mohit bhandwal

@amity.uz

assistant professor, department of IT and Engineering
Amity university in Tashkent

mohit bhandwal


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https://researchid.co/mohit5410

RESEARCH, TEACHING, or OTHER INTERESTS

Energy, Mechanical Engineering, Pollution, Modeling and Simulation
29

Scopus Publications

263

Scholar Citations

7

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Human Health Status Detection from the Tongue Image Using Embedded Image Processing
    Mohit Bhandwal, Mukesh Roy, Pratap Patil
    Embedded Systems for Biomedical Applications, 2025
    The tongue, one of the most critical organs of the human body, may divulge substantial indications of an individual’s health. For hundreds of years, traditional medicine has documented the association between what we would currently term general health issues and the properties of the tongue through its technique of tongue diagnosis. Because of the breakthroughs in image processing technology, it is feasible to analyze an individual’s tongue image for a variety of health conditions. Extracting and examining features such as color, texture, coating, and form via high-resolution tongue images with computer vision techniques help analyze potentially useful traits. Upon verification with patterns established in connection with a variety of medical disorders, this property may reveal an annotated potential anomaly or a marker of illness. The use of image processing to ascertain the health status of the tongue provides numerous benefits, including early disease detection and its non-invasive nature. Automated image evaluation through machine learning algorithms and pattern recognition techniques may dramatically improve evaluating tongue pictures at all levels of aid organizations. As a result, image processing in the medical area may improve existing diagnostic methodologies by giving unbiased, quantifiable data for a more complete understanding of health. The technique of gathering older diagnostic knowledge with new technologies presents numerous novel possibilities for the creation of a new set of disease treatment and preventive care paradigms. Due to innovative technical solutions, an increase in research and development in this field can eventually lead to the enhancement of other medical applications.
  • Integration of Digital Twin and Computational Engineering for Sustainable Infrastructure Management
    Neha Gupta, Piyush Jain, Manik Rakhra, Naina Chaudhary, Mohit Bhandwal, Pratap Patil
    Proceedings of the 2025 14th International Conference on System Modeling and Advancement in Research Trends Smart 2025, 2025
    Digital Twins (DTs) are dynamic data-driven digital doppelganger of physical infrastructure assets with constantly synchronized capabilities with the underlying asset. Integrated with computational engineering techniques, such as high fidelity simulation, reduced order modelling, data assimilation and physics informed learning, DTs support proactive operations, maintenance and retrofit decisions to increase sustainability on the asset life cycle. In this study, an integrated architecture and workflow was proposed with four steps: trusted information management based on BIM/IFC and ISO 19650 standard; multiphysics simulation based on FMI co-simulation; real-time state estimation and continuous model update; and sustainability assessment according to ISO 14040/44 LCAs and ASCE/ISI Envision. A multi-fuel bridge and district-energy case study is used to illustrate how the proposed DT framework can predict performance, analyse risk, and optimise action in terms of important carbon footprint, cost, resilience and service efficiency measures. The decision making layer is formalised in the form of a multi objective optimisation problem which accounts for structural reliability as well as the life cycle impact. Furthermore, reduced order models and physics informed neural networks (PINNs) are exploited to achieve their goal with a real-time computational capability and without compromising accuracy. Overall, the proposed approach offers a practical way for asset owners and engineers to operationalise Digital Twins that are both useful, usable and popular for sustainable infrastructural management.
  • Machine Learning Approaches for Predictive Maintenance in Smart Manufacturing Environments
    Naina Chaudhary, Pratap Patil, Tanveer Baig Z, Mohit Bhandwal, Manik Rakhra, Prabha Kiran
    Proceedings of the 2025 14th International Conference on System Modeling and Advancement in Research Trends Smart 2025, 2025
