Dr. Harsh Nagar

@manipal.edu

Assistant Professor, School of Computer Engineering, MIT Manipal
Manipal Institute of Technology, Manipal, India

Dr. Harsh Nagar


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https://researchid.co/nagarharsh33

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Engineering, Artificial Intelligence, Human-Computer Interaction
21

Scopus Publications

257

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Introduction, Importance, Concept, and Application of Smart Farming in Horticulture
    Harsh Nagar, Ambuj, Rajendra Machavaram
    Smart Horticulture Production Integrating Advanced Technologies, 2026
    The agricultural sector is undergoing a significant transformation through Smart Farming, driven by advancements in information and communication technologies (ICTs). This chapter delves into the evolving relationship between technology and horticulture, beginning with a historical overview of traditional farming practices and the growing need for innovation to address global challenges. It introduces the concept of Smart Farming, its components, and its integration into modern horticulture. The role of technologies such as the Internet of Things (IoT), data analytics, and precision agriculture is explored, illustrating how they enhance productivity, sustainability, and resource efficiency. Key innovations—including sensors, automation, robotics, and drones—are examined for their role in modernizing horticultural practices. The chapter also highlights practical applications such as crop monitoring, precision irrigation, pest and disease control, and supply chain optimization. Real-world case studies provide evidence of the tangible benefits and transformative potential of Smart Farming. Furthermore, the discussion addresses barriers to adoption and offers strategies to overcome them. Looking ahead, the chapter explores 2anticipated technological advancements and emphasizes the critical role of policy and regulation in supporting smart horticulture initiatives. Overall, this chapter serves as a comprehensive guide for researchers, practitioners, and policymakers interested in the future of technology-driven horticulture.
  • Enhancing Bitcoin Address Unlikability: Addressing Gaps in the Evicting-Filling Attack and Proposing a Holistic Defense Framework
    Nitin Varshney, Harsh Nagar, Tirth Bhadeshiya
    Communications in Computer and Information Science, 2026
  • Advancements in Machine Learning: Algorithms, Applications, and Emerging Research Directions
    Harsh Nagar, Nitin Varshney, Twinkal Chavda, Krunal Vaghela
    Lecture Notes in Networks and Systems, 2026
  • Blockchain-Enhanced Federated Learning for Privacy-Preserving Model Training in Cloud Computing Systems
    Nitin Varshney, Harsh Nagar, Dhaval Pambhar, Krunal Vaghela
    Lecture Notes in Networks and Systems, 2026
  • Optimizing novel drying process for Carissa carandas pulp: process parameters, drying kinetics, and thin-layer drying model validation
    Ankit Kumar, Sandeep Kumar, Rakesh Gehlot, Rekha Phogat, Amit Verma, Harsh Nagar
    Cogent Food and Agriculture, 2026
    Foam-mat drying is widely used for fruit pulps; however, limited studies have systematically integrated process optimization, drying kinetics, and functional powder properties for underutilized fruits such as Carissa carandas. This study aimed to optimize foam-mat drying of C. carandas pulp to produce high-quality powder. Different levels of methyl cellulose, xanthan gum, and gum tragacanth, and whipping time were evaluated using Taguchi’s orthogonal array design. Optimized foams were dried at varying temperatures (45–90 °C) and thicknesses (5 and 10 mm). According to Taguchi’s optimization, the best-performing treatments [methyl cellulose (0.4%), xanthan gum (0.3%), and gum tragacanth (0.1%), and whipping time (20 min)] yielded lower foam density (0.172 g/cm3), higher foam expansion (550%), better foam stability (100%), lower moisture content (7.00%), and bulk density (0.192 g/cm3). The effective moisture diffusivity ranged from 3.93 × 10−1 to 2.50 × 10−9 m2/s across all treatments, while activation energy values were 27.58 and 22.07 kJ/mol for 5 and 10 mm foams, respectively. Among the tested models, the Hasibuan & Daud and Midilli et al. models provided the best fit for drying curves under various drying conditions. The study demonstrates the potential of foam-mat drying for producing stable and functional powder from C. carandas, contributing to improved utilization and quality assurance of underutilized fruit resources.
