ANKUR JAIN

@iftmuniversity.ac.in

Assistant Professor, Department of Computer Science & Engineering
IFTM University

ANKUR JAIN


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https://researchid.co/ankur1101

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science
6

Scopus Publications

551

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Enhancing e-learning with brain-computer interface in education
    Ankur Jain, Prithu Sarkar, Abhishika Sharma, Neelu Jain, Amit Verma, Pankaj Dadheech
    Concepts and Applications of Brain Computer Interfaces, 2025
    The field of brain-computer interface (BCI) technology is developing quickly, and its fast growth could transform e-learning by bringing more engaging and intuitive educational experiences to the platform. To enhance educational outcomes, the integration of BCI systems into virtual learning environments is examined in this chapter. Brain-computer interfaces (BCIs) can monitor engagement levels, adjust learning materials dynamically to maximize comprehension and retention, and customize educational content to each individual's cognitive state by using real-time neural data. The study includes a thorough analysis of the impact that BCI technology has on students' academic performance, motivation, and engagement in a range of learning situations. The goal of this research is to provide effective methods for integrating BCI into e-learning platforms, resolve any issues, and assess the general effectiveness of this strategy. User input and experimental trials will be used to achieve this. The results suggest that e-learning with BCI enhancements.
  • Exploring the Effectiveness of Machine Learning Algorithms for Tomato Leaf Disease Classification Using Multiple Image
    Ashish Nagila, Abhishek Kumar Mishra, Neelu Trivedi, Ritu Nagila, Kanishk Trivedi, et al.
    2025 4th Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5 0 Otcon 2025, 2025
    Tomato cultivation is critical to global food security; however, disease outbreaks can severely impact yield and quality. This study investigated the efficacy of different machine learning algorithms in categorizing tomato leaf diseases using diverse image sources. The algorithms examined were Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Random Forest (RF). Our results demonstrated that the CNN model surpassed the other two algorithms, attaining the best level of accuracy in categorizing tomato leaf illnesses. To improve the accuracy of categorization, we used a soft-voting classifier based on the results of various algorithms, creating a hybrid model. The soft voting classifier exhibited exceptional enhancement in precision and resilience, surpassing the performance of the separate models to a large degree, with 97.13 accuracy. These findings indicate that utilizing the advantages of several machine-learning algorithms can result in better performance in classifying plant diseases, presenting a promising avenue for future research in precision agriculture. This approach offers a promising solution for the accurate and efficient diagnosis of tomato crops, facilitating timely intervention and improving yield management.
  • Optimising the Allocation of Resources in Cloud Computing Using Machine Learning
    Zameer Ahmed Adhoni, Syeda Imrana Fatima, Rajdeep Singh, S. Shalini, Ankur Jain, Shaik Rehana Banu, Damian Dziembek
    Recent Trends in Engineering and Science for Resource Optimization and Sustainable Development, 2025
    In the digital age, optimizing resource allocation in cloud computing is of the utmost importance. In order to improve resource allocation in cloud systems, this study investigates the integration of machine learning approaches. With its 50capacity for adaptation and learning from data, machine learning presents exciting possibilities for addressing the dynamic and intricate nature of cloud resource management. In order to increase the effectiveness and cost-effectiveness of resource allocation in cloud computing, this paper explores the advantages, difficulties, and practical applications of employing machine learning. The challenge of resource distribution in auctions is difficult for cloud computing. But because it is NP-hard, the resource allocation problem cannot be addressed in polynomial time. In this investigation, we define, formulate, and assess the multi-dimensional cloud resource allocation problem. Additionally, we provide two methods that employ both logistic and linear regressions to forecast resource allocation. The prediction model may ensure that the resource utilisation and allocation accuracy in the practical solution are startlingly close to those of the ideal allocation solution by learning a small-scale training set. The outcomes of the experiments demonstrate that the suggested approach has a positive impact on resource distribution in cloud computing.
