Abhijit Mitra

@symlaw.ac.in

Assistant Professor at Symbiosis Law School Pune, SIU
Symbiosis Law school Pune

Abhijit Mitra


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https://researchid.co/abhijit.mitra

RESEARCH, TEACHING, or OTHER INTERESTS

Law, Multidisciplinary, Social Sciences
8

Scopus Publications

2

Scholar Citations

1

Scholar h-index

Scopus Publications

  • Cyber stalking and Voyeurism: Understanding the psychological and legal implications in the digital age
    Rule of Law in Context Globalisation and Indian Resonances to Sustainable Development, 2025
  • Deep Learning based Intelligent Spectrum Sensing Framework Optimizing Dynamic Radio Resource Allocation
    Ravi Kumar M, Mahesh Kumar A. S, Abbas Thajeel Rhaif Alsahlanee, Bhargav H K, Amit Barve, et al.
    4th IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2025, 2025
    Efficient spectrum utilization in today's wireless communications requires intelligent spectrum sensing because dynamic spectrum access depends on it for resource allocation. This study presents a new deep learning framework for spectrum sensing which uses convolutional neural networks together with long short-term memory networks. Decision-making processes in real-time employ hybrid architecture which analyses both spectrum data spatial patterns as well as its temporal evolution through reinforcement learning mechanisms. The spectrum sensing framework using deep learning achieved 97.8% accuracy in detecting spectrum holes while reaching 95.3% precision in identifying primary users through its implementation which resulted in a 42% better spectrum utilization than conventional energy detection methods. Under -20dB to 20dB SNR conditions the system maintained steady performance that generated false alarms less than 0.03 times per observation. The proposed system design provides improved spectrum detection capabilities and resource distribution capabilities which makes it applicable for on-going wireless networks in congested urban spaces.
  • Advanced IoT Routing Algorithms for Improved Food Delivery Services with Temperature Control
    Abhijit Mitra, Ramakrishnan Raman
    2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems Adics 2024, 2024
    Food delivery services have grown rapidly, making ensuring food quality and safety harder. This article investigates how Internet of Things (IoT) technologies might improve food delivery efficiency and dependability by improving routing methods and temperature management. It provides a unique delivery route optimization method that uses real-time GPS and traffic data from IoT devices. It reduces delivery times, fuel use, and environmental effects. Simulation and real-world testing show that the suggested routing algorithm outperforms the proposed techniques. Furthermore, it addresses the crucial problem of food delivery temperature management. IoT-enabled temperature sensors and control systems monitor and manage perishable food temperatures during delivery. It reduces food waste and improves food safety by keeping food products within temperature ranges. It performed comprehensive environmental tests to evaluate our temperature control system and examined the findings for temperature stability and food quality maintenance. IoT technology can improve food delivery efficiency, dependability and safety. For food delivery services aiming to improve operations using IoT, this paper offers insights and suggestions.
  • Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems
    Manjunath Kamath K, Samata Mehta.S, Akshaya H. L, Shilpashree N, Girish Jadhav, et al.
    4th IEEE International Conference on Mobile Networks and Wireless Communications Icmnwc 2024, 2024
    Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cognitive frameworks, Agentic AI goes beyond traditional rule-based and reactive models, enabling autonomous systems to make informed decisions, anticipate future states, and learn from experience. This paper explores the theoretical foundations and practical applications of Agentic AI, highlighting its potential to transform a variety of fields, including robotics, autonomous driving, finance, and healthcare. Through a detailed review of state-of-the-art research, we illustrate how cognitive architectures such as ACT-R and Soar, combined with advanced RL techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), contribute to the development of AI agents with human-like reasoning and decision-making capabilities. Experimental results demonstrate that Agentic AI significantly outperforms conventional AI approaches in terms of adaptability, learning efficiency, and decision accuracy. The findings suggest that Agentic AI offers a robust framework for creating intelligent systems capable of complex problem-solving, long-term planning, and proactive behavior, paving the way for the next generation of AI-driven applications.
  • Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
    A. S Mahesh Kumar, Meenakshi Maindola, Vimuktha E Salis, Abhijit Mitra, Tabitha Janumala, et al.
    2024 1st International Conference on Software Systems and Information Technology Ssitcon 2024, 2024
    There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an $F 1$ score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is $\\mathbf{9 6. 1 \\%}$. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices.
  • Optimizing Resource Allocation for Secure Communication in IoT Ecosystems
    Suresh Bysani Venkata Naga, Arnav Kotiyal, Suruchi Singh, K. B. V. Brahma Rao, Abhijit Mitra, et al.
    International Conference on Distributed Systems Computer Networks and Cybersecurity Icdscnc 2024, 2024
    The proliferation of social media has made sites like Twitter a treasure trove of information for diagnosing mental health problems like depression. By examining trends in user-generated material, this research presents a machine learning system with the goal of identifying Twitter profiles exhibiting symptoms of sadness. We made sure to include tweets from a wide range of demographics in our dataset by collecting them from both people who reported having depression and a control group. Text, facial expressions, and interaction patterns were all subjected to feature extraction analysis. We used sentiment analysis and natural language processing (NLP) techniques to identify depressive symptoms in language and emotions. A number of ML models were trained and assessed for accuracy, precision, and recall; these models included Neural Networks, Random Forests, and Support Vector Machines (SVMs). Our approach outperforms baseline algorithms in identifying depressed symptoms, according to the data. Providing a scalable method for early diagnosis of depression, this research adds to the continuing efforts in digital psychiatry. It could aid in prompt intervention and support for affected patients. Responsible use of technology in mental health monitoring requires further discussion of privacy implications and ethical considerations when implementing such models. Several indicators were used to objectively analyze the performance of the machine learning models. With 89% accuracy, 86% precision, and 88% recall, the Neural Network model was the top performer. Additionally, the Random Forest model performed admirably, achieving 85% accuracy, 83% precision, and 84% recall. A recall of 81%, precision of 80%, and accuracy of 82% were all rather respectable for the SVM model.
  • IoT-Enhanced Workplace Safety for Real-Time Monitoring and Hazard Detection for Occupational Health
    Ramakrishnan Raman, Abhijit Mitra
    International Conference on Artificial Intelligence for Innovations in Healthcare Industries Icaiihi 2023, 2023
    This paper discusses using Internet of Things (IoT) technology to improve workplace safety via real-time monitoring and danger identification, addressing occupational health issues. IoT devices and sensors are used in more sectors to make workplaces safer. A network of sensors may gather real-time data from various workplace locations to monitor ambient conditions, equipment functioning, and personnel actions. The system develops a comprehensive IoT infrastructure for occupational safety. This infrastructure includes environmental sensors for temperature, humidity, air quality, and noise. Wearable sensors are also investigated for worker vital signs and mobility. The data is sent to a central platform where powerful analytics and IoT discover dangers, abnormalities, and hazardous trends. Integration of danger detection techniques with real-time warnings and notifications is vital to the analysis. Hazardous circumstances trigger quick alerts for workers and supervisors. This proactive strategy allows quick risk mitigation and accident prevention. The system also addresses data privacy and security in the IoT framework, suggesting methods for protecting sensitive data while enabling data exchange.
  • Decentralisation at the Grassroots: Status of Panchayats Extension to Scheduled Areas of Jharkhand
    Sachchidanand Prasad, Abhijit Mitra, Bhupesh Gopal Chintamani, Gitanjali Shrivastava, Kshitij Naikade, et al.
    Academic Journal of Interdisciplinary Studies, 2023
    The term decentralisation is now universally accepted. The quality of governance enhances through decentralisation. In the time of post-globalisation, it allows citizens to express their views regarding the process of developmental work in their area. This paper examines the status of grassroots-level implementation of Panchayats Extension (PESA) to Fifth Scheduled Areas of Jharkhand. The empirical study conducted shows that Jharkhand still requires to strengthen and promote the practice of decentralization, so that gram sabha enjoys the power envisaged under PESA Act, 1996. The historical deprivation of Jharkhand during the period when it was a part of United Bihar, is also responsible for the present state of development at the local level. After the inception of Jharkhand as a new state, it initially suffered from unstable governments and delays in the election process at the panchayats level. This did not allow tribal peoples to participate in decentralized governance. This paper suggests some policy implications which can improve the level of decentralized authority in the scheduled area of Jharkhand.
 
