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
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.
Abdul Bashiru Jibril, Frederick Pobee, Saikat Gochhait, and Ritesh Chugh
Informa UK Limited
Aakash Bhandary, Vruti Dobariya, Gokul Yenduri, Rutvij H. Jhaveri, Saikat Gochhait, and Francesco Benedetto
Institute of Electrical and Electronics Engineers (IEEE)
Effective energy management is crucial for sustainability, carbon reduction, resource conservation, and cost savings. However, conventional energy forecasting methods often lack accuracy, suggesting the need for advanced approaches. Artificial intelligence (AI) has emerged as a powerful tool for energy forecasting, but its lack of transparency and interpretability poses challenges for understanding its predictions. In response, Explainable AI (XAI) frameworks have been developed to enhance the transparency and interpretability of black-box AI models. Accordingly, this paper focuses on achieving accurate household energy consumption predictions by comparing prediction models based on several evaluation metrics, namely the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The best model is identified by comparison after making predictions on unseen data, after which the predictions are explained by leveraging two XAI frameworks: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These explanations help identify crucial characteristics contributing to energy consumption predictions, including insights into feature importance. Our findings underscore the significance of current consumption patterns and lagged energy consumption values in estimating energy usage. This paper further demonstrates the role of XAI in developing consistent and reliable predictive models.
Saikat Gochhait, Deepak K. Sharma, Rajkumar Singh Rathore, and Rutvij H. Jhaveri
Bentham Science Publishers Ltd.
Aim: Load forecasting with for efficient power system management Background:: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method:: 1D CNN BI-LSTM model incorporating convolutional layers. Result:: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion:: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.
Omar Alsayyed, Tareq Hamadneh, Hassan Al-Tarawneh, Mohammad Alqudah, Saikat Gochhait, Irina Leonova, Om Parkash Malik, and Mohammad Dehghani
MDPI AG
In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos’ digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.
Soumya Pandey and Saikat Gochhait
AIP Publishing
Siddharth Mantraratnam, Saikat Gochhait, Ahmed J. Obaid, and A. H. Radie
AIP Publishing
Shalini Sinha, Saikat Gochhait, Ahmed J. Obaid, Azmi Shawkat Abdulbaqi, Watheq Naeem Alwan, Mohammed Ibrahim Mahdi, and Muthmainnah
AIP Publishing
Ambuj Mohan, Saikat Gochhait, Ahmed J. Obaid, Muthmainnah, and Miguel Cardoso
AIP Publishing
Selvakumar Thirumalaisamy, Kamaleshwar Thangavilou, Hariharan Rajadurai, Oumaima Saidani, Nazik Alturki, Sandeep kumar Mathivanan, Prabhu Jayagopal, and Saikat Gochhait
MDPI AG
Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies.
Sweta Sharma and Saikat Gochhait
AIP Publishing
Sujata Roy, Jeyalakshmi Jeyabalan, Saikat Gochhait, Poonkuzhali Sugumaran, and M. Michael Gromiha
International Information and Engineering Technology Association
Puneet Gore, Saikat Gochhait, and Samrat Ray
IEEE
Due to rapid industrialization and the use of sheet metal for various purposes, the cold rolling mill has achieved the utmost importance. The cold rolling process runs at room temperature under oil lubrication. Cold rolling affects the drawability ofsteel; therefore, the need arises to determine the factors leadingto a decrease in accuracy. By knowing this, we can increase the efficiency of the cold rolling mill. Various experiments were conducted mathematical model was created to calculate the roll gap. The emulsion was prepared, and oils were tested for their thickness. Some conclusions were derived, film thickness depends upon the film and type of contact. As speed increases, the stabilityof the mill decreases.
Srijit Nair, Saikat Gochhait, and Manisha Paliwal
IEEE
The healthcare sector has been enhanced over the last several years by the development of many applications. Industry 4.0 neuro-technologies - Machine Learning, the Internet of Things (IoT), and Blockchain - have revolutionized healthcare. The rise of portable health care devices able to monitor patients' vital signs has improved the quality of healthcare systems. Blockchain-based solutions provide immutable health records with authenticity and transparency. As Industry 4.0 transforms the health care industry, society's goals and production growth are driving the industry towards Health Industry 5.0 as a result. A human-centered solution that enhances healthcare treatment is the core idea of Health Industry 5.0. The three pillarsare Human Centric, Sustainable, and Resilient. The purpose of this study is to discuss the emerging neuro-technologies in healthcare and the possibilities of Health Industry 5.0.
M.S. Priyadarshini, Annareddy Sravani, Saikat Gochhait, Ankit Bhatt, Mohit Bajaj, and Mohamed Metwally Mahmoud
IEEE
Street lighting is an important concept on which we have to focus more because we spend more than 40 percent of the allotted budget towards it. In the present scenario most of the street lighting systems are inefficient in reducing the cost. An effective solution is reducing the energy consumption. This paper deals with monitoring and controlling the street lights through IoT and energy consumption reduction of the street lights in a smart way. The proposed system can make the street light to glow with different intensities as per the scheduled timings also depending on the traffic as well as on climatic conditions Transformer and relays are used to reduce the intensity of the light by reducing the voltage. Sensing the darkness and detection of objects can be carried out by Light Dependent Sensor (LDR) and Passive Infrared (PIR) sensors. Master node (Raspberry Pi) and Slave node (Arduino) communicate each other through RF module. Current and Potential transformers are used to measure the current and voltage readings respectively and the values will be uploaded to server and these can be monitored from anywhere in the world. This project helps not only to the government but also to the educational institutions, offices and industries.
Saikat Gochhