ASHUTOSH MISHRA

@amrita.edu

Assistant Professor (Sr. Gr) in Department of Electronics and Communication Engineering
Amrita Vishwa Vidyapeetham



              

https://researchid.co/ashutosh.mishra

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Multidisciplinary

42

Scopus Publications

615

Scholar Citations

12

Scholar h-index

18

Scholar i10-index

Scopus Publications


  • A Comprehensive Survey on AgriTech to Pioneer the HCI-Based Future of Farming
    Ashutosh Mishra and Shiho Kim
    Springer Nature Switzerland


  • Irregular situations in real-world intelligent systems
    Ashutosh Mishra and Shiho Kim
    Elsevier

  • A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques
    Dimple Tiwari, Bharti Nagpal, Bhoopesh Singh Bhati, Ashutosh Mishra, and Manoj Kumar
    Springer Science and Business Media LLC
    AbstractSentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.


  • Preface


  • Advanced data-driven approaches for intelligent olfaction
    Shiv Nath Chaudhri, Ashutosh Mishra, and Navin Singh Rajput
    IGI Global
    Advanced data-driven approaches have transformed the development of intelligent systems, gaining recognition from researchers and industrialists. Data plays a critical role in shaping intelligent systems, including artificial olfaction systems (AOS). AOS has evolved from manual feature extraction to leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs) for automated feature extraction. This chapter comprehensively overviews the synergy between data-driven approaches and CNNs in intelligent AOS. CNNs have significantly improved the accuracy and efficiency of scent and odor detection in AOS by automating feature extraction. Exploiting abundant data and leveraging CNN capabilities can enhance AOS performance. However, challenges and opportunities remain, requiring further research and development for optimal utilization of data-driven approaches in intelligent AOS.


  • Understanding the salient features related to resource management in broadband wireless networks
    Ramkumar Jayaraman, Devandar Rao, Manoj Kumar, and Ashutosh Mishra
    Wiley

  • A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles
    Pamul Yadav, Ashutosh Mishra, and Shiho Kim
    MDPI AG
    Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in MARL for CAVs. This paper provides a comprehensive survey on MARL for CAVs. A classification-based paper analysis is performed to identify the current developments and highlight the various existing research directions. Finally, the challenges in current works are discussed, and some potential areas are given for exploration to overcome those challenges. Future readers will benefit from this survey and can apply the ideas and findings in their research to solve complex problems.

  • A novel data-driven technique to produce multi- sensor virtual responses for gas sensor array-based electronic noses
    Sumit Srivastava, Shiv Nath Chaudhri, Navin Singh Rajput, and Ashutosh Mishra
    Walter de Gruyter GmbH
    Abstract Accurate detection of gas/odor requires highly selective gas sensor. However, the high-performance classification of gases/odors can be achieved using partial-selective gas sensors. Since 1980s, an array of broadly tuned (partial-selective) gas sensors have been used in several fields of science and engineering, and the resulting gas sensing systems (GSS) are popularly known as electronic noses (e-Noses). The combination of similar or different sensors in the array indirectly compensates for the requirement of high selectivity in GSS. Further, e-Nose’s performance inevitably depends on the salient features drawn from the initial responses of the gas sensor array (GSA). So obtained features are referred to as the responses of virtual sensors (VS). In this paper, we have proposed the three-input and three-output (TITO) technique to derive efficient virtual sensor responses (VSRs) which outperform its well-published peer technique. A GSA consisting of four elements is used to demonstrate the proposed technique. Our proposed technique augments the VSRs by four times compared to its peer. The efficacy of our proposed technique has been tested using nine fundamental classifiers, viz., linear support vector machine (100%), decision tree (97.5%), multi-layer perceptron neural network (100%), K-nearest neighbor (85%), logistic regression (100%), Gaussian process with radial basis function (95%), linear discriminant analysis (97.5%), random forest (100%), and AdaBoost (95%). Ten-fold cross-validation has been used to minimize the biasing impact of the intra- and inter-class variance. With the result, four classifiers successfully provide an accuracy of 100 percent. Hence, we have proposed and vindicated an efficient technique.

