VISHAL DUTT
@cuchd.in
Technical Trainer
Chandigarh University
Vishal Dutt is an accomplished Senior Research Associate at AVN Innovations, with extensive experience in academia and industry. He is a renowned freelance trainer for Android and Google Cloud, having served in this capacity since 2016. With over 7 years of academic teaching experience, he has authored over 50 publications in well-known and peer-reviewed National and International Scopus Journals, Conferences, and Book Chapters. He has contributed to the editorial process of two books with Wiley, Eureka publications, and is currently working on three more publications with Wiley.
EDUCATION
MCA (Gold Medalist)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Computer Science Applications, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Neeraj Bhargava, Ritu Bhargava, Kapil Chauhan, Pramod Singh Rathore, and Vishal Dutt
Apple Academic Press
Abhishek Kumar, Swarn Avinash Kumar, Vishal Dutt, S. Shitharth, and Esha Tripathi
Wiley
Srinivasa Rao Burri, D. K. Agarwal, Vishal Dutt, and Aradhya Pokhriyal
IEEE
There is presently no cure for Parkinson's disease (PD), a degenerative and devastating neurological ailment that affects millions of people worldwide. The field of PD could benefit significantly from quantum computing, an emerging technology that has the potential to revolutionize healthcare research. After introducing Parkinson's disease (PD), its symptoms, and current treatments, this paper dives into the role that quantum computing could play in PD diagnosis and treatment. The research explains how quantum machine learning algorithms might enhance the accuracy of PD diagnosis and prediction by analyzing complicated datasets, including genetic data and medical pictures, to uncover biomarkers for early identification. In addition, the feasibility of using quantum computing to expedite drug discovery and back up personalized medicine approaches in PD treatment is investigated. The constraints and ethical concerns of applying quantum computing to healthcare are also discussed. Research is needed to address the limits of quantum computing technology. Still, the study does imply that quantum computing has the potential to dramatically improve healthcare applications, particularly in the field of neurological illnesses.
Narayan Vyas and Vishal Dutt
IEEE
Precision agriculture relies on the early detection and isolation of crop diseases, and this research details how the You Only Look Once, Version 8 (YOLOv8) algorithm was used for the PlantVillage dataset. This research looks at how Deep Learning (DL) and Computer Vision (CV) could streamline and improve the diagnostic process, a problem with conventional disease detection approaches. The YOLOv8 model is trained and evaluated using the PlantVillage dataset, which consists of high-resolution photos encompassing different classes of crops and diseases. It is found that YOLOv8 outperformed other popular Machine Learning (ML) models in identifying agricultural diseases with 95% accuracy, 90% precision, 95% recall, 92% F1 score, and 90% specificity. Parameter optimization, advanced network architectures, and integration of the Internet of Things (IoT) and drones for real-time disease monitoring are just some of the future research directions proposed in this study, along with discussions of the difficulties posed by data availability, computational complexity, and resource requirements. YOLOv8's successful application to the PlantVillage dataset demonstrates its potential to automate and improve crop disease diagnosis, leading to more effective and environmentally friendly farming methods.
Srinivasa Rao Burri, Mamadou Yero Diallo, Lakshay Sharma, and Vishal Dutt
IEEE
This paper examines how AI has revolutionised drug development and medical research using the ChEMBL dataset. The primary study areas are AI-driven therapeutic target identification., computational approaches in drug development., drug repurposing for COVID-19 therapies., and AI methods for natural leather flaw detection. Target selection must balance novelty and confidence., and AI-driven therapeutic target identification is considered. Structure-based virtual screening and profound learning predictions of ligand properties and target activities are considered for application in scaling up to broader chemical spaces. AI is used to discover new links between drugs., targets., and diseases and treat COVID-19. The paper also highlights this field's enforcement challenges and offers solutions. A Generative Adversarial Network (GAN)-based automatic flaw identification system for natural leather is another topic of study. The results show that the suggested strategy is economical and accurate., despite limitations and biases. AI has revolutionised medical diagnostics., medication development., and precision medicine., making this work meaningful. This paper”s findings offer a cross-disciplinary perspective on artificial intelligence's potential in healthcare., revealing knowledge gaps and suggesting further research.