  • Climate-Intelligent Agriculture: Robotic and UAV Approaches for Resilient Crop Systems
    Harsh Nagar, Rajendra Machavaram
    Innovations in Climate Resilient Agriculture, 2025
  • Enhanced PTP Security Framework for TSN: A Comprehensive Approach to Secure TLV Management with Extended Analysis
    Twinkal Chavda, Dhavalkumar Pambhar, Harsh Nagar
    2025 Artificial Intelligence and Smart Technologies for Sustainability Conference Aists 2025, 2025
    Time-Sensitive Networking (TSN) is what makes deterministic communication possible in Industry 4.0. It depends on the Precision Time Protocol (PTP) to keep clocks synchronized in every microsecond, which is important for manufacturing automated car systems and industrial automation. Nevertheless Berardi et al. (2023) demonstrated that vulnerabilities in PTP's Type Length Value (TLV) frames allow for low-bandwidth attacks, such as DISABLE_PORT, which de-synchronize clocks causing errors of up to 8 ms and jeopardizing operational integrity. We present Secure-Tl V-ptp, a robust framework to strengthen PTP through required TLV authentication, dynamic key management, network segmentation and machine learning-based anomaly detection. Extending Berardi et al.'s fundamental work, we present a formal security model a new mathematical formulation for clock drift prediction and attack likelihood and a thorough analysis of TSN security trends from 2020 to 2025. With clock drift decreased to 35 ns and latency overhead of 4555 µs, well within TSN's rigorous criteria, comprehensive studies spanning 2 to 20 nodes show a 97.5% reduction in attack success rates. Our method for finding anomalies is 94% accurate, which is far better than previous standards. Also, a new graphs and tables show the speed and scalability. Secure-TLV- PTP enhances TSN security by tackling TLV vulnerabilities with a scalable, low-latency solution therefore guaranteeing consistent synchronization for next-generation industrial networks.
  • Sentiment Analysis-Driven Stock Price Forecasting Using Natural Language Processing (NLP) and Predictive Analytics
    Rahul Jain, Nitin Varshney, Kruti Rathod, Harsh Nagar, Rajul Suthar, Bansi Chavda
    2025 Artificial Intelligence and Smart Technologies for Sustainability Conference Aists 2025, 2025
    Forecasting stock prices helps investors and data analyst make smart trading choices. A lot of the traditional approaches are available for making predictions are based on how stock prices have changed over a time and a lot of different technical factors, such as markers and patterns are there. Nevertheless, these methods often fail to determine the mood of investors, which can be influenced by many different factors like news stories, financial reports, and social media posts etc. This study looks into how combining Predictive Analytics and Natural Language Processing (NLP) together can make predictions more accurate for any stocks. In this research paper we involve many modern Sentiment Analysis methods like BERT and TF-IDF to determine how the market feels about many factors that affect investors. These features are based on cutting edge Machine Learning models like Random Forest and Gradient Boosting to make predictions highly accurate. This study's findings demonstrate that sentiment data is far more crucial and useful rather than traditional forecasting algorithms for predicting stock prices. This research makes a huge impact in the field of financial data analytics by introducing new approach based on sentiment that aims to improve the accuracy of stock market trend predictions. This methodology optimizes algorithms and provides retail investors a AI -generated insights to fill institutional traders' knowledge gaps. By assessing source credibility, we address ethical challenges like bots propagating fake news to affect people's feelings. Our approach is reliable in uncertain markets because it perfectly balances new ideas with taking responsibility.
  • Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor
    Vijay Mahore, Peeyush Soni, Prakhar Patidar, Harsh Nagar, Arjun Chouriya, Rajendra Machavaram
    Smart Agricultural Technology, 2024
    The tractor serves as a crucial power source in agricultural operations. However, the tractor's power often remains underutilized due to a mismatch between the tractor and implement, considering specific field conditions. To enhance system output, it becomes vital to acquire data on performance-related parameters for the tractor-implement combination. In this study we develop a real-time Instrumented Tractor Performance Monitoring System (ITPMS) using the Internet-of-Things (IoT). This system consists of a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer measures tillage depth, while the three-point hitch dynamometer used to measure data on draft force. Proximity sensors are installed on a two-wheel drive (2WD) tractor to measure forward speed and drive-wheel slip. We establish a dedicated web server using a Google® Firebase® project to store data from all sensors through Raspberry Pi. Additionally, we design a web interface and a mobile application to provide real-time data generated from the sensors. Field experiments were done to evaluate and monitor the performance parameters of the tractor-implement combination utilising the developed ITPMS. The results demonstrate that the system effectively monitors the performance parameters necessary for tractor-implement combination. Furthermore, the system's capability to update data to the IoT server in real-time is validated. Overall, the development and implementation of this Raspberry Pi based IoT system, provides a reliable and efficient solution for real-time performance monitoring of instrumented tractors.
  • Assessment of sensor-based automatic smart watering unit for paddy nurseries under Indian perspective
    Vinod Choudhary, Rajendra Machavaram, Naseeb Singh, Harsh Nagar
    Smart Agricultural Technology, 2024
    A sensor-based automatic watering unit was developed to irrigate the paddy seedling trays. The watering unit comprised six flat spray nozzles with a direct current (DC) water pump and flow sensor, and these were kept at the top of the conveyor frame, which was actuated by a DC water pump with a solenoid valve to drop the water in trays. The actuation of the DC solenoid valve was controlled by a limit switch fitted to the conveyor frame, which sensed the moving trays on the conveyor. The performance of the developed unit was evaluated, and process optimization was performed using the Inscribed Central Composite Design in Response Surface Methodology. The effect of main operating parameters- chain conveyor speed (0.123, 0.196, and 0.27 m/s), spray operating pressure (1.5, 3.0, and 4.5 kg/cm2), and spray height (10,30 and 50 cm) on the performance of the sensor-based automatic watering unit was studied in terms of the amount of water spray requirement and coefficient of water spray uniformity in trays. The responses of the unit at its optimal parameter operation (spray operating pressure 4.5 kg/cm2, spray height 10 cm, and chain conveyor speed 0.14 m/s) were found to be 1.31 L/tray as the amount of water spray requirement and 92.41 % as the coefficient of water spray uniformity of the unit. Hence, it can be an efficient substitute with significantly less power consumption 41 W with a higher working capacity of 780 trays/h for watering into the paddy tray nursery. The developed sensor-based automatic smart watering unit saves irrigation water per square area of 68.90 %, 76.13 %, and 82.31 % compared to existing drip, sprinkler, and flood irrigation methods in mat-type paddy nurseries. The developed sensor-based automatic smart watering unit saves the watering cost INR 11,567.16 (140.02 USD) per hectare, reducing cost by 63.7 % compared to manual watering in the mat-type nursery preparation. Thus, an automatic smart watering unit in a paddy tray nursery offers consistent and uniform watering from an Indian perspective, promoting uniform germination, crop growth, and establishment. It conserves water by ensuring the right amount of water is applied at the right time, avoiding overwatering or under-watering.
