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
62
Scopus Publications
1280
Scholar Citations
17
Scholar h-index
27
Scholar i10-index
Scopus Publications
Uncertainty-Aware Volumetric Transformer with Dual Spatial-Channel Attention for Lung Nodule Classification B. N. Patil, TK Rama Krishna Rao, Nurilla Mahamatov, Elangovan Muniyandy, Arun Prasad.VK, et al. International Journal of Advanced Computer Science and Applications, 2026 Lung cancer is also among the most common causes of cancer-related deaths in the world, and the earliest possible detection of the cancer through computed tomography (CT) is important in the enhancement of patient survival. Nevertheless, accurate diagnosis is still a challenge as the nodules are small and indistinct, inter-rater consistency among radiologists, and the traditional deep learning systems have limited capacity to handle volumetric interactions and give interpretable and confidence-aware forecasts. This research suggests an uncertainty-cognizant Transformer-Enhanced Dual-Level Attention Network (TDA-Net) to classify lung nodules in CT images to deal with these issues. The suggested architecture combines a 3D Swin Transformer backbone and sequential spatial and channel attention fusion to be able to model both localized structural and global volumetric context. Moreover, Monte Carlo dropout is used in inference to measure predictive uncertainty and allows low-confidence cases to be identified and sent to a radiologist. The model is tested on a publicly available lung CT dataset, and it has an accuracy of 98.3% with high sensitivity to small nodules in the feature space. There is a separation of classes in the feature space, and the uncertainty rate is 5.1%. The findings of the experiment indicate that TDA-Net can be used as a supportive decision-making tool to diagnose lung cancer with the help of computers because it has better discriminative performance and uncertainty awareness when compared to the baseline models. Moreover, distinguishable uncertainty of predictions and uncertainty of models are present. Predictive uncertainty is measured through the variance of softmax probability distributions through stochastic forward passes, which is related to the ambiguity of data. Monte Carlo dropout is used to estimate model uncertainty as a Bayesian approximation, which represents parameter-level uncertainty due to a small amount of training data.
Artificial Intelligence in Robotics: Revolutionizing Industrial Automation and Beyond Chamandeep Kaur, Awatef Balobaid Proceedings 1st International Conference on Frontier Technologies and Solutions Icfts 2025, 2025 AI in robotics involves the application of efficient algorism and computational techniques that allows robots to operate independently, learn from the environment and make efficient decisions. However, in current studies, mostly contact methods that cannot be learned and do not respond to change are used despite the progress made in this regard. These approaches often fail to respond in real-time to a certain issue, which can result in inefficiency and low overall operational performance. To overcome these shortcomings, this paper presents a new technique in which DRL (Deep Reinforcement Learning) is used for motion control of a robotic arm in industrial automation. DRL allows the robotic system to learn the best motion profiles from playing a game with its surroundings with reduced chances of a less optimum setting due to changing environmental conditions. The most important peculiarities of the proposed approach include integration of enhanced neural structures and realization of automatic learning and decision-making.The effectiveness of the DRL approach is shown to be considerably higher than that of traditional ones, with accuracy reaching 99.3%. Such outcomes speak to the prospects to transform robotics through DRL knowledge and skills to enhance real-time adaptation and increase effectiveness. Lastly, this research enhances the existing literature in AI & Robotics and accordingly establishes the foundation for further developments of intelligent automation systems in various fields.