  • Evaluation of Growth Responses of Lettuce and Energy Efficiency of the Substrate and Smart Hydroponics Cropping System
    Monica Dutta, Deepali Gupta, Sangeeta Sahu, Suresh Limkar, Pawan Singh, Ashutosh Mishra, Manoj Kumar, and Rahim Mutlu
    MDPI AG
    Smart sensing devices enabled hydroponics, a concept of vertical farming that involves soilless technology that increases green area. Although the cultivation medium is water, hydroponic cultivation uses 13 ± 10 times less water and gives 10 ± 5 times better quality products compared with those obtained through the substrate cultivation medium. The use of smart sensing devices helps in continuous real-time monitoring of the nutrient requirements and the environmental conditions required by the crop selected for cultivation. This, in turn, helps in enhanced year-round agricultural production. In this study, lettuce, a leafy crop, is cultivated with the Nutrient Film Technique (NFT) setup of hydroponics, and the growth results are compared with cultivation in a substrate medium. The leaf growth was analyzed in terms of cultivation cycle, leaf length, leaf perimeter, and leaf count in both cultivation methods, where hydroponics outperformed substrate cultivation. The results of the ‘AquaCrop simulator also showed similar results, not only qualitatively and quantitatively, but also in terms of sustainable growth and year-round production. The energy consumption of both the cultivation methods is compared, and it is found that hydroponics consumes 70 ± 11 times more energy compared to substrate cultivation. Finally, it is concluded that smart sensing devices form the backbone of precision agriculture, thereby multiplying crop yield by real-time monitoring of the agronomical variables.

  • Energy-efficient hybrid node localisation underwater wireless sensor network schemes
    Parul Gupta, Wajahat Gh. Mohd, Nitin Goyal, Sachin Kumar Gupta, and Ashutosh Mishra
    Inderscience Publishers


  • Design and Analysis of Novel Microstrip-Based Dual-Band Compact Terahertz Antenna for Bioinformatics and Healthcare Applications
    Sandeep Kumar, Akhilendra Pratap Singh, and Ashutosh Mishra
    Ram Arti Publishers
    This paper presents a compact microstrip-based dual-band antenna for terahertz (THz) technology, catering to the increasing demand for high-frequency, high-gain, and wideband THz antennas. THz technology has numerous applications, including its demands in bioinformatics and healthcare. To address this need, the proposed antenna operates in two frequency bands: 3.6 THz to 4.3 THz and 5 THz to 5.7 THz, enabling its use in THz band communication. The antenna design features a microstrip patch with two transverse slots and one longitudinal slot as a radiator, fed with a microstrip line. The transverse slots enable dual-band resonance, while the longitudinal slots enhance bandwidth and efficiency. Using a 10µm thick polyamide material with a dielectric constant of 3.55, the antenna achieves a compact size of 40 × 40 µm2, lightweight construction, high radiation efficiency, and a wide impedance bandwidth. Simulation results confirm good impedance matching characteristics, with minimal voltage standing wave ratio and return loss of -10dB or less. The antenna exhibits an impedance bandwidth of -10dB at 700 GHz, a peak radiation efficiency of 85%, a peak gain of 7.86 dB, and an omnidirectional radiation pattern. These favorable attributes position the proposed antenna as an excellent choice for various THz applications, particularly in bioinformatics and healthcare applications.

