IntelliFog Architecture for Fog Computing Environment using Machine Learning Ruchika Bindal, Mandeep Kaur, Righa Tandon 2026 IEEE 15th International Conference on Communication Systems and Network Technologies Csnt 2026, 2026 og computing has become a very promising paradigm to assist Internet of Things (IoT) based applications that require latency sensitivity and high data volumes, especially in smart agriculture. Nonetheless, it is not easy to perform tasks efficiently and make intelligent decisions at the fog layer. In this paper, the author suggests IntelliFog, a three-level architecture of IoT devices that are combined with fog nodes and cloud infrastructure that is supplemented with machine learning to process data intelligently. An algorithm called SAML is presented as a hybrid machine learning based algorithm that provides a possibility to classify and prioritize agricultural data at the fog layer and execute tasks faster and more accurately. To check the proposed approach, the experimental analysis is based on the time of execution and prediction accuracy. The findings indicate that IntelliFog has a high level of accuracy of 99.60 per cent and the execution time is reduced significantly than the current methods. The results show that the IntelliFog architecture proposed is suitable to support smart agriculture applications because it enhances performance and responsiveness of fog computing environments.og computing has become a very promising paradigm to assist Internet of Things (IoT) based applications that require latency sensitivity and high data volumes, especially in smart agriculture. Nonetheless, it is not easy to perform tasks efficiently and make intelligent decisions at the fog layer. In this paper, the author suggests IntelliFog, a three-level architecture of IoT devices that are combined with fog nodes and cloud infrastructure that is supplemented with machine learning to process data intelligently. An algorithm called SAML is presented as a hybrid machine learning based algorithm that provides a possibility to classify and prioritize agricultural data at the fog layer and execute tasks faster and more accurately. To check the proposed approach, the experimental analysis is based on the time of execution and prediction accuracy. The findings indicate that IntelliFog has a high level of accuracy of 99.60 per cent and the execution time is reduced significantly than the current methods. The results show that the IntelliFog architecture proposed is suitable to support smart agriculture applications because it enhances performance and responsiveness of fog computing environments. F
Implementation of Neural Network Control Mechanism for Grid Connected Wind-Solar PV Charging Station Amanpreet Kaur, Babita Sharma, Mandeep Kaur 2025 International Conference on Electronics and Computing Communication Networking Automation Technologies Icec2nt 2025, 2025 This work proposes a novel approach to enhance Grid-connected wind-solar PV charging stations face with challenges like fluctuating energy supply, inefficient resource usage, and the necessity for adaptive real-time control. Traditional control methods, like PI controllers, often fall short in optimizing system performance under these dynamic conditions, resulting in inadequate power supply for EV charging. To tackle these hurdles, this study proposes a pioneering approach employing neural network (NN) controllers to enhance grid-connected wind-solar PV charging stations' operation. NN controllers dynamically adjust charging station operations based on real-time data inputs, offering superior adaptability and efficiency. By integrating wind and solar power generation with intelligent NN control mechanisms, the system adeptly responds to varying environmental conditions and grid demands, ensuring more effective utilization of renewable energy sources. The proposed NN controller-based system targets enhancing the reliability, sustainability, and economic feasibility of grid-connected charging stations. Simulations showcase the effectiveness and stability of this approach in integrating renewable energy into transportation infrastructure. Performance evaluation can be conducted using Matlab/Simulink Software.
Smart irrigation for soil moisture prediction using hybrid AI and fog computing Ruchika Bindal, Mandeep Kaur, Righa Tandon 2025 IEEE 14th International Conference on Communication Systems and Network Technologies Csnt 2025, 2025 Irrigation control is essential for optimizing water usage in smart agricultural systems, especially where there is water scarcity. The fast development of IoT and fog computing brings up new opportunities for developing new and advanced systems that are both efficient and flexible. Further integration of fog computing with machine learning will bring more developments to smart agriculture systems. This paper has framed an irrigation supervision algorithm to predict irrigation needs in the smart agriculture system. The methodology uses a hybrid algorithm that incorporates Gradient Boosting (GB), Logistic Regression (LR) and Random Forest (RF). The system accurately forecasts the irrigation requirement by processing ecological information such as humidity, temperature, and time-based factors. 97.78% of accuracy illustrates the reliability of the resultant model, and initial processing of the data illustrates that information is concise and accurate. Recall, precision and F1- score are computed as metrics for the performance. The system is integrated into fog computing and machine learning to manage real-time data processing to achieve faster and more accurate predictions.