  • Machine Learning for Employee Turnover Prediction
    Geetha Manoharan, Viyyapu Pushpa, Arati V. Deshpande, Melanie Lourens, M. K. Sharma, Ankur Jain
    Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024
    The main problem is that employees leaving the company has a big affect on its costs, efficiency, and effectiveness. By accurately estimating employee turnover, businesses can take proactive steps to keep good employees and lower the costs of hiring and teaching new ones. This research looks at how machine learning techniques can be used to predict staff turnover by looking at things like employee demographics, job satisfaction, success indicators, and engagement levels. Training and testing of the models were done with a dataset that included both old and new data from a big organisation. The results of this research are very helpful for human resource managers who are trying to fix problems with disengaged employees and make policies that keep employees. The research say that machine learning systems can accurately predict how many employees will leave a company.
  • An analysis of medical images using deep learning
    Ankur Jain, Muddada Murali Krishna, Sai Nitisha Tadiboina, Kapil Joshi, Yerrolla Chanti, K. Sai Krishna
    2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2023, 2023
    The use of AI models in health care system and the life sciences is expanding. In this paper, we will take a look at the present state of the art and address the unanswered issues regarding the development of Ai technologies as clinical decision support tools. A review, which included a critical examination of papers published from 1990 and 2022, led the study's most challenging aspects.First, we demonstrate the structural distinction between ML and DL methods. Methods for training, validating, and testing ML models, as well as feature extraction, are described. In DL, models are provided as multi-layered artificial neural networks for direct image analysis. Data management includes technical stages like as image labelling, picture annotation, data standardization, and federated learning. After that, we divide the following into their own subsections: sample size computation, including frequent trials in AI methods; data augmentation strategies for coping with limited or unequal data; and the understandability of AI models. Finally, the advantages and disadvantages of ML and DL in introducing AI applications to diagnostic imaging are compared and contrasted. Biomedical and healthcare systems rank high on the list of important topics for AI applications, with medical imaging ranking as one of the most relevant and promising fields in which to apply such technology. Gaining insight into the specific difficulties associated with developing and deploying such systems in healthcare situations is helpful.
  • Detailed Investigation of Influence of Machine learning (ML) and Big Data on Digital Transformation in Marketing
    Ankur Jain, K K Ramachandran, Shikha Sharma, Trishu Sharma, Prakash Pareek, Bhasker Pant
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
    The study explores with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Computer vision and computer vision have improved a wide range of industries, including medical diagnoses, data display and procedures, science and research, and so. Just as preparing for a sport may be risky for individuals who are prone to injury, learning from contaminated or erroneous data can be costly. As described in the article Approaching Data Science, incorrectly trained algorithms result in expenses for a corporation rather than savings. Because incorrectly labeled, missing, or irrelevant data might impair the accuracy of any algorithm, organizations must be able to vouch for the quality and integrity of any data sets, along with their sources.

