PREESAT BISWAS

Verified @gmail.com

Assistant Professor
Government Engineering College, Jagdalpur

PREESAT BISWAS


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https://researchid.co/preesat

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Signal Processing, Oncology (nursing)
23

Scopus Publications

170

Scholar Citations

7

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs
    Preesat Biswas, Aishwarya Mishra, Rinku Sharma Dixit, Abhishek Dwivedi, Shailee Lohmor Choudhary, et al.
    SN Computer Science, 2025
  • Batch Size Optimization in CNN Models for Chest X-ray Image Analysis: An Analytical Investigation
    Prabira Kumar Sethy, Raman Sahu, Preesat Biswas, Akshay Shirole, Santi Kumari Behera
    Lecture Notes in Networks and Systems, 2025
  • CROSS-MODAL EMBEDDINGS: A COMPREHENSIVE SURVEY OF TEXT-IMAGE REPRESENTATION LEARNING
    Preesat Biswas
    International Journal of Applied Mathematics, 2025
    The fusion of visual and textual modalities through cross-modal embeddings has become a critical research direction in computer vision and natural language processing. This paper investigates advanced embedding models that enable shared semantic understanding between text and images, with a focus on improving cross-modal retrieval performance. We analyze joint and coordinated embedding methods such as CLIP, DeViSE, and the proposed Cross-Modal Semantic Embedding Hashing (CMSEH) and Visual-Textual Fusion Network (VTFN). These models utilize contrastive and generative learning strategies to bridge the semantic gap across modalities. Extensive experiments on benchmark datasets—NUS-WIDE and MIR-Flickr25K—demonstrate that CMSEH significantly outperforms traditional approaches, achieving up to 82% mAP in text-to-image retrieval. An ablation study further confirms the effectiveness of semantic fusion and hashing components in enhancing retrieval accuracy. Our findings highlight the scalability, efficiency, and robustness of the proposed models, underscoring their potential for real-world applications such as visual search, image captioning, and visual question answering. This work also identifies current research gaps—such as modality imbalance, interpretability, and language bias—and outlines future directions for building fair, generalizable, and context-aware multimodal systems.
  • DESIGN AND IMPLEMENTATION OF SRAM ARCHITECTURE FOR MULTISTAGE RING OSCILLATOR PUF AND READ-CURRENT DISCHARGE-BASED PUF
    Preesat Biswas
    International Journal of Applied Mathematics, 2025
    This research explores the design and implementation of a configurable 10-transistor (10T) SRAM architecture for enhancing the security of Physical Unclonable Functions (PUFs) in resource-constrained environments. The primary focus is to develop two PUF variants: a Multistage Ring Oscillator (RO) PUF and a read-current discharge-based entropy source PUF. The proposed SRAM architecture leverages the robustness and configurability of the 10T SRAM cell to improve the stability, reliability, and uniqueness of the generated PUF responses. This study investigates key performance metrics including uniqueness, reliability, bit error rate (BER), key error rate (KER), uniformity, stability, and randomness. Additionally, the entropy characteristics of the read-current discharge-based PUF are analyzed to assess its suitability as a source of randomness. The impact of environmental factors such as voltage and temperature variations on the PUF performance is also examined. The results show that the 10T SRAM architecture effectively enhances the stability and uniqueness of PUF responses, making it a promising candidate for low-power, secure identity generation and authentication in Internet of Things (IoT) and embedded system applications. The proposed architecture provides a viable solution for improving the reliability and efficiency of PUFs, with potential for further optimization in future work.
  • Lipid droplet segmentation using U-net convolutional neural network architecture
    Lipsarani Jena, S. Shanthi, A. Geetha Devi, Prabira Kumar Sethy, Santi Kumari Behera, et al.
    Aip Conference Proceedings, 2024
  • Rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net
