Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)—FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32—for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT’s in medical image processing.

Hyperspectral imagery, capturing information beyond the visible spectrum, is essential for many remote sensing applications. This paper introduces a novel approach utilizing a 3D Convolutional Neural Network (CNN) enhanced with self-attention mechanism to address the limitations of traditional architectures in hyperspectral image classification. The main problem tackled is the effective extraction of both spatial and spectral features from complex hyperspectral datasets. Our proposed model simultaneously analyzes spatial and spectral dimensions with improved accuracy and adaptability through self-attention. We systematically evaluate the performance of 1D CNN, 2D CNN, and 3D CNN models, alongside Multi-Column Neural Network (MCNN) and Hybrid Spectral Network (Hybrid SN) models, comparing them to our proposed approach. The evaluation, conducted on the Indian Pines and Salinas datasets, highlights significant improvements in classification accuracy and adaptability. Specifically, the proposed 3D CNN with self-attention achieves accuracy values of 98.45% and 91.32% on the Indian Pines and Salinas datasets, respectively. This paper provides critical insights for data scientists and remote sensing experts, contributing to informed decision-making in selecting CNN models tailored to specific application requirements and advancing the field of hyperspectral image classification (HSIC). Furthermore, our approach shows improved handling of the complex spectral variability and spatial heterogeneity inherent in hyperspectral data.

The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection.

The progress of technology has played a crucial role in enhancing various fields such as education. Universities in Saudi Arabia offer free education to students and follow specific admission policies. These policies usually focus on features and scores such as the high school grade point average, general aptitude test, and achievement test. The main issue with current admission policies is that they do not fit with all majors, which results in high rates of failure, dropouts, and transfer. Another issue is that all mentioned features and scores are cumulatively calculated, which obscures some details. Therefore, this study aims to explore admission criteria used in Saudi Arabian universities and the factors that influence students’ choice of major. First, using data mining techniques, the research analyzes the relationships and similarities between the university’s grade point average and the other student admission features. The study proposes a new Jaccard model that includes modified Jaccard and approximated modified Jaccard techniques to match the specifications of students’ data records. It also uses data distribution analysis and correlation coefficient analysis to understand the relationships between admission features and student performance. The investigation shows that relationships vary from one major to another. Such variations emphasize the weakness of the generalization of the current procedures since they are not applicable to all majors. Additionally, the analysis highlights the importance of hidden details such as high school course grades. Second, this study employs machine learning models to incorporate additional features, such as high school course grades, to find suitable majors for students. The K-nearest neighbor, decision tree, and support vector machine algorithms were used to classify students into appropriate majors. This process significantly improves the enrolment of students in majors that align with their skills and interests. The results of the experimental simulation indicate that the K-nearest neighbor algorithm achieves the highest accuracy rate of 100%, while the decision tree algorithm’s accuracy rate is 81% and the support vector machine algorithm’s accuracy rate is 75%. This encourages the idea of using machine learning models to find a suitable major for applicants.

The development of e-healthcare systems requires the application of advanced technologies, such as blockchain technology. The main challenge of applying blockchain technology to e-healthcare is to handle the impact of the delay that results from blockchain procedures during the communication and voting phases. The impacts of latency in blockchains negatively influence systems’ efficiency, performance, real-time processing, and quality of service. Therefore, this work proposes a modified model of a blockchain that allows delays to be avoided in critical situations in healthcare. Firstly, this work analyzes the specifications of healthcare data and processes to study and classify healthcare transactions according to their nature and sensitivity. Secondly, it introduces the concept of a fair-proof-of-stake consensus protocol for block creation and correctness procedures rather than famous ones such as proof-of-work or proof-of-stake. Thirdly, the work presents a simplified procedure for block verification, where it classifies transactions into three categories according to the time period limit and trustworthiness level. Consequently, there are three kinds of blocks, since every category is stored in a specific kind of block. The ideas of time period limits and trustworthiness fit with critical healthcare situations and the authority levels in healthcare systems. Therefore, we reduce the validation process of the trusted blocks and transactions. All proposed modifications help to reduce computational costs, speed up processing times, and enhance security and privacy. The experimental results show that the total execution time using a modified blockchain is reduced by about 49% compared to traditional blockchain models. Additionally, the number of messages using modified blockchain is reduced by about 53% compared to the traditional blockchain model.

Recently, distributed systems have become the backbone of technological development. It serves as the foundation for new trends technologies such as blockchain, the internet of things and others. A distributed system provides fault tolerance and decentralization, where a fault in any component does not result in a whole system failure. In addition, deep learning model enables processing data to find patterns, which helps in classification, regression, prediction, and clustering. This work employs deep learning to handle faults within distributed systems in three scenarios. Firstly, a faulty processor may not be able to produce the right output. Therefore, deep learning model uses the inputs and outputs of other processors to find patterns and produces the proper output of the faulty processor. Secondly, if a faulty possessor corrupts its inputs as well, then the deep learning model learns from the inputs and the outputs of successful processors and produces the proper output of the faulty processor, even with corrupted inputs. Thirdly, for unrelated data, in which the patterns of the input of the faulty processors differ from the patterns of the inputs of successful ones. In this case, the model is able to discover the new pattern and to be labeled as unknown. In the experiments, we use deep learning models like VGG16, VGG19, AlexNet LSTM and ResNet34, to investigate the performance of the deep learning in the three mentioned scenarios. For unstructured datasets, the accuracy of the models is affected by the size of the faulty data. The accuracy of all models lies between 60% when the size of the faulty data is 90%, and 96%, when the size of the faulty data is 90%. The structured datasets are not significantly affected by the portion of the faulty data and the accuracy reaches 99%.