Debnath Bhattacharyya
@kpritech.ac.in
Professor, Computer Science and Engineering
Kommuri Pratap Reddy Institute of Technology
Currently, I am a Full-Time Professor and Director at KPRIT, Ghanpur, Hyderabad, India, and Visiting Professor at University of Johannesburg, South Africa.
I worked as an Invited International Professor at Lincoln University, KL, Malaysia (2017-2021).
I am a former Foreign Professor, Department of Multimedia Engineering, Hannam University, South Korea.
I received my Ph.D. (Tech., Computer Science and Engineering) from the University of Calcutta, Kolkata. I received M.Tech (Computer Science and Engineering) from West Bengal University of Technology, Kolkata, India.
I am a Member of ACM, ACM SIGKDD, IEEE Senior Member (Since 2010), Life Member of CSI, India, Senior Member of IACSIT, Singapore and Senior Member of IAENG, Hong Kong. I have been selected as ACM Distinguished Speaker (2017-20).
I am the Editor of Many International Journals (indexed by Scopus, SCI, and Web of Science). I visited various Foreign Countries for attending Conferences and delivery of lectures.
EDUCATION
PhD (Tech.), 2012, University of Calcutta
M.Tech (CSE), 2008, West Bengal University of Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Multidisciplinary
Scopus Publications
Scopus Publications
Jampani Satish Babu, Smitha Chowdary Ch, Debnath Bhattacharyya, and Yungcheol Byun
MDPI AG
The Agronomy Editorial Office retracts the article entitled “Estimating the Crop Acreage of Menthol Mint Crop from Remote Sensing Satellite Imagery Using ANN” [...]
Sangeeta Parshionikar and Debnath Bhattacharyya
Elsevier BV
Rajesh Bose, Shrabani Sutradhar, Haraprasad Mondal, Debnath Bhattacharyya, and Sandip Roy
Springer Science and Business Media LLC
AbstractFish farming plays a pivotal role in meeting the ever-increasing global demand for fish and seafood. Yet, fish farmers face formidable challenges in maintaining ideal water conditions and safeguarding their stock from avian predators. This research introduces a comprehensive solution that harnesses IoT technology, real-time monitoring, and bird deterrent mechanisms to heighten the efficiency of fish farming. Through IoT sensors, critical water parameters like temperature, pH, turbidity, and more are continuously monitored, providing real-time data accessible via an intuitive web application and SMS alerts. Motion detection, using passive inferred (PIR) based sensors, activates a water-spraying mechanism to repel birds and safeguard fish, thus eliminating the necessity for expensive and potentially harmful net enclosures. Our experiments underscore the system’s precision, with a mere 0.40 °C average temperature difference compared to traditional measurement tools. This integrated approach not only enhances sustainability and productivity but also reduces manual labour, minimizes losses, and preserves the environment, rendering fish farming more efficient and economically viable.
Shrabani Sutradhar, Sudipta Majumder, Rajesh Bose, Haraprasad Mondal, and Debnath Bhattacharyya
Elsevier BV
Yu-Chen Hu, Pelin Angin, Haiming Liu, and Debnath Bhattacharyya
Elsevier BV
Shrabani Sutradhar, Sunil Karforma, Rajesh Bose, Sandip Roy, Sonia Djebali, and Debnath Bhattacharyya
Elsevier BV
Rajesh Bose, Shrabani Sutradhar, Debnath Bhattacharyya, and Sandip Roy
Springer Science and Business Media LLC
AbstractThe Trustworthy Healthcare Cloud Storage Auditing Scheme (TCSHAS) represents a progressive solution for resolving trust-related issues linked to third-party auditors (TPAs) within traditional healthcare cloud storage audit systems. As the healthcare industry increasingly relies on cloud storage, concerns surrounding security and privacy have grown, motivating the development of a unique incentive mechanism. This mechanism leverages the non-tamperable and traceable features of blockchain technology to encourage TPAs to uphold honesty and reliability. By organizing TPAs as a group of nodes on the blockchain, a system of mutual surveillance is established, enabling diligent monitoring and penalization of any malicious actions. TCSHAS encompasses a comprehensive system model that incorporates smart contracts to manage transaction-related matters, including dispute resolution. Performance evaluations have confirmed its efficiency and suitability for real-world healthcare applications. Our experiments demonstrate that TCSHAS performs well in terms of gas consumption and exhibits scalability as the number of participant’s increases. Compared with other common smart contracts, TCSHAS maintains a balanced level of complexity, incorporates strong security measures, offers comprehensive auditing capabilities, and remains flexible. To further enhance our research, we can explore ways to optimize TCSHAS scalability and performance, such as through shading, as the volume of healthcare data continues to grow. In addition, we can investigate the integration of advanced privacy technologies or AI-based auditing. These advancements will reinforce the role of TCSHAS in establishing trust and security in healthcare cloud storage audits, making it highly relevant in real-world healthcare settings. Ultimately, TCSHAS contributes to improving trust and security in healthcare cloud storage auditing, ensuring responsible management of sensitive healthcare data.
