Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

The rise of artificial intelligence (AI) revolutionized both cybersecurity defenses and cybercriminals' methods to exploit vulnerabilities. Cybercriminals continue to exploit previously undiscovered vulnerabilities, known as zero-day attacks, posing severe threats to cybersecurity. These attacks are particularly challenging to detect, as they target unknown weaknesses in systems before security teams can respond or act. Traditional intrusion detection systems (IDS) rely heavily on pre-existing attack signatures, making them ineffective against zero-day threats. Machine learning (ML) algorithms have recently become a promising solution for enhancing IDS capabilities by identifying anomalies and predicting potential vulnerabilities in real time. This review paper explores how cutting-edge AI techniques, specifically ML, DL, and federated learning (FL), are harnessed to counter zero-day attacks. AI is used to defend against cyberattacks that exploit vulnerabilities unknown to existing security software. This research explores different AI methods used in cybersecurity, analyzes the data used to train these AI models, and evaluates how well various algorithms perform in actual cyberattacks. Moreover, key challenges in deploying ML for zero-day detection are highlighted, including handling imbalanced data, generalization across diverse types of attacks, and the trade-offs between accuracy and computational cost. The paper outlines future research directions to enhance AI-based zero-day attack defenses and strengthen proactive cybersecurity strategies.

Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.

Identification and precise classification of plants are crucial in improving plant quality and economic viability, particularly in an industrial setting. In a faster-growing world, there is a growing demand for fully automated tomato detection and grading systems. Within the past few years, applying deep learning in detecting and classifying tomatoes into different classes has gained popularity. This study aims to build a new framework for the efficient automated harvesting of tomato plants based on deep learning. The new model integrates the capabilities of Particle Swarm Optimization (PSO) and the You Only Look Once version-8 (YOLOv8) architecture for better hyperparameter optimization and improved performance results. For the first time, it classifies and detects ripe, semi-ripe, and unripe tomatoes, in addition to diseased and rotten tomatoes. To validate the efficacy of the proposed YOLO-v8 network’s performance, three experiments were conducted employing a unique dataset, collected from different sources. Firstly, two experiments were conducted to formally confirm whether or not the utilized data augmentation technique significantly improved, one with data augmentation and another one with the end-to-end framework without data augmentation. Secondly, the proposed YOLOv8 was compared with other YOLO versions, e.g., YOLOv3, YOLOv5, S-YOLOv5, YOLOv7, and YOLOv8. Thirdly, the proposed framework was compared with many cutting-edge object detection architectures for tomato harvesting, e.g., Convolutional neural network (CNN), Mask R-CNN and color analysis, and other models based on handcrafted features. The enhancements made to the original YOLO-v8 network have attained promising results. The experiments on the collection of different datasets reveal that the proposed model performs with the highest precision, recall, F1-score, and mean average precision (mAP) of 0.89, 0.9, 0.89, and 0.89 (mAP@0.5:0.95), respectively, exceeding other models. This framework offers a viable and useful solution for class detection and location identification of tomato plants.