    Predictive maintenance (PdM) is a key enabler for Industry 4.0. Predictive maintenance has one goal: to predict equipment failures in the future to minimize downtimes (and resulting costs) in production. Today's intelligent smartmanufacturing environment of sensors, big data analytics and machine learning (ML) has enabled around-the-clock machine health surveillance. This manuscript provides a review and a critical evaluation of state-of-the-art ML methodologies for PdM by categorizing the methodologies in supervised, unsupervised and reinforcement learning. The applicability of these methods in the fault detection, remaining useful life (RUL) estimation, and anomaly detection is investigated. A case study using vibration and temperature datasets obtained from CNC machinery is used to show the comparative performance of Random Forest (RF), Support Vector Machines (SVM), and Long Short Shane Memory (LSTMs) Neural Networks. According to evaluation performances including accuracy, precision, recall and mean absolute error (MAE), LSTM-based models outperformed the conventional ML approaches in terms of modeling the temporal dynamics of sensor data. It also discusses the problems such as data imbalance, model interpretability, and real-time application, and provides recommendations for hybrid architecture and edgecomputing implementation. Accordingly, a framework for ML algorithm selection and implementation in PdM systems in smart manufacturing scenarios is proposed in this study, aiming at increased reliability, safety, and cost efficiency.
  • Blockchain-Enabled Secure Data Transmission Framework for Next-Generation Sensing Applications
    Pratap Patil, Mohit Bhandwal, G. Prakash Babu, Manik Rakhra, Naina Chaudhary, Prabha Kiran
    Proceedings of the 2025 14th International Conference on System Modeling and Advancement in Research Trends Smart 2025, 2025
    Sensing systems of the next generation, such as smart cities, self-driving vehicles and industrial IoT, require a system of data transmission to be secure and efficient to permit integrity, confidentiality and trust. The traditional centralized security measures can be exploited with the single points of breakdown, data manipulation, and unauthorized access. In this paper, a data transmission system based on blockchain is suggested to offer secure data transmission through the combination of cryptographic hashing, digital signatures, and decentralized consensus to exchange immutable and verifiable sensing data. The architecture uses light edge gateways, which use lightweight blockchain nodes to reduce the latency with a high throughput. A mathematical model is created to determine transaction verification time, probability of data integrity and energy usage. Experimental simulations of Hyperledger Fabric have shown that the proposed framework minimizes the probability of data tampering by 94, throughput by 28, and latency by 17 per cent relative to non-blockchain base protocols. The outcomes prove that blockchain is effective in enabling next-generation sensing application with limited performance trade-offs.
  • Optimization of Energy-Efficient Wireless Sensor Networks for Industrial Process Automation
    Jitendra Kumar, Naina Chaudhary, Mohit Bhandwal, Pratap Patil, G. Prakash Babu, Z. Tanveer Baig
    Proceedings of the 2025 14th International Conference on System Modeling and Advancement in Research Trends Smart 2025, 2025
    With the growing importance of process networks in an industrial setting, dense wireless sensor networks (WSNs) must guarantee high reliability and bounded latency while being subject to stringent power restrictions. In order to achieve this objective, this paper proposes a joint clustering-powerduty (JCPD) optimization framework to reduce the network energy consumptions without sacrificing the end-to-end delay and packet delivery constraints observed in industrial control loops. In order to meet these requirements, the framework employs TSCH and collision avoidance and interference resiliency is achieved by deploying a first-order radio model. Variables for optimization are transmission power, sampling rate, duty cycle, cluster-head selection and routing in the presence of limitations on connectivity, coverage, queue stability and per-flow deadlines. Relaxation and inequality-based log-convex approximations of the problems are developed for the convex approximation and a distributed algorithm based on an ADMM is developed at the level of cluster-heads to solve the mixed-integer program optimally. In addition, to allow sensor nodes to be employed as well, a lightweight version of the heuristic (JCPD-Lite) is suggested. Simulation studies on the industrial network topologies with 50 to 300 nodes show an energy savings up to 38–55 % compared to LEACH/ PEGASIS and TSCH-only schedulers, with 99.9% packet delivery and latency of less than 50 ms.
  • AI-Driven Computational Models for Real-Time Structural Health Monitoring Using IoT Sensing Systems
    Mohit Bhandwal, Roopali Gupta, Naina Chaudhary, Z. Tanveer Baig, Pratap Patil, Akash Sanghi
    Proceedings of the 2025 14th International Conference on System Modeling and Advancement in Research Trends Smart 2025, 2025
    Structural Health Monitoring (SHM) is essential in the aetiology and maintenance of the safety and durability of civil infrastructures. The current manuscript outlines an Artificial Intelligence (AI) powered computational framework for real-time SHM using sensing Internet of Things (IoT) systems. The proposed architecture has multiple sensor modalities incorporated into edge-based ai models, namely strain gauges, accelerometers, and vibration sensors, to enable the concept of detecting anomalies in their early stages and locating the damage. A combination deep learning approach of Convolutional Neural Networks (CNNs) and Long ShortTerm Memory (LSTM) networks is used in combination to process heterogeneous sensor time series data in real time. Experimental evaluations performed with simulated datasets from bridges and buildings show that the model achieves an accuracy of more than 95 per cent in categorizing faults, and the mean squared error (MSE) reduction of 18 per cent with regard to the conventional baseline techniques. This study highlights the game-changing power of AI-IoT combination in terms of predictive maintenance, which can lead to cost savings, improved safety and resilience of critical infrastructure.
