Jimmy Singla

Bio: Jimmy Singla is an academic researcher from Lovely Professional University. The author has contributed to research in topics: Medical diagnosis & Computer science. The author has an hindex of 8, co-authored 47 publications receiving 181 citations. Previous affiliations of Jimmy Singla include Punjab Technical University.

Fake News Detection Using Machine Learning approaches: A systematic Review

Abstract: The easy access and exponential growth of the information available on social media networks has made it intricate to distinguish between false and true information. The easy dissemination of information by way of sharing has added to exponential growth of its falsification. The credibility of social media networks is also at stake where the spreading of fake information is prevalent. Thus, it has become a research challenge to automatically check the information viz a viz its source, content and publisher for categorizing it as false or true. Machine learning has played a vital role in classification of the information although with some limitations. This paper reviews various Machine learning approaches in detection of fake and fabricated news. The limitation of such and approaches and improvisation by way of implementing deep learning is also reviewed.

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Abstract: Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

Medical Expert Systems for Diagnosis of Various Diseases

Abstract: Diseases should be treated well and on time. If they are not treated on time, they can lead to many health problems and these problems may become the cause of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In an effort to address such problems, studies made attempts to design and develop expert systems which can provide advice for physicians and patients to facilitate the diagnosis and recommend treatment of patients. This review paper presents a comprehensive study of medical expert systems for diagnosis of various diseases. It provides a brief overview of medical diagnostic expert systems and presents an analysis of already existing studies. General Terms Artificial intelligence, Expert system, Medical knowledge.

Security Assured CNN-Based Model for Reconstruction of Medical Images on the Internet of Healthcare Things

Abstract: Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

Comparative study of Mamdani-type and Sugeno-type fuzzy inference systems for diagnosis of diabetes

Abstract: Fuzzy inference systems for diagnosis of diabetes are developed using Mamdani-type and Sugeno-type fuzzy models. The outcome obtained by two fuzzy inference systems is evaluated. This paper summarizes the essential variation among the Mamdani-type and Sugeno-type fuzzy inference systems. MATLAB fuzzy logic toolbox is used for the simulation of both the models. This also confirms which one is a superior choice of the two fuzzy inference systems for diagnosis of diabetes.

Heart Disease: A Textbook of Cardiovascular Medicine

Abstract: This third edition of what has now become a well-established textbook in cardiovascular medicine is again edited by Dr Eugene Braunwald with the assistance of 65 other authors who read like a Who's Who of American Cardiology. Since the second edition, 12 new chapters have been added or substituted and others have been significantly revised. The first volume includes Part I on "Examination of the Patient" and Part II on "Normal and Abnormal Circulatory Function." The second volume deals with specific diseases. Part III, "Diseases of the Heart, Pericardium and Vascular System," includes new sections on "Risk Factors for Coronary Artery Disease," "The Pathogenesis of Atherosclerosis," and "Interventional Catheterization Techniques." Part IV, "Broader Perspectives on Heart Disease and Cardiologic Practice," includes new chapters on "Genetics and Cardiovascular Disease," "Aging in Cardiac Disease," and "Cost Effective Strategies in Cardiology." The last 200 pages of the book (Part V) are devoted to

Knowledge-Based Systems

Abstract: Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Abstract: Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Abstract: Automated fault detection is an important part of a quality control system. It has the potential to increase the overall quality of monitored products and processes. The fault detection of automotive instrument cluster systems in computer-based manufacturing assembly lines is currently limited to simple boundary checking. The analysis of more complex nonlinear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. We present a novel approach for automated Fault Detection and Isolation (FDI) based on deep learning. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the approach models the different spatial/temporal patterns found in the data. The approach can successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established FDI methods.