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Hybrid Approach of Deep Convolution Neural Network and Transfer Learning for Tuberculosis Prediction

Hybrid Approach of Deep Convolution Neural Network and Trans...

Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Rinki Singh Jagrati Malviya Sabita Ram Bhawan Singh Deepak Garg Department of Computer Science and Engineering Satyug Darshan Institute of Engineering and Technology Faridabad Haryana Department of Computer Science and Engineering Satyug Darshan Institute of Engineering and Technology Faridabad Sdgi Global University Gaziyabad Bennett university Greater Noida

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Low and middle-income countries (LMICs) grapple with various difficulties in their campaign to curb the tuberculosis (TB) outbreak, including persistent socioeconomic health inequities, a lack of medical professionals in the field, and inadequate healthcare systems in underdeveloped areas. The rate of tuberculosis-related deaths is rising, and the causes of treatment failure remain unclear. In the healthcare industry, predictive algorithms also known as machine learning and information analysis approaches have shown to be helpful in identifying correlations between various characteristics that may influence a disease's course. The procedure of diagnosing tuberculosis has accelerated due to the advancement of computer technology. Machine learning and data analytics approaches have proven useful in the healthcare sector for determining connections between different features that could impact the course of a disease. Our work proposes a Hybrid strategy to handle unbalanced, less-category X-ray images, utilizing deep Convolutional Neural Networks (CNNs) in conjunction with transfer learning. We examine the efficacy and efficiency of random sampling method for training neural networks and discover that it has a remarkable influence on the classification of medical images. Our techniques and findings point to a possible route for quicker and more precise TB diagnosis in LMIC medical facilities.

关键词： Training Accuracy Tuberculosis Convolution Transfer learning Neural networks Medical services Convolutional neural networks Medical diagnostic imaging X-ray imaging

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Evaluating the Severity of Parkinson's Disease Using Machine Learning Classifiers

Evaluating the Severity of Parkinson's Disease Using Machine...

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Rinki Singh Deepti Goyal Shikha Arora Jagrati Malviya Deepak Garg Department of Computer Science and Engineering Satyug Darshan Institute of Engineering and Technology Faridabad Haryana Bennett university Greater Noida

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

A neurodegenerative disorder, Parkinson's disease worsens with time and significantly lowers the quality of life for people who have it. It largely affects how humans use their motor systems. Parkinson's disease symptoms encompass trembling stiffness, slowness of movement, and trouble walking. It also causes changes in thinking and behavior. In contemporary times, an extensive study in this area have yielded promising outcomes, surpassing conventional approaches., machine learning (ML) techniques offer good solutions in the medical industry by processing and analyzing data with high accuracy and a shorter processing time. This paper employs voice samples, a very accurate automated method utilizing machine learning was developed to identify Parkinson's illness. The author suggested leveraging a variety of machine learning classifiers Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), K-nearest neighbor (KNN) and Support Vector Machines (SVM) to balance a Parkinson's disease dataset combining an enhanced hybrid SMOTE methodology. The study highlighted the importance of dataset balance in medical classification. Different classifiers with and without SMOTE were assessed using performance metrics. With unbalanced data, the suggested method classified Parkinson's disease with 97% accuracy. The goal of this non-invasive method is to more accurately diagnose patients and identify their ailment at an affordable stage.

关键词： Support vector machines Radio frequency Accuracy Machine learning algorithms Parkinson's disease Machine learning Nearest neighbor methods Motors Medical diagnostic imaging Random forests

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Specific Heat of Nanofluids—An Experimental Investigation 46th

Specific Heat of Nanofluids—An Experimental Investigation

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46th National Conference on Fluid Mechanics and Fluid Power, FMFP 2019

作者： Anand, Tushar Mallick, Soumya Suddha Satyug Darshan Institute of Engineering and Technology Faridabad121002 India Mechanical Engineering Department Thapar University Patiala147001 India

ISBN: (纸本)9789811606977

This paper presents an experimental investigation of the specific heat of nanofluids. While a lot of research has been done on nanofluids, it is still inconclusive, and there is no agreement on the findings between various research groups. Also, most of the research has been done in areas of improvement of thermal conductivity but not of specific heat, which is equally important since it influences the heat-carrying capacity of a system. In the present work, the specific heat of different nanoparticles—silver, SWCNT, zinc oxide, aluminum oxide—has been investigated experimentally at different temperatures and particle sizes. Deficiencies of existing models are brought out, and a new correlation is presented. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heat transfer Nanofluid Nanoparticles Particle size distribution Temperature

