saikat Gochhait
@siu.edu.in
Assistant Professor Academic Level 12 7th Pay CPC
Symbiosis International Deemed University
Dr. Saikat Gochhait teaches at Symbiosis Institute of Digital & Telecom Management, Symbiosis International Deemed University Pune, India and Neurosciences Research Institute-Samara State Medical University, Russia. He is Ph.D and Post-Doctoral Fellow from the UEx, Spain and National Dong Hwa University, Taiwan. He was Awarded DITA and MOFA Fellowship in 2017 and 2018. His research publication with foreign authors is indexed in Scopus, ABDC, and Web of Science. He is a Senior IEEE member.
EDUCATION
Post Doctoral Fellow - Uex, Spain
Post Doctoral Fellow - National Dong Hwa University, Taiwan
PhD - Sambalpur University
RESEARCH INTERESTS
Technology Management
Marketing
Healthcare
Entrepreneurship
FUTURE PROJECTS
Neurosciences
NeuroMarketing
Applications Invited
Collaborators
Entreprenuership
Women Entrepreneurs
Applications Invited
Collaborators
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Saikat Gochhait, Deepak K. Sharma, and Mrinal Bachute
University of Basrah - College of Engineering
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential features from electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integrates a feature extraction module, densely connected residual block (DCRB), long short-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decision making processes, making it a valuable tool for stakeholders in the electricity sector.
Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, and Shubham Kumar
IGI Global
Accurate disease prognosis is crucial for improved healthcare outcomes. Artificial intelligence (AI) offers immense potential in this domain, but traditional “black-box” models lack interpretability. This chapter explores the integration of Explainable AI (XAI) with Green AI, a resource-efficient and sustainable approach to AI development. They discuss how XAI can enhance trust in Green AI models for disease prognosis, mitigate potential biases, and promote responsible AI development. They highlight the challenges of balancing interpretability with efficiency and propose future research directions to unlock the full potential of XAI for Green AI-powered disease prognosis. This approach has the potential to revolutionize healthcare by providing accurate, transparent, and environmentally friendly tools for early disease detection and improved patient outcomes.
Mohit Yadav, Priyank Kumar Singh, Saikat Gochhait, Nisha Gaur, and Puwakpitiyage Gayan Dhanushka Wijethilaka
IGI Global
This chapter explores the potential of green AI and big data informatics for personalized disease prediction in clinical decision making. Green AI prioritizes efficiency, minimizing computational resources needed to analyze vast healthcare datasets. Big data informatics provides the platform to manage and analyze these datasets for knowledge discovery. This chapter delves into how green AI algorithms optimize resource utilization while big data platforms leverage diverse patient data for more accurate, individual risk assessments. The applications in clinical decision-making encompass early detection, risk stratification, and personalized treatment plans. However, ethical considerations regarding data privacy, bias, and potential job displacement require careful attention. Finally, the future directions highlight advancements in green AI efficiency, explainable models, and integration with other health technologies, paving the way for a future of proactive healthcare and patient empowerment.
Priyank Kumar Singh, Mohit Yadav, Saikat Gochhait, and P. G. S. Amila Jayarathne
IGI Global
In this chapter, the authors aim to discuss the significance of integrating AI prediction and green computing in the healthcare field to improve disease diagnosis, treatment, and patient care and minimise the adverse effects on the environment. The methodology employed is the systematic literature review (SLR) approach. The results show that combining green practices with AI prediction enhances the effectiveness and sustainability of the healthcare system. Practical implications are that there is a need for frequent policy updates and practical staff training to improve environmental management. The authors focus on the real-world implications and provide tactical recommendations for healthcare organisations that want to adopt green computing strategies successfully. A strategic perspective should be used with top management's support and all employees' involvement to achieve the organisation's future vision regarding these measures.
Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, Nisha Gaur, and Shubham Kumar
IGI Global
Clinical trial design is undergoing a revolution fueled by artificial intelligence (AI) and translational bioinformatics. This chapter explores how AI techniques like machine learning and deep learning are being harnessed to analyze vast datasets of biological and clinical information. By integrating these insights with translational bioinformatics, researchers can identify promising drug candidates, select patients most likely to benefit from treatment, and design more efficient and targeted clinical trials. Real-world examples showcase the application of AI in immuno-oncology patient selection, drug discovery for rare diseases, predicting Alzheimer's trial outcomes, and virtual patient recruitment for cardiovascular studies. While challenges like data quality and ethical considerations exist, AI and translational bioinformatics hold immense promise for accelerating drug development, bringing life-saving therapies to patients faster.