Srinivasa Rao Burri, Vasundhara Vijay Ghorpade, Vishal Dutt, and Kumari Lipi
IEEE
Health Assistant Bot is an advanced AI-powered virtual health assistant to enhances patients' ability to receive more individualized care. Health Assistant Bot's capabilities in symptom recognition, physician recommendation, and essential health information provision are discussed in this study, along with its creation and evaluation. Extensive testing and analysis have shown that the system is accurate, effective, and user-friendly. The problems of false information and data privacy are addressed by Health Assistant Bot, guaranteeing its ethical and responsible use in healthcare settings. By harnessing technology, Health Assistant Bot supplements human caretakers and gives patients more agency in their treatment. By invisibly integrating state-of-the-art technology into healthcare practices, the virtual assistant paves the way for care centered on the patient. The proposed model of RNN gave an accuracy of 90.20%, precision of 88.50%, recall of 88% and Fl Score of 89.20%. Embrace the transformative potential of the Health Assistant Bot, which encourages better healthcare outcomes through co-production and gives people more agencies.
Hardik Dhiman, Narayan Vyas, Ashutosh Pagrotra, and Vishal Dutt
IEEE
The research proposes improving night-time driving safety by handling high-resolution car projector lights. Internet of Things (IoT) and Computer Vision technology are employed in this strategy. The technology constantly monitors traffic data to adjust the light output and leave room for incoming traffic to prevent accidents. The proposed approach integrates computer vision, IoT, and high-resolution projection lighting to enhance traffic safety and lower the likelihood of accidents. The system will be managed by the You Only Look Once (YOLO) algorithm to adjust the light projection in response to the presence of other cars on the road, and the suggested method will automatically change the vehicle's high beam to prevent shining into the path of approaching traffic. The proposed architecture has the potential to significantly enhance night-time driving security, decreasing the danger of harm or death and significantly cutting the frequency of crashes.
Ashutosh Pagrotra, Narayan Vyas, Hardik Dhiman, and Vishal Dutt
IEEE
In recent years, there have been significant advancements in Remotely Operated Vehicles (ROVs), particularly in air and underwater applications. However, the progress in land-based ROVs has been slow, necessitating innovative approaches to address the existing challenges. This research paper presents a comprehensive analysis of the current state of land-based ROV technology, focusing on its limitations. A comprehensive solution is proposed, which combines well-established technology from the gaming industry, such as steering wheel controls and accelerators, with high-performance vehicle control systems to overcome these shortcomings. The suggested approach aims to enhance the control and precision of land-based ROVs using imitation learning techniques, thereby increasing operator safety during critical missions. The paper highlights the primary features and capabilities of the proposed model while underscoring its potential to revolutionize land-based robotic systems. The researchers aspire to advance the field of remote vehicles operating in challenging terrains by providing valuable insights derived from their study. The conclusions drawn from this research significantly impact the future development and utilization of land-based ROVs, paving the way for safer and more efficient operations across various sectors and applications.
Rajasrikar Punugoti, Vishal Dutt, Abhishek Kumar, and Neha Bhati
IEEE
Cardiovascular Disease (CVD) affects deaths and hospitalisations. Clinical data analytics struggles to predict heart disease survival. This report compares machine learning-based cardiovascular disease prediction studies. The authors use a Kaggle dataset of 70,000 records and 16 features to show a SMOTE model with hyperparameter-optimized classifiers. Random Forest outperforms KNN with 13 elements in cardiovascular disease prediction. Naive Bayes outperforms SVM on complete feature sets. The proposed model achieves 86% accuracy, and the optimised SMOTE technique outperforms the traditional SMOTE technique in all metrics. This study analyses the strengths and weaknesses of existing models for making cardiovascular disease predictions with machine learning and suggests a promising new method.
Venkata Raghuveer Burugadda, Vishal Dutt, Mamta, and Narayan Vyas
IEEE
This study investigates the advantages and disadvantages of using a variety of machine learning approaches to estimate an individual person's risk of Cardiovascular Disease (CVD). Not all relevant risk variables specific to an individual may be considered by conventional risk assessment methods because they rely on predetermined risk factors. Algorithms trained on big data sets can identify trends and anticipate individual risks, allowing for more precise and targeted measures against CVD. Different machine learning algorithms are tested for their ability to predict CVD risk using a dataset comprising clinical and demographic factors. Examples of these algorithms are logistic regression, decision trees, random forests, and Support Vector Machines (SVM). Additionally, we investigate how feature-selection strategies and model hyperparameter adjustment can improve ML model efficiency. The study reveals the most critical risk factors for CVD and underlines the potential of machine learning techniques to enhance personalized CVD risk prediction. This research took advantage of data from over 14,000 patients, including detailed information on their demographics, medical histories, and daily routines. The solution outperformed conventional approaches with an accuracy of 90% after training machine learning models on this varied dataset. The personalized data-driven strategy shows potential for improving CVD risk prediction and preventative measures.