    Shrani Sethy, Santi Kumari Behera, J. Ramadevi, Prabira Kumar Sethy, Preesat Biswas
    Lecture Notes in Networks and Systems, 2024
  • Durum Wheat Classification Using Feature Selection, Bayesian Optimization and Support Vector
    Nabin Kumar Naik, Prabira Kumar Sethy, Appari Geetha Devi, Rajat Amat, Santi Kumari Behera, et al.
    2024 4th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2024, 2024
    Wheat is the primary component of the majority of everyday food items, and acquiring high-quality wheat grains is a crucial concern for the production of food products. Recognizing the types of durum wheat is vital during processing in food-processing facilities. A dataset that included two varieties of durum wheat and extraneous substances was gathered. The objective of this study was to identify the minimum number of features from a pool of 236 morphological, color, wavelet, and gaborlet features, which can yield the highest accuracy with minimal difference between validation and test accuracy for two kinds of durum wheat: starchy durum wheat and vitreous durum wheat and foreign elements. This study proposes a machine learning approach to identify the optimal set of features for distinguishing between starchy, vitreous durum wheat, and foreign elements. This approach comprises feature selection, optimization, and classification. First, five feature selection techniques, MRMR, ChiSquare, Relief, ANOVA, and Kruskal-Wallis with SVM, were evaluated for identification of durum wheat. After conducting the analysis, it was found that out of the 236 features, a set of 50 features yielded significant performance. However, it also suffers of decreasing 2-3% decrease in accuracy. To compensate for this, a Bayesian optimization technique was introduced with SVM, which achieved a validation accuracy of 99.8% and test accuracy of 99.6%. This methodology helps to identify vitreous durum wheat in the food-processing chain.
  • Breast Cancer Detection: A Convolutional Neural Network based Approach for Robust Benign and Malignant Mass Identification Across Varied Breast Density
    Ankita Patra, Santi Kumari Behera, J. Ramadevi, Prabira Kumar Sethy, Preesat Biswas, et al.
    Isml 2024 Intelligent Systems and Machine Learning Conference, 2024
    This paper presents a groundbreaking methodology for the identification of breast cancer using a Convolutional Neural Network (CNN) that has been specifically engineered to exhibit resilience in accurately distinguishing between benign and malignant masses in X-ray images. By effectively utilizing a meticulously curated dataset comprising 5040 images sourced from Kaggle, wherein each category is represented by an equal distribution of 2520 images, the Convolutional Neural Network (CNN) attains a commendable training accuracy of 84.13%. The remarkable ability of the model to discern intricate patterns among diverse breast densities is truly commendable, as it effectively tackles a crucial facet of breast cancer detection. During the testing process, it is noteworthy that the Convolutional Neural Network (CNN) consistently upholds a commendable level of accuracy, specifically measuring 75.92%. This outcome serves as a testament to the efficacy of CNN in real-world scenarios. This study highlights the inherent capacity of this convolutional neural network (CNN)-derived methodology as a potent instrument for the timely identification of breast cancer, presenting a promising pathway towards enhanced precision in diagnosis and, ultimately, better prognoses for patients.
  • Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques
    Ankita Patra, Preesat Biswas, Santi Kumari Behera, Nalini Kanta Barpanda, Prabira Kumar Sethy, et al.
    Journal of Intelligent Systems, 2024
    In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.
  • Revolutionizing Wire Arc Additive Manufacturing: Advances in Geometric Accuracy and Surface Finish Optimization
    Preesat Biswas, Akula Rajitha, V Revathi, H Pal Thethi, Safaa Halool Mohammed, et al.
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