  • “Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud”
    Ambuj, Harsh Nagar, Ayan Paul, Rajendra Machavaram, Peeyush Soni
    Robotics and Autonomous Systems, 2024
  • Deep learning aided computer vision system for automated linear type trait evaluation in dairy cows
    Indu Devi, Naseeb Singh, Kuldeep Dudi, Rakesh Ranjan, Surender Singh Lathwal, Divyanshu Singh Tomar, Harsh Nagar
    Smart Agricultural Technology, 2024
  • Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification
    Ayan Paul, Rajendra Machavaram, Ambuj, Dheeraj Kumar, Harsh Nagar
    Computers and Electronics in Agriculture, 2024
  • AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage
    Harsh Nagar, Rajendra Machavaram, Pranav Kulkarni, Peeyush Soni
    Systems Science and Control Engineering, 2024
  • Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning
    Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Vijay Mahore, Prakhar Patidar
    Cogent Engineering, 2024
  • An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning
    Harsh Nagar, Rajendra Machavaram, Ambuj, Peeyush Soni, Subhajit Saha, T. Subhash Chandra Bose
    Systems Science and Control Engineering, 2024
  • Utilizing Fine-Tuned YOLOv8 Deep Learning Model for Greenhouse Capsicum Detection and Growth Stage Determination
    Ayan Paul, Ambuj, Harsh Nagar, Rajendra Machavaram
    7th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2023 Proceedings, 2023
  • Detection of Cotton Plants Using the YOLOv7 Deep Learning Model
    Arjun Chouriya, Peeyush Soni, E.V. Thomas, Vijay Mahore, Prakhar Patidar, Harsh Nagar
    2023 2nd International Conference on Futuristic Technologies Incoft 2023, 2023
  • An IoT-Enabled Tractor Data Sensing System for Precision Agriculture
    Vijay Mahore, Prakhar Patidar, Peeyush Soni, Harsh Nagar, Arjun Chouriya, Arpita Paul
    2023 2nd International Conference on Futuristic Technologies Incoft 2023, 2023
  • A Data-Driven Approach to Forecast Engine Torque of an Agricultural Tractor Across Varied Operational Range Using Machine Learning
    Harsh Nagar, Rajendra Machavaram, Ayan Paul, Peeyush Soni, Vijay Mahore, Arjun Chouriya, Ambuj
    2023 2nd International Conference on Futuristic Technologies Incoft 2023, 2023
  • Application of Artificial Intelligence for Fuel Consumption Prediction of a Tractor in Different Operating Conditions
    Harsh Nagar, Rajendra Machavaram
    2022 IEEE 7th International Conference for Convergence in Technology I2ct 2022, 2022

RECENT SCHOLAR PUBLICATIONS

  • Optimizing novel drying process for Carissa carandas pulp: process parameters, drying kinetics, and thin-layer drying model validation
    A Kumar, S Kumar, R Gehlot, R Phogat, A Verma, H Nagar
    Cogent Food & Agriculture 12 (1), 2657608 , 2026
    2026.0
  • Introduction, Importance, Concept, and Application of Smart Farming in Horticulture
    H Nagar, R Machavaram
    Smart Horticulture Production 1, 1-37 , 2026
    2026.0
  • Blockchain-Enhanced Federated Learning for Privacy-Preserving Model Training in Cloud Computing Systems
    N Varshney, H Nagar, D Pambhar, K Vaghela
    World Conference on Information Systems for Business Management, 379-390 , 2025
    2025.0
  • Enhancing Bitcoin Address Unlikability: Addressing Gaps in the Evicting-Filling Attack and Proposing a Holistic Defense Framework
    N Varshney, H Nagar, T Bhadeshiya
    International Conference on Advancements in Smart Computing and Information … , 2025
    2025.0
  • Climate-Intelligent Agriculture: Robotic and UAV Approaches for Resilient Crop Systems
    H Nagar, R Machavaram
    Innovations in Climate Resilient Agriculture, 391-416 , 2025
    2025.0
    Citations: 4
  • An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning
    H Nagar, R Machavaram, Ambuj, P Soni, S Saha, TSC Bose
    Systems Science & Control Engineering 12 (1), 2385332 , 2024
    2024.0
    Citations: 2
  • AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage
    H Nagar, R Machavaram, P Kulkarni, P Soni
    Systems Science & Control Engineering 12 (1), 2347936 , 2024
    2024.0
    Citations: 12
  • Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning
    H Nagar, R Machavaram, Ambuj, P Soni, V Mahore, P Patidar
    Cogent Engineering 11 (1), 2311810 , 2024
    2024.0
    Citations: 8
  • Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor
    V Mahore, P Soni, P Patidar, H Nagar, A Chouriya, R Machavaram
    Smart Agricultural Technology 9, 100530 , 2024
    2024.0
    Citations: 21
  • Assessment of sensor-based automatic smart watering unit for paddy nurseries under Indian perspective
    V Choudhary, R Machavaram, N Singh, H Nagar
    Smart Agricultural Technology 8, 100518 , 2024
    2024.0
    Citations: 5
  • Deep learning aided computer vision system for automated linear type trait evaluation in dairy cows
    I Devi, N Singh, K Dudi, R Ranjan, SS Lathwal, DS Tomar, H Nagar
    Smart Agricultural Technology 8, 100509 , 2024
    2024.0
    Citations: 12
  • Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud
    H Nagar, A Paul, R Machavaram, P Soni
    Robotics and Autonomous Systems 178, 104723 , 2024
    2024.0
    Citations: 23
  • Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification
    A Paul, R Machavaram, D Kumar, H Nagar
    Computers and Electronics in Agriculture 219, 108832 , 2024
    2024.0
    Citations: 128
  • Fields of Balance: An Extensive Analysis of Conventional and Organic Agriculture in the Contemporary Era
    V Gupta, R Bagora, RP Singh, N Nandeha, D Monya, H Nagar, D Singh
    International Journal of Environment and Climate Change 14 (2), 545-554 , 2024
    2024.0
  • A data-driven approach to forecast engine torque of an agricultural tractor across varied operational range using machine learning
    H Nagar, R Machavaram, A Paul, P Soni, V Mahore, A Chouriya
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-7 , 2023
    2023.0
    Citations: 4
  • Detection of Cotton Plants Using the YOLOv7 Deep Learning Model
    A Chouriya, P Soni, EV Thomas, V Mahore, P Patidar, H Nagar
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-4 , 2023
    2023.0
    Citations: 3
  • An IoT-Enabled Tractor Data Sensing System for Precision Agriculture
    V Mahore, P Patidar, P Soni, H Nagar, A Chouriya, A Paul
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-4 , 2023
    2023.0
    Citations: 4
  • Utilizing Fine-Tuned YOLOv8 deep learning model for greenhouse capsicum detection and growth stage determination
    A Paul, H Nagar, R Machavaram
    2023 7th International Conference on I-SMAC (IoT in Social, Mobile … , 2023
    2023.0
    Citations: 23
  • Application of artificial intelligence for fuel consumption prediction of a tractor in different operating conditions
    H Nagar, R Machavaram
    2022 IEEE 7th International conference for Convergence in Technology (I2CT), 1-7 , 2022
    2022.0
    Citations: 7
  • Machine Learning Based Generalised Tractor Fuel Consumption Prediction Model Integrated with Cloud-Based Serverless Spatial Platforms
    H Nagar, A Pathak, R Machavaram, V Mahore, P Soni, P Patidar
    Available at SSRN 4512884 , 0
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification
    A Paul, R Machavaram, D Kumar, H Nagar
    Computers and Electronics in Agriculture 219, 108832 , 2024
    2024.0
    Citations: 128
  • Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud
    H Nagar, A Paul, R Machavaram, P Soni
    Robotics and Autonomous Systems 178, 104723 , 2024
    2024.0
    Citations: 23
  • Utilizing Fine-Tuned YOLOv8 deep learning model for greenhouse capsicum detection and growth stage determination
    A Paul, H Nagar, R Machavaram
    2023 7th International Conference on I-SMAC (IoT in Social, Mobile … , 2023
    2023.0
    Citations: 23
  • Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor
    V Mahore, P Soni, P Patidar, H Nagar, A Chouriya, R Machavaram
