This research proposes a Federated Multi-Modal Deep Learning Framework (FedMM-AD) with explainability features and privacy-preserving techniques for early-stage AD prediction. Our federated model was constructed using MRI, PET, and CT scan data, protecting patient privacy and security while enabling cross-institution training of the model without sharing any patient information. The FedMM-AD model’s accuracy, AUC, sensitivity, and specificity were 98.59%, 97.3%, and 98.2%, respectively, according to the experiment conducted on the ADNI and OASIS datasets. In order to increase the model’s transparency and help doctors identify key brain regions for AD, we integrated explainable AI techniques employing cross-attention. The findings show that when additional stakeholders, like clinicians, are properly involved, the approach can be beneficial and have an impact.

Blockchain-based federated learning (BCFL) is emerging as a promising solution to these challenges. Unlike traditional machine learning frameworks, including standard federated learning, BCFL offers a secure framework for distributed analysis of agricultural big data while integrating a transparent reward system via smart contracts. The development of smart agriculture requires data analysis methods that preserve privacy while encouraging collaboration among stakeholders. This review analyses studies (2018-2023) to assess how the integration of blockchain in federated learning (BCFL) addresses this dual challenge. Three key findings emerge: (1) 78% of BCFL implementations improve model auditability while maintaining accuracy > 85% (average difference of -9.2% vs. conventional FL); (2) Reward smart contracts reduce participant dropout rates by 40%; and (3) Only 12% of existing frameworks integrate differential privacy (DP) mechanisms. Our analysis reveals key barriers such as scalability (average latency of 2.7 s/transaction) and the heterogeneity of agricultural devices. Identified future directions include the adoption of lightweight blockchains (IOTA) and hybrid FL-DP protocols. This comprehensive review examines the integration of federated learning and blockchain in the agricultural domain. By analysing current trends, technical challenges, and future perspectives, this work aims to establish a framework for decentralized, secure, and privacy-preserving solutions. Unlike traditional approaches, our analysis highlights the potential of this synergy to optimize distributed agricultural data management, strengthen supply chain traceability, and improve decision-making through collaborative AI models. By identifying existing limitations (latency, scalability, interoperability), we pave the way for future research aimed at making these technologies accessible to farms of various sizes, while ensuring transparency and energy efficiency.

Brain tumors, whether benign or malignant, present significant challenges in medical diagnosis and treatment. Timely and precise detection is critical for effective intervention and patient outcomes. This study introduces a pioneering method for brain tumor detection, employing a fusion of Convolutional Neural Networks (CNN) and Vision Transformer (ViT) architectures. By integrating these models, we exploit their complementary features in image analysis, particularly in medical imaging contexts. Our research assesses the performance of this integrated CNN-ViT framework across various brain tumor imaging modalities and clinical scenarios using extensive experimentation on benchmark datasets. Results validate the robustness and accuracy of our approach, achieving a remarkable precision, recall rates, and overall accuracy of 98%.

This study aimed to propose a detection approach for plant disease based on deep learning (DL) algorithms. The study sought to discover diseases affecting three plants, which are: Common Rust, Vercospora Leaf Spot, Northern Leaf Blight for Corn Plant, And Early Blight, Late Blight for Potato, And Bacterial Spot, Septoria Leaf Spot, Target Spot for Tomato. To implement this approach, three deep learning algorithms were used including VGG16, VGG19, and CNN. This model was trained on a dataset contain 25272 image from the Kaggle database. The results demonstrated that the proposed approach could accurately detect plant diseases. In comparison, the VGG19 algorithm has a high detection accuracy of up to 95%, while the VGG16 algorithm has a detection accuracy of up to 86%. Then followed the CNN algorithm, which had detection accuracy up to as 86%.

Machine learning and artificial intelligence have led to notable progress in the field of deep learning and the identification of text within natural scene images in the past few years. Despite notable advancements, the effectiveness of deep learning and text detection in natural scene images, particularly for Arabic language, is frequently constrained by the scarcity of comprehensive datasets containing various multilingual scripts. YOLO (You Only Look Once) is a widely used deep learning neural network that has gained immense popularity for its versatility in handling diverse machine learning tasks, primarily in the field of computer vision. The YOLO algorithm has progressively garnered recognition for its exceptional performance to solving a complex problems, noisy data, as well as overcoming various challenges encountered in real-world scenarios. Our experiments provide a concise examination of text detection algorithms based on convolutional neural networks (CNNs), especially different versions of the YOLO models using a data augmentation technique applied to "SYPHAX" dataset, our new dataset of multilingual scripts in the wild. The objective of this paper is to offer insights into the future of YOLO in the field, highlighting potential research directions that can enhance text detection systems.

