Dr Chamandeep Kaur

Dr Chamandeep Kaur has been working as a Lecturer in the Department of Computer Science and Information Technology at Jazan University, Saudi Arabia, for over a decade. She received her PhD in Computer Science and Engineering from JJT University, India and MCA from Punjab Technical University, India. She has over 15 years of experience in teaching, research, education, industry, and consulting.
Her research areas include IoT, cloud computing, big data, machine learning, computer networks and security.
She has made several innovative and outstanding contributions to academic research. She has contributed over 25 publications in Scopus, SCIE, Elsevier, WoS and International peer-reviewed impact journals, several patents, and books. She is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the International Association of Engineers. She has been awarded teaching and research merits by Jazan University, Saudi Arabia. She can be reached at

Scopus Publications

Implementation of cloud based IoT technology in manufacturing industry for smart control of manufacturing process
Sohail Imran Khan, Chamandeep Kaur, Mohammed Saleh Al Ansari, Iskandar Muda, Ricardo Fernando Cosio Borda, and B. Kiran Bala
Springer Science and Business Media LLC



A study analyzing the major determinants of implementing Internet of Things (IoT) tools in delivering better healthcare services using regression analysis
Chamandeep Kaur, Mohammed Saleh Al Ansari, Nisha Rana, Bhadrappa Haralayya, Yaisna Rajkumari, and K. C. Gayathri
BENTHAM SCIENCE PUBLISHERS
The new advancements in healthcare systems are influenced majorly by the adoption of the Internet of Things (IoT). This is especially important in light of the present state of affairs in the healthcare, social welfare, and energy sectors. By understanding the interconnected problems such as energy efficiency and sustainable development, it may be possible to enhance the well-being of both humans and the environment. The incorporation of sensors and other intelligent devices is crucial to the accomplishment of the aims of sustainable development. In todays rise in population, there is a key area in which the latest scientific developments really need to be put into practice: public health. For the sake of the well-being of future generations, it is essential toconduct research on the ways in which the SDGs have an impact on the uses of sensors and the Internet of Things in human environments. Peoples lives are being influenced by applications of technology, sensor networks, intelligent systems, and the Internet of Things (IoT), all of which are having a positive impact on the environmental sustainability and energy efficiency of the world.The digitization and application of intelligent systems and the Internet of Things devices are carried out in blocks of analysis, organized in a variety of disciplines, in urbanized settings, and in human-inhabited communities; nonetheless, they all have a similar center of gravity, which is the trilogy: human, technology, and sustainability. The management of effective and healthy resources, enhanced governance, and programs that encourage the adoption of new technological solutions are all necessary for sustainable development in better healthcare services. The study is focused on the major determinants of implementing Internet of Things (IoT) tools in delivering better healthcare services.


Advanced hybrid CNN-Bi-LSTM model augmented with GA and FFO for enhanced cyclone intensity forecasting
Franciskus Antonius Alijoyo, Taviti Naidu Gongada, Chamandeep Kaur, N. Mageswari, J.C. Sekhar, Janjhyam Venkata Naga Ramesh, Yousef A.Baker El-Ebiary, and Zoirov Ulmas
Elsevier BV



Using IoT to evaluate the effectiveness of online interactive tools in healthcare
K. Suresh Kumar, Chinmaya Kumar Nayak, Chamandeep Kaur, and Ahmed Hesham Sedky
Wiley



Implementation of a neuro-fuzzy- based classifier for the detection of types 1 and 2 diabetes
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
Wiley



An intelligent IoT-based healthcare system using fuzzy neural networks
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
Wiley