Pneumonia Prediction Using Deep Learning Yashika Girdhar, Babita Sharma, Mandeep Kaur, Gurleen Kaur Proceedings 2025 7th International Conference on Computational Intelligence and Communication Technologies Ccict 2025, 2025 an important worldwide health problem is pneumonia, which mostly affects vulnerable groups including the elderly and small children. Reducing death rates and improving patient outcomes depend on an accurate and prompt diagnosis of pneumonia. Conventional diagnostic techniques such as radiologists interpreting chest X-rays (CXR), might be subjectively interpreted incorrectly and take a lot of time. In this study, we look into the automatic classification of cases Pneumonia from Chest X-rays (CXR) images, employing deep learning algorithms specifically Convolutional Neural Networks (CNN). A proposed research used a database of 5,216 chest x-ray images, with the number of samples classified as pneumonia being 3,875 and classified as normal 1,341. To serve as a benchmark, a more basic Support Vector Machine model (SVM) was utilized to compare the results of CNN model. The CNN model outperformed the SVM model, which obtained 97.03% accuracy, with a greater accuracy of 97.42% after training. The improved performance of CNN in identifying pneumonia was facilitated by its automated extraction of hierarchical characteristics from pictures. This study offers healthcare practitioners a useful tool by showcasing the ability of deep learning models to increase diagnostic speed and accuracy. The model's handling of multi-class classification, better generalization across a variety of datasets, and system integration into clinical settings for real-time diagnostic help will be the main areas of future effort.
Precision medicine and personalized treatment Harpreet Kaur Channi, Ramandeep Sandhu, Mandeep Kaur, Deepika Ghai Advancing Healthcare Through Decision Intelligence Machine Learning Robotics and Analytics in Biomedical Informatics, 2025
Zero-Trust Approach for Secure Healthcare System Zero Trust Learning Applications in Modern Network Security, 2025
Smart text extraction system for Bank Cheque Images using DWT and dynamic thresholding Neha Thakur, Deepika Ghai, Sunpreet Kaur Nanda, Sandeep Kumar, Mandeep Kaur Smart Electronic Devices Artificial Intelligence Machine Learning and the Future, 2025 Bank cheques are largely utilized for financial dealings or transactions, with millions processed daily worldwide. A significant challenge in cheque management is the high cost and time involved in processing, which could be mitigated by automating the cheque processing system. While considerable research has focused on extracting certain fields—such as the date, signature, and legal and courtesy amounts—less attention has been given to fields like the bank logo, bank name, and payee’s name. This chapter presents a novel and effective approach for automatically extracting various data fields from bank cheque images, aiming for improved precision, recall, and reduced processing time. This chapter introduces a hybrid technique that integrates Discrete Wavelet Transform (DWT) with dynamic thresholding and a logical AND operator for data extraction. Text features exhibit abrupt variations and distinct edges when transformed using wavelets. Initially, edges are identified in the input grayscale image through 2D DWT, resulting in detailed sub-bands that include both text and non-text regions. Next, utilizing dynamic thresholding, morphological dilation techniques are used to join individual text regions inside these sub-bands. Finally, the logical AND operator and area-based filtering techniques are employed to precisely identify the data fields within the bank cheque images. The proposed technique outperforms the currently available methods in terms of Precision Rate (PR), Recall Rate (RR), and Processing Time (PT) for obtaining relevant data fields under varied circumstances, according to MATLAB experiments conducted on both public and own datasets.