  • SYNCHRONOUS FEDERATED LEARNING BASED MULTI UNMANNED AERIAL VEHICLES FOR SECURE APPLICATIONS
    Itika Sharma, Sachin Kumar Gupta, Ashutosh Mishra, and Shavan Askar
    Scalable Computing: Practice and Experience
    Unmanned Aerial Vehicles (UAVs), also known as drones, have rapidly gained popularity due to their widely employed applications in various industries and fields, including search and rescue, agriculture, industry, military operations, safety, and more. Additionally, drones assist with tasks such as search and rescue efforts, pandemic virus containment, crisis management, and other critical operations. Due to their unique capabilities in image, video, and information collection, a multi-UAV system plays a crucial role in these activities. However, such images and video data involve individual privacy. Therefore, such multi-UAV applications have an indigenous tradeoff of privacy preservation. We have proposed a Federated Learning (FL) based approach for ensuring privacy in multi-UAV applications. The proposed methodology utilizes a synchronous FL approach and the Convolutional Neural Network (CNN) to ensure security. The model parameters are protected by using a secure aggregation. Results demonstrate that the proposed approach outperforms existing techniques in terms of accuracy and precision.

  • Synergetic Effect of Complementary Nature of Hyperspectral and LiDAR Data for High Performance LULC Classification
    S. N. Chaudhri, A. Mishra, N. S. Rajput, Y. Mallikarjuna Rao, and M. V. Subramanyam
    IEEE
    Smart cities are being developed by using well-planned schemes based on a wide variety of data from different sources. Remote sensing is a highly valued technology that provides multimodal information using Hyperspectral and LiDAR data. Such sensor responses are capable of sketching footprints for smart city planning. We have shown the synergetic effect of complementary nature of both Hyperspectral and LiDAR data. It facilitates the land-use/land-cover (LULC) classification to provide precise footprints that subsequently aids on smart city planning. We have demonstrated the proof of concept using datasets provided by National Ecological Observatory Network (NEON). The geographical location covered in the scene being captured in the form of Hyperspectral Image consisting three major classes, viz., vegetation, soil, and road. The results of experiment have shown an overall classification accuracy of 98.61% with synergetic effect along with the performance improvement of 1.96% and 5.39% with respect to Hyperspectral and LiDAR data, respectively. In this experiment, a widely popular neural network architecture Convolutional Neural Network (CNN) has been used as the classifier for performance assessment.

  • Neuromorphic Hardware Accelerators
    Pamul Yadav, Ashutosh Mishra, and Shiho Kim
    Springer International Publishing

  • AI Accelerators for Cloud and Server Applications
    Rakesh Shrestha, Rojeena Bajracharya, Ashutosh Mishra, and Shiho Kim
    Springer International Publishing

  • Artificial Intelligence and Hardware Accelerators
    Springer International Publishing

  • Intelligent Monitoring of Disinfectants
    Dharmendra Kumar, Ashutosh Mishra, Shiv Nath Chaudhri, and Navin Singh Rajput
    Springer International Publishing

  • Artificial Intelligence Accelerators
    Ashutosh Mishra, Pamul Yadav, and Shiho Kim
    Springer International Publishing

  • Preface


  • Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network
    Ramkumar Jayaraman, Baskar Manickam, Suresh Annamalai, Manoj Kumar, Ashutosh Mishra, and Rakesh Shrestha
    MDPI AG
    A 5G wireless network requires an efficient approach to effectively manage and segment the resource. A Centralized Radio Access Network (CRAN) is used to handle complex distributed networks. Specific to network infrastructure, multicast communication is considered in the performance of data storage and information-based network connectivity. This paper proposes a modified Resource Allocation (RA) scheme for effectively handling the RA problem using a learning-based Resource Segmentation (RS) technique. It uses a modified Random Forest Algorithm (RFA) with Signal Interference and Noise Ratio (SINR) and position coordinates to obtain the position coordinates of end-users. Further, it predicts Modulation and Coding Schemes (MCS) for establishing a connection between the end-user device and the Remote Radio Head (RRH). The proposed algorithm depends on the accuracy of positional coordinates for the correctness of the input parameters, such as SINR, based on the position and orientation of the antenna. The simulation analysis renders the efficiency of the proposed technique in terms of throughput and energy efficiency.

