J. P. Verma

Nirma University

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Priyam Verma

Centrale Marseille

Sanjay Garg

Jaypee University of Engineering and Technology

Sudeep Tanwar

Nirma University

Abdel‐Salam G. Abdel‐Salam

Qatar University

S. P. Raychaudhuri

Sacramento VA Medical Center

Rachna Jain

Ambedkar University Delhi

Swati Jain

Nirma University

S. G. Borkar

Raisoni Group of Institutions

R. P. Singh

Jawaharlal Nehru Krishi Vishwa Vidyalaya

Madhuri Bhavsar

Nirma University

Cooperative Institutions

Nirma University

Indian Agricultural Research Institute

Banaras Hindu University

Chaudhary Charan Singh Haryana Agricultural University

University of Delhi

Chandigarh University

Institute of Management Technology

Amity University

Galgotias University

Govind Ballabh Pant University of Agriculture and Technology

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) (2022)

Jay S. Raval J. P. Verma Sardar N. M. Islam Rachna Jain Narina Thakur

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm.

Feature (linguistics)

10.1109/acait56212.2022.10137923

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Citations (3)

General Awareness about Epilepsy in a Cohort of Female and Male Students: A Statistical Comparison

International Journal of Epilepsy (2018)

Harinder Jaseja J. P. Verma Man Mohan Mehndiratta A K Govila Jyoti Tiwari

Abstract Background and Objective A survey on general awareness toward epilepsy was conducted on a cohort of female and male medical and paramedical students that represent a model educated section of the society. It is, therefore, imperative that this section of the society acquires correct knowledge about epilepsy. Materials and Methods The subjects were undergraduate students from medical and paramedical institutes in Gwalior region who were gathered to celebrate the National Epilepsy Day on November 17, 2016. A self-administered questionnaire of 50 questions on general awareness about epilepsy was completed at the very beginning before the celebrations. The results were subjected to various statistical analyses. Results A total of 251 respondents (149 females and 102 males) in the age group of 17 to 31 years participated in the survey. All the respondents had heard about epilepsy. One-hundred fifty-eight participants were from medical and dental institutes, whereas 93 participants were from paramedical institutes. In general, the female participants exhibited significantly greater general knowledge about epilepsy in comparison to the male respondents. Conclusion The results indicate a favorable degree of awareness about epilepsy. A definite knowledge gap was observed between the medical and paramedical students. However, the awareness still needs to be enhanced even among students pursuing medical and paramedical courses through various programs apart from their academic curriculum, as a certain degree of misconceptions and superstitious beliefs associated with epilepsy continues to prevail even in this educated section of the society.

10.1055/s-0038-1636946

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Citations (1)

A New Approach for Processing Raster Geospatial Big Data in Distributed Environment

Lecture notes in networks and systems (2022)

Anupama J. P. Verma Sapan H. Mankad Sanjay Garg Pankaj Bodani

SPARK (programming language)

Raster data

Spatial Query

10.1007/978-981-19-1018-0_8

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Application of Discriminant Function Analysis

J. P. Verma

Discriminant function analysis, also known as discriminant analysis (DA), is used to classify a subject into one of the two groups on the basis of some independent traits. This chapter discusses the details of DA and the procedure of using SPSS in getting the solution. In discriminant analysis, a discriminant function is used to classify an individual or cases into two categories. If the function is effective for a set of data, the percentage of correct classification of cases in the classification table increases. The terminologies involved in it are explained. The chapter then discusses the steps involved in using SPSS for DA teaches how to to apply discriminant analysis in explorative studies. The procedure of developing discriminant function, the power of discriminant model in a study, and writing the results of DA in standard format are also explained.

Discriminant function analysis

Optimal discriminant analysis

Multiple discriminant analysis

10.1002/9781119206767.ch10

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Citations (1)

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Ravindrakumar Purohit J. P. Verma Rachna Jain Madhuri Bhavsar

Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However, creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semi-supervised task where the algorithm learns to recognize abnormality while being taught the normal class.