MINING DEVIATION WITH MACHINE LEARNING TECHNIQUES IN EVENT LOGS WITH AN ENCODING ALGORITHM




Hybrid MLP-GRU Federated Learning Framework for Industrial Predictive Maintenance
K. Praveena, M. Misba, Chamandeep Kaur, Mohammed Saleh Al Ansari, Veera Ankalu. Vuyyuru, and S Muthuperumal
IEEE
Assuring the dependability and effectiveness of industrial gear, cutting downtime, and lowering maintenance costs all depend on predictive maintenance. We provide a hybrid MLP-GRU model-based Federated Learning-Enabled Advanced Predictive Maintenance Framework in this work for defect prediction and detection in industrial machinery. By using federated learning approaches, the framework is made to effectively utilize the combined intelligence of dispersed datasets while maintaining data security and privacy. Three distinct datasets, representing various types of equipment and failure scenarios, are integrated into the framework: the IMS Bearing Dataset, C-MAPSS Dataset, and Pump Sensor Dataset. The records are carefully combined into a training dataset by means of integration and preprocessing, which makes it easier to create a hybrid MLP-GRU model that can recognize intricate temporal correlations and fault patterns. The model may learn from a variety of sources without centralizing sensitive data thanks to the Federated Learning architecture, which facilitates collaborative model training across dispersed data subsets. Across dispersed datasets, the optimization layer effectively updates model parameters while decreasing loss functions by utilizing sophisticated optimization methods. The adapted framework's efficacy in defect detection and prognosis tasks across a range of industrial machinery types and fault circumstances has been demonstrated through training and assessment. All things considered, the suggested framework is a major step forward in industrial machinery predictive maintenance, providing a scalable, accurate, and privacy-preserving approach for proactive failure identification and prediction. Its use of hybrid MLP-GRU model architecture and federated learning approaches shows promising outcomes of 0.94% accuracy in practical industrial applications.


Enhancing Early Heart Disease Prediction through Optimized CNN-GRU Algorithms: Advanced Techniques and Applications
Ravindra Changala, M. Misba, Muthurasu N, Chamandeep Kaur, Veera Ankalu Vuyyuru, and Ananthi R K
IEEE
Enhancing early heart disease prediction involves refining algorithms and incorporating advanced medical data analysis techniques to identify risk factors and symptoms, enabling proactive intervention and improved patient outcomes. This paper presents a pioneering approach to enhance the early prediction of heart disease through meticulously optimized Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) algorithms. Early detection of cardiovascular ailments is imperative for mitigating their adverse effects on individual health and healthcare systems. Our methodology leverages the synergistic capabilities of CNNs and GRUs, combining convolutional layers' feature extraction prowess with recurrent units' temporal sensitivity to encapsulate the holistic essence of cardiovascular dynamics. Furthermore, transfer learning from large-scale biomedical datasets enhances our model's generalizability, facilitating seamless integration into real-world clinical workflows. Beyond technical advancements, our work holds significant societal implications, democratizing access to early heart disease detection and intervention and catalyzing a shift towards proactive health management. By empowering healthcare practitioners with a reliable tool for preemptive diagnosis, our optimized CNN-GRU algorithms promise to alleviate the burden of cardiovascular morbidity and mortality. Embracing a multidisciplinary ethos, this research exemplifies the transformative potential of interdisciplinary collaboration in addressing complex healthcare challenges. As we navigate towards precision cardiovascular medicine, the implications of our work resonate in shaping a future where early disease prediction translates into tangible improvements in public health outcomes and quality of life. An optimized CNN-GRU model achieving an accuracy of 98.7% was implemented in Python, surpassing other comparison models in performance.


Maximizing Learning Outcomes through Fuzzy Inference System and Graph Theory Based on Learning Analytics
J. Chandra Sekhar, Balajee J., Sanjiv R. Godla, Vuda Sreenivasa Rao, Yousef A. B. El-Ebiary, and Chamandeep Kaur
Engineering and Technology Publishing
—Teachers are urged to explore innovative instructional methods, including technology integration and personality-oriented approaches, to enhance learning outcomes and foster better upbringing. The unique tactic proposed in this study involves incorporating learning analytics and feedback data into pedagogy improvement efforts. Teachers get access to visual classroom data about the active learning facilitation strategies they use in their classes using the automated feedback platform TEACHActive. In addition to discussing the system’s information flow from an autonomous observation model to the feedback data, including the technological architecture, the study also examines the core necessity of the TEACHActive system improving teaching practises through reflection. To gather these data, a fuzzy inference method and graph theory are used. By combining graph theory and fuzzy logic, the conventional approach is innovatively modified in order to enhance instruction. By utilising these strategies, teachers can enhance their pedagogical practises and individualise learning experiences. The integration of the TEACHActive automated feedback platform, which utilizes learning analytics and feedback information, to improve teaching practices and personalize learning experiences through the fusion of graph theory and fuzzy logic, resulting in enhanced education outcomes. This research fills the gap in conventional instructional techniques by introducing TEACHActive, a system that integrates learning analytics and feedback data through fuzzy inference and graph theory. By providing insightful information on active learning strategies, the study uniquely improves teaching practices, enhancing student learning and improving academic results. The study’s unique approach offers teachers valuable insights into active learning techniques, leading to better pedagogical procedures for improved learning and upbringing results. The method is feasible to run fuzzy inference systems and statistical analysis using Matlab software. The results show that four variables have the most impact on how well pedagogical processes work.