Pankaj Kumar, Sudhir Bhandari, and Vishal Dutt
IEEE
This study primarily examines how well four pretrained deep learning models perform in identifying eye disorders using four metrics we developed: recall, precision, accuracy, and F1 Score. With the help of universal custom layers, the models are adjusted, and the outcomes are examined. The study then suggests an ensemble method that uses majority voting to combine the probabilistic outputs of the top-performing models. The suggested methodology outperforms state-of-the-art algorithms in experiments using a publicly available dataset, with average values for Recall, Precision, Accuracy, and F1 Score of 81.25%, 83.68%, 95.17%, and 79.12%, respectively. The work shows how well-trained deep learning models can identify eye illnesses and have the potential to improve public health, especially in mass screening programs.
Uday Shankar Sekhar, Narayan Vyas, Vishal Dutt, and Abhishek Kumar
IEEE
This research aimed to evaluate numerous deep-learning models for Alzheimer's disease detection using several different neuroimaging techniques. Ten recent studies were selected for comparison based on their methodology, conclusions, and limitations. The Generative Adversarial Network (GAN) algorithm is applied fictitiously to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and results are provided. A comparison was made between the results of the GAN algorithm and the selected studies. Evaluation metrics were presented in a table and a graph. The study concludes that ensemble models and multi-modal approaches improve Alzheimer's Disease detection performance. However, there is a need for further work to be done to address issues, including limited sample sizes and a lack of external validation.
Rajasrikar Punugoti, Vishal Dutt, Abhineet Anand, and Neha Bhati
IEEE
The Internet of Things (IoT) will disrupt medicine the most. Healthcare uses edge computing, deep learning, IoT, and machine learning for real-time analytics, monitoring, and data analysis. This research will evaluate, expand, overcome difficulties, and improve IoT-based healthcare systems. The article explores healthcare IoT and edge computing’s benefits and drawbacks. Edge computing can improve healthcare delivery, cost, and results, according to the authors. Researchers offer wearable sensors, smart health gateways, early warning score health monitoring, and artificial intelligence case studies. The authors evaluate edge computing’s enabling technologies, applications, and obstacles in healthcare and provide solutions. Existing research lack solid empirical evaluation, hyper-specialization in one healthcare field, and inadequate study of hurdles and difficulties. This research also includes various IoT services, utilization rate graphs, major drivers driving edge-IoT operations graph, IoT adoption graph, and recent Medicare technologies diagram.
Rohit Raturi, Abhishek Kumar, Narayan Vyas, and Vishal Dutt
IEEE
Data anomalies are found using anomaly detection. Generative adversarial networks (GANs) can produce synthetic data and learn complex patterns. Temporal dependencies and relevant features are needed to identify timeseries anomalies. A GAN is trained to detect anomalies in timeseries data. The model performs better with synthetic data. Traditional ML models struggle with the difficulties of anomaly identification in time-series data due to issues such as dealing with high dimensionality, capturing temporal correlations, and detecting infrequent events with skewed class distributions. This study tested machine learning techniques for anomaly identification in time-series data using the Yahoo! Webscope S5 dataset. In terms of F1 score (0.912), precision (0.S99), recall (0.925), AUC-ROC (0.976), TP rate (0.925), and FP rate (0.045), the suggested approach beat the baseline methods. The suggested method uses GANs to generate synthetic data for anomaly identification in time-series data.