RECENT SCHOLAR PUBLICATIONS

  • Understanding the salient features related to resource management in broadband wireless networks
    R Jayaraman, D Rao, M Kumar, A Mishra
    Resource Management in Advanced Wireless Networks, 81-97 2025

  • Some Aspects of Hardware Trojan in Integrated Circuits and Its Detection and Prevention
    VH Gaidhane, ARA Rajak, J Nayak, A Mishra, M Goswami
    Hardware Security: Challenges and Solutions, 239-258 2025

  • Security Challenges and Solutions in IoT: Analyzing Threats, Architectures, and Policies
    Y Jamwal, P Kumari, A Mishra, N Goel, M Goswami
    Hardware Security: Challenges and Solutions, 215-238 2025

  • Physical Layer Security for Future Wireless Communication Systems
    K Gunaseelan, R Dhanusuya, ARA Rajak, A Mishra
    Hardware Security: Challenges and Solutions, 159-189 2025

  • Power Up IoT: How Hardware-Assisted Blockchain Is Transforming Connectivity in Health Care
    S Mubeena, PK Jawahar, AR Abdul Rajak, A Mishra
    Hardware Security: Challenges and Solutions, 139-157 2025

  • Hardware Security: Challenges and Solutions
    A Mishra, M Goswami, M Kumar, NS Rajput
     2025

  • Intelligent classification of coal seams using spontaneous combustion susceptibility in IoT paradigm
    A Mishra, SK Gupta
    International Journal of Coal Preparation and Utilization 44 (7), 757-779 2024

  • A Comprehensive Survey on AgriTech to Pioneer the HCI-Based Future of Farming
    A Mishra, S Kim
    International Conference on Intelligent Human Computer Interaction (IHCI 2024

  • Transforming Industry using Digital Twin Technology
    A Mishra, ME Barachi, M Kumar
     2024

  • Utilizing machine learning for the assessment of mosquito repellent effectiveness and decision support in product selection
    D Kumar, A Verma, M Kumar, V Maurya, A Mishra
    International Journal of Sustainable Building Technology and Urban 2023

  • Synchronous federated learning based multi unmanned aerial vehicles for secure applications
    I Sharma, SK Gupta, A Mishra, S Askar
    Scalable Computing: Practice and Experience 24 (3), 191-201 2023

  • A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques
    D Tiwari, B Nagpal, BS Bhati, A Mishra, M Kumar
    Artificial Intelligence Review 56 (11), 13407-13461 2023

  • Design and Analysis of Novel Microstrip-Based Dual-Band Compact Terahertz Antenna for Bioinformatics and Healthcare Applications.
    S Kumar, AP Singh, A Mishra
    International Journal of Mathematical, Engineering & Management Sciences 8 (5) 2023

  • Intelligent monitoring of disinfectants
    D Kumar, A Mishra, SN Chaudhri, NS Rajput
    IoT, Big Data and AI for Improving Quality of Everyday Life: Present and 2023

  • Synergetic effect of complementary nature of hyperspectral and lidar data for high performance lulc classification
    SN Chaudhri, A Mishra, NS Rajput, YM Rao, MV Subramanyam
    2023 3rd International Conference on Intelligent Technologies (CONIT), 1-6 2023

  • Correction to: Review of ML and AutoML Solutions to Forecast Time-Series Data
    A Alsharef, K Aggarwal, Sonia, M Kumar, A Mishra
    Archives of Computational Methods in Engineering 30 (5), 3473-3473 2023

  • Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
    SM Biju, A Mishra, M Kumar
     2023

  • Irregular situations in real-world intelligent systems
    A Mishra, S Kim
    Advances in Computers 134, 1-31 2023

  • A comprehensive survey on multi-agent reinforcement learning for connected and automated vehicles
    P Yadav, A Mishra, S Kim
    Sensors 23 (10), 4710 2023

  • 6G network for connecting CPS and industrial IoT (IIoT)
    R Shrestha, A Mishra, R Bajracharya, S Sinaei, S Kim
    Cyber-Physical Systems for Industrial Transformation, 17-38 2023

MOST CITED SCHOLAR PUBLICATIONS

  • Review of ML and AutoML Solutions to Forecast Time‑Series Data
    A Alsahref, K Aggarwal, Sonia, M Kumar, A Mishra
    Archives of Computational Methods in Engineering 2022
    Citations: 137

