Breast cancer has been the most frequent diagnosed cancer and the leading cause of cancer-related deaths among women worldwide, mainly due to delayed detection. Early diagnosis significantly improves prognosis and long-term survival rates. Various techniques, including imaging, sensors, and molecular biotechnology, have been developed to facilitate early detection. This review provides a comprehensive analysis of these diagnostic techniques, emphasizing precision, patient comfort, and cost-effectiveness. Additionally, it explores the emerging role of wearable technologies, such as smart bras and real-time monitoring devices, in revolutionizing breast cancer detection. The review concludes by discussing the limitations of current diagnostic methods and proposing future directions for enhancing early detection and improving patient outcomes.

According to the World Health Organization, cardiovascular diseases (CVDs) account for an estimated 17.9 million deaths annually. CVDs refer to disorders of the heart and blood vessels such as arrhythmia, atrial fibrillation, congestive heart failure, and normal sinus rhythm. Early prediction of these diseases can significantly reduce the number of annual deaths. This study proposes a novel, efficient, and low-cost transformer-based algorithm for CVD classification. Initially, 56 features were extracted from electrocardiography recordings using 1,200 cardiac ailment records, with each of the four diseases represented by 300 records. Then, random forest was used to select the 13 most prominent features. Finally, a novel transformer-based algorithm has been developed to classify four classes of cardiovascular diseases. The proposed study achieved a maximum accuracy, precision, recall, and F1 score of 0.9979, 0.9959, 0.9958, and 0.9959, respectively. The proposed algorithm outperformed all the existing state-of-the-art algorithms for CVD classification.

The demand of the Internet of Things (IoT) has witnessed exponential growth. These progresses are made possible by the technological advancements in artificial intelligence, cloud computing, and edge computing. However, these advancements exhibit multiple challenges, including cyber threats, security and privacy concerns, and the risk of potential financial losses. For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an accuracy of 99.90% to classify nine cyber-attacks. Multiple other performance metrices have been evaluated to validate the efficacy of the proposed scheme. In addition, comparison has been done with existing state-of-the-art schemes. The findings of the proposed study can significantly contribute to the development of secure intrusion detection for IIoT systems.

Non-communicable diseases, such as diabetes, are the leading cause of mortality worldwide. Effective diabetes management is crucial for ensuring the well-being of diabetics. Existing glucose monitoring technologies are often invasive and uncomfortable, eliciting anxiety among patients. Non-invasive procedures offer a promising solution for these issues, but their widespread adoption is restricted by cost and accuracy constraints. This study investigates the use of green light Photoplethysmography (PPG) signals for non-invasive blood glucose monitoring. A custom-designed PPG acquisition setup was developed to collect PPG data from 80 subjects under controlled conditions. Simultaneously, reference capillary blood glucose readings were obtained using a lancing device to serve as the gold standard. Signals were enhanced by applying different processing techniques and 32 features were extracted, which were scaled and subjected to correlation analysis to retain the highly correlated features. Feature engineering further optimized the feature set, which was then used to train and validate regression models. Model performance was evaluated using R ² (coefficient of determination), mean absolute error (MAE), and bias analysis across glucose ranges. Among the tested models, the Random Forest Regression (RFR) showed the best performance with an R ² value of 0.92 and MAE of 4.8 mg/dL. Predicted glucose levels demonstrated minimal bias across glucose ranges, with mean differences of −5.11 ± 0.80 mg/dL (<90 mg/dL), −3.68 ± 1.24 mg/dL (90–120 mg/dL), and −5.61 ± 0.75 mg/dL (≥120 mg/dL). The findings demonstrate that PPG signals in the green light spectrum effectively reflect blood glucose levels, supporting their potential for non-invasive glucose monitoring.

The versatile nature of Visual Sentiment Analysis (VSA) is one reason for its rising profile. It isn't easy to efficiently manage social media data with visual information since previous research has concentrated on Sentiment Analysis (SA) of single modalities, like textual. In addition, most visual sentiment studies need to adequately classify sentiment because they are mainly focused on simply merging modal attributes without investigating their intricate relationships. This prompted the suggestion of developing a fusion of deep learning and machine learning algorithms. In this research, a deep feature-based method for multiclass classification has been used to extract deep features from modified ResNet50. Furthermore, gradient boosting algorithm has been used to classify photos containing emotional content. The approach is thoroughly evaluated on two benchmarked datasets, CrowdFlower and GAPED. Finally, cutting-edge deep learning and machine learning models were used to compare the proposed strategy. When compared to state-of-the-art approaches, the proposed method demonstrates exceptional performance on the datasets presented.