  • Trends and Development of the Digital Economy in Uzbekistan: A Sectoral Analysis (2016-2025)
    Naina Chaudhary, Balvinder Shukla, Sujit Prasad, Pratap Patil, Mohit Bhandwal, Manik Arora
    Proceedings of the 2025 International Conference on Technology Enabled Economic Changes Intech 2025, 2025
    This study investigates Uzbekistan's digital economy development over the years 2016 through 2025 by assessing the different sectors to which it drives its growth. The analysis relies on official statistical data to assess the publishing of books and the development of web portals and film production along with other digital-related activities. The analysis determines fundamental development patterns alongside sector-specific contributions and policymaking requirements which enable digital transformation of the Uzbekistan national economy. Research results demonstrate Uzbekistan's economic transformation through digital services and content development while providing guidance for policy decisions and scholarly study and industrial sector needs.
  • Predicting Career Transitions Through Insights from Academic Background and Workforce Dynamics Using Machine Learning
    Rajneesh Kler, Gurinder Singh, Naina Chaudhary, Bobur Abdullaev, Mohit Bhandwal, Danish Ather
    Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024
    The increased dynamism in modern employment landscapes has emanated to the frequent career changes and, therefore, the importance of accustoming to the factors explaining occupational change is growing. The paper discusses the various machine learning models in the prediction of career changes with the help of the data set that contains record number of 38444 and has 22 features including the academic background of the person, job satisfaction, skills gap, and the growth of the industry. Therefore, the investigations conducted regarding the connections between these features and career mobility offer practical recommendations for various applications of HR analytics, career guidance, and workforce planning. Logistic regression, decision tree and random forests algorithms were used from the family of machine learning algorithms. Of the models presented, the random forest model was found to be most effective with an accuracy of 85%. In the feature correlation analysis we determined other factors that deem to influence this factors for example field of study, levels of job satisfaction and growth in the industry. Demographic factors are also identified as critical drivers of career choices in the study. The research is useful to identify opportunities for the support of predictive analytics in context to current issues pertaining to talent management, talent development, and the overall talent retention. This research extends prior literature in the field of HR analytics by providing a quantitative approach for analyzing career mobility.
  • The Role of Artificial Intelligence in Advancing Fracture Mechanics: Modeling, Prediction, and Data Analysis
    Vivek Srivastava, Nitin Kumar Gupta, Ojas Raturi, Nalin Somani, Mohit Bhandwal, N aina Chaudhary
    Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024
    With the Internet of Things (loT) and the Industrial Internet of Things (1IoT) as key paradigms, industries and societies have been advanced by allowing systems to interconnect and making intelligent decisions. This paper examines the progress of the loT and IloT by specifically define the taxonomy, communication protocols and system integration for the two technologies. Whenever loT solutions are deployed, taxonomies provide the categorization of diverse constituents, functional relationships, and compositional hierarchies within these arrangements. MQTT, DDS, and OPC UA are discussed as important protocols and their functions, differences, and performance characteristics are compared and analyzed. The paper also aims to look at the integration framework of the IloT as this has the key function of ensure the compatibility between industrial operations and smart technologies. The concepts garnered through loT and IloT are explained by applying them in fields such as Smart Cities, Healthcare, Manufacturing and logistics etc. Nevertheless, some issues still present major questions such as scalability, interoperability and security. That is why this work underscores that there is a need to develop and improve protocols, which are necessary to overcome these challenges. As a synthesis of the current knowledge regarding loT and IloT, this work offers a valuable guide to researchers and practitioners trying to meet the great challenges of building reliable and efficient solutions to next generation interconnected systems.
  • Refining Large Language Model Query Optimization: An Adaptive Semantic Approach