    Smart Agricultural Technology 9, 100530 , 2024
    2024.0
    Citations: 21
  • AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage
    H Nagar, R Machavaram, P Kulkarni, P Soni
    Systems Science & Control Engineering 12 (1), 2347936 , 2024
    2024.0
    Citations: 12
  • Deep learning aided computer vision system for automated linear type trait evaluation in dairy cows
    I Devi, N Singh, K Dudi, R Ranjan, SS Lathwal, DS Tomar, H Nagar
    Smart Agricultural Technology 8, 100509 , 2024
    2024.0
    Citations: 12
  • Cloud-driven serverless framework for generalised tractor fuel consumption prediction model using machine learning
    H Nagar, R Machavaram, Ambuj, P Soni, V Mahore, P Patidar
    Cogent Engineering 11 (1), 2311810 , 2024
    2024.0
    Citations: 8
  • Application of artificial intelligence for fuel consumption prediction of a tractor in different operating conditions
    H Nagar, R Machavaram
    2022 IEEE 7th International conference for Convergence in Technology (I2CT), 1-7 , 2022
    2022.0
    Citations: 7
  • Assessment of sensor-based automatic smart watering unit for paddy nurseries under Indian perspective
    V Choudhary, R Machavaram, N Singh, H Nagar
    Smart Agricultural Technology 8, 100518 , 2024
    2024.0
    Citations: 5
  • Climate-Intelligent Agriculture: Robotic and UAV Approaches for Resilient Crop Systems
    H Nagar, R Machavaram
    Innovations in Climate Resilient Agriculture, 391-416 , 2025
    2025.0
    Citations: 4
  • A data-driven approach to forecast engine torque of an agricultural tractor across varied operational range using machine learning
    H Nagar, R Machavaram, A Paul, P Soni, V Mahore, A Chouriya
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-7 , 2023
    2023.0
    Citations: 4
  • An IoT-Enabled Tractor Data Sensing System for Precision Agriculture
    V Mahore, P Patidar, P Soni, H Nagar, A Chouriya, A Paul
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-4 , 2023
    2023.0
    Citations: 4
  • Detection of Cotton Plants Using the YOLOv7 Deep Learning Model
    A Chouriya, P Soni, EV Thomas, V Mahore, P Patidar, H Nagar
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-4 , 2023
    2023.0
    Citations: 3
  • An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning
    H Nagar, R Machavaram, Ambuj, P Soni, S Saha, TSC Bose
    Systems Science & Control Engineering 12 (1), 2385332 , 2024
    2024.0
    Citations: 2
  • Machine Learning Based Generalised Tractor Fuel Consumption Prediction Model Integrated with Cloud-Based Serverless Spatial Platforms
    H Nagar, A Pathak, R Machavaram, V Mahore, P Soni, P Patidar
    Available at SSRN 4512884 , 0
    Citations: 1
  • Optimizing novel drying process for Carissa carandas pulp: process parameters, drying kinetics, and thin-layer drying model validation
    A Kumar, S Kumar, R Gehlot, R Phogat, A Verma, H Nagar
    Cogent Food & Agriculture 12 (1), 2657608 , 2026
    2026.0
  • Introduction, Importance, Concept, and Application of Smart Farming in Horticulture
    H Nagar, R Machavaram
    Smart Horticulture Production 1, 1-37 , 2026
    2026.0
  • Blockchain-Enhanced Federated Learning for Privacy-Preserving Model Training in Cloud Computing Systems
    N Varshney, H Nagar, D Pambhar, K Vaghela
    World Conference on Information Systems for Business Management, 379-390 , 2025
    2025.0
  • Enhancing Bitcoin Address Unlikability: Addressing Gaps in the Evicting-Filling Attack and Proposing a Holistic Defense Framework
    N Varshney, H Nagar, T Bhadeshiya
    International Conference on Advancements in Smart Computing and Information … , 2025
    2025.0
  • Fields of Balance: An Extensive Analysis of Conventional and Organic Agriculture in the Contemporary Era
    V Gupta, R Bagora, RP Singh, N Nandeha, D Monya, H Nagar, D Singh
    International Journal of Environment and Climate Change 14 (2), 545-554 , 2024
    2024.0