  • Evaluation of growth responses of lettuce and energy efficiency of the substrate and smart hydroponics cropping system
    M Dutta, D Gupta, S Sahu, S Limkar, P Singh, A Mishra, M Kumar, R Mutlu
    Sensors 23 (4), 1875 2023
    Citations: 57

  • A comprehensive survey on multi-agent reinforcement learning for connected and automated vehicles
    P Yadav, A Mishra, S Kim
    Sensors 23 (10), 4710 2023
    Citations: 42

  • Effective Resource Allocation Technique to Improve QoS in 5G Wireless Network
    R Jayaraman, B Manickam, S Annamalai, M Kumar, A Mishra, R Shrestha
    Electronics 2023
    Citations: 40

  • NDSRT: An efficient virtual multi-sensor response transformation for classification of gases/odors
    A Mishra, NS Rajput, G Han
    IEEE Sensors Journal 17 (11), 3416-3421 2017
    Citations: 29

  • Authorized traffic controller hand gesture recognition for situation-aware autonomous driving
    A Mishra, J Kim, J Cha, D Kim, S Kim
    Sensors 21 (23), 7914 2021
    Citations: 28

  • In-Cabin Monitoring System for Autonomous Vehicles
    A Mishra, S Lee, D Kim, S Kim
    Sensors 22 (12), 4360 2022
    Citations: 27

  • A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques
    D Tiwari, B Nagpal, BS Bhati, A Mishra, M Kumar
    Artificial Intelligence Review 56 (11), 13407-13461 2023
    Citations: 25

  • Synchronous federated learning based multi unmanned aerial vehicles for secure applications
    I Sharma, SK Gupta, A Mishra, S Askar
    Scalable Computing: Practice and Experience 24 (3), 191-201 2023
    Citations: 18

  • A novel principal component-based virtual sensor approach for efficient classification of gases/odors
    SN Chaudhri, NS Rajput, A Mishra
    Journal of Electrical Engineering 73 (2), 108-115 2022
    Citations: 17

  • An intelligent in-cabin monitoring system in fully autonomous vehicles
    A Mishra, J Kim, D Kim, J Cha, S Kim
    2020 International SoC Design Conference (ISOCC), 61-62 2020
    Citations: 13

  • Artificial Intelligence and Hardware Accelerators
    A Mishra, J Cha, H Park, S Kim
    Springer 2023
    Citations: 12

  • HCI based in-cabin monitoring system for irregular situations with occupants facial anonymization
    A Mishra, J Cha, S Kim
    International Conference on Intelligent Human Computer Interaction, 380-390 2020
    Citations: 12

  • A novel modular ANN architecture for efficient monitoring of gases/odours in real-time
    A Mishra, NS Rajput
    Materials Research Express 5 (4), 045904 2018
    Citations: 12

  • A novel autonomous taxi model for smart cities
    NS Rajput, A Mishra, A Sisodia, I Makarov
    2018 IEEE 4th World Forum on Internet of Things (WF-IoT), 625-628 2018
    Citations: 12

  • Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles
    A Mishra, J Cha, S Kim
    Computational Intelligence and Neuroscience 2022 2022
    Citations: 11

  • Performance evaluation of normalized difference based classifier for efficient discrimination of volatile organic compounds
    A Mishra, NS Rajput, D Singh
    Materials Research Express 5 (9), 095901 2018
    Citations: 11

  • Leakage current minimization in dynamic circuits using sleep switch
    A Mishra, RA Mishra
    2012 Students Conference on Engineering and Systems, 1-6 2012
    Citations: 11

  • Security and privacy in cyberspace
    O Kaiwartya, K Kaushik, SK Gupta, A Mishra, M Kumar
    Springer Nature 2022
    Citations: 9

  • Single Neuron for Solving XOR like Nonlinear Problems
    A Mishra, J Cha, S Kim
    Computational Intelligence and Neuroscience 2022 2022
    Citations: 9