    Ayush Thakur, Naina Chaudhary, Astha Gupta, Gurinder Singh, Amresh Kumar Choubey, Mohit Bhandwal
    Proceedings 4th International Conference on Technological Advancements in Computational Sciences Ictacs 2024, 2024
    This paper presents a novel approach to improve how questions interact with LLMs in this paper is presented. To this end, we developed an index called Query Semantic Complexity (QSC) that quantifies how challenging a question is. We also developed a method by the name Adaptive Semantic Query Optimization (ASQO) which alters the manner that it processes questions depending on their level of difficulty. Our approach attempts to try striking the middle ground between providing exact responses and employing the computer resources. Our concepts were tried on various LLMs such as GPT-3, T511B, as well as BERT-large that we discussed in this work. The results included great enhancements in speed in its response to questions, and precision of the answers provided. We also applied our method to examples from science and technical writing in practice. It turned out to be most effective when dealing with challenging questions and with large language generators. Overall this research provide a promising approach to making the LLMs perform better when responding to questions.
  • Arduino-Based Monitoring of Microclimatic Variables for Precision Agriculture in Sugarcane Cultivation
    Naina Chaudhary, Gurinder Singh, Danish Ather, Rajneesh Kler, Mohit Bhandwal
    2023 4th International Conference on Computation Automation and Knowledge Management Iccakm 2023, 2023
  • Design and Analysis of Flapping Bird Aerial Robot with Lift and Drag Force
    Mohit Bhandwal, Naina Chaudhary, Manik Arora, Pratap Paraji Patil, Nitin Kumar Gupta
    Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
  • AI-Driven Personalized Travel Planning: Enhancing Tourist Experiences in Uzbekistan
    Manik Arora, Naina Chaudhary, Mohit Bhandwal, Tanveer Baig, Pratap Patil
    Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
  • Predicting Student Performance with Machine Learning Algorithms
    Pratap Patil, Naina Chaudhary, Sujit Prasad, Mohit Bhandwal, Manik Arora, Gurinder Singh
    Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
  • Emission Reduction from Diesel Engine Using Alkali Solution, and Carbon Black in Union with Catalytic Convertor
    Mohit Bhandwal, R. K. Tyagi
    Lecture Notes in Mechanical Engineering, 2022
  • A new method to reduce the harmful gases and particulate matter emitted from the vehicles
    Mohit Bhandwal, R.K. Tyagi
    Materials Today Proceedings, 2022
  • Disambiguation of Catalytic Converter with Fluid Compounds using Automation and Reducing Cold Start Time with PCM
    Mohit Bhandwal, , Tyagi R K, and
    Journal of Engineering Research Kuwait, 2022
  • Investigation of Types of Technical Levitation and Mathematical Modeling of the Action of Many Composites Non-contact Electromechanical Mechanism
    F. M. Matmurodov, Basant Singh Sikarwar, Mohit Bhandwal
    Lecture Notes in Mechanical Engineering, 2021
  • Pandemic 2020, Challenges, and Measures for Post Revival
    Rahul Gupta, Manjula Shastri, Amit K. Pandey, Mohit Bhandwal
    Lecture Notes in Mechanical Engineering, 2021
  • Tailoring the Thermal Conductivity of Paraffin and Low-cost Device for Measuring thermal conductivity of Phase Change Material
    Mohit Bhandwal, Amritanshu Verma, Basant Singh Sikarwar
    Journal of Physics Conference Series, 2019
  • Optimizing the performance of catalytic convertor using turbulence devices in the exhaust system
    Tanmay Agrawal, Vivek Kumar Banerjee, Basant Singh Sikarwar, Mohit Bhandwal
    Lecture Notes in Mechanical Engineering, 2019
  • A novel system based on the principle of electrochemical treatment to reduce exhaust emission from gasoline-operated engine
    Prem Pal, Priyanka Sharma, Ajay Sharma, Mohit Bhandwal
    Lecture Notes in Mechanical Engineering, 2019
  • Reduction in exhaust emission using constantan catalyst in the diesel engine
    Vivek Kumar Banerjee, Tanmay Agrawal, Basant Singh Sikarwar, Mohit Bhandwal
    Lecture Notes in Mechanical Engineering, 2019
  • A novel system for exhaust emission reduction of diesel engine by using electrochemical technique
    Priyanka Sharma, Prem Pal, Ashutosh Mishra, Mohit Bhandwal, Ajay Sharma
    Lecture Notes in Mechanical Engineering, 2019
  • Estimate the performance of catalytic converter using turbulence induce devices
    Mohit Bhandwal, R. Tyagi, B. Sikarwar
    International Journal of Engineering Transactions B Applications, 2018
  • The effect of using the turbulence enhancement unit before the catalytic converter in diesel engine emissions
    Mohit Bhandwal, Manthan Kumar, Manish Sharma, Utkarsh Srivastava, Anmol Verma, R. K. Tyagi
    International Journal of Ambient Energy, 2018
  • Modelling of Blood Flow in Stenosed Arteries
    Mukesh Roy, Basant Singh Sikarwar, Mohit Bhandwal, Priya Ranjan
    Procedia Computer Science, 2017
  • Effect of creating turbulence on the performance of catalytic converter
    International Journal of Performability Engineering, 2016
  • Ecofriendly catalytic converter to reduce biochemical effect of exhaust gases
    Der Pharma Chemica, 2015