    Wire Arc Additive Manufacturing (WAAM) continues to be a dynamic area of research, with the pursuit of enhanced performance parameters guiding advancements in the field. This study introduces a groundbreaking WAAM method distinguished by its adaptive control strategy, integrating a suite of algorithms to achieve superior outcomes. The proposed method excels in critical aspects such as layer thickness control, thermal imaging accuracy, path planning efficiency, in-situ monitoring reliability, surface tension optimization, and machine learning model performance. A comprehensive comparative analysis, presented through tables and figures, highlights the consistent superiority of the proposed method across diverse parameters. Visualizations, including line charts, pie charts, stacked bar charts, scatter plots, bubble charts, and waterfall charts, offer a nuanced perspective on performance distribution and relationships between key parameters. The proposed method’s adaptability, precision, and versatility position it as a promising advancement in WAAM technologies, contributing to the ongoing evolution of additive manufacturing processes. As the additive manufacturing landscape continues to evolve, this research serves as a foundational resource for advancing the capabilities of WAAM methods, pushing the boundaries of what is achievable in modern manufacturing technologies.
  • DeepOvaNet: A Comprehensive Deep Learning Framework for Predicting and Diagnosing Ovarian Cancer in Women Across Menopausal Transitions
    Ashis Das, M Chilakarao, Preesat Biswas, Prabira Kumar Sethy, Mukesh Kumar Dalai, et al.
    2024 4th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2024, 2024
  • Evaluation of optimization techniques with support vector machine for identification of dry beans
    Nabin Kumar Naik, Prabira Kumar Sethy, Rajat Amat, Santi Kumari Behera, Preesat Biswas
    Indonesian Journal of Electrical Engineering and Computer Science, 2023
  • Adaptation issues of machine learning in safety digitization
    Gyana Ranjana Panigrahi, Nalini Kanta Barpanda, Komma Anitha, Shanti Rathore, Preesat Biswas, et al.
    Indonesian Journal of Electrical Engineering and Computer Science, 2023
  • Estimating the Low Cost Probability Error in QAM Using the SRRC Filter with Other Filter
    Preesat Biswas, Bharti Baghel, Shirin Khan, M. R. Khan
    Lecture Notes in Networks and Systems, 2023
  • Predicted New Adaptive Linear and Nonlinear Signal in Different QAM Using SRRC Filter
    Preesat Biswas, Suman Sahu, Ishan Sen Chowdhury, Khemlal Dhruw, Hemant Kumar Binjhekar, et al.
    Smart Innovation Systems and Technologies, 2023
  • Breast cancer detection using bimodal image fusion: Thermography and mammography images
    Onkologia I Radioterapia, 2022
  • Brain tumor magnetic resonance images classification based machine learning paradigms
    Baby Pattanaik, Komma Anitha, Shanti Rathore, Preesat Biswas, Prabira Sethy, et al.
    Wspolczesna Onkologia, 2022
  • Evaluation of Transfer Learning Model for Mango Recognition
    Chanki Pandey, Prabira Kumar Sethy, Santi Kumari Behera, Sharad Chandra Rajpoot, Bitti Pandey, et al.
    Smart Innovation Systems and Technologies, 2021
  • A Novel Approach of Digital Communication using 256QAM and Raised Cosine Filter
    Preesat Biswas, Shanti Rathore, M.R. Khan, Prabira Kumar Sethy
    Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing Iccsp 2020, 2020
  • Algorithm Design Simulation Performance Analysis of MIMO GMSK System for Radio Communication on AWGN Channel
    Preesat Biswas, Chanki Pandey, Ashish Kumar Thakur, M.R. Khan, Shanti Rathore
    Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing Iccsp 2020, 2020
  • Minimum Time Delay and More Efficient Image Filtering Brain Tumour Detection with the help of MATLAB
    Yogesh Kumar Sahu, Chanki Pandey, Preesat Biswas, M.R. Khan, Shanti Rathore
    Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing Iccsp 2020, 2020
  • Quality Evaluation of Pomegranate Fruit using Image Processing Techniques
    Chanki Pandey, Prabira Kumar Sethy, Preesat Biswas, Santi Kumari Behera, M.R. Khan
    Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing Iccsp 2020, 2020
  • Detection of coronavirus disease (COVID-19) based on deep features and support vector machine
    Prabira Kumar Sethy, Santi Kumari Behera, Pradyumna Kumar Ratha, Preesat Biswas
    International Journal of Mathematical Engineering and Management Sciences, 2020