Shashank Mittal, Priyank Kumar Kumar Singh, Saikat Gochhait, and Shubham Kumar
IGI Global
AI is rapidly transforming the field of epidemiology. This chapter explores how AI integrates data analysis, predictive modeling, disease surveillance, and diagnostic tools to significantly improve public health outcomes. AI-driven methodologies enhance diagnostic accuracy, improve disease surveillance efficiency, and aid in developing better predictive models, all of which contribute to improved public health strategies. AI seamlessly integrates with traditional epidemiological approaches, paving the way for a new era in combating infectious diseases. Advancements in AI hold immense promise for the future of public health, with possibilities for real-time disease surveillance, personalized medicine, and more accurate predictive modeling. However, broader adoption and responsible use of AI require careful consideration of ethical issues, data privacy concerns, and collaboration among stakeholders. Ultimately, leveraging AI effectively has the potential to improve public health outcomes, ensure equitable access to healthcare, and enhance global preparedness for health crises.
Priyank Kumar Singh, Mohit Yadav, Saikat Gochhait, and Puwakpitiyage Gayan Dhanushka Wijethilaka
IGI Global
The burgeoning field of AI-powered healthcare prognosis offers immense potential, but traditional data center infrastructure creates a significant environmental footprint. This chapter advocates for energy-efficient AI algorithms and hardware alongside renewable energy integration (solar, wind) to minimize reliance on fossil fuels. Robust security measures and privacy-preserving techniques are crucial to protect sensitive patient data used in AI models. Finally, scalable cloud-based infrastructure with containerization and auto-scaling ensures efficient handling of growing data volumes and user demands. By prioritizing these principles, we can create a sustainable and secure future where AI empowers healthcare prognosis, improving patient outcomes for generations to come.
Saikat Gochhait
IGI Global
CDN is constituted of three basic components. A content provider is somebody entrusting the URI namespace of the Web objects to be dispersed. The content provider's server contains all such objects. A CDN provider can be some owner party that enables transportation conveniences to content providers to deliver content in a timely and reliable manner. They may employ geographically distributed caching and/or replica servers (surrogates or edge servers) to duplicate content. Together they may form what we call a web cluster. End users are the customers who use content from the content provider's website.
Saikat Gochhait
IGI Global
Cloud computing is gaining momentum as a subscription-oriented paradigm providing on-demand payable access to virtualized IT services and products across the net. It is a breakthrough technology that is offering on-demand access to various services across the network. Auto-scaling, though quite an attractive proposition to customers and naïve cloud service providers, has its own share of issues and challenges. This work was an attempt to classify and appreciate the auto scaling framework while outlining its challenges. Many effective and efficient auto scaling strategies are being deployed by cloud giants like Amazon AWS, Microsoft Azure, etc.
Saikat Gochhait, Yogesh Singh Rathore, Irina Leonova, Mahima Shanker Pandey, Bal Krishna Saraswat, Santosh Kumar Maurya, Hare Ram Singh, and Nidhi Bansal
Institute of Advanced Engineering and Science
<p>URL stands for uniform resource locator are the addresses of the unique resources on the internet. We all need URLs to access any type of resource on the internet, such as any web page, and document. Sometimes URLs can be long, irrelative and unattractive and unable to send sometimes via email. So, for this, we proposed a URL shortener web application based on the Python-Django platform which is fast and makes your long URLs in the shortest form which you can share on social media platforms. It makes all the messy, unattractive URLs short and shareable. Writing paper proposed a premium section in our application that gives access to the customizable URLs and analytics of your shorten URLs. Customizable URLs are the URLs you create by your own keywords. By creating a premium profile with the application, you can create your own URLs by using your own keywords. We have considered security a major part of the application that prevents the short URLs from being hacked or redirected to any advertising website or content. We store all the data related to the URL to show you the best view of your analytics and update it regularly. Main contribution in this field that for web application that provides users with a fast, secure and shortest URL for their using long URLs. Comparatively to other services that are currently available, the application provides superior security, availability, and confidentiality.</p>
Osama Al-Baik, Saleh Alomari, Omar Alssayed, Saikat Gochhait, Irina Leonova, Uma Dutta, Om Parkash Malik, Zeinab Montazeri, and Mohammad Dehghani
MDPI AG
A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator’s attack on a pufferfish and (ii) exploitation based on the simulation of a predator’s escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.