Abhishek Kumar, Swarn Avinash Kumar, Vishal Dutt, Ashutosh Kumar Dubey, and Sushil Narang
Universidad Internacional de La Rioja
Sriramakrishnan Chandrasekaran, Vishal Dutt, Narayan Vyas, and Abhineet Anand
IEEE
Most machine learning models have had tremendous success in implementing prediction analysis on dePD end diseases such as brain tumors, making it an ambitious goal to apply machine learning to medical research discoveries. In the case of Parkinson's disease, for example, early diagnosis and understanding might allow patients to adopt preventative measures before the onset of clinical symptoms. In cases when no effective therapies exist, machine learning mPDe provides a means of making an early diagnosis and thereby improving patient outcomes. A loss of brain function, like in the case of Dementia, impairs the ability of the rest of the body to function normally. In medicine, the application of machine learning models is known as “quantum intelligence,” and it is used to determine which drug combinations work best. In terms of technology and automation, quantum computing or intelligence is among the highest. We're attempting to apply quantum intelligence to a dataset about Parkinson's disease. Models of machine learning and deep learning, such as random forests, decision trees, convolutional neural networks, recurrent neural networks, and so on, are applied to a variety of generic datasets. OASIS is a specialized neuroimaging dataset with a variety of MRI patient dimensions. This can be incorporated into future studies. We are employing 3D brain pictures to diagnose the tumour using machine learning models, and MRI has multiple facets to be taken into account before arriving at a diagnosis. Magnetic Resonance Imaging (MRI) has two variations, known as T1 and T2. When it comes to fMRI, PET, and similar technologies, we've got you covered. Decision trees and random forest algorithms from machine learning, along with neural networks and RNNs from deep learning, were used to attain the desired accuracy.
Sriramakrishnan Chandrasekaran, Rashmi Agrawal, Vishal Dutt, and Narayan Vyas
IEEE
Health care is playing a major role in designing any kind of prediction model and there is a large amount of data related to different diseases is available over the globe. A major part of machine learning lies in the identification of the data related to different diseases and make them helpfulfor people. The major implementation is in the identification ofthe safest medical data systems if the new challenging task where we can implement quantum computing as the main lead to process the medical data and making the new data systems which are most secured to manage the medical data which is useful to identify the fastest method to form a drug and also to manage in the identification of the safe drug mechanism for thedisease which is not even in identification. Quantum computingis a challenging area that is like an extension to the current scenario of medical imaging. Improved healthcare system using machine learning and quantum computing gives a new age to medical sciences to identify the approximate solutions to the different medical diseases. This article works on the implementation of such medical advancements to a specificdisorder based on human genetics and DNA sequencing. The Quantum computing approach succeeded in recognizing the approximate medical sequences for a particular disease.
Sriramakrishnan Chandrasekaran, Vishal Dutt, Narayan Vyas, and Raj Kumar
IEEE
In terms of social, psychological, physical, technological, and other elements, the educational system is undergoing significant transformation. Today, education is becoming a joint venture between the state, the market, and the community. Alternative education and training providers that place a greater emphasis on employability provide a problem, and university professors represent a particular breed of career academics that remain cut off from developments in the outside world. The sentiment analysis of student comments is presented in this work using a combination of Methodologies based on lexicons and machine learning. The textual feedback, which is often gathered around the conclusion of a semester, offers helpful insights into the general quality of teaching and makes insightful recommendations for ways to enhance instructional design. The article describes a sentiment analysis model trained using TF-IDF and linguistic characteristics to look at the opinions expressed by participants in their textual feedback. Additionally, a comparison among the existing sentiment analysis techniques is done.
Abhishek Kumar, SwarnAvinash Kumar, Vishal Dutt, Ashutosh Kumar Dubey, and Vicente García-Díaz
Elsevier BV
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Conducted numerous research training workshops and seminars, focusing on advanced topics and techniques in the field of Computer Science and Engineering.
Developed and implemented training modules to enhance research skills and foster a culture of innovation among students and faculty members.
Mentored and guided students in their research projects, helping them explore new avenues and refine their methodologies.
Facilitated collaboration among research teams, fostering interdisciplinary research projects, and encouraging knowledge sharing.
Played a vital role in establishing research partnerships with industry and academia to enhance the research ecosystem at Chandigarh University.
Actively contributed to research publications, presenting papers at national and international conferences and journals.
Received the Outstanding Teaching Award for his contributions to the development and delivery of research-based contributions.
Conducted Seminars and workshops of GCP in various colleges and Universities.
Received consistently positive feedback from students in R & D training courses, with an average rating of 4.7 out of 5 for course content, delivery, and instructor expertise.