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Performance Analysis of Liver Disease Prediction Using Supervised Machine Learning Classification Models 1

Performance Analysis of Liver Disease Prediction Using Super...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Ramveer Sharma, Rahul Chaudhary, Shalbha Yadav, Deepika Jain, Jaishree Kumar, Pawan Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Raj Kumar Garg Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India R.D. Engineering College Department of Management Studies UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Teerthanker Mahaveer University Department of Computer Science & Engineering UP Moradabad India Satyug Darshan College of Engineering Department of Computer Science Faridabad India

ISBN: (纸本)9798350367720

Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, detoxification, nutrition and glycogen storage, carbohydrate metabolism, vitamin and mineral storage etc. It has been found from various survey reports that across the world, 58 million people are suffering from liver disorders and nearly 01 million liver disorder patients are present in India. The growth rate of liver disorder patients is increasing rapidly worldwide due to a sharp rise in obesity, eating more junk foods, more alcohol consumption and unhealthy lifestyles. People who have liver disorders problem need medical treatment at the correct time and early disease prediction helps to reduce the number of deaths. The research focuses on designing an intelligent system that helps for prediction liver disorder by using various classification models of supervised machine learning and it also helps to prevent becoming India a global hub of liver diseases by 2030. The research emphasis on comparative performance analysis of different classification algorithms and ensemble methods. It also helps to select best accurate model for prediction of the liver diseases. © 2024 IEEE.

关键词： Self-supervised learning

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Correction to: Analysing the reliability factors of a robot utilized within an FMC comprising two machines and one robot (International Journal on Interactive Design and Manufacturing (IJIDeM), (2025), 10.1007/s12008-025-02237-2)

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International Journal on Interactive Design and Manufacturing 2025年第6期19卷 4533-4533页

作者： Tiwari, Anant Kumar, Shailendra Sharma, Rajiv Kumar Mehdi, Husain Saroha, Manju Mechanical Engineering Department National Institute of Technology HP Hamirpur India Mechanical Engineering Department Satyug Darshan Institute of Engineering and Technology Haryana Faridabad India Mechanical Engineering Department Meerut Institute of Engineering and Technology UP Meerut India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Faculty of Engineering and Technology Bhagat Phool Singh Mahilla Vishwavidyalaya Khanpur Kalan HR Sonipat India

In this article, the affiliation details for author’s Shailendra Kumar and Husain Mehdi were incorrectly given as Shailendra Kumar22Mechanical engineering Department, satyug darshan institute of engineering and technology, Meerut, UP, India Husain Mehdi2,32Mechanical engineering Department, satyug darshan institute of engineering and technology, Meerut, UP, India 3Centre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab 140417, India But should have been Shailendra Kumar22Mechanical engineering Department, satyug darshan institute of engineering & technology Faridabad, Haryana, India Husain Mehdi3,43Mechanical engineering Department, Meerut institute of engineering and technology, Meerut, UP, India 4Centre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab 140417, India The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025.

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Efficient power management unit for IIOT edge computing applications

Research Square

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Research Square 2021年

作者： Mukul, Mukul Sharma Shiv, Shiv Kumar Rashmi, Rashmi Chawla Satyug Darshan Institute of Engineering and Technology India J.C. Bose University of Science & Technology YMCA India

Applications of Industrial Internet of Things (IIoT) are comprehended in mass domains, and this reflects the prominence of IoT. However, the main con of IoT applications is that it requires continuous power to be drawn by various components to operate all the time. Energy harvesting provides a potential solution to this problem in many applications;however, this continual energy usage remains main IoT con. The energy generated by ambient sources is inadequate to run system application, and a need for better Power Management Unit (PMU) persists. This research paper proposes a PMU framework for energy efficiency in edge computing application with three layers of IoT architecture. Design-build on this deployment, constitutes sleep and awake scheduling, with calculative idle intervals. The transitions of states exercise an energy-efficient solution for the entire system. The design is tested on ZYBO Zynq-7000 (XC7Z010-1CLG400C) ARM/FPGA SoC board for development and evaluation. Simulation results demonstrate the task scheduling of our proposed architecture and energy consumption details. In brief, the proposed design proves to be a beneficial option, in particular, surpassing kin designs for industrial automation. © 2021, CC BY.