An introduction to generative AI tools for education 2030 Ramandeep Sandhu, Harpreet Kaur Channi, Deepika Ghai, Gagandeep Singh Cheema, Mandeep Kaur Integrating Generative AI in Education to Achieve Sustainable Development Goals, 2024 The year 2030 marks a significant juncture in the evolution of education, where Generative Artificial Intelligence (AI) tools are poised to revolutionize the learning experience. In education society, the importance of generative AI is to improve the accessibility of learning at the global level so that personalized learning experiences can be provided to every learner as per their needs. This chapter explores the multifaceted role of generative AI tools in reshaping educational practices, envisioning a future where these tools foster personalized, adaptive, and engaging learning environments. Generative AI tools, characterized by their ability to create and adapt content autonomously, are instrumental in tailoring educational materials to individual learner needs. This chapter surveys the landscape of generative AI applications in education, including content generation, interactive simulations, intelligent tutoring systems, and dynamic learning pathways. These tools aim to provide adaptive, context-aware learning experiences that cater to diverse learning styles and preferences. The adaptability of generative AI tools extends to the creation of personalized learning pathways. By leveraging data analytics and machine learning algorithms, these tools dynamically adjust content delivery, pacing, and complexity, ensuring that each learner's educational journey is optimized for their unique requirements. The discussion encompasses the potential of generative AI tools to support both formal and informal learning settings. Generative AI tools also play a crucial role in promoting inclusivity in education. By generating diverse and culturally relevant content, these tools contribute to breaking down barriers and addressing disparities in access to quality education. This chapter explores how generative AI can be leveraged to create content that resonates with learners from different backgrounds, fostering a more inclusive educational landscape.
Connecting Cities: IoST Innovation for Smarter Cities Mandeep Kaur, Rajni Aron 2024 International Conference on Recent Innovation in Smart and Sustainable Technology Icrisst 2024, 2024 The Internet of Things (IoT) is becoming the next big thing in the history of the Internet. It is important to figure out what areas IoT can be used and what study problems these areas offer. IoT will be important in every part of human lives, from smart towns and healthcare to smart farming, smart living, smart shopping, and intelligent environments. Even though IoT-enabling technologies have come a long way in the past few years, many problems still need to be fixed. Turning a city into a "smart city" is very difficult, but it can be done with the help of technologies that enable the Internet of Smart Things (IoST). The Internet of Things (IoT) idea grew out of different types of end systems, so there will be many study problems to solve. This paper talks about how the growth of smart cities will be achieved by improving general infrastructure with the help of IoT and what problems this might cause.
Analysis of IoT devices data using bayesian learning on fog computing Heena Wadhwa, Htet Ne oo, Mandeep Kaur, Amanpreet Kaur, Pardeep Singh Tiwana Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
Fog computing and its role in development of smart applications Mandeep Kaur Saroa, Rajni Aron Proceedings 16th IEEE International Symposium on Parallel and Distributed Processing with Applications 17th IEEE International Conference on Ubiquitous Computing and Communications 8th IEEE International Conference on Big Data and Cloud Computing 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications Ispa Iucc Bdcloud Socialcom Sustaincom 2018, 2018
RECENT SCHOLAR PUBLICATIONS
