ADIL ABDULHUR ABUSHANA ALNASRAWI

In real-world surveillance environments, face recognition and tracking remain challenging due to partial occlusion, pose variation, illumination changes, and background clutter. This paper presents a robust hybrid framework that integrates fuzzy genetic algorithms (FGA) with deep coupled feature learning for multi-face recognition and tracking. The proposed system comprises three main modules: (1) face detection and pre-processing using the Multi-task Cascaded Convolutional Network (MTCNN), (2) deep coupled ResNet embeddings that jointly learn identity and appearance-invariant representations, and (3) a fuzzy rule-based genetic optimizer that adaptively refines tracking decisions based on uncertainty in motion, appearance similarity, and occlusion levels. The novelty of this work lies in the fusion of fuzzy inference with evolutionary search to guide the genetic optimization process—allowing dynamic adaptation to noisy and uncertain visual conditions. Moreover, probabilistic data association filters (PDAF) and conditional joint likelihood filters (CJLF) are employed to further enhance temporal consistency under occlusion and appearance variation. The results confirm that fuzzy evolutionary optimization, when coupled with deep feature learning, significantly improves robustness and stability for real-time face tracking in complex, dynamic scenes.

The study introduces The PDRFSVM framework which combines PSPNet-based segmentation with multi-branch feature extraction from DenseNet121 and ResNet50V2 and a Vision Transformer (ViT) to achieve fuzzy classification through Fuzzy Support Vector Machine (Fuzzy SVM). The ViT component uses self-attention to model long-range spatial dependencies which allows it to extract detailed lesion characteristics that include subtle discoloration patterns and irregular boundaries. The proposed framework combines heterogeneous deep features through structured fusion which enables it to effectively represent complex disease patterns across different environmental conditions. The experimental evaluation across various deep learning models shows that ResNet50V2 achieves strong baseline performance through optimization using both SGD and Adam while the PDRFSVM framework reaches higher accuracy of 98.2% which exceeds TLFDNN and standalone ResNet50V2 performance. Fuzzy SVM integration into the system provides a classification method that utilizes continuous membership values to create a grading system for disease intensity instead of using binary classification. The system helps precision agriculture by enabling farmers to detect problems early and create treatment strategies. The proposed approach presents a strong solution which detects Tuta absoluta and tomato leaf diseases at an early stage while assessing their severity through an interpretable system which agricultural systems can use in real-world applications.

novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed. The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT. The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigenlips approach, their proposed approach by. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the method.

The number of expertise’s in the medical domain about the breast cancer is limited. Many patients have to wait too long to get their result from the check-up. The system can be used to help patients, he number of expertise’s in the medical domain about the breast cancer is limited. Many patients have to wait too long to get their result from the check-up. The system can be used to help patients

To: The Academic and Administrative Staff of [University Kufa] Subject: Letter of Appreciation and Gratitude Date: 2020- 2026 Dear Esteemed Colleagues, It is with great pleasure and pride that we extend our deepest gratitude to all of you for your outstanding dedication and tireless efforts during the current academic year. Your commitment to academic excellence and your professional handling of administrative tasks have played a pivotal role in the success of our institution. The progress we have achieved in [mention a specific achievement, e.g., research rankings/quality assurance] is a direct result of your collective hard work and unwavering spirit. We truly value your contributions and look forward to more milestones together. Wishing you continued success and prosperity in your academic journey.

To share my research   At the conference held in Dubai on the date of 2018--5-27