Computer Engineering, Computer Science Applications, Information Systems, Software
10
Scopus Publications
15
Scholar Citations
3
Scholar h-index
Scopus Publications
Investigating the system and service quality of online learning portals in North India leading to establishment of new paradigms Sidhu Harmaninder Jit Singh, Maninderjit Singh Khanna, Inderpreet Kaur, Akhilendra Kumar Khare Aip Conference Proceedings, 2024 As world has been growing dynamically paradigms of teaching has undergone drastic changes. Relationship of a teacher-student has changed to interactive classroom study which was used to be one-way communication during ancient times. User friendly and online data libraries have replaced repositories of books. Nowadays learning is available 24 hours, hazel free and easily available for students. This paper gives information about recent online system for learning, which is analyzed for the regular learner from northern part of India that cover Himachal Pradesh, Haryana, Jammu and Kashmir, Punjab along with union territories of Chandigarh and Delhi using online learning portal (OLLP) system. In this paper effective implementation of OLLP system could be analyzed which giving focuses on conduct of online courses to check qualities of OLLP system, along with in ensuring, subscribers employability and service quality. Hypothesis has been developed to explore the significance of age, level of education and place of residence with various factors involving courses through online education portals and data collected through questionnaire is analyzed.
Strawberry Leaf Disease Severity Decoded for Agriculture: A Federated Learning CNN Approach Satvik Vats, Maninderjit Singh Khanna, Vinay Kukreja, Shiva Mehta 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024 In this research paper, agricultural diseases can be detected using innovative technologies. To discuss classification approaches to strawberry leaf disease using a Federated Learning (FL) framework and Convolutional Neural Networks (CNN). A study assessing leaf disease severity in four grades used datasets from four customers. Using FL in this context enables local data analysis from different sources. To protect privacy and keep all training on-site, averages are federated to create a comprehensive global model. To found that the FL-CNN model can accurately grade strawberry leaf disease severity. Four clients performed well in precision, recall, F1 score, and overall accuracy in an analysis of four clients. The model results could be understood more nuancedly by calculating macro-, micro-, and average-weighted results. As for the standardized performance metrics across clients, including macro averages, they were 85.19%, 82.06 %, 86.61 %, 83.10 %, etc. It has consistently emphasized the stability of its disease severity classification since its founding. Weighted average values, which consider class imbalance, also indicated the model's strength, with 84.95% and scores between 81.5% and 79.0%. These microaverages were 84.97 percent, 81.60 percent, and ovmaxacing respectively. The study's design, which involves converting local data into a one-stop global model based on federated averaging, hints at how FL has a tremendous advantage for agricultural applications (compared to centralized learning) in situations where privacy and distributed training are crucial. To used this technique successfully in our study to develop more precise and refined models for disease detection, which improved measured results on disease detection, which are crucial for effective crop management and implementing strategies for controlling or preventing any given disease.
AI in Agriculture: A Federated Learning CNN Approach to Detecting Almond Leaf Disease Satvik Vats, Maninderjit Singh Khanna, Vinay Kukreja, Shiva Mehta 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024 The proposes an original method of detecting and classifying almond leaf diseases with a CNN-federated learning framework. Aside from the four severity levels of almond leaf diseases, there are also four kinds of clients. There is no loss of independent client-side confidential data or subversion of a global model based on localized data input. A primary objective of the project was to increase accuracy and efficiency in disease classification for almond cultivation with the help of distributed computing power. Four different clients with different local data characteristics were used to train the CNN model. Diseases are graded based on severity, facilitating accurate and timely detection, which is critical to crop management success. The model's performance has been carefully evaluated locally and globally for its accuracy and reliability. The macro averages across clients also reflect the model's class-wise performance. The highest score was 94.68. The weighted average (which takes class frequency into account) also reached 94.69, showing consistency across classes. As usual, the highest of all the micro averages is 94.68, which is what people need to understand performance when class distributions are not balanced. This study constitutes an important step in agricultural AI, particularly in detecting and classifying plant diseases. In addition to being statistically significant, these results also have practical implications. As a result of this accomplishment, agriculture can now benefit from more advanced and decentralized AI that preserves privacy.
