Saurabh Kr. Srivastava

Bio: Saurabh Kr. Srivastava is an academic researcher from ABES Engineering College. The author has contributed to research in topics: Support vector machine & Kernel method. The author has an hindex of 4, co-authored 14 publications receiving 64 citations. Previous affiliations of Saurabh Kr. Srivastava include Galgotias University & Jaypee Institute of Information Technology.

Machine Learning: A Review on Binary Classification

Abstract: In the field of information extraction and retrieval, binary classification is the process of classifying given document/account on the basis of predefined classes. Sockpuppet detection is based on binary, in which given accounts are detected either sockpuppet or non-sockpuppet. Sockpuppets has become significant issues, in which one can have fake identity for some specific purpose or malicious use. Text categorization is also performed with binary classification. This research synthesizes binary classification in which various approaches for binary classification are discussed.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Abstract: Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Geometric total plaque area is an equally powerful phenotype compared with carotid intima–media thickness for stroke risk assessment: A deep learning approach

Abstract: Currently, carotid intima-media thickness (cIMT) and geometric total plaque area (gTPA) are computed manually and thus are tedious and prone to interobserver and intraobserver variabilities. This s...

Role of Kernel Parameters in Performance Evaluation of SVM

Abstract: Identifying performance of classifier is a challenging task. SVM plays an important role in classification. Here different kernel parameters are used as a tuning parameter to improve the classification accuracy. There are mainly four different types of kernels (Linear, Polynomial, RBF, and Sigmoid) that are popular in SVM classifier. The paper presents SVM classification results with above mentioned kernels on two different datasets (Diabetic Retinopathy dataset and Lung Cancer dataset). To evaluate the performance of the classifier we have used True positive rate, False Positive rate, Precision, Recall, F-measure and accuracy as performance measures of SVM. Finally we evaluated that SVM with linear kernel performs best among all.

A new backpropagation neural network classification model for prediction of incidence of malaria.

Abstract: Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classified as low and high risk regions using a backpropagation neural network (BPNN). However, this approach yielded low performance and stability necessitating development of a more robust model. We hypothesized that by spiking neuron models in simulating the characteristics of a neuron, which when embedded with a BPNN, could improve the performance for the assessment of malaria prone regions. To this end, we created an inter-spike interval (ISI)-based BPNN (ISI-BPNN) architecture that uses a single-pass spiking learning strategy and has a parallel structure that is useful for non-linear regression tasks. Existing malaria dataset comprised of 1296 records, that met these attributes, were used. ISI-BPNN showed superior performance, and a high accuracy. The benchmarking results showed reliability and stability and an improvement of 11.9% against a multilayer perceptron and 9.19% against integrate-and-fire neuron models. The ISI-BPNN model is well suited for deciphering the risk of acquiring malaria as well as other diseases in prone regions of the world.

Implementation of Machine Learning Model to Predict Heart Failure Disease

Abstract: In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon’s point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Abstract: This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis

Abstract: Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.