RECENT SCHOLAR PUBLICATIONS

  • Integration of Digital Twin and Computational Engineering for Sustainable Infrastructure Management
    N Gupta, P Jain, M Rakhra, N Chaudhary, M Bhandwal, P Patil
    2025 14th International Conference on System Modeling & Advancement in … , 2025
    2025
  • Optimization of Energy-Efficient Wireless Sensor Networks for Industrial Process Automation
    J Kumar, N Chaudhary, M Bhandwal, P Patil, GP Babu, ZT Baig
    2025 14th International Conference on System Modeling & Advancement in … , 2025
    2025
  • AI-Driven Computational Models for Real-Time Structural Health Monitoring Using IoT Sensing Systems
    M Bhandwal, R Gupta, N Chaudhary, ZT Baig, P Patil, A Sanghi
    2025 14th International Conference on System Modeling & Advancement in … , 2025
    2025
  • Machine Learning Approaches for Predictive Maintenance in Smart Manufacturing Environments
    N Chaudhary, P Patil, T Baig, M Bhandwal, M Rakhra, P Kiran
    2025 14th International Conference on System Modeling & Advancement in … , 2025
    2025
  • Blockchain-Enabled Secure Data Transmission Framework for Next-Generation Sensing Applications
    P Patil, M Bhandwal, GP Babu, M Rakhra, N Chaudhary, P Kiran
    2025 14th International Conference on System Modeling & Advancement in … , 2025
    2025
  • Trends and Development of the Digital Economy in Uzbekistan: A Sectoral Analysis (2016–2025)
    N Chaudhary, B Shukla, S Prasad, P Patil, M Bhandwal, M Arora
    2025 International Conference on Technology Enabled Economic Changes (InTech … , 2025
    2025
  • Human Health Status Detection from the Tongue Image Using Embedded Image Processing
    M Bhandwal, M Roy, P Patil
    Embedded Systems for Biomedical Applications, 272-295 , 2025
    2025
    Citations: 1
  • Refining Large Language Model Query Optimization: An Adaptive Semantic Approach
    A Thakur, N Chaudhary, A Gupta, G Singh, AK Choubey, M Bhandwal
    2024 4th International Conference on Technological Advancements in … , 2024
    2024
  • Predicting career transitions through insights from academic background and workforce dynamics using machine learning
    R Kler, G Singh, N Chaudhary, B Abdullaev, M Bhandwal, D Ather
    2024 4th International Conference on Technological Advancements in … , 2024
    2024
    Citations: 37
  • The Role of Artificial Intelligence in Advancing Fracture Mechanics: Modeling, Prediction, and Data Analysis
    V Srivastava, NK Gupta, O Raturi, N Somani, M Bhandwal, ...
    2024 4th International Conference on Technological Advancements in … , 2024
    2024
  • Smart Contracts for Ensuring Data Integrity in Cloud Storage with Blockchain
    K Bhurani, A Dogra, P Agarwal, P Shrivastava, TP Singh, M Bhandwal
    EAI Endorsed Transactions on Scalable Information Systems 11 (6) , 2024
    2024
    Citations: 2
  • Arduino-based monitoring of microclimatic variables for precision agriculture in sugarcane cultivation
    N Chaudhary, G Singh, D Ather, R Kler, M Bhandwal
    2023 4th International Conference on Computation, Automation and Knowledge … , 2023
    2023
    Citations: 60
  • Predicting Student Performance with Machine Learning Algorithms