RECENT SCHOLAR PUBLICATIONS

  • Detection of Pneumonia Utilising Deep learning-based Feature Extraction
    A Jain, D Bharadwaj
    Vascular and Endovascular Review 8 (12s), 34-42 , 2025
    2025
  • Exploring the Effectiveness of Machine Learning Algorithms for Tomato Leaf Disease Classification Using Multiple Image
    A Nagila, AK Mishra, N Trivedi, R Nagila, K Trivedi, A Jain
    2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025
    2025
    Citations: 1
  • Machine Learning for Employee Turnover Prediction
    G Manoharan, V Pushpa, AV Deshpande, M Lourens, MK Sharma, A Jain
    2024 International Conference on Innovative Computing, Intelligent … , 2025
    2025
    Citations: 3
  • Optimising the Allocation of Resources in Cloud Computing Using Machine Learning
    ZA Adhoni, SI Fatima, R Singh, S Shalini, A Jain, SR Banu, D Dziembek
    Recent Trends In Engineering and Science for Resource Optimization and … , 2025
    2025
  • Enhancing E-Learning With Brain-Computer Interface in Education
    A Jain, P Sarkar, A Sharma, N Jain, A Verma, P Dadheech
    Concepts and Applications of Brain-Computer Interfaces, 461-474 , 2025
    2025
  • Conceptual and Comparative Analysis of Deep Neural Network (DNN) Models and its Application in Image Recognition
    A Jain, D Bharadwaj
    Journal of Computational Analysis and Applications (1572-9206) 34 (8), 2712-2723 , 2024
    2024
  • Improving service management using machine learning
    G Manoharan, KJ Velmurugan, AS Chandra, M Ravichand, A Jain
    Recent Technological Advances in Engineering and Management, 198-202 , 2024
    2024
    Citations: 3
  • USING LEARNING ABILITIES OF COMPUTATION IN COUNTING
    T Jain, A Jain, R Saxena
    Jñānābha 54 (1), 41-48 , 2024
    2024
  • Nurturing the Fields: A Guide to IoT in Agriculture
    DDB Harpreet Singh Chawla, Ankur Jain
    https://www.store.bookrivers.com/product/nurturing-the-fields-a-guide-to-iot … , 2023
    2023
  • Data Analytics and Digital Transformation Demystified: A Comprehensive Guide for Beginners
    AJ Dr. Deepankar Bharadwaj, Harpreet Singh Chawla
    https://www.store.bookrivers.com/product/data-analytics-and-digital … , 2023
    2023
  • Empowering Human Health: an Introduction to Deep Learning for Healthcare
    A Jain, DD Bharadwaj, HS Chawla
    https://www.store.bookrivers.com/product/empowering-human-health-an … , 2023
    2023
  • An analysis of medical images using deep learning
    A Jain, MM Krishna, SN Tadiboina, K Joshi, Y Chanti, KS Krishna
    2023 3rd International Conference on Advance Computing and Innovative … , 2023
    2023
    Citations: 11
  • DEEP LEARNING APPROACH FOR STRENGTHEN DETECTION OF CORONAVIRUS DISEASE
    AN ANKUR JAIN, BHARTI JAIN, DR. DEEPANKAR BHARADWAJ, HARPREET SINGH CHAWLA ...
    IN Patent App. 202,211,047,325 , 2022
    2022
  • A SYSTEM FOR INFORMATION MANAGEMENT IN COMPUTERIZED INJECTORS
    A JAIN, DRD BHARADWAJ, B JAIN, RM KANASE, DRD SUDHA, ...
    IN Patent App. 202,211,010,459 , 2022
    2022
  • DIFFERENT COLOR DETECTION USING AI & PYTHON
    MRMK MR. SANJEEV BHARDWAJ, MR. ASHISH NAGILA, PROF. VAIBHAV TRIVEDI, PROF ...
    IN Patent App. 202,211,028,727 , 2022
    2022
  • SOCIAL DISTANCE AND MONITORING USING IOT AND PYTHON
    MRA NAGILA, MRS BHARDWAJ, PV TRIVEDI, PN TRIVEDI, MRA JAIN
    2022
  • DEEP LEARNING BASED DETECTION OF SECURITY ATTACKS USING IN WIRELESS SENSOR NETWORK
    MRA JAIN, MRHS CHAWLA, PV TRIVEDI, PN TRIVEDI, ...
    IN Patent App. 202,211,028,744 , 2022
    2022
  • ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS BASED SMART PARKING SYSTEM IN SMART CITY
    AJ ASHISH NAGILA, SANJEEV BHARDWAJ, PROF. VAIBHAV TRIVEDI
    IN Patent App. 202,211,025,486 , 2022
    2022
  • Detailed investigation of influence of machine learning (ML) and big data on digital transformation in marketing
    A Jain, KK Ramachandran, S Sharma, T Sharma, P Pareek, B Pant
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 22
  • PLANT LEAF DISEASE DETECTION USING MACHINE LEARNING
    A NAGILA, S BHARDWAJ, PV TRIVEDI, A JAIN
    IN Patent App. 202,211,021,962 , 2022
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • Taking the edge off with espresso: Scale, reliability and programmability for global internet peering