关键词： Internet of things

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Performance Analysis of Liver Disease Prediction Using Supervised Machine Learning Classification Models

Performance Analysis of Liver Disease Prediction Using Super...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Ramveer Singh Rahul Sharma Shalbha Chaudhary Deepika Yadav Jaishree Jain Pawan Kumar Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Science & Engineering Raj Kumar Garg Institute of Technology Ghaziabad UP India Department of Management Studies R.D. Engineering College Ghaziabad UP India Department of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Computer Science & Engineering Teerthanker Mahaveer University Moradabad UP India Department of Computer Science Satyug Darshan College of Engineering Faridabad India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

关键词： Surveys Analytical models Obesity Liver diseases Computational modeling Predictive models Performance analysis Minerals Metabolism Diseases

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Using hierarchies in online social networks to determine link prediction 1

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International Conference on Soft Computing and Signal Processing, ICSCSP 2018

作者： Ahuja, Ravinder Singhal, Vipul Banga, Alisha Jaypee Institute of Information Technology Noida India Satyug Darshan Institute of Engineering and Technology FaridabadHaryana India

ISBN: (数字)9789811333934

ISBN: (纸本)9789811333927

Social hierarchy is one of the most basic concepts in sociology and analysis of networks. In today’s world, online social networks are very popular, and thus, there is a need to analyze social hierarchies in the form of complex networks. Social hierarchies can help in improving link prediction, optimize page rank, provide efficient query results, etc. These networks can provide us with important information about social interactions and how to efficiently use it. Social network will be modeled as directed graph, and then, social hierarchies are explored by converting directed graph to directed acyclic graph. By converting to directed acyclic graph (DAG), we can partition the graph into different levels which will divide the network into multiple hierarchies. The topmost level signifies highest social ranking, while bottom ones represent the least ranking. Most algorithms widely used calculate DAG in O(n3), which is fine for small graphs, but when dealing with large social networks, it is not practically feasible. Thus here, we devise a new method to compute DAG. In this method, we will remove all the cycles from graph G′. In addition to this, we use a two-phase algorithm—compute and refine. The result is used to improve link prediction. The algorithm used two orders faster than the basic algorithm. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Social networking (online)

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Mental stress detection in university students using machine learning algorithms 1

Mental stress detection in university students using machine...

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1st International Conference on Pervasive Computing Advances and Applications, PerCAA 2019

作者： Ahuja, Ravinder Banga, Alisha Jaypee Institute of Information Technology Noida201304 India Satyug Darshan Institute of Engineering and Technology Faridabad121002 India

Mental stress is a major issue nowadays, especially among youngsters. The age that was considered once most carefree is now under a large amount of stress. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. In this paper, we are calculating the mental stress of students one week before the exam and during the usage of the internet. Our objective is to analyze stress in the college students at different points in his life. The effect that exam pressure or recruitments stress has on the student which often goes unnoticed. We will perform an analysis on how these factors affect the mind of a student and will also correlate this stress with the time spent on the internet. The dataset was taken from Jaypee institute of Information technology and it consisted of 206 student’s data. Four classification algorithms Linear Regression, Naïve Bayes, Random Forest, and SVM is applied and sensitivity, specificity, and accuracy are used as a performance parameter. The accuracy and performance of data are further enhanced by applying 10-Fold Cross-Validation. The highest accuracy recorded was by Support Vector Machine (85.71%). © 2019 The Authors. Published by Elsevier Ltd.

关键词： Students

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Mental Stress Detection in University Students using Machine Learning Algorithms

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Procedia Computer Science 2019年 152卷 349-353页

作者： Ravinder Ahuja Alisha Banga Jaypee Institute of Information Technology Noida 201304 India Satyug Darshan Institute of Engineering and Technology Faridabad-121002 India

关键词： Classification algorithms Mental Stress Perceived Stress Scale Sensitivity and Specificity

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