A Deep CNN Self-Attention Model for Multidimensional Speech Quality Prediction Using Crowdsourced Datasets Annapurna Gummadi, Chamandeep Kaur, Deepak Gupta, Vuda Sreenivasa Rao, Santosh Tripurana, et al. 2025 5th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2025, 2025 Predicting voice quality is critical for improving communication systems and the user experience. In this article, we offer a unique technique to multidimensional speech quality prediction using a Deep Convolutional Neural Network (CNN) enriched with self-attention techniques and crowdsourced datasets. The proposed model incorporates both spatial and temporal information from speech signals via numerous convolutional layers, enabling it to accurately represent the intricate patterns and features seen in speech data. Furthermore, self-attention methods are used to let the algorithm to focus on relevant parts of the input data, increasing its discriminative capacity and tolerance to interference. Transfer learning is utilized to leverage pre-trained representations from large-scale speech datasets, enabling the model to generalize well to diverse speech characteristics and environments. By fine-tuning the pre-trained model on the specific crowdsourced dataset, we adapt it to the task of speech quality prediction while mitigating the need for large annotated datasets, which are often costly and time-consuming to obtain. Experimental results demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance on various metrics for multidimensional speech quality prediction tasks. Furthermore, extensive ablation studies are conducted to analyze the contributions of different components of the model, highlighting the importance of both the CNN architecture and self-attention mechanisms. Overall, our findings suggest that the proposed deep CNN self-attention model, combined with transfer learning from crowdsourced datasets, presents a promising approach for accurate and efficient speech quality prediction in diverse real-world scenarios
Improving Skin Lesion Diagnosis with Grasshopper-Optimized ResNet-50 Architecture Ravindra Changala, Chamandeep Kaur, Gande Soma Raju, Abdul Rahman Mohammed Al Ansari, S. Suma Christal Mary, et al. Proceedings of 2025 AI Driven Smart Healthcare for Society 5 0 Adsoc5 0 2025, 2025 Skin lesion diagnosis helps doctors find and treat melanoma and skin cancer early. Computer systems that use CNNs and deep learning methods recently proved highly capable at identifying different types of skin lesions. This study develops an enhanced approach by combining the ResNet-50 architecture with the GOA algorithm to improve diagnostic outcomes and broaden the ability to detect various conditions. ResNet-50, a popular CNN architecture, drawing inspiration from the foraging behaviour of grasshoppers, which effectively traverse search areas to discover optimum solutions. With the goal of improving feature extraction and classification performance, the Grasshopper-Optimized ResNet-50 model attempts to address typical deep learning difficulties including overfitting and convergence stagnation. The GOA improves hyperparameter tuning for ResNet-50 by using grasshoppers' natural searching method in optimization. This system optimizes feature acquisitions while preventing model fitting errors and learning rate slowdowns. The framework demonstrates excellent results in data testing by reaching 97% accuracy and 93% F1 while showing strong improvements in performance measurement. Research shows how optimization algorithms based on natural phenomena boost the effectiveness of deep learning methods in medical image analysis. The Grasshopper-Optimized ResNet-50 model demonstrates strong capabilities in skin lesion identification and brings valuable solutions to medical systems and patient care.
Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network Meenal R Kale, Chamandeep Kaur, Indrajeet Kumar, Haider Sharif Mahdi, Vijay Uprikar, et al. 2025 5th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2025, 2025 The ability to recognize sign language movements, especially Arabic Sign Language (ArSL), is necessary to increase the conversation accessibility for the those who are hard of hearing or deaf. However, there are a number of drawbacks to the current ASLR techniques, such as their dependency on manually created features, lack of datasets, and inefficiencies in real-time applications. This study suggests a novel method that utilize Deep Convolutional Neural Network (DCNN) model designed especially for ASLR applications to overcome these difficulties. The suggested DCNN overcomes the drawbacks of conventional handmade feature-based techniques by automatically extracting discriminative features from unprocessed sign language images using hierarchical feature learning. Preprocessing techniques such as normalization and scaling are applied to improve the 7,857 completely labeled images in the RGB Arabic Alphabet Sign Language (AASL) the data set, which is provided for ASLR model evaluation and training. The results of the study demonstrate how successful the recommended DCNN model is, as evidenced by its astounding 99% accuracy on the AASL dataset. Comparison with similar approaches like VGG-16, ResNet-18, and other Efficient Net based architecture, shows the accuracy to which the proposed method can classify Arabic sign language alphabets. The findings presented evidence for how the chosen architecture for DCNNs can significantly enhance ASLR improvement, open up opportunities for improving communication interaction with Deaf and hard-of-hearing people and their integration. In doing the proposed work, we use Python.
