J. Jayashree

Bio: J. Jayashree is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Confusion matrix. The author has an hindex of 2, co-authored 3 publications receiving 12 citations.

Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) – A hybrid expert system for diagnosis of diabetes

Abstract: In worldwide 415 million of peoples are affected by diabetics in the year of 2015, that is increased from the year of 2012. Based on the survey, it clearly shows the diabetics are one of the dangerous diseases because it leads to create several risk of early death. Due to the seriousness of the diabetic, it has been detected in early stage by creating expert system. During this process, the expert system has several issues such as accuracy of prediction due to the huge dimension of the diabetic feature that reduce the entire efficiency of the system. So, in this paper introduced the evolutionary correlated gravitational search algorithm (ECGS) for selecting the optimized features. The introduced method analyzes each diabetic feature according to the correlation and mutual information is selected with minimum computation time and cost. The selected features are processed by genetic optimized Hopfield neural network (GHNN) for predicting the diabetic related features effectively. Then the efficiency of the system is implemented using MATLAB tool that utilizes the Pima Indian Diabetic Dataset for analyzing the efficiency of introduced diabetic expert system. The efficiency of the system is evaluated in terms of using mean square error rate, F-measurer, accuracy, confusion matrix and ROC curve.

Hybrid swarm intelligent redundancy relevance (RR) with convolution trained compositional pattern neural network expert system for diagnosis of diabetes

Abstract: According to the World Health Organization (WHO) report, 438 million peoples are affected by diabetics in the upcoming year of 2030. Due to the seriousness of the diabetics’ disease, it has to be predicted in the earlier stage but the minimum symptoms of diabetic failure to predict in earlier stage. So, the automatic and earlier diabetic prediction system needs to be created for eliminating the serious factors in medical field. There are several earlier diabetic prediction system is created with the help of hybridized machine learning techniques but they are difficult to process the huge dimension of data as well as consume more time for predicting diabetic related features. For overcoming the above issues, in this paper introduces the swarm intelligent redundancy relevance (RR) along with convolution trained compositional pattern neural network for predicting the diabetic disease. Initially, the diabetic data has been collected from Pima Indian Diabetic dataset, the dimensionality of data is reduced by swarm intelligence RR techniques, the selected features are trained by layers of convolution networks that helps to speed up the diabetic prediction process. Finally, diabetic classification process is done by compositional pattern neural network and the efficiency is evaluated using MATLAB based experimental results.

Linear Discriminant Analysis Based Genetic Algorithm with Generalized Regression Neural Network – A Hybrid Expert System for Diagnosis of Diabetes

Abstract: Among the applications enabled by expert systems, disease diagnosis is a particularly important one. Nowadays, diabetes is found to be a complex health issue in human life. There has been a wide range of intelligent methods proposed for early detection of diabetes. The objective of this paper is to propose an expert system for better diagnosis of diabetes. The methodology of the proposed framework is classified as two stages: (a) Linear Discriminant Analysis (LDA) based genetic algorithm for feature selection, (b) Generalized Regression Neural Network (GRNN) for classification. The proposed a genetic algorithm with Linear Discriminant Analysis (LDA) based feature selection for not only reduce the computation time and cost of the disease diagnosis but also improved the accuracy of classification. The performance of the method is evaluated using the calculation of accuracy, confusion matrix and Receiver-Operating Characteristic (ROC). The proposed method is compared with other existing methods for evaluating the performance and accuracy. The LDA based Genetic Algorithm (GA) with GRNN produces the accuracy of 80.2017% with a ROC of 0.875.

Image Enhancement using ESRGAN for CNN based X-Ray Classification

Abstract: There is tremendous amount of computational power in artificial intelligence models like computing variety of complex mathematical calculations and recognizing objects. In the past six to seven years, the amount of computing power used by record-breaking AI models doubled frequently in the time span of months. An interesting way in which these models learn and progress is through deep learning. Deep learning is an intelligent machine’s way in which machines learn without being supervised by us and grants them the power to recognize speech, translate, and even make or take data-driven decisions. Machines consider this as a studying method, inspired by the architecture of the human brain and how we learn. An important deep learning method where we train the machines on information that is unlabeled is called unsupervised learning. A strong part of neural networks that are utilized for unsupervised learning is Generative Adversarial Networks. When it comes to applications on images quality improvement, Super Resolution GAN (SRGAN) have a key role to play in it. It was proposed by researchers at Twitter. The motive of this GAN is to super resolute the fine textures of the image by upscaling it. In order to enhance the images further, ESRGAN is used. As the name suggests, ESRGAN is an implementation of SRGAN and uses some added components of SRGAN.