Exploring IoT-enabled machine learning approaches for soil quality monitoring in agriculture: a systematic review R Bindal, M Kaur, R Tandon International Journal of Machine Learning and Cybernetics 17 (6), 306 , 2026 2026
IntelliFog Architecture for Fog Computing Environment Using Machine Learning R Bindal, M Kaur, R Tandon 2026 IEEE 15th International Conference on Communication Systems and Network … , 2026 2026
Zero-Trust Approach for Secure Healthcare System R Sandhu, HK Channi, D Ghai, M Kaur Zero-Trust Learning, 387-409 , 2025 2025 Citations: 2
Smart text extraction system for Bank Cheque Images using DWT and dynamic thresholding N Thakur, D Ghai, SK Nanda, S Kumar, M Kaur Smart Electronic Devices, 176-199 , 2025 2025
Smart irrigation for soil moisture prediction using hybrid AI and fog computing R Bindal, M Kaur, R Tandon 2025 IEEE 14th International Conference on Communication Systems and Network … , 2025 2025 Citations: 1
Precision medicine and personalized treatment HK Channi, R Sandhu, M Kaur, D Ghai Advancing Healthcare through Decision Intelligence, 151-174 , 2025 2025 Citations: 3
Resource allocation using a hybrid evolutionary model and machine learning M Kaur, R Aron, H Wadhwa, R Tandon, HN Oo, G Kaur Cloud and Fog Optimization-based Solutions for Sustainable Developments, 217-241 , 2024 2024
Management of metropolitan mobility for public transport and smart vehicles with fog computing R Tandon, A Verma, M Kaur, H Wadhwa, D Asirvatham Cloud and Fog Optimization-based Solutions for Sustainable Developments, 48-76 , 2024 2024 Citations: 1
From Concept to Reality: The Iterative Path of Smart City Implementation G Kaur, M Kaur, R Tandon, A Singh, R Kaur 5G Enabled Technology for Smart City and Urbanization System, 116-127 , 2024 2024
Optimizing resource allocation for energy efficiency in fog cloud computing environments M Kaur, R Aron, S Seth 2024 IEEE 13th International Conference on Communication Systems and Network … , 2024 2024 Citations: 3
Connecting Cities: IoST Innovation for Smarter Cities M Kaur, R Aron 2024 International Conference on Recent Innovation in Smart and Sustainable … , 2024 2024 Citations: 1
Impact of Artificial Intelligence Techniques on Green Applications M Kaur, R Aron, R Tandon, H Wadhwa, G Kaur, R Sandhu, D Ghai Artificial Intelligence Techniques for Sustainable Development, 64-85 , 2024 2024 Citations: 4
An introduction to generative AI tools for education 2030 R Sandhu, HK Channi, D Ghai, GS Cheema, M Kaur Integrating generative AI in education to achieve sustainable development … , 2024 2024 Citations: 96
The Fusion of Fog Computing and Intelligent Technologies for Parkinson's Disease Care H Wadhwa, M Kaur, O Sharma, HN Oo, R Tandon, G Kaur Intelligent Technologies and Parkinson’s Disease: Prediction and Diagnosis … , 2024 2024 Citations: 1
A comprehensive exploration of machine learning and iot applications for transforming water management M Kaur, R Aron, H Wadhwa, R Tandon, HN Oo, R Sandhu Innovations in Machine Learning and IoT for Water Management, 25-50 , 2024 2024 Citations: 12
SACF: An Innovative and Secure Architectural Approach for Cybersecurity using Fog Computing Environments M Kaur, R Aron, S Seth 2023 9th International Conference on Signal Processing and Communication … , 2023 2023 Citations: 1
Analysis of IoT devices data using bayesian learning on fog computing H Wadhwa, M Kaur, A Kaur, PS Tiwana Artificial Intelligence, Blockchain, Computing and Security Volume 2, 194-199 , 2023 2023 Citations: 12
Exploring the Core Components of Cloud Computing and Its Architecture M Kaur, S Sharma 2023 14th International Conference on Computing Communication and Networking … , 2023 2023 Citations: 1
Cnn-based smart waste management system in fog computing environment M Kaur, R Aron, H Wadhwa, HN Oo 2023 IEEE 12th International Conference on Communication Systems and Network … , 2023 2023 Citations: 15
An energy-efficient load balancing approach for fog environment using scientific workflow applications M Kaur, R Aron Distributed computing and optimization techniques: Select proceedings of … , 2022 2022 Citations: 14