 Received: 7 October 2022 / Accepted: 28 December 2022 / Published: 5 January 2023

RECENT SCHOLAR PUBLICATIONS

  • Intersection of claim for Scheduled Tribe Status and Identity Politics among the Kurmi Mahto of Chotanagpur Region in India
    S Prasad, A Mitra
    Contemporary Voice of Dalit, 2455328X231207500 , 2023
    2023
    Citations: 2
  • Decentralisation at the Grassroots: Status of Panchayats Extension to Scheduled Areas of Jharkhand
    S Prasad, A Mitra, BG Chintamani, G Shrivastava, K Naikade, A Shelke
    Academic Journal of Interdisciplinary Studies 12 ((1) January 2023), 280 , 2023
    2023
  • Exploring the relationship between Law & Governance in the Indian context
    A Mitra
    HNLU JOURNAL OF LAW & SOCIAL SCIENCES 7 (Jan-Dec 2021), 302-310 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Intersection of claim for Scheduled Tribe Status and Identity Politics among the Kurmi Mahto of Chotanagpur Region in India
    S Prasad, A Mitra
    Contemporary Voice of Dalit, 2455328X231207500 , 2023
    2023
    Citations: 2
  • Decentralisation at the Grassroots: Status of Panchayats Extension to Scheduled Areas of Jharkhand
    S Prasad, A Mitra, BG Chintamani, G Shrivastava, K Naikade, A Shelke
    Academic Journal of Interdisciplinary Studies 12 ((1) January 2023), 280 , 2023
    2023
  • Exploring the relationship between Law & Governance in the Indian context
    A Mitra
    HNLU JOURNAL OF LAW & SOCIAL SCIENCES 7 (Jan-Dec 2021), 302-310 , 2021
    2021