In the twenty-first century, machine learning is a significant part of daily life for everyone. Today, it is adopted in many different applications, such as object recognition, object classification, and medical purposes. This research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves. Typically, farmers are unaware of diseases on plant leaves and adopt manual disease detection methods. Their production often decreases as the virus spreads. However, due to a lack of essential infrastructure, quick identification needs to be improved in many regions of the world. It is now feasible to diagnose diseases using mobile devices as a result of the increase in mobile phone usage globally and recent advancements in computer vision due to deep learning. To conduct this research, firstly, a dataset was created that contained images of money plant leaves that had been split into two primary categories, specifically (i) healthy and (ii) unhealthy. This research collected thousands of images in a controlled environment and used a public dataset with exact dimensions. The next step was to train a deep model to identify healthy and unhealthy leaves. Our trained YOLOv5 model was applied to determine the spots on the exclusive and public datasets. This research quickly and accurately identified even a small patch of disease with the help of YOLOv5. It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. This research is beneficial for farmers since it allows them to recognize diseased leaves as soon as they noted and take the necessary precautions to halt the disease’s spread. This research aimed to provide the best hyper-parameters for classifying and detecting the healthy and unhealthy parts of leaves in exclusive and public datasets. Our trained YOLOv5 model achieves 93 % accuracy on a test set.

An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity.

Electrocardiography (ECG) constitutes a perfect and primary diagnostic tool for measuring the different morbidity conditions of the heart in the context of different heart diseases and arrhythmia. Various studies have proposed different techniques of classification of ECG features and defined the parametric structure of different features of ECG. This paper is primarily designed to provide a more accurate classification by inducting the concept of a model-driven environment (MDE). Such induction works on the basis of reusable factors that are fitted to the state-of-the-art parametric structure of the ECG features. Some issues and challenges related to the embedding process are highlighted in the form of research questions. The aim of this paper is to provide the solutions to these research questions. The literature review is completed in two phases. In the first phase, those articles are collected that have been published in IEEE Xplore, ACM Library, Science Direct, and Springer, from 2008 to 2017.The second phase as a part of the execution stage is completed at the three different levels of the rectification process by adhering to the Kitchen ham guideline. At the first level, articles are filtered according to title and abstract. At the second level, articles are filtered according to specific eligibility criteria, and at the last level, articles are selected based on the skills of different domain experts (authors) by checking the quality assessment parameters. The significance of MDE in the classification of different ECG features is reflected in their compatibility with the research questions. Furthermore, future directions are proposed that depict the significance of dependencies involvement in classification analysis of ECG. These future directions are identified based on the planning and execution of our operational investigation and our critical observation of the existing gaps between dependencies of features classification that is the major cause of cardiovascular diseases.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern‐era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body’s internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).