    P Patil, N Chaudhary, S Prasad, M Bhandwal, M Arora, G Singh
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 12
  • Design and Analysis of Flapping Bird Aerial Robot with Lift and Drag Force
    M Bhandwal, N Chaudhary, M Arora, PP Patil, NK Gupta
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
  • AI-driven personalized travel planning: Enhancing tourist experiences in Uzbekistan
    M Arora, N Chaudhary, M Bhandwal, T Baig, P Patil
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 11
  • Disambiguation of catalytic converter with fluid compounds using automation and reducing cold start time with PCM
    M Bhandwal, RK Tyagi
    J. Eng. Res 10 , 2022
    2022
    Citations: 1
  • A new method to reduce the harmful gases and particulate matter emitted from the vehicles
    M Bhandwal, RK Tyagi
    Materials Today: Proceedings 56, 3623-3626 , 2022
    2022
    Citations: 2
  • Pandemic 2020, Challenges, and Measures for Post Revival
    R Gupta, M Shastri, AK Pandey, M Bhandwal
    Advances in Interdisciplinary Engineering: Select Proceedings of FLAME 2020 … , 2021
    2021
    Citations: 4
  • Investigation of Types of Technical Levitation and Mathematical Modeling of the Action of Many Composites Non-contact Electromechanical Mechanism
    FM Matmurodov, BS Sikarwar, M Bhandwal
    Advances in Engineering Design: Select Proceedings of FLAME 2020, 327-335 , 2021
    2021
    Citations: 4
  • Emission Reduction from Diesel Engine Using Alkali Solution, and Carbon Black in Union with Catalytic Convertor
    M Bhandwal, RK Tyagi
    International Conference on Energy, Materials Sciences & Mechanical … , 2020
    2020
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Modelling of blood flow in stenosed arteries
    M Roy, BS Sikarwar, M Bhandwal, P Ranjan
    Procedia Computer Science 115, 821-830 , 2017
    2017
    Citations: 68
  • Arduino-based monitoring of microclimatic variables for precision agriculture in sugarcane cultivation
    N Chaudhary, G Singh, D Ather, R Kler, M Bhandwal
    2023 4th International Conference on Computation, Automation and Knowledge … , 2023
    2023
    Citations: 60
  • Predicting career transitions through insights from academic background and workforce dynamics using machine learning
    R Kler, G Singh, N Chaudhary, B Abdullaev, M Bhandwal, D Ather
    2024 4th International Conference on Technological Advancements in … , 2024
    2024
    Citations: 37
  • Modelling and simulation of brake disc for thermal analysis
    N Gupta, M Bhandwal, BS Sikarwar
    Indian Journal of Science and Technology 10 (17), 1-5 , 2017
    2017
    Citations: 17
  • Predicting Student Performance with Machine Learning Algorithms
    P Patil, N Chaudhary, S Prasad, M Bhandwal, M Arora, G Singh
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 12
  • The effect of using the turbulence enhancement unit before the catalytic converter in diesel engine emissions
    M Bhandwal, M Kumar, M Sharma, U Srivastava, A Verma, RK Tyagi
    International Journal of Ambient Energy 39 (1), 73-77 , 2018
    2018
    Citations: 12
  • AI-driven personalized travel planning: Enhancing tourist experiences in Uzbekistan