Healthcare Data Management Optimization Using LSTM and GAN-Based Predictive Modeling: Towards Effective Health Service Delivery
Ravindra Changala, Chamandeep Kaur, Nr. Rutuparna Satapathy, Veera Ankalu Vuyyuru, Kathari Santosh, and M. Ponni Valavan
IEEE
In the rapidly evolving landscape of healthcare, effective data management plays a pivotal role in enhancing health service delivery. This study explores the application of Long Short-Term Memory (LSTM) and Generative Adversarial Network (GAN) based predictive modelling to optimize healthcare data management. The proposed framework leverages LSTM to capture temporal dependencies in healthcare data, allowing for improved prediction of patient outcomes and resource utilization. GANs are employed to generate synthetic healthcare data. The primary objectives of this research include the development of an advanced predictive modeling system for healthcare data and the optimization of health service delivery processes. By harnessing the power of LSTM and GANs, the study aims to address challenges related to data heterogeneity, imbalances, and scarcity. The proposed LSTM-GAN model shows better accuracy with 98.5% which is 4 % higher when compared with SVM and GRU. The implications of this work extend beyond predictive modeling, influencing policy-making and strategic decision processes in the pursuit of effective, accessible, and patient-centric healthcare services.


Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management
Franciskus Antonius Alijoyo, Deepak A. Vidhate, Chamandeep Kaur, V Nivedan, V. Kanpur Rani, and A. Balakumar
IEEE
The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future.


Enhancing Cyber-Physical Systems Resilience: Adaptive Self-Healing Security Using Long Short-Term Memory Networks
Franciskus Antonius Alijoyo, Chamandeep Kaur, Afsana Anjum, Veera Ankalu Vuyyuru, and B Kiran Bala
IEEE
Cyber-Physical Systems (CPS) form the backbone of critical infrastructures, integrating computational and physical processes to enhance efficiency and automation. However, the increasing interconnectivity exposes these systems to diverse cyber threats, necessitating proactive security measures. This research seeks to advance the security of Cyber-Physical Systems (CPS) through the implementation of a self-healing mechanism driven by neural networks. CPS, pivotal in critical infrastructures, have become increasingly susceptible to a myriad of cyber threats owing to their intricate interconnectivity. The paramount significance of this research is rooted in the creation of a dynamic and intelligent defense system capable of autonomously identifying, responding to, and recuperating from cyber-physical attacks. The traditional CPS security landscape has grappled with static and rule-based approaches, struggling to keep pace with the dynamic nature of contemporary cyber threats. Moreover, the recovery processes in place have been predominantly manual and time-consuming. This research addresses these longstanding issues by introducing LSTM into the CPS security framework. This incorporation represents a paradigm shift, ushering in an era of adaptive resilience. The novelty of the research lies in the seamless integration of neural networks, enabling the system to learn from past incidents and adapt to emerging threats. The proposed self-healing mechanism emphasizes real-time threat detection, allowing for swift responses and the automation of the recovery phase, ultimately reducing downtime associated with security incidents. the integration of self-healing mechanisms using Long Short-Term Memory (LSTM) networks proves to be a promising approach for advancing cybersecurity in Cyber-Physical Systems (CPS), with the proposed model achieving an impressive accuracy of 99%. The research not only tackles existing vulnerabilities but also pioneers a transformative approach to CPS security, leveraging the capabilities of neural networks to create a more robust and adaptive defense mechanism against evolving cyber threats.