Emerging Trends in Agritech: Federated Learning CNN-Based Jackfruit Leaf Disease Severity Detection Satvik Vats, Maninderjit Singh Khanna, Vinay Kukreja, Shiva Mehta 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024 This paper uses federated learning and a Convolutional Neural Network (CNN) to present a new case study in the categorization of jackfruit leaf disease. Information from four customers helps zero in on the illness severity. This paradigm preserves data privacy and collaborative learning by using federated learning without storing sensitive information in a central location. Part of the task is one that defines mild (2650 %, or roughly one in four), severe (51-75 %, almost two out of three) and critical cases( 76-100 %). In this case, we use a federated learning model whereby data from all four clients are sent up to get an average global model. Locally tail Performance measures such as Accuracy, Precision and Recall, F1-Score, and Support are used in the results. These indicate how well an individual model does at classifying a patient’s condition severity. We examined customer performance in terms of Macro, Micro and Weighted Averages to give a balanced view on model capabilities. When compared against the results analysis data, these numbers offered strong validation for our model’s prowess. For example, the Macro Average values were between 88.44 % and 92.04 %, meaning that all classes performed equally regardless of how many times they appeared in a passage. The range of these values was 88.42 % to 92.06 %. Results arising from the Micro Average, which are especially effective in situations of class imbalance, perform admirably as well. They average between 88.40 % and 92.04 %. This study constitutes another brick for farm technology. It reveals how federated learning can solve problems of complicated, manyclient data sets for disease classification even while maintaining security and accuracy.
Federated Learning CNN Empowers Precision In Agriculture: A Case Study in Soybean Leaf Disease Satvik Vats, Maninderjit Singh Khanna, Vinay Kukreja, Shiva Mehta 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2024, 2024 The research paper that we are referring to here uses a new approach. It employs Federated Learning (FL) combined with Convolutional Neural Networks (CNNs) to achieve soybean leaf disease classification across five different classes of disease and five clients, hj1 through hj5. By integrating a thorough analysis of the results, our investigation offers outstanding accuracy, recall, and F1-Score values for every client-class pair. In particular, for client hj1, we attain a precision of 95.71%, recall at 95.72%, and F1-Score equal to 98% on gh-l; initial accuracy is also up above the symbolic line with no glossary provided—above all higher than that coupled with your offer! This well-working Client hj2 offers excellent results, with a precision of 89.70 %, recall of 89.73 % and F1 Score of 89.66 for the gh-1 data set, staying remarkably accurate at the level Price: gh-1 Client hj3 has a similar high precision, recall, and F1-Score of 95.43% and 95.44%, respectively, with an accuracy level of 98%. For client hj4, the precision is 90.72%, recall is 91.02%, and F1-Score rates at 90.76 for gh-1 with an accuracy rate of guess what? Client HJ5 retains excellent performance. For GH-1, they achieve a precision of 93.49%, a recall of 93.57%, and an F1-Score of 86%. This translates to final grade accuracy upwards of the magical two-digit percentage mark at human standards: results greater than or equal to nine. The computed averages further demonstrate the robustness of the model, with macro average precision, recall, and F1-Score values coming to 95.71 %, 89.69 %, and 95.43%, respectively. The weighted means show highly correlated performance metrics: precision, 95.71%; recall, 89.70%; F1-Score = 95.43%. Average precision, recall, and F1-Score remain the same for micro: 95.71%, 89.65%, and 95.43%.
Cloud's Transformative Involvement in Managing BIG-DATA ANALYTICS for Securing Data in Transit, Storage and Use: A Study Harmaninder Jit Singh Sidhu, Maninderjit Singh Khanna Pdgc 2020 2020 6th International Conference on Parallel Distributed and Grid Computing, 2020 with the advent of Cloud Computing a new era of computing has come into existence. No doubt, there are numerous advantages associated with the Cloud Computing but, there is other side of the picture too. The challenges associated with it need a more promising reply as far as the security of data that is stored, in process and in transit is concerned. This paper put forth a cloud computing model that tries to answer the data security queries; we are talking about, in terms of the four cryptographic techniques namely Homomorphic Encryption (HE), Verifiable Computation (VC), Secure Multi-Party Computation (SMPC), Functional Encryption (FE). This paper takes into account the various cryptographic techniques to undertake cloud computing security issues. It also surveys these important (existing) cryptographic tools/techniques through a proposed Cloud computation model that can be used for Big Data applications. Further, these cryptographic tools are also taken into account in terms of CIA triad. Then, these tools/techniques are analyzed by comparing them on the basis of certain parameters of concern.