Novelty Detection

10.1109/incacct57535.2023.10141778

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Citations (2)

Studies on the nutrition of Xanthomonas malvacearum and its relationship to the free amino acids, organic acids and sugars of resistant and susceptible cotton cultivars.

Acta Phytopathologica Academiae Scientiarum Hungaricae (1974)

J. P. Verma Ravi P. Singh

Xanthomonas

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A discriminant analysis of team cohesiveness among high-performance and low-performance elite Indian volleyball players

Studies in physical Culture &Tourism (2012)

J. P. Verma Pintu Modak J. P. Bhukar Sanjeev Kumar

Aim of Study. This study investigates group cohesion among elite Indian male volleyball players. The study had two purposes; firstly, to assess the role of different parameters of team cohesiveness (Group-Task and Group-Social) among the high and low performance volleyball players; and secondly, to develop a discriminant model for classifying volleyball players into high or low performance groups on the basis of group cohesion parameters. Material and Methods. Two hundred eight male elite volleyball players from India were selected for the study. The Group Environment Questionnaire was administered to the subjects prior to the tour- nament and the data were obtained on four parameters: Group Integration-Task (GI-T), Group Integra- tion-Social (GI-S), Individual Attraction to the Group-Task (IAG-T) and Individual Attraction to the Group-Social (IAG-S). On the basis of the performance of the teams forty eight samples were retained for the final study in such a manner that 24 volleyball players were from the first five teams and twen- ty four subjects were from the last five teams. The data was analyzed using the SPSS ver. 17.0. Results. The mean values of all the four group cohesion parameters were significantly higher among high performance volleyball players in comparison with low performance players. Further, a discrimi- nant model was prepared to classify volleyball players into high and low performance groups on the basis of cohesion variables. A disrciminant function Z was developed (Z = -5.88 +0.11 (GI-T) +0.12 (GI- S) -0.05 (IAG-T) +0.15 (IAG-S)). The attained discriminant model classified correctly 75% of the cases in the sample. Conclusions. The variable Individual Attraction to the Group-Social (IAG-S) had the highest discrimi- nating power among the four group cohesion parameters. The discriminant function Z developed in the study classified the male volleyball players into the low performance category, if its value was positive and into the high performance category, if negative.

Group cohesiveness

Cohesion (chemistry)

Elite

Discriminant function analysis

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Citations (4)

Index

J. P. Verma

10.1002/9781119206767.index

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A deep learning based approach for trajectory estimation using geographically clustered data

SN Applied Sciences (2021)

Aditya Shrivastava J. P. Verma Swati Jain Sanjay Garg

Abstract This study presents a novel approach to predict a complete source to destination trajectory of a vehicle using a partial trajectory query. The proposed architecture is scalable to extremely large-scale data with respect to the dense road network. A deep learning model Long Short Term Memory (LSTM) has been used for analyzing the temporal data and predicting the complete trajectory. To handle a large amount of data, clustering of similar trajectory data is used that helps in reducing the search space. The clusters based on geographical locations and temporal values are used for training different LSTM models. The proposed approach is compared with the other published work on the parameters as Average distance error and one step prediction accuracy The one-step prediction accuracy is as good as 81% and Distance error are .33 Km. Our proposed approach termed Clustered LSTM is outperforming in both the parameters when compared with other reported results. The proposed solution is a clustering-based predictive model that effectively contributes to accurately handle the large scale data. The outcome of this study leads to improvise the navigation systems, route prediction, traffic management, and location-based recommendation systems.

10.1007/s42452-021-04556-x

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Citations (12)

Studies on the host preference of Heliothis obsoleta F. (Lepidoptera : Noctuidae).

Madras Agricultural Journal (1970)

Sandeep K. Sharma J. P. Verma Yamini Mathur R. C. Batra

Heliothis

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