Remarkable performance on computer vision, and especially on pattern recognition field has been known for a long time to be produced by Deep learning algorithms. It is clear that amongst the successful applications in the pattern recognition domain, Arabic handwriting recognition (AHR) is a must. In this survey, we use two deep networks: Deep Belief Network (DBN) and Convolutional Neural Networks (CNN), for Arabic handwritten script (AHS) recognition. Despite the triumph of DBN and CNN methods, over-fitting is able to take place on these networks thanks to the massive number of parameters. In order to fight over-fitting, we have deeply inquired two regularization techniques called Dropout and DropConnect. While training with the two regularization methods, a randomly chosen subsets of activations/weights are dropped. Consequently, the assessment on the HACDB database to treat character level proves shows an improvement of classification error rate once adding Dropout and DropConnect techniques.

As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this study, a deep learning model based on Support Vector Machine (SVM) named Deep SVM (DSVM) is represented. We applied the dropout technique on the Deep SVM (DSVM). It is worth noting that this model has an inherent capacity to choose data points crucial to classify good generalization capacities. The deep SVM is built by a stack of SVMs permitting to extracting/learning automatically features from the raw images and to realize classification, too. We chose and tested the Multi-class Support Vector Machine with an RBF kernel, as non-linear discriminative features for classification, on Handwritten Arabic Characters Database (HACDB). Further to these advantages, our model is safeguarded against over-fitting because of strong performance of dropout. Simulation outcomes prove the efficiency of the suggested model.

Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI) of the hand. Secondly, we combine multiple descriptors to extract the palmprint texture information, which are Gray-Level Co-occurrence Matrix (GLCM) and the Gabor filters using feature level fusion. These descriptors have been broadly applied in various tasks, specifically in the image processing domain to analyze the image texture. Then, we apply the generalized discriminant analysis (GDA) to reduce the length of the feature vectors and their redundancies. Finally, we classify these final resulting features by developing the SVM method which supports several kernel functions to reach a best recognition rate. We have conducted extensive experiments on the “CASIA-Palmprint” and “PolyU-palmprint” datasets. The obtained results of the proposed approach provide promising results compared to other well-known state-of-the-art approaches.

Handwriting recognition ranks among the highest and the most triumphant applications in the pattern recognition domain. Despite being a developed field, many enquiries are still needed and still represent a defiance mainly for the Arabic Handwritten Script (AHS). Recently, more regard has been given to Support Vector Machines (SVM) classifier for script recognition. Nevertheless, it has not been put in application yet to the handwritten Arabic field if compared with the other methods like ANN, CNN, RNN and HMM. SVMs for AHS recognition is examined in this paper. Handcrafted feature is handled as input by the suggested method and gets going with a supervised learning algorithm. We chose the Multi-class Support Vector Machine with an RBF kernel and we tested it on Handwritten Arabic Characters Database (HACDB) as well. It was proven that the proposed method was effective thanks to the simulation results. We compared the well-functioning of this method with character recognition reliabilities coming from state-of-the-art Arabic OCR which resulted in commendatory outcomes.

Hand gestures are natural and intuitive communication way for the human being to interact with his environment. They serve to designate or manipulate objects, to enhance speech, or communicate in a noisy place. They can also be a separate language. Gestures can have different meanings according to the language or culture. They can also be a way to interact with machines. The subject of our research concerns the design and development of computer vision methods for recognizing hand gestures by a mobile device. We have proposed a system based on SVM for recognizing various hand gestures. The system consists of four steps: hand segmentation, smoothing, feature extraction and classification. The idea here is to allow the smartphone to perform all necessary steps to recognize gestures without the need to connect to a computer in which a database is located to perform training process. With this system, all steps can be done by the smartphone. In this paper, for image acquisition, frontal camera of the smartphone is used. After that frames are gotten from the video, the color sampling is done which is followed by making binary representation of the hand, and then contours representing the hand were described with convex polygons to get information about fingertips and finally the input gesture was recognized using proper classifier.