    M Arora, N Chaudhary, M Bhandwal, T Baig, P Patil
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 11
  • Tailoring the thermal conductivity of paraffin and low-cost device for measuring thermal conductivity of phase change material
    M Bhandwal, A Verma, B Singh Sikarwar
    Journal of Physics: Conference Series 1369 (1), 012022 , 2019
    2019
    Citations: 6
  • Optimizing the performance of catalytic convertor using turbulence devices in the exhaust system
    T Agrawal, VK Banerjee, BS Sikarwar, M Bhandwal
    Advances in Interdisciplinary Engineering: Select Proceedings of FLAME 2018 … , 2019
    2019
    Citations: 6
  • Effect of creating turbulence on the performance of catalytic converter
    M KUMAR, M BHANDWAL, M SHARMA, A VERMA, U SRIVASTAVA, ...
    International Journal of Performability Engineering 12 (2), 115 , 2016
    2016
    Citations: 6
  • Ecofriendly catalytic converter to reduce biochemical effect of exhaust gases
    J Malhotra, M Bhandwal, RK Tyagi, A Kalia, S Pandey, A Rahul
    Der Pharma Chem 7 (12), 56-61 , 2015
    2015
    Citations: 5
  • Pandemic 2020, Challenges, and Measures for Post Revival
    R Gupta, M Shastri, AK Pandey, M Bhandwal
    Advances in Interdisciplinary Engineering: Select Proceedings of FLAME 2020 … , 2021
    2021
    Citations: 4
  • Investigation of Types of Technical Levitation and Mathematical Modeling of the Action of Many Composites Non-contact Electromechanical Mechanism
    FM Matmurodov, BS Sikarwar, M Bhandwal
    Advances in Engineering Design: Select Proceedings of FLAME 2020, 327-335 , 2021
    2021
    Citations: 4
  • Emission Reduction from Diesel Engine Using Alkali Solution, and Carbon Black in Union with Catalytic Convertor
    M Bhandwal, RK Tyagi
    International Conference on Energy, Materials Sciences & Mechanical … , 2020
    2020
    Citations: 3
  • Estimate the performance of catalytic converter using turbulence induce devices
    M Bhandwal, RK Tyagi, BS Sikarwar
    IJE Trans. B 31, 696-705 , 2018
    2018
    Citations: 3
  • Tailoring the thermal conductivity of paraffin wax by nano-fillers for thermal storage applications
    BS Sikarwar, A Chopra, M Bhandwal, M Kumar, DK Avasthi
    Proceedings of the 24th National and 2nd International ISHMT-ASTFE Heat and … , 2017
    2017
    Citations: 3
  • Smart Contracts for Ensuring Data Integrity in Cloud Storage with Blockchain
    K Bhurani, A Dogra, P Agarwal, P Shrivastava, TP Singh, M Bhandwal
    EAI Endorsed Transactions on Scalable Information Systems 11 (6) , 2024
    2024
    Citations: 2
  • A new method to reduce the harmful gases and particulate matter emitted from the vehicles
    M Bhandwal, RK Tyagi
    Materials Today: Proceedings 56, 3623-3626 , 2022
    2022
    Citations: 2
  • Human Health Status Detection from the Tongue Image Using Embedded Image Processing
    M Bhandwal, M Roy, P Patil
    Embedded Systems for Biomedical Applications, 272-295 , 2025
    2025
    Citations: 1
  • Disambiguation of catalytic converter with fluid compounds using automation and reducing cold start time with PCM
    M Bhandwal, RK Tyagi
    J. Eng. Res 10 , 2022
    2022
    Citations: 1