Advancing Healthcare Anomaly Detection: Integrating GANs with Attention Mechanisms
Thakkalapally Preethi, Afsana Anjum, Anjum Ara Ahmad, Chamandeep Kaur, Vuda Sreenivasa Rao, Yousef A.Baker El-Ebiary, and Ahmed I. Taloba
The Science and Information Organization
— Early illness diagnosis, treatment monitoring


Federated Convolutional Neural Networks for Predictive Analysis of Traumatic Brain Injury: Advancements in Decentralized Health Monitoring
Tripti Sharma, Desidi Narsimha Reddy, Chamandeep Kaur, Sanjiv Rao Godla, R. Salini, Adapa Gopi, and Yousef A.Baker El-Ebiary
The Science and Information Organization
— Traumatic Brain Injury (TBI) is a significant global health concern, often leading to long-term disabilities and cognitive impairments. Accurate and timely diagnosis of TBI is crucial for effective treatment and management. In this paper, we propose a novel federated convolutional neural network (FedCNN) framework for predictive analysis of TBI in decentralized health monitoring. The framework is implemented in Python, leveraging three diverse datasets: CQ500, RSNA


Monitoring of operational conditions of fuel cells by using machine learning
Andip Babanrao Shrote, K Kiran Kumar, Chamandeep Kaur, Mohammed Saleh Al Ansari, Pallavi Singh, Bramah Hazela, and Madhu G C
European Alliance for Innovation n.o.
The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.


Optimizing Mobile Advertising with Reinforcement Learning and Deep Neural Networks
Divya Nimma, Chamandeep Kaur, Gunjan Chhabra, V. Selvi, Divya Tyagi, and A. Balakumar
IEEE
Mobile advertising is also closely associated with contemporary digital marketing channels, where the projective targeting is critical in regard to businesses’ potential performance. This research makes use of a combination of machine learning and reinforcement learning (RL) to determine the best place to post an ad considering the users’ demographic and behavior. Deepening the neural networks (DNNs) in this regard, the method is expected to advance the efficiency of mobile advertising while delivering more relevant content to the target user. Purchasing tendency predictions in the report are based on Python-based models through a dataset from Kaggle that has incorporated user demographics (Gender, Age, Estimated Salary) and purchasing patterns. The missing values are first handled using the different techniques then normalization of the numerical data and one hot coding of the features such as the Device Type and the Location. Automatic feature are extracted with the help of DNNs which obviate the process of feature extraction. This approach proposed yielded an accuracy level of 98% a higher level as compared to the CNN and LSTM models. For decision-making reinforcement learning is used with the help of Deep Q-Network (DQN) and Policy gradient. DQNs predict Q-values for the actions (ad displays) and choose the best action, while Policy Gradient methods directly learn the policy by modifying the probabilities related to the actions based on the users’ behavior. The training techniques used on both models include backpropagation and stochastic gradient descent, which enables the RL agent in the continuous implementation of better ad placement strategies. Here they used DNN to train the features and RL to optimize the training and then improve the on average click through rates and conversion rates which provides a better strategy towards advertising.


EXPLORING THE INFLUENCE OF ARTIFICIAL INTELLIGENCE TECHNOLOGY IN MANAGING HUMAN RESOURCE MANAGEMENT




Role of artificial intelligence for the curative field: A review
Shaista Sabeer, Ayasha Siddiqua, Afsana Anjum, Sunanda Kondapalli, Chamandeep Kaur, and Ahmed Unnisa Begum
AIP Publishing



A novel vehicle tracking approach using random forest classifier for disaster management system along with R-CNN for enhancing the performance
Hussein Tuama Hazim, Chamandeep Kaur, Sambhrant Srivastava, Iskandar Muda, Harish Chander Anandaram, and Mohammed Saleh Al Ansari
AIP Publishing



An overview of 3d printing (additive manufacturing in powder-based methods) materials, methods, mechanical properties, and applications
R. Raffik, Raghavan Santhanam, Chamandeep Kaur, S. Seenivasan, and K. Somasundaram
IGI Global
The multi-component alloys with special technique of additive manufacturing or 3D printing creates the novel material to enhance the mechanical characteristics, excellent formability, and maximum potency. Because these techniques were able to compose the layer-by-layer process with various materials like titanium, nickel alloys, and aluminium matrix materials, for creating the complex based geometry shapes, this additive technique recreates the material with layer-by-layer on the substrate with the help of powder materials with selected process parameters. The selected materials of additive manufacturing possess oxidation performances, creep resistances, high hardness, hydrogen properties, compressive strength, and tensile strength are in maximum level, and the post-heat treatments are well built on the substrate layers. Therefore, this chapter was utilized to identify the correctness of manufacturing procedures, selection of materials, and application-oriented areas.