Debnath Bhattacharyya, N. Thirupathi Rao, Eali Stephen Neal Joshua, and Yu-Chen Hu
Springer Science and Business Media LLC
R. S. Raghav, Debnath Bhattacharyya, Dinesh Kumar Anguraj, and Tai‑hoon Kim
Springer Science and Business Media LLC
R. S. Raghav, Debnath Bhattacharyya, Dinesh Kumar Anguraj, and Tai-hoon Kim
Springer Science and Business Media LLC
Bhanu Prakash Doppala, Debnath Bhattacharyya, Midhun Chakkravarthy, and Tai-hoon Kim
Springer Science and Business Media LLC
Eali Stephen Neal Joshua, Debnath Bhattacharyya, Thirupathi Rao Nakka, and Yung-Cheol Byun
International Information and Engineering Technology Association
Jampani Babu, Smitha Ch, Debnath Bhattacharyya, and Yungcheol Byun
MDPI AG
Acreage estimates are crucial for forecasting menthol mint acreage, as crop output figures fluctuate from year to year in response to fluctuations in the market price of menthol mint oil. Thus, there are yearly fluctuations in the maximum price that farmers can obtain. Since low production arises from low rates, and high production results from high prices, these acreage estimate studies may be useful in lowering uncertainty regarding menthol mints’ output. The widespread adoption of remote sensing technologies for assessing crop acreage at both the national and international levels can be attributed to their low cost, ease of use, and flexibility. The extent of an area planted with menthol mint in the Vishakhapatnam district of Andhra Pradesh, India, was estimated using Sentinel-2A satellite data for that year. After conducting a comprehensive ground survey, the area of the menthol mint crop was estimated using an adaptive maximum chance-type set of rules for taluk-level statistics. According to the research, the Bheemunipatnam taluk in the Vishakhapatnam district was the most productive in growing menthol mint. Using customer and manufacturer accuracies of 89.13% and 87.23%, along with the average accuracy (90.67%) and kappa rate (0.9), the total acreage of menthol mint crop in the study region was estimated to be around 58,000,284.70 ha (0.844). A further aim in this study was to estimate the acreage planted with early and late menthol mint. Around 26,123.50 ha and 29,911.40 ha were found to be home to early menthol mint and late menthol mint, respectively. This method shows promise for early- and late-stage crop acreage assessment of menthol mint using a localised degree of precision.
Debnath Bhattacharyya, Eali Stephen Neal Joshua, N. Thirupathi Rao, and Tai-hoon Kim
MDPI AG
Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It helps them create more food by solving a variety of challenges. India’s sugarcane crop is popular and lucrative. Long-term crops that require water do not need specific soil. They need water; the ground should always have adequate water due to the link between cane growth and evaporation. This research focuses on forecasting soil moisture and classifying sugarcane output; sugarcane has so many applications that it must be categorized. This research examines these claims: The first phase model predicts soil moisture using two-level ensemble classifiers. Secondly, to boost performance, the proposed ensemble model integrates the Gaussian probabilistic method (GPM), the convolutional neural network (CNN), and support vector machines (SVM). The suggested approach aims to correctly anticipate future soil moisture measurements affecting crop growth and cultivation. The proposed model is 89.53% more accurate than conventional neural network classifiers. The recommended models’ outcomes will assist farmers and agricultural authorities in boosting production.
