Alzheimer’s disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer’s disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer’s disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer’s disease detection and highlights future research directions.

In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks (CNNs), a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight (BLB), Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80% of the data, and a testing set, which makes up the remaining 20%. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

Stroke detection using artificial intelligence (AI) continues to show promise in advancing medical imaging diagnostics, particularly in improving accuracy

With the growing prevalence of cybercrime, botnets have emerged as a significant threat, infiltrating an increasing number of legitimate computers annually. Challenges arising for organizations, educational institutions, and individuals as a result of botnet attacks include distributed denial of service (DDoS) attacks, phishing attacks, and extortion attacks, generation of spam, and identity theft. The stealthy nature of botnets, characterized by constant alterations in network structures, attack methodologies, and data transmission patterns, poses a growing difficulty in their detection. This paper introduces an innovative strategy for mitigating botnet threats. Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. Differential evolution is a population-based meta-heuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. The results demonstrate that our model achieves 99.99% accuracy, 99.49% precision, 98.98% recall, and 99.23% F1-score, which outperform the state-of-the-art P2P detection approaches.