MINING DEVIATION WITH MACHINE LEARNING TECHNIQUES IN EVENT LOGS WITH AN ENCODING ALGORITHM Journal of Theoretical and Applied Information Technology, 2024
Uncertainty-Aware Volumetric Transformer with Dual Spatial-Channel Attention for Lung Nodule Classification. BN Patil, TK Rao, N Mahamatov, E Muniyandy, AP VK, C Kaur, ... International Journal of Advanced Computer Science & Applications 17 (2) , 2026 2026
Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems M DSouza, R Pradhan, C Kaur, SSC Mary, J Preshiya 2025 2nd International Conference on Integration of Computational … , 2025 2025
A Scalable Machine Learning Framework for Predictive Analytics and Employee Performance Enhancement in Large Enterprises. JS Kanwar, RS Kartha, C Kaur, BV Nandakishore, E Muniyandy, VS Rao, ... International Journal of Advanced Computer Science & Applications 16 (8) , 2025 2025 Citations: 2
Optimizing Personalized Cancer Treatment Plans using Deep Reinforcement Learning in IoT-Enabled Healthcare Systems R Balakrishna, S Patra, C Kaur, NM Aljawarneh 2025 International Conference on Intelligent Computing and Knowledge … , 2025 2025
Artificial Intelligence in Robotics: Revolutionizing Industrial Automation and Beyond C Kaur, A Balobaid 2025 International Conference on Frontier Technologies and Solutions (ICFTS … , 2025 2025 Citations: 2
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), 020038 , 2025 2025 Citations: 6
Improving Skin Lesion Diagnosis with Grasshopper-Optimized ResNet-50 Architecture R Changala, C Kaur, GS Raju, ARM Al Ansari, SSC Mary, II Raj 2025 AI-Driven Smart Healthcare for Society 5.0, 212-217 , 2025 2025
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) 19 (2 … , 2025 2025 Citations: 67
A Deep CNN Self-Attention Model for Multidimensional Speech Quality Prediction Using Crowdsourced Datasets A Gummadi, C Kaur, D Gupta, VS Rao, S Tripurana, P Nagaraj 2025 Fifth International Conference on Advances in Electrical, Computing … , 2025 2025 Citations: 2
Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network MR Kale, C Kaur, I Kumar, HS Mahdi, V Uprikar 2025 Fifth International Conference on Advances in Electrical, Computing … , 2025 2025 Citations: 1
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 2024 Citations: 6
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 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 2024 Citations: 16
Revolutionizing Image Analysis: The Integration of MLP and GRU Architectures in Advanced Computer Vision D Nimma, AS Sengar, C Kaur, SA Khan, SD Anushna, II Raj 2024 IEEE 1st International Conference on Green Industrial Electronics and … , 2024 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 2024 Citations: 6
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 2024 Citations: 6
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 2024 Citations: 15
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 2024 Citations: 9
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 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 2024 Citations: 11
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 2023 Citations: 268
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 2023 Citations: 99
The cloud computing and internet of things (IoT) C Kaur International Journal of Scientific Research in Science, Engineering and … , 2020 2020 Citations: 69
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) 19 (2 … , 2025 2025 Citations: 67
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 2022 Citations: 67
Comparative analysis of artificial intelligence and its powered technologies applications in the finance sector S Tyagi, T Jindal, SH Krishna, SM Hassen, SK Shukla, C Kaur 2022 5th International Conference on Contemporary Computing and Informatics … , 2022 2022 Citations: 53
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 2022 Citations: 50
The vital role of virtual private network (VPN) in making secure connection over internet world YK Sharma, C Kaur International Journal of Recent Technology and Engineering (IJRTE) 8 (6 … , 2020 2020 Citations: 48
Incorporating sentimental analysis into development of a hybrid classification model: A comprehensive study D Kaur International Journal of Health Sciences 6, 1709-1720 , 2022 2022 Citations: 37
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 2024 Citations: 31
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 2021 Citations: 27
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 2024 Citations: 26
Recent advances in artifact removal techniques for EEG signal processing A Bisht, C Kaur, P Singh Intelligent Communication, Control and Devices: Proceedings of ICICCD 2018 … , 2019 2019 Citations: 26
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 2023 Citations: 20
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 2023 Citations: 20
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 2023 Citations: 17
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 2022 Citations: 17
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 2024 Citations: 16
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 2020 Citations: 16
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 2024 Citations: 15