RECENT SCHOLAR PUBLICATIONS

  • DESIGN AND IMPLEMENTATION OF SRAM ARCHITECTURE FOR MULTISTAGE RING OSCILLATOR PUF AND READ-CURRENT DISCHARGE-BASED PUF
    P Biswas
    International Journal of Applied Mathematics 38 (1s), 249-269 , 2025
    2025
  • Cross-modal embeddings: A comprehensive survey of text-image representation learning
    P Biswas
    International Journal of Applied Mathematics 38 (1s), 237-248 , 2025
    2025
    Citations: 2
  • TEACHING MODEL OF EARTHQUAKE PRONE WATER TANK
    DGPKDMKSRDDASDVKMRTDPBDSS Rathore
    IN Patent 441449-001 , 2025
    2025
  • AI BASED HEART OBSERVATION DEVICE
    DSSRRRPRKPDPBKKDPVDACURDP Biswas
    IN Patent 446409-001 , 2025
    2025
  • Unleashing Agricultural Precision: A Deep Learning Paradigm for Papaya
    AK Ratha, NK Barpanda, PK Sethy
    Proceedings of 5th International Conference on Recent Trends in Machine … , 2025
    2025
  • Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs
    P Biswas, A Mishra, RS Dixit, A Dwivedi, SL Choudhary, S Tiwari, ...
    SN Computer Science 6 (2), 137 , 2025
    2025
    Citations: 2
  • COMBINING OF RLS EQUALIZER AND LSM EQUALIZER IN ADAPTIVE SIGNAL WITH VARIOUS QAM SRRC FILTER
    DRP BISWAS
    CA Patent 1,230,366 , 2025
    2025
  • Basic Antenna & Wave Propagation With Its Matlab-Volume-II
    P Biswas, S Rathore, MR Khan
    Blue Rose Publishers , 2025
    2025
    Citations: 1
  • AI BASED DEVICE FOR DETERMINING CROP YIELD
    BSDNPMSSAMASMAKDPBDS Rathore
    IN Patent 426923-001 , 2024
    2024
  • Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques
    A Patra, P Biswas, SK Behera, NK Barpanda, PK Sethy, ...
    Journal of Intelligent Systems 33 (1), 20240172 , 2024
    2024
    Citations: 7
  • Neurokognitives multisensorisches Trainings system zur Verbesserung der sportichen koordinierung
    MAS Dr.Preesat Biswas, MOHD. ARSH KHAN, DR. JAGANNATH PATRA, DR. RAMESH ...
    CN Patent DE 202024103319 U1 , 2024
    2024
  • KEYBOARD ASSISTANT DEVICE WITH GESTURE CONTROL INTERFACE
    YCMPMDPSMASDPBFMMSBU Dewangan
    IN Patent 419117-001 , 2024
    2024
  • Batch size optimization in CNN models for chest X-ray image analysis: an analytical investigation
    PK Sethy, R Sahu, P Biswas, A Shirole, SK Behera
    International Conference on ICT for Sustainable Development, 267-280 , 2024
    2024
    Citations: 3
  • SOLAR AND WIND ENERGY BASED VEHICLE CHARGING STATION
    DSR Chavan Udaya Kiran, Dr.Preesat Biswas, Dr. Ruhi Uzma Sheikh, Dr.Rajani ...
    GB Patent 6,375,740 , 2024
    2024
  • Lipid droplet segmentation using U-Net convolutional neural network architecture
    L Jena, S Shanthi, AG Devi, PK Sethy, SK Behera, P Biswas
    AIP Conference Proceedings 3122 (1), 030021 , 2024
    2024
    Citations: 1
  • IOT BASED SMOKE AND HEAT DETECTOR
    DASDPNDPBDVKSMKBRKPGDKRPCP Kumar
    IN Patent 413268-001 , 2024
    2024
  • Revolutionizing wire arc additive manufacturing: advances in geometric accuracy and surface finish optimization
    P Biswas, A Rajitha, V Revathi, HP Thethi, SH Mohammed, DK Yadav
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024
    Citations: 1
  • AUTOMATIC TYRE INFLATION SYSTEM FOR VEHICLES
    KDMASDPBSPMDSDRMDKSDSMDLNTMK Rajasekhar
    IN Patent 414458-001 , 2024
    2024
  • Breast Cancer Detection: A Convolutional Neural Network based Approach for Robust Benign and Malignant Mass Identification Across Varied Breast Density
    A Patra, SK Behera, J Ramadevi, PK Sethy, P Biswas, HS Bhoi
    2024 Intelligent Systems and Machine Learning Conference (ISML), 280-283 , 2024
    2024
  • AI BASED SECURITY DEVICE FOR SCHOOLS
    SKSDNMRRDMKGDMDNDVKMDP Biswas.
    IN Patent 410993-001 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Brain tumor magnetic resonance images classification based machine learning paradigms
    B Pattanaik, K Anitha, S Rathore, P Biswas, P Sethy, S Behera
    Contemporary Oncology/Współczesna Onkologia 26 (4), 268-274 , 2022
    2022
    Citations: 34
  • Minimum time delay and more efficient image filtering brain tumour detection with the help of MATLAB
    YK Sahu, C Pandey, P Biswas, MR Khan, S Rathore
    2020 International Conference on Communication and Signal Processing (ICCSP … , 2020
    2020
    Citations: 16