Kritika Sood, Saikat Gochhait, and Manisha Paliwal
Springer Nature Singapore
Kriti Majumder, Saikat Gochhait, and Manisha Paliwal
Springer Nature Singapore
Ibraheem Abu Falahah, Osama Al-Baik, Saleh Alomari, Gulnara Bektemyssova, Saikat Gochhait, Irina Leonova, Om Parkash Malik, Frank Werner, and Mohammad Dehghani
Tech Science Press
Naga Venkata Yaswanth Lankadasu, Devendra Babu Pesarlanka, Ajay Sharma, Shamneesh Sharma, and Saikat Gochhait
IEEE
Skin cancer is one the most frequent type of cancer in the world. Early detection and diagnosis are vital for effective treatment. Deep learning has been determined to be efficacious in the categorization of skin cancer. In this paper the author has presented a deep learning approach for classifying skin cancer. The algorithm was trained on approximately 10000 photos of skin cancer. In the approach author has used convolutional neural network (CNN) for skin cancer classification. The CNN model is next trained on a collection of skin data tagged as benign or malignant. In the validation of our method is done using a publicly accessible database of skin images. To train dataset, our method obtains ~92% accuracy. For the test set, the model achieves an accuracy of more than 95%. The model can accurately categorize both benign and malignant skin cancer. The predicted model is a useful method for skin cancer early detection and treatment.
Saira Yaqub, Saikat Gochhait, Hafiz Abdul Haseeb Khalid, Syeda Noreen Bukhari, Ayesha Yaqub, and Muhammad Abubakr
IEEE
WhatsApp has become a widely used medium to communicate in the modern era of technology, fostering diverse conversations and expressions among millions of users worldwide. This research introduces a robust analytical tool, the “WhatsApp Chat Ana-lyzer,” crafted to dissect and visualize the multifaceted landscape of group chats on WhatsApp. Imbued with Python's prowess and fortified by Streamlit, matplotlib, and Seaborn, the tool transcends conventional analyses by providing nuanced insights into user behavior, message statistics, and emerging content trends. In this research, we embark on an exploratory journey to decipher the complex dynamics embedded within WhatsApp group chats. By amalgamating sophisticated data preprocessing techniques, advanced statistical analyses, and captivating visualizations, the “WhatsApp Chat Analyzer” stands as a testament to our commitment to unraveling the facts of modern digital communication.
Sakshi, Chetan Sharma, Shamneesh Sharma, Tushar Sharma, and Saikat Gochhait
IEEE
Mustard plants are a crucial agricultural commodity for food and oil production. However, they are frequently vulnerable to various diseases that can substantially reduce crop yield. Early identification and diagnosis of these illnesses are essential for efficient management and control, ensuring sustainable production and agricultural output continuity. This research presents a new method for categorizing mustard leaf diseases using Convolutional Neural Networks (CNNs). The study utilizes sophisticated machine learning algorithms to differentiate between healthy and diseased mustard leaves, which is crucial for maintaining agricultural output and guaranteeing food security. The study involves training CNN architectures—Sequential CNN, ResNet-50, VGG, and AlexNet— on a meticulously curated dataset comprising images of healthy and diseased mustard leaves. The classifier's effectiveness is validated through comprehensive testing, demonstrating significant precision, recall, and F1-score advancements over conventional methods. This approach provides an efficient tool for disease detection in mustard crops and contributes to sustainable agricultural practices, aligning with the global goal of food security and environmental sustainability.
Venkateswara Reddy Lakkireddy, R. Madana Mohana, B. Rama Ganesh, Lakkireddy Udanth Reddy, Saikat Gochhait, and Shrish Chogle
IEEE
Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
GRANT DETAILS
Department of Science and Industrial Research , Govt of India with Grant of Rs 13,000,00
Ministry of Foreign Affairs, Taiwan with Grant of Rs 12,000,00
University of Deusto, Spain with Research Grant of Rs 2,000,00
University of Extremadura, Spain with Research Grant of Rs 2,000,00
Samara State Medical University, Russia with Research Visit grant of Rs 2,500,00
Symbiosis International Deemed University with Travel and Research Grant of 4,000,000
INDUSTRY EXPERIENCE
IFGL Refractories Ltd