MOST CITED SCHOLAR PUBLICATIONS
A systematic study of load balancing approaches in the fog computing environment: M. Kaur, R. Aron M Kaur, R Aron The Journal of supercomputing 77 (8), 9202-9247 , 2021 2021 Citations: 140
An introduction to generative AI tools for education 2030 R Sandhu, HK Channi, D Ghai, GS Cheema, M Kaur Integrating generative AI in education to achieve sustainable development … , 2024 2024 Citations: 96
Focalb: Fog computing architecture of load balancing for scientific workflow applications M Kaur, R Aron Journal of Grid Computing 19 (4), 40 , 2021 2021 Citations: 46
An energy-efficient load balancing approach for scientific workflows in fog computing M Kaur, R Aron Wireless Personal Communications 125 (4), 3549-3573 , 2022 2022 Citations: 43
Fog computing and its role in development of smart applications MK Saroa, R Aron 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications … , 2018 2018 Citations: 43
Equal distribution based load balancing technique for fog-based cloud computing M Kaur, R Aron International Conference on Artificial Intelligence: Advances and … , 2020 2020 Citations: 18
A novel load balancing technique for smart application in a fog computing environment M Kaur, R Aron International Journal of Grid and High Performance Computing (IJGHPC) 14 (1 … , 2022 2022 Citations: 16
Cnn-based smart waste management system in fog computing environment M Kaur, R Aron, H Wadhwa, HN Oo 2023 IEEE 12th International Conference on Communication Systems and Network … , 2023 2023 Citations: 15
An energy-efficient load balancing approach for fog environment using scientific workflow applications M Kaur, R Aron Distributed computing and optimization techniques: Select proceedings of … , 2022 2022 Citations: 14
A comprehensive exploration of machine learning and iot applications for transforming water management M Kaur, R Aron, H Wadhwa, R Tandon, HN Oo, R Sandhu Innovations in Machine Learning and IoT for Water Management, 25-50 , 2024 2024 Citations: 12
Analysis of IoT devices data using bayesian learning on fog computing H Wadhwa, M Kaur, A Kaur, PS Tiwana Artificial Intelligence, Blockchain, Computing and Security Volume 2, 194-199 , 2023 2023 Citations: 12
Fog clustering-based architecture for load balancing in scientific workflows M Kaur, R Aron Proceedings of International Conference on Computational Intelligence and … , 2022 2022 Citations: 6
Impact of Artificial Intelligence Techniques on Green Applications M Kaur, R Aron, R Tandon, H Wadhwa, G Kaur, R Sandhu, D Ghai Artificial Intelligence Techniques for Sustainable Development, 64-85 , 2024 2024 Citations: 4
Precision medicine and personalized treatment HK Channi, R Sandhu, M Kaur, D Ghai Advancing Healthcare through Decision Intelligence, 151-174 , 2025 2025 Citations: 3
Optimizing resource allocation for energy efficiency in fog cloud computing environments M Kaur, R Aron, S Seth 2024 IEEE 13th International Conference on Communication Systems and Network … , 2024 2024 Citations: 3
Zero-Trust Approach for Secure Healthcare System R Sandhu, HK Channi, D Ghai, M Kaur Zero-Trust Learning, 387-409 , 2025 2025 Citations: 2
Smart irrigation for soil moisture prediction using hybrid AI and fog computing R Bindal, M Kaur, R Tandon 2025 IEEE 14th International Conference on Communication Systems and Network … , 2025 2025 Citations: 1
Management of metropolitan mobility for public transport and smart vehicles with fog computing R Tandon, A Verma, M Kaur, H Wadhwa, D Asirvatham Cloud and Fog Optimization-based Solutions for Sustainable Developments, 48-76 , 2024 2024 Citations: 1
Connecting Cities: IoST Innovation for Smarter Cities M Kaur, R Aron 2024 International Conference on Recent Innovation in Smart and Sustainable … , 2024 2024 Citations: 1
The Fusion of Fog Computing and Intelligent Technologies for Parkinson's Disease Care H Wadhwa, M Kaur, O Sharma, HN Oo, R Tandon, G Kaur Intelligent Technologies and Parkinson’s Disease: Prediction and Diagnosis … , 2024 2024 Citations: 1