Industry 4.0: A study of india's readiness as preferred investment destination in automotive and auto component industry Maninderjit Singh Khanna, Harmaninder Jit Singh Sidhu, Rajni Bansal Pdgc 2020 2020 6th International Conference on Parallel Distributed and Grid Computing, 2020 Industry4.0 was originated in the Germany who defines major technological changes in manufacturing and laid down certain protocols for worldwide competitiveness of German industry. As the new era of ‘smart’ factory is about to begin, in which computers are connected with robotics remotely and use machine learning programs that can control the automatic machines with ease. In this paper, the basic inspiration of industry4.0 will be shared. The analysis of the effectiveness of Government of India's ‘Make in India’ initiative on manufacturing industry is assceesd. In the end, India's competitiveness in automotive industry and India readiness as preferred investment destination by all major automobiles giants will be discussed. And further some of the Government of India's initiative to boost up Auto Sector is also discussed.
Semantic web services in clouds for semantic computing Gurparkash Singh Kang, Jaiteg Singh, Maninderjit Singh Khanna Proceedings of the International Conference on Advances in Computing and Artificial Intelligence Acai 2011, 2011 The progression of interleaved domains like software engineering, natural language processing, artificial intelligence, programming languages and cloud computing has resulted in evolution of concept called semantic computing. Semantic computing is responsible drilling out the requisite information and semantically analyzes the same for efficient machine training and logic development purposes. Ontology based semantic web services are indispensible for the success of the semantic computing. One can deploy cloud computing to bridge gap between distributed data sources and their use through web services
RECENT SCHOLAR PUBLICATIONS
Investigating the system and service quality of online learning portals in North India leading to establishment of new paradigms SHJ Singh, MS Khanna, I Kaur, AK Khare AIP Conference Proceedings 2816 (1), 070001 , 2024 2024
A study of user predilection on UPI based online payment system with reference to Northern India MS Khanna, HJS Sidhu, A Khare AIP Conference Proceedings 2916 (1), 030017 , 2023 2023 Citations: 1
Exploring the economic and social impact of Covid-19 on India R Bansal, D Tandon, SK Misra, MS Khanna RECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY … , 2023 2023
Cloud's Transformative Involvement in Managing Big-Data Analytics for Securing Data in Transit, Storage And Use: A Study HJS Sidhu, MS Khanna 2020 Sixth International Conference on Parallel, Distributed and Grid … , 2020 2020 Citations: 4
Industry 4.0: A study of india's readiness as preferred investment destination in automotive and auto component industry MS Khanna, HJS Sidhu, R Bansal 2020 sixth international conference on parallel, distributed and grid … , 2020 2020 Citations: 7
Semantic Web services in clouds for semantic computing GS Kang, J Singh, MS Khanna Proceedings of the international conference on advances in computing and … , 2011 2011 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Industry 4.0: A study of india's readiness as preferred investment destination in automotive and auto component industry MS Khanna, HJS Sidhu, R Bansal 2020 sixth international conference on parallel, distributed and grid … , 2020 2020 Citations: 7
Cloud's Transformative Involvement in Managing Big-Data Analytics for Securing Data in Transit, Storage And Use: A Study HJS Sidhu, MS Khanna 2020 Sixth International Conference on Parallel, Distributed and Grid … , 2020 2020 Citations: 4
Semantic Web services in clouds for semantic computing GS Kang, J Singh, MS Khanna Proceedings of the international conference on advances in computing and … , 2011 2011 Citations: 3
A study of user predilection on UPI based online payment system with reference to Northern India MS Khanna, HJS Sidhu, A Khare AIP Conference Proceedings 2916 (1), 030017 , 2023 2023 Citations: 1
Investigating the system and service quality of online learning portals in North India leading to establishment of new paradigms SHJ Singh, MS Khanna, I Kaur, AK Khare AIP Conference Proceedings 2816 (1), 070001 , 2024 2024
Exploring the economic and social impact of Covid-19 on India R Bansal, D Tandon, SK Misra, MS Khanna RECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY … , 2023 2023
