Dr Chamandeep Kaur
@jazanu.edu.sa
Lecturer
Jazan University
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
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
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
K. Suresh Kumar, Chinmaya Kumar Nayak, Chamandeep Kaur, and Ahmed Hesham Sedky
Wiley
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
Wiley
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi, and D. Suganthi
Wiley
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.
Shaista Sabeer, Ayasha Siddiqua, Afsana Anjum, Sunanda Kondapalli, Chamandeep Kaur, and Ahmed Unnisa Begum
AIP Publishing
Hussein Tuama Hazim, Chamandeep Kaur, Sambhrant Srivastava, Iskandar Muda, Harish Chander Anandaram, and Mohammed Saleh Al Ansari
AIP Publishing
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.
Yousef Methkal Abd Algani, Orlando Juan Marquez Caro, Liz Maribel Robladillo Bravo, Chamandeep Kaur, Mohammed Saleh Al Ansari, and B. Kiran Bala
Elsevier BV
Nagendar Y, Chamandeep Kaur, Noor Firdoos Jahan, Aravinda K, Kskn Venkata Ramana Devi, and Ginni Nijhawan
IEEE
The current research experimentally examines the elements that influence the usage of cloud-based computation in organizations where the strategy is regarded as strategic for performing their operation. To examine the elements that impact the intent to use cloud computing, a study framework was created that incorporates the elements discovered in the technological acceptance model (TAM) with other exterior elements like high-level management assistance, firm size, training, interaction, and technical complication. The assumptions are tested using data gathered from 150 firms in Andalusia (Spain). The findings of this research demonstrate which key aspects must be examined and the way they are linked. They also demonstrate the organizational needs that should be regarded by businesses that want to use an actual management paradigm tailored to the digital financial system, particularly those associated with cloud computing.
Lipsa Das, Chamandeep Kaur, Ayasha Siddiqua, Durdana Taranum, Ganesh Vasudeo Manerkar, and Ajay Rana
IEEE
Authentication, access control, confidentiality, integrity, on-repudiation, and vacuity come obligatory security Mobile Payment operations. The authentication process consists of two-way including stoner verification and origin verification. Authentication consists of two ways which includes validating the stoner and determining the provenance of the data source. Access control can give authorized individualities access to the payment system while precluding unauthorized people from penetrating the payment system. To avoid unresistant assaults on sale data, the information must also be kept nonpublic. The vacuity of the payment system guarantees that it's accessible. Data integrity prevents data from being tampered with, and non-repudiation verifies that the communication was transferred by a specific stoner. This paper describes a new security medium to ameliorate data integrity during the sale using a mobile payment operation.
A Satchidanandam, R. Mohammed Saleh Al Ansari, A L Sreenivasulu, Vuda Sreenivasa Rao, Sanjiv Rao Godla, and Chamandeep Kaur
The Science and Information Organization
— The goal of artistic style translation is to combine an image's substance with an equivalent image's spirit of innovation. Current approaches are unable to consistently capture complex stylistic elements and maintain uniform stylization over semantic segments, which results in artefacts. Also suggest a novel approach which blends subjective loss algorithms using deep networks of neurons with segmentation using semantics to address these issues. By guaranteeing contextually-aware design distribution together with information preservation, the combination improves general aesthetic correctness during the styling transmission process. With this technique, perceptive components are extracted using both the subject matter and the style photos using previously trained deep neural systems. These components combine to provide perceptive loss coefficients, which are subsequently included into the design of a Generative Adversarial Network (GAN). For offering the representation a better grasp of the meaning contained in any given image, an automatic segmenting module is subsequently implemented. This historical data directs the style transferring process, producing an additional precise and sophisticated transition. The outcomes of our experiments confirm the efficacy of this method and demonstrate improved visual accuracy over earlier approaches. The use of semantic segmentation and loss of perceptual information algorithms together provide a significant 95.6% improvement in visual accuracy. This method effectively overcomes the drawbacks of earlier approaches, providing precise and trustworthy transference of style and constituting a noteworthy advancement in the field of imaginative style transfer. The final output graphics further demonstrate the importance of the recommended approach by deftly integrating decorative elements into functionally significant places.
