Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, clinical datasets also suffer from this imbalance problem, and the imbalanced class distributions pose several issues in the training of classifiers. Consequently, classifiers suffer from low accuracy, precision, recall, and a high degree of misclassification, etc. We performed a brief literature review on the class imbalanced learning scenario. This study carries the empirical performance evaluation of six classifiers, namely Decision Tree, k-Nearest Neighbor, Logistic regression, Artificial Neural Network, Support Vector Machine, and Gaussian Naïve Bayes, over five imbalanced clinical datasets, Breast Cancer Disease, Coronary Heart Disease, Indian Liver Patient, Pima Indians Diabetes Database, and Coronary Kidney Disease, with respect to seven different class balancing techniques, namely Undersampling, Random oversampling, SMOTE, ADASYN, SVM-SMOTE, SMOTEEN, and SMOTETOMEK. In addition to this, the appropriate explanations for the superiority of the classifiers as well as data-balancing techniques are also explored. Furthermore, we discuss the possible recommendations on how to tackle the class imbalanced datasets while training the different supervised machine learning methods. Result analysis demonstrates that SMOTEEN balancing method often performed better over all the other six data-balancing techniques with all six classifiers and for all five clinical datasets. Except for SMOTEEN, all other six balancing techniques almost had equal performance but moderately lesser performance than SMOTEEN.

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

Major depressive disorder (MDD) is a mental ailment marked by a loss of interest in activities, persistent depression, and hopelessness. MDD has been on the rise in society in recent decades for varied reasons and has spurred suicidal tendencies among individuals. Early detection, continuous monitoring, and effective treatment are crucial for its impact on quality of life and society. EEG signal models the brain’s electrical activities and has emerged as a potential tool to assess the depression status of a person. Due to advancements in sensor technology, fast, convenient, and cost-effective EEG acquisition is now possible, resulting in many EEG-based healthcare monitoring applications in recent years. This work proposes an EEG-headset-based smart monitoring system for real-time diagnosis of MDD in the Internet of Medical Things (IoMT) framework. In this study, we proposed a novel Linear Graph Convolution Network-Transformer-based deep learning approach for categorizing MDD through a time-frequency analysis of EEG signals. The Stockwell transform (S-transform) is employed to exploit the spectro-temporal information from the EEG and the resulting 2D representation is then fed into customized Linear Graph Convolution Network for MDD detection. We have utilized theWeighted Focal Binary Hinge Loss function, specifically designed for customized Linear Graph Convolution Network, to improve learning and handle unbalanced input. Subsequently, a novel Transformer model is designed to refine the MDD classification further. The proposed methodology named SLiTRANet, blends spectral analysis with the S-transform, graph-based learning with Linear Graph Convolution Network, and the sequence modeling capability of the Transformer. The proposed SLiTRANet model can be further integrated within an IoMT framework for automated real-time MDD diagnosis using EEG signals. The proposed methodology is evaluated on two publicly available datasets, MODMA and HUSM datasets. The evaluation results demonstrate the superior performance of the proposed SLiTRANet framework against the existing pre-trained and hybrid deep learning models, achieving remarkable accuracy, sensitivity, specificity, and precision rates of 99.92%, 99.90%, 99.95%, and 99.97%, respectively on HUSM dataset followed by an equally good performance on MODMA dataset with an accuracy of 99.68%. The proposed comprehensive approach implemented on two varied datasets highlights significant advancements in depression detection by outperforming state-of-art approaches.