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The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. [ABSTRACT FROM AUTHOR]

With the advancements in the era of artificial intelligence, blockchain, cloud computing, and big data, there is a need for secure, decentralized medical record storage and retrieval systems. While cloud storage solves storage issues, it is challenging to realize secure sharing of records over the network. Medi-block record in the healthcare system has brought a new digitalization method for patients' medical records. This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital. It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record. This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records. It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing, such as using Blockchain technology, Access Control, Privacy-Preserving, Proxy Re-Encryption, and Service-On- Chain approach. Finally, we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems. [ABSTRACT FROM AUTHOR]

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized. While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase (LMuMOGWO) for tackling feature selection problems. The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer (MOGWO): a Lévy flight and a mutation operator. The Lévy flight, a type of random walk with jump size determined by the Lévy distribution, enhances the global search capability of MOGWO, with the objective of maximizing classification accuracy while minimizing the number of selected features. The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy. As feature selection is a binary problem, the continuous search space is converted into a binary space using the sigmoid function. To evaluate the classification performance of the selected feature subset, the proposed approach employs a wrapper-based Artificial Neural Network (ANN). The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO, BMOGWO-S (based sigmoid), BMOGWO-V (based tanh) as well as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (BMOPSO). The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem. Moreover, the proposed approach outperforms existing approaches in most cases in terms of classification error rate, feature reduction, and computational cost. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model’s poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6. [ABSTRACT FROM AUTHOR]