Moresh Mukhedkar, Chamandeep Kaur, Divvela Srinivasa Rao, Shweta Bandhekar, Mohammed Saleh Al Ansari, Maganti Syamala, and Yousef A.Baker El-Ebiary
The Science and Information Organization
— Reliable classification of Land Use and Land Cover (LULC) using satellite images is essential for disaster management, environmental monitoring, and urban planning. This paper introduces a unique method that combines a Convolutional Neural Network (CNN) with Human Group-based Particle Swarm Optimization (HPSO) and Ant Colony Optimization (ACO) algorithms to improve the accuracy of LULC classification. The suggested hybrid HPSO-ACO-CNN architecture effectively solves the issues with feature selection, parameter optimization
A. Leela Sravanthi, Sameh Al-Ashmawy, Chamandeep Kaur, Mohammed Saleh Al Ansari, K. Aanandha Saravanan, and Veera Ankalu. Vuyyuru
The Science and Information Organization
— Diabetes is a major health issue that affects people all over the world. Accurate early diagnosis is essential to enabling adequate therapy and prevention actions. Through the use of electronic health records and recent advancements in data analytics, there is growing interest in merging multimodal medical data to increase the precision of diabetes prediction. In order to improve the accuracy of diabetes prediction, this study presents a novel hybrid optimisation strategy that seamlessly combines machine learning techniques. In order to merge many models in a way that maximises efficiency while enhancing prediction accuracy, the study employs a collaborative learning technique. This study makes use of two separate diabetes database datasets from Pima Indians. A feature selection process is used to streamline error-free classification. A third method known as Binary Grey Wolf-based Crow Search Optimisation (BGW-CSO)
Chamandeep Kaur, Abdul Rahman Mohammed Al-Ansari, Taviti Naidu Gongada, K. Aanandha Saravanan, Divvela Srinivasa Rao, Ricardo Fernando Cosio Borda, and R. Manikandan
The Science and Information Organization
— Effective patient treatment and care depend heavily on accurate disease diagnosis. The availability of multi-modal medical data in recent years, such as genetic profiles, clinical reports, and imaging scans, has created new possibilities for increasing diagnostic precision. However, because of their inherent complexity and variability, analyzing and integrating these varied data types present significant challenges. In order to overcome the difficulties of precise medical disease diagnosis using multi-modal data, this research suggests a novel approach that combines Transfer Learning (TL) and Deep Neural Networks (DNN). An image dataset that included images from various stages of Alzheimer's disease (AD) was collected from kaggle repository. In order to improve the quality of the signals or images for further analysis, a Gaussian filter is applied during the preprocessing stage to smooth out and reduce noise in the input data. The features are then extracted using Gray-Level Co-occurrence Matrix (GLCM). TL makes it possible for the model to use the information gained from previously trained models in other domains, requiring less training time and data. The trained model used in this approach is AlexNet. The classification of the disease is done using DNN. This integrated approach improves diagnostic precision particularly in scenarios with limited data availability. The study assesses the effectiveness of the suggested method for diagnosing AD, focusing on evaluation metrics such as accuracy, precision, miss rate, recall, F1-score, and the Area under the Receiver Operating Characteristic Curve (AUC-ROC). The approach is a promising tool for medical professionals to make more accurate and timely diagnoses, which will ultimately improve patient outcomes and healthcare practices. The results show significant improvements in accuracy (99.32%).
Ayasha Siddiqua, Afsana Anjum, Sunanda Kondapalli, and Chamandeep Kaur
IEEE
The proposed approach utilizes real-time techniques to aid in regulating and monitoring of solar power plants through the Internet of Things(IoT). Traditional PLC technology is insufficient for remote access and monitoring of solar power station operations. Therefore, the IoT and Machine learning (ML) are used to the administration of solar power plants. The term “Internet of Things” refers to the integration of several physical technologies with cloud applications. Each component of the Internet of Things (IoT) must have a certain minimum level of processing power, data security measures, and communication capabilities. This innovative technology may be used to monitor and boost solar power output. Since servomotors may be used to rotate solar panels in response to changes in the sun’s angle, the efficiency with which they produce electricity can be increased. Designing a photovoltaic system, constructing the analogue circuitry for accurate voltage and current measurements, and developing a website to provide the monitored data in an approachable graphical format are all necessary steps towards this goal. Since the web server is located on a WAN (Wide Area Network), it is accessible from anyplace with an Internet connection.