Seng-Phil Hong and Debnath Bhattacharyya
University North
The mining of patient data in the health care industry is becoming an increasingly important field because of the direct effect it has on the lives of patients. In the field of medicine, one use of data mining is the early diagnosis of medical diagnostic conditions. However, extracting information from medical records is a laborious process that involves a lot of time and effort. Communities that are dominated by females have an elevated risk of developing breast cancer. Even though mammography is one of the most common ways to use computer-assisted diagnostics, there is still a chance that breast cancer will not be found even if it is one of the most common ways to find and screen for the disease. This indicates that just thirty percent of breast cancers are diagnosed at the appropriate time. Digital image pre-processing includes grayscale-to-binary conversion, noise reduction, and character separation. Most picture recognition algorithms employ statistical, syntactic, and template matching. Neural networks and support vector machines have enabled recent photo identification advances. This article discusses the second stage of the pre-processing procedure, which is adding a filter to the image after it has been segmented in order to make it seem more appealing. It works to identify the area of interest and improve the image by removing the breast border in order to apply filtering algorithms. The breast image's edge is reconstructed using morphological processes in the segmentation method that has been proposed, and breast masses are found by subtracting the two images. In addition, a modified bi-level histogram and homomorphic filters were used in order to improve the image's quality by reducing noise and enhancing contrast.
N. Thirupathi Rao, Eali Stephen Neal Joshua, and Debnath Bhattacharyya
Chapman and Hall/CRC
Nakka Marline Joys, N. Thirupathi Rao, and Debnath Bhattacharyya
Chapman and Hall/CRC
Mohamed Hamdi, Nakka Marline Joys, Debnath Bhattacharyya, and N. Thirupathi Rao
Springer Nature Singapore
S. NagaMallik Raj, Eali Stephen Neal Joshua, Nakka Thirupathi Rao, and Debnath Bhattacharyya
Springer Nature Singapore
Chaitanya Nagolu, Chandramani Cheekula, Durga Sai Kiran Thota, K. Padmanaban, and Debnath Bhattacharyya
IEEE
Forest fires provide a serious risk to the natural world and human lives, making effective detection and response essential. This research study presents a smart sensor-based IoT forest fire detection system that focuses on machine learning methods. To detect and categorize forest fires in real-time, the proposed system combines data from sensors, including temperature sensors and smoke detectors. The proposed system performance is assessed by using a labeled dataset and explaining the algorithms' advantages and disadvantages, including potential difficulties and constraints for forest fire detection. This study also proposes future work to address these challenges and enhance the suggested system's performance. This research study contributes to the creation of reliable and effective innovations to identify wildfires that leverage the power of machine learning algorithms and highlights the need for continued research and development in this important area.
Boppana Sudheer Kumar, Dasari Likhitha, Gadhamsetty Nikhil Kumar, Aishik Ghosh, Raja G, and Debnath Bhattacharyya
IEEE
Video defogging is a process that aims to improve the visibility and quality of video captured in foggy or hazy conditions. It involves several steps, including analyzing the captured video, estimating the amount of fog or haze in each frame, and applying algorithms to remove or reduce the effect of fog or haze. There are various techniques used for video defogging, such as image fusion, dark channel prior, and atmospheric scattering models. Image fusion involves combining multiple images of the same scene to create a clear image, while dark channel prior exploits statistical regularities to estimate the haze layer. Atmospheric scattering models use light scattering properties in the atmosphere to estimate the amount of haze. Video defogging has numerous applications, including surveillance, traffic monitoring, and outdoor video recording. It can improve the visibility of captured video, making it easier to identify objects and events in the scene.
S. NagaMallik Raj, S. Neeraja, N. Thirupathi Rao, and Debnath Bhattacharyya
Springer Nature Singapore
B. Dinesh Reddy, N. Thirupathi Rao, and Debnath Bhattacharyya
Springer Nature Singapore
Eali Stephen Neal Joshua, N. Tirupati Rao, Debnath Bhattacharyya, and Nakka Marline Joys
Springer Nature Singapore