Review and Analysis of Techniques to Mitigate Sub Synchronous Resonance using FACTS Devices

Abstract: Flexible AC Transmission System devices are reactive power compensation devices employed to suppress the sub synchronous resonance (SSR) in the systems. The main important issue in the transmission lines of power system is SSR and it occurs during the condition, when series compensator is employed to improve the reactive power. It occurs at sub synchronous frequencies due to Electrical instability occurs as the result of shaft failures. Different FACTS devices such as the Static Synchronous Reactor (SSR), Static Synchronous Series Compensator (SSSC), Static Synchronous Compensator, Thyristor Controlled Series Compensator (TCSC), and Unified Power Flow Controller (UPFC) are analysed and reviewed in terms of SSR mitigation to develop a new strategy to mitigate SSR in the system. The new Multilevel inverter-based control strategy will be designed and developed based on the review and analysis using MATLAB Simulink to mitigate the SSR.

Classification and diagnosis of diabetes

Abstract: 1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review

Abstract: Diabetes Mellitus (DM) is a condition induced by unregulated diabetes that may lead to multi-organ failure in patients Thanks to advances in machine learning and artificial intelligence, which enables the early detection and diagnosis of DM through an automated process which is more advantageous than a manual diagnosis Currently, many articles are published on automatic DM detection, diagnosis, and self-management via machine learning and artificial intelligence techniques This review delivers an analysis of the detection, diagnosis, and self-management techniques of DM from six different facets viz, datasets of DM, pre-processing methods, feature extraction methods, machine learning-based identification, classification, and diagnosis of DM, artificial intelligence-based intelligent DM assistant and performance measures It also discusses the conclusions of the previous study and the importance of the results of the study Also, three current research issues in the field of DM detection and diagnosis and self-management and personalization are listed After a thorough screening procedure, 107 main publications from the Scopus and PubMed repositories are chosen for this study This review provides a detailed overview of DM detection and self-management techniques which may prove valuable to the community of scientists employed in the area of automatic DM detection and self-management

Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus

Abstract: Diabetes mellitus (DM) is a combination of metabolic disorders characterized by elevated blood glucose levels over a prolonged duration. Undiagnosed DM can give rise to a host of associated complications like retinopathy, nephropathy and neuropathy and other vascular abnormalities. In this background, machine learning (ML) approaches can play an essential role in the early detection, diagnosis and therapeutic monitoring of the disease. Recently, several research works have been proposed to predict the onset of DM. To this end, we develop a stacking-based evolutionary ensemble learning system “NSGA-II-Stacking” for predicting the onset of Type-2 diabetes mellitus (T2DM) within five years. For this purpose, publicly accessible Pima Indian diabetes (PID) dataset is utilized. As a data pre-processing step, the missing values and outliers are identified and imputed with the median values. For base learner selection, a multi-objective optimization algorithm is utilized which simultaneously maximizes the classification accuracy and minimizes the ensemble complexity. As for model combination, k -nearest neighbor (K-NN) is employed as a meta-classifier that combines the predictions of the base learners. The comparative results demonstrate that the proposed NSGA-II-Stacking method significantly outperforms several individual ML approaches and conventional ensemble approaches. In terms of performance metrics, the proposed system achieves the highest accuracy of 83.8 %, sensitivity of 96.1 %, specificity of 79.9 %, f-measure of 88.5 % and area under ROC curve of 85.9 %.

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Abstract: The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent on its network structure. Thus, the output of ANNs has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of the logical rule in the ANN. The work presents an integrated representation of k-satisfiability (kSAT) in a mutation hopfield neural network (MHNN). Neuron states of the hopfield neural network converge to minimum energy, but the solution produced is confined to the limited number of solution spaces. The MHNN is incorporated with the global search capability of the estimation of distribution algorithms (EDAs), which typically explore various solution spaces. The main purpose is to estimate other possible neuron states that lead to global minimum energy through available output measurements. Furthermore, it is shown that the MHNN can retrieve various neuron states with the lowest minimum energy. Subsequent simulations performed on the MHNN reveal that the approach yields a result that surpasses the conventional hybrid HNN. Furthermore, this study provides a new paradigm in the field of neural networks by overcoming the overfitting issue.

Smart healthcare disease diagnosis and patient management: Innovation, improvement and skill development

Abstract: Data mining (DM) is an instrument of pattern detection and retrieval of knowledge from a large quantity of data. Many robust early detection services and other health-related technologies have developed from clinical and diagnostic evidence in both the DM and healthcare sectors. Artificial Intelligence (AI) is commonly used in the research and health care sectors. Classification or predictive analytics is a key part of AI in machine learning (ML). Present analyses of new predictive models founded on ML methods demonstrate promise in the area of scientific research. Healthcare professionals need accurate predictions of the outcomes of various illnesses that patients suffer from. In addition, timing is another significant aspect that affects clinical choices for precise predictions. In this regard, the authors have reviewed numerous publications in this area in terms of method, algorithms, and performance. This review paper summarized the documentation examined in accordance with approaches, styles, activities, and processes. The analyses and assessment techniques of the selected papers are discussed and an appraisal of the findings is presented to conclude the article. Present statistical models of healthcare remedies have been scientifically reviewed in this article. The uncertainty between statistical methods and ML has now been clarified. The study of related research reveals that the prediction of existing forecasting models differs even if the same dataset is used. Predictive models are also essential, and new approaches need to be improved.