    KK Yap, M Motiwala, J Rahe, S Padgett, M Holliman, G Baldus, M Hines, ...
    Proceedings of the Conference of the ACM Special Interest Group on Data … , 2017
    2017
    Citations: 455
  • DDoS attack algorithm using ICMP flood
    N Gupta, A Jain, P Saini, V Gupta
    2016 3rd International Conference on Computing for Sustainable Global … , 2016
    2016
    Citations: 42
  • Detailed investigation of influence of machine learning (ML) and big data on digital transformation in marketing
    A Jain, KK Ramachandran, S Sharma, T Sharma, P Pareek, B Pant
    2022 2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 22
  • Internet distance prediction using node-pair geography
    A Jain, J Pasquale
    2012 IEEE 11th International Symposium on Network Computing and Applications … , 2012
    2012
    Citations: 13
  • An analysis of medical images using deep learning
    A Jain, MM Krishna, SN Tadiboina, K Joshi, Y Chanti, KS Krishna
    2023 3rd International Conference on Advance Computing and Innovative … , 2023
    2023
    Citations: 11
  • Machine Learning for Employee Turnover Prediction
    G Manoharan, V Pushpa, AV Deshpande, M Lourens, MK Sharma, A Jain
    2024 International Conference on Innovative Computing, Intelligent … , 2025
    2025
    Citations: 3
  • Improving service management using machine learning
    G Manoharan, KJ Velmurugan, AS Chandra, M Ravichand, A Jain
    Recent Technological Advances in Engineering and Management, 198-202 , 2024
    2024
    Citations: 3
  • Exploring the Effectiveness of Machine Learning Algorithms for Tomato Leaf Disease Classification Using Multiple Image
    A Nagila, AK Mishra, N Trivedi, R Nagila, K Trivedi, A Jain
    2025 4th OPJU International Technology Conference (OTCON) on Smart Computing … , 2025
    2025
    Citations: 1
  • Improving The Quality of Service in Mobile Ad-hoc Network Using ant Colony Optimization
    A Jain, R Choudhary
    International Journal 4 (6) , 2014
    2014
    Citations: 1
  • Detection of Pneumonia Utilising Deep learning-based Feature Extraction
    A Jain, D Bharadwaj
    Vascular and Endovascular Review 8 (12s), 34-42 , 2025
    2025
  • Optimising the Allocation of Resources in Cloud Computing Using Machine Learning
    ZA Adhoni, SI Fatima, R Singh, S Shalini, A Jain, SR Banu, D Dziembek
    Recent Trends In Engineering and Science for Resource Optimization and … , 2025
    2025
  • Enhancing E-Learning With Brain-Computer Interface in Education
    A Jain, P Sarkar, A Sharma, N Jain, A Verma, P Dadheech
    Concepts and Applications of Brain-Computer Interfaces, 461-474 , 2025
    2025
  • Conceptual and Comparative Analysis of Deep Neural Network (DNN) Models and its Application in Image Recognition
    A Jain, D Bharadwaj
    Journal of Computational Analysis and Applications (1572-9206) 34 (8), 2712-2723 , 2024
    2024
  • USING LEARNING ABILITIES OF COMPUTATION IN COUNTING
    T Jain, A Jain, R Saxena
    Jñānābha 54 (1), 41-48 , 2024
    2024
  • Nurturing the Fields: A Guide to IoT in Agriculture
    DDB Harpreet Singh Chawla, Ankur Jain
    https://www.store.bookrivers.com/product/nurturing-the-fields-a-guide-to-iot … , 2023
    2023
  • Data Analytics and Digital Transformation Demystified: A Comprehensive Guide for Beginners
    AJ Dr. Deepankar Bharadwaj, Harpreet Singh Chawla
    https://www.store.bookrivers.com/product/data-analytics-and-digital … , 2023
    2023
  • Empowering Human Health: an Introduction to Deep Learning for Healthcare
    A Jain, DD Bharadwaj, HS Chawla
    https://www.store.bookrivers.com/product/empowering-human-health-an … , 2023
    2023
  • DEEP LEARNING APPROACH FOR STRENGTHEN DETECTION OF CORONAVIRUS DISEASE
    AN ANKUR JAIN, BHARTI JAIN, DR. DEEPANKAR BHARADWAJ, HARPREET SINGH CHAWLA ...
    IN Patent App. 202,211,047,325 , 2022
    2022
  • A SYSTEM FOR INFORMATION MANAGEMENT IN COMPUTERIZED INJECTORS
    A JAIN, DRD BHARADWAJ, B JAIN, RM KANASE, DRD SUDHA, ...
    IN Patent App. 202,211,010,459 , 2022
    2022
  • DIFFERENT COLOR DETECTION USING AI & PYTHON
    MRMK MR. SANJEEV BHARDWAJ, MR. ASHISH NAGILA, PROF. VAIBHAV TRIVEDI, PROF ...
    IN Patent App. 202,211,028,727 , 2022
    2022