  • DeepOvaNet: a comprehensive deep learning framework for predicting and diagnosing ovarian cancer in women across menopausal transitions
    A Das, M Chilakarao, P Biswas, PK Sethy, MK Dalai, SK Behera
    2024 Fourth International Conference on Advances in Electrical, Computing … , 2024
    2024
    Citations: 15
  • Algorithm design simulation performance analysis of MIMO GMSK system for radio communication on AWGN channel
    P Biswas, C Pandey, AK Thakur, MR Khan, S Rathore
    2020 international conference on communication and signal processing (ICCSP … , 2020
    2020
    Citations: 13
  • Quality evaluation of pomegranate fruit using image processing techniques
    C Pandey, PK Sethy, P Biswas, SK Behera, MR Khan
    2020 International Conference on Communication and Signal Processing (ICCSP … , 2020
    2020
    Citations: 12
  • Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. 2020
    PK Sethy, SK Behera, PK Ratha, P Biswas
    Binary–95.13% Multiclass–85.35% Binary–96.51% Multiclass–87.3 , 2020
    2020
    Citations: 12
  • Detection of coronavirus disease (COVID-19) based on Deep Features and Support Vector Machine
    P Kumar Sethy, S Kumari Behera, P Kumar Ratha, P Biswas
    Preprints, Apr , 2020
    2020
    Citations: 12
  • Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques
    A Patra, P Biswas, SK Behera, NK Barpanda, PK Sethy, ...
    Journal of Intelligent Systems 33 (1), 20240172 , 2024
    2024
    Citations: 7
  • Evaluation of optimization techniques with support vector machine for identification of dry beans
    NK Naik, PK Sethy, R Amat, SK Behera, P Biswas
    Indonesian Journal of Electrical Engineering and Computer Science 32 (2 … , 2023
    2023
    Citations: 7
  • Detection of coronavirus Disease (COVID-19)
    PK Sethy, SK Behera, PK Ratha, P Biswas
    based on Deep Features and Support Vector Machine , 2020
    2020
    Citations: 5
  • Durum wheat classification using feature selection, bayesian optimization and support vector
    NK Naik, PK Sethy, AG Devi, R Amat, SK Behera, P Biswas
    2024 Fourth International Conference on Advances in Electrical, Computing … , 2024
    2024
    Citations: 4
  • Evaluation of Transfer Learning Model for Mango Recognition
    C Pandey, PK Sethy, SK Behera, SC Rajpoot, B Pandey, P Biswas, ...
    Intelligent Manufacturing and Energy Sustainability: Proceedings of ICIMES … , 2021
    2021
    Citations: 4
  • WITHDRAWN: A novel approach of various QAM with roll off factor variation using raised cosine filter and SRRC filter for analysis of BER and SNR
    P Biswas, S Rathore, MR Khan
    Materials Today: Proceedings , 2021
    2021
    Citations: 4
  • Batch size optimization in CNN models for chest X-ray image analysis: an analytical investigation
    PK Sethy, R Sahu, P Biswas, A Shirole, SK Behera
    International Conference on ICT for Sustainable Development, 267-280 , 2024
    2024
    Citations: 3
  • Breast cancer detection using bimodal image fusion: Thermography and mammography images.
    PK Sethy, S Shanthi, K Anitha, AG Devi, P Biswas
    Onkologia i Radioterapia 16 (6) , 2022
    2022
    Citations: 3
  • Cross-modal embeddings: A comprehensive survey of text-image representation learning
    P Biswas
    International Journal of Applied Mathematics 38 (1s), 237-248 , 2025
    2025
    Citations: 2
  • Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs
    P Biswas, A Mishra, RS Dixit, A Dwivedi, SL Choudhary, S Tiwari, ...
    SN Computer Science 6 (2), 137 , 2025
    2025
    Citations: 2
  • Support Vector machine classifier for wheat grain identification based on grid search optimization technique
    NK Naik, PK Sethy, M Panigrahi, SK Behera
    International Conference on ICT for Sustainable Development, 237-245 , 2023
    2023
    Citations: 2
  • Rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net
    S Sethy, SK Behera, J Ramadevi, PK Sethy, P Biswas
    International Conference on Recent Trends in Machine Learning, IOT, Smart … , 2023
    2023
    Citations: 2
  • Brain tumour localization using image processing techniques
    PK Sethy, BB Pattnaik, S Dash
    2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile … , 2022
    2022
    Citations: 2