of plant 1 species and diseases taken under real culture conditions from several geographical areas.

This paper presents a pedestrian detection approach based on Multi-block Local Binary Pattern (MB-LBP) features and Weighted Region Covariance Matrix (WRCM). Multistage classifiers are used to increase the processing speed and reliability of the detection system. Using the modified 3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD) techniques, the region of interest is determined. Once the pedestrian region is identified, the front end of the multistage classifier quickly determines wherever pedestrians may be present, while the back end confirms whether the first descriptor did classify correctly. The experimental results demonstrated that our approach performed well in real-time application.