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
Chamandeep Kaur, M. Sunil Kumar, Afsana Anjum, M. B. Binda, Maheswara Reddy Mallu, and Mohammed Saleh Al Ansari
Engineering and Technology Publishing
J. Lysa Eben, Chamandeep Kaur, Mathews T Thelly, and Parimita
IEEE
Most of India’s population relies on farming for their livelihood. Every year brings new advances in agricultural technology. The use of wireless sensors in contemporary agriculture is crucial. The employment of wireless sensors across a wide range of agricultural applications has a profoundly favourable effect on crop productivity and economic efficiency. The mushroom business is still relatively young and modest compared to other sectors of India’s agricultural market. The white button mushroom is India’s most commercially significant fungus and is a global favourite. It may be grown anywhere with a suitable climate and soil, but most of the world’s supply comes from North India in the winter. For mycelia to flourish, the ideal temperature range is between 22 and 25 degrees Celsius. In contrast, the sweet spot for developing fruit bodies is between 14 and 18 degrees Celsius with high relative humidity. Equipment for maintaining a steady temperature and pasteurising the mushrooms, should be present in the rooms used for growing. The primary objective of this study is to automate the mushroom production plant and keep track of the crop room’s environmental state to reduce the amount of human care required for the mushroom plant. The Internet of Things has also modernized agriculture industries, creating an environmental monitoring and regulating system that is used in the mushroom farm in this work. Using the Speak internet platform, users may monitor ecological conditions in a mushroom farm on their Android mobile, including the ambient temperature, relative humidity, carbon dioxide level, and light intensity. The Arduino prototype platform is employed as the sensors station’s controller, ensuring that the plant parameters are within the user-specified range. Using a pair of low-power ESP8266 as Wi-Fi modems, the current parameter status is relayed to a distant monitoring station. The integrated Arduino uses the programming environment to write the controller’s code in the Arduino programming language, run debugging and compilation processes, and finally burn the code onto the microcontroller (IDE).
Chamandeep Kaur, Tuhina Panda, Subhasis Panda, Abdul Rahman Mohammed Al Ansari, M. Nivetha, and B. Kiran Bala
IEEE
Machine learning is widely used in many aspects of healthcare. The development of medical technology has made it possible to gather better data for early disease symptom diagnosis. This study makes an effort to categorize Alzheimer’s disorder. Alzheimer’s disease is a fatal disorder that may result in memory loss and mental impairment. To prepare for medical attention, this needs early disease diagnosis. Magnetic resonance imaging (MRI) can be used to accurately and non-invasively diagnose Alzheimer’s disease. Effective feature extraction and segmentation techniques are necessary for the accurate diagnosis of MRI images. Utilizing MRI data of the brain’s white matter, grey matter, and cerebrospinal fluid, feature selection is carried out. Random forest trees are used in standard machine learning methods like regression and classification. The results of the utilized method were next contrasted with those of other machine learning techniques. As a result, RF model-based interpolation analysis surpasses the RF non-imputation method with greater accuracy, specificity, sensitivity, f-measure, and ROC.
Chamandeep Kaur, Samar Mansour Hassen, Mawahib Sharafeldin Adam Boush, and Harishchander Anandaram
Engineering and Technology Publishing
In a Wireless Sensor Network (WSN), Numerous cost-effective and energy-constrained sensor nodes are typically used. In a typical Wireless Sensor Network, a single Base Station (BS) gathers information from the whole network, which contributes to concerns including latency, network failure, and congestion. The overwhelming proportion of energy consumption, as well as the energy hole limitation, significantly degrades the overall system performance and network lifetime, which is owing to the sensor nodes that are near the BS consuming more energy. To tackle this problem, it’s essential to determine the perfect spot for mobile sink nodes, which minimizes the power consumed and so increases the network's lifespan. In this work, an effective strategy is designed and developed to detect the location of a mobile sink considering factors such as distance, estimated energy, and fairness, using Deep learning-based energy prediction with an adjacency cell score model. In addition, the predicted energy is determined by employing the Deep Maxout Network (DMN). However, a Minimum distance of 137.364, maximal residual energy of 30.903, maximum standardized fairness of 64.426, maximum network duration of 60, and maximum standardized throughput of 60.613 was obtained using the proposed adjacency-based cell score + Deep Maxout Network.
Yousef Methkal Abd Algani, B. Nageswara Rao, Chamandeep Kaur, B. Ashreetha, K. V. Daya Sagar, and Yousef A. Baker El-Ebiary
The Science and Information Organization