Recognizing Tourist Movement Networks Using Big Data Analysis and a Median Support Based Graph Approach




Leaf disease identification and classification using optimized deep learning
Yousef Methkal Abd Algani, Orlando Juan Marquez Caro, Liz Maribel Robladillo Bravo, Chamandeep Kaur, Mohammed Saleh Al Ansari, and B. Kiran Bala
Elsevier BV



Cyber Extortion Unveiled: The Evolution, Tactics, Challenges, and Future of Ransomware
Salahaldeen Duraibi, Chamandeep Kaur, and A.B. Pawar
IEEE
The adaptability and malicious efficiency of ransomware, a sort of malicious software that encrypts files or restricts system access until a ransom, frequently in the form of cryptocurrency, is delivered to the attacker, is unmatched by other computer dangers. This article covers ransomware's unrelenting evolution, from its historical roots to sophisticated modern varieties that use cutting-edge encryption and the ominous “double extortion” tactic. Attacks with ransomware offer serious risks, including financial hardships, breaches of data privacy, interruptions of vital infrastructure, and psychological effects on victims. The paper explores encryption methods, such as the AES and RSA algorithms, and finds common entry vectors for ransomware, including social engineering, phishing emails, and software weaknesses. It highlights difficulties in ransomware mitigation, including dangers in the supply chain, human weaknesses, resource limitations, ransomware's dynamic nature, encryption and evasion strategies, data exfiltration, and cryptocurrency transactions. Application whitelisting, behavioral analysis, and threat intelligence are all examples of mitigation methods. Response strategies like data backups and incident response plans are examples of mitigation strategies. The efficacy analysis highlights the necessity for a comprehensive strategy by highlighting strengths and shortcomings. The review advocates for interdisciplinary integration to combat the evolving ransomware threat landscape by outlining promising approaches and future research needs, such as multi-modal authentication techniques, resilience-centric security measures, ransomware attribution techniques, and user-centric security designs.


An Approach with Machine Learning for Heart Disease Risk Prediction
Fathe Jeribi, Chamandeep Kaur, and A.B. Pawar
IEEE
Heart disease is a prominent cause of death worldwide, needing novel techniques for early detection and care. This study looks into the potential of machine learning in predicting heart illness and addresses the limitations of existing risk assessment approaches. To ensure data quality, large, high-quality datasets are collected, and data preparation techniques are used. For heart disease risk prediction, several machine learning methods are used, with an emphasis on feature selection and engineering. The study underlines the necessity of collaboration among healthcare practitioners, data scientists, and patients in addressing data quality, privacy, and ethical concerns. The outcomes of this study show that machine learning has the potential to improve risk assessment, early identification, and individualized therapy of heart disease. Machine learning is critical in the identification of arrhythmias, image analysis, individualized treatment strategies, and medication development. Machine learning models in clinical decision support systems can enhance patient care and outcomes. Despite the encouraging results, the study recognizes issues such as data quality, class imbalance, model interpretability, and privacy concerns. Ethical issues, clinical validation, and regulatory compliance are also important factors to consider when applying machine learning in healthcare. The study emphasizes the need to work together to fully realize the promise of machine learning, assuring improved patient outcomes and lowering the global burden of cardiovascular disease.

RECENT SCHOLAR PUBLICATIONS

Cloud computing visualization for resources allocation in distribution systems
C Dash, MSA Ansari, C Kaur, YAB El-Ebiary, YMA Algani, BK Bala
AIP Conference Proceedings 3137 (1) 2025



Optimizing Mobile Advertising with Reinforcement Learning and Deep Neural Networks
D Nimma, C Kaur, G Chhabra, V Selvi, D Tyagi, A Balakumar
2024 International Conference on Artificial Intelligence and Quantum 2024



Hybrid Deep Learning Framework for Dynamic and Energy-Efficient Workload Migration in Cloud Computing Environments
M D'Souza, C Kaur, AS Bisht, D Nimma, G Dhanalakshmi, MKM Faizal
2024 International Conference on Communication, Control, and Intelligent 2024



A Study Analyzing the Major Determinants of Implementing Internet of Things (IoT) Tools in Delivering Better Healthcare Services Using Regression Analysis
C Kaur, MS Al Ansari, N Rana, B Haralayya, Y Rajkumari, KC Gayathri
Advanced Technologies for Realizing Sustainable Development Goals: 5G, AI 2024



Healthcare Data Management Optimization Using LSTM and GAN-Based Predictive Modeling: Towards Effective Health Service Delivery
R Changala, C Kaur, NR Satapathy, VA Vuyyuru, K Santosh, MP Valavan
2024 International Conference on Data Science and Network Security (ICDSNS), 1-6 2024



Enhancing Early Heart Disease Prediction through Optimized CNN-GRU Algorithms: Advanced Techniques and Applications
R Changala, M Misba, C Kaur, VA Vuyyuru, A RK
2024 Third International Conference on Electrical, Electronics, Information 2024



Hybrid MLP-GRU Federated Learning Framework for Industrial Predictive Maintenance
K Praveena, M Misba, C Kaur, MS Al Ansari, VA Vuyyuru, ...
2024 Third International Conference on Electrical, Electronics, Information 2024



Implementation of a Neuro‐Fuzzy‐Based Classifier for the Detection of Types 1 and 2 Diabetes
C Kaur, MS Al Ansari, VK Dwivedi, D Suganthi
Advances in Fuzzy‐Based Internet of Medical Things (IoMT), 163-178 2024



Using IoT to Evaluate the Effectiveness of Online Interactive Tools in Healthcare
K Suresh Kumar, CK Nayak, C Kaur, AH Sedky
Advances in Fuzzy‐Based Internet of Medical Things (IoMT), 239-253 2024



Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management
FA Alijoyo, DA Vidhate, C Kaur, V Nivedan, VK Rani, A Balakumar
2024 IEEE 3rd International Conference on Electrical Power and Energy 2024



Advancing Healthcare Anomaly Detection: Integrating GANs with Attention Mechanisms.
T Preethi, A Anjum, AA Ahmad, C Kaur, VS Rao, YAB El-Ebiary, AI Taloba
International Journal of Advanced Computer Science & Applications 15 (6) 2024



Enhancing Cyber-Physical Systems Resilience: Adaptive Self-Healing Security Using Long Short-Term Memory Networks
FA Alijoyo, C Kaur, A Anjum, VA Vuyyuru, BK Bala
2024 International Conference on Advances in Computing, Communication and 2024



Federated Convolutional Neural Networks for Predictive Analysis of Traumatic Brain Injury: Advancements in Decentralized Health Monitoring.
T Sharma, DN Reddy, C Kaur, SR Godla, R Salini, A Gopi, ...
International Journal of Advanced Computer Science & Applications 15 (4) 2024



Advanced hybrid CNN-Bi-LSTM model augmented with GA and FFO for enhanced cyclone intensity forecasting
FA Alijoyo, TN Gongada, C Kaur, N Mageswari, JC Sekhar, JVN Ramesh, ...
Alexandria Engineering Journal 92, 346-357 2024



An Intelligent IoT‐Based Healthcare System Using Fuzzy Neural Networks
C Kaur, MS Al Ansari, VK Dwivedi, D Suganthi
Advances in Fuzzy‐Based Internet of Medical Things (IoMT), 121-133 2024



MINING DEVIATION WITH MACHINE LEARNING TECHNIQUES IN EVENT LOGS WITH AN ENCODING ALGORITHM
VVJR KRISHNAIAH, BS RAO, MRS DUGGINENI VEERAIAH, ...
Journal of Theoretical and Applied Information Technology 102 (3) 2024



’Maximizing Learning Outcomes through Fuzzy Inference System and Graph Theory Based on Learning Analytics
JC Sekhar, J Balajee, SR Godla, VS Rao, YA El-Ebiary, C Kaur
Journal of Advances in Information Technology 15 (6) 2024



Exploring the influence of artificial intelligence technology in managing human resource management
M Orosoo, N Raash, K Santosh, C Kaur, D Bani-Younis, M Rengarajan
J Theor Appl Inf Technol 101 (23), 7847-7855 2023



Cyber Extortion Unveiled: The Evolution, Tactics, Challenges, and Future of Ransomware
S Duraibi, C Kaur, AB Pawar
2023 International Conference on Computational Science and Computational 2023



An Approach with Machine Learning for Heart Disease Risk Prediction
F Jeribi, C Kaur, AB Pawar
2023 International Conference on Computational Science and Computational 2023

MOST CITED SCHOLAR PUBLICATIONS

Leaf disease identification and classification using optimized deep learning
YM Abd Algani, OJM Caro, LMR Bravo, C Kaur, MS Al Ansari, BK Bala
Measurement: Sensors, 100643 2023
Citations: 149



Automated Registration of Multiangle SAR Images Using Artificial Intelligence
P Chopra, VS Gollamandala, AN Ahmed, SBGT Babu, C Kaur, NA Prasad, ...
Mobile Information Systems 2022, 1 - 10 2022
Citations: 64



The cloud computing and internet of things (IoT)
C Kaur
International Journal of Scientific Research in Science, Engineering and 2020
Citations: 61



Chronic Kidney Disease Prediction Using Machine Learning
MSAA Chamandeep Kaur, M. Sunil Kumar, Afsana Anjum, M. B. Binda, Maheswara ...
Journal of Advances in Information Technology (JAIT) 14 (2), 384-391 2023
Citations: 60



Challenges in internet of things towards the security using deep learning techniques
KC Ravikumar, P Chiranjeevi, NM Devarajan, C Kaur, AI Taloba
Measurement: Sensors 24, 100473 2022
Citations: 37



Implementation of cloud based IoT technology in manufacturing industry for smart control of manufacturing process
SI Khan, C Kaur, MS Al Ansari, I Muda, RFC Borda, BK Bala
International Journal on Interactive Design and Manufacturing (IJIDeM), 1-13 2023
Citations: 35



The vital role of VPN in making secure connection over internet world
DC Kaur
International Journal of Recent Technology and Engineering (IJRTE) ISSN 2022
Citations: 35



Incorporating sentimental analysis into development of a hybrid classification model: A comprehensive study
D Kaur
International Journal of Health Sciences 6, 1709-1720 2022
Citations: 32



A Mysterious and Darkside of The Darknet: A Qualitative Study
A Anjum, DC Kaur, S Kondapalli, MA Hussain, AU Begum, SM Hassen, ...
Webology 18 (number 4), 285 - 294 2021
Citations: 25



Regulating and monitoring IoT controlled solar power plant by ML
A Siddiqua, A Anjum, S Kondapalli, C Kaur
2023 International Conference on Computer Communication and Informatics 2023
Citations: 17



Challenges Faced by Big Data and Its Orientation in the Field of Business Marketing
HM Mourad, C Kaur, M Aarif
International Journal of Mechanical and Production Engineering Research and 2020
Citations: 16



Utilizing the random forest algorithm to enhance Alzheimer’s disease diagnosis
C Kaur, T Panda, S Panda, ARM Al Ansari, M Nivetha, BK Bala
2023 Third International Conference on Artificial Intelligence and Smart 2023
Citations: 15



A design for the Bandwidth improvement for the microstrip patch antenna for wireless network sensor
S Mawahib, C Kaur
International Journal of Scientific Research in Computer Science Engineering 2022
Citations: 15



Advanced hybrid CNN-Bi-LSTM model augmented with GA and FFO for enhanced cyclone intensity forecasting
FA Alijoyo, TN Gongada, C Kaur, N Mageswari, JC Sekhar, JVN Ramesh, ...
Alexandria Engineering Journal 92, 346-357 2024
Citations: 12



& Bala, BK (2022). Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning
KB Naidu, BR Prasad, SM Hassen, C Kaur, MS Al Ansari, R Vinod
Measurement: Sensors 24, 100545 2022
Citations: 12



The empirical analysis of artificial intelligence approaches for enhancing the cyber security for better quality
BS Rawat, D Gangodkar, V Talukdar, K Saxena, C Kaur, SP Singh
2022 5th International Conference on Contemporary Computing and Informatics 2022
Citations: 11



Analysis Of Security Threats, Attacks In The Internet Of Things
A Anjum, A Siddiqua, S Sabeer, S Kondapalli, C Kaur, K Rafi
Int. J. Mech. Eng 6, 2943-2946 2021
Citations: 10



Machine learning based library management system
K Geetha, GS Rao, C Kaur, KK Kumar
2022 6th International Conference on Electronics, Communication and 2022
Citations: 8



Federated Convolutional Neural Networks for Predictive Analysis of Traumatic Brain Injury: Advancements in Decentralized Health Monitoring.
T Sharma, DN Reddy, C Kaur, SR Godla, R Salini, A Gopi, ...
International Journal of Advanced Computer Science & Applications 15 (4) 2024
Citations: 7



A novel hybrid deep learning framework for detection and categorization of brain tumor from magnetic resonance images
YM Abd Algani, BN Rao, C Kaur, B Ashreetha, KVD Sagar, YAB El-Ebiary
International Journal of Advanced Computer Science and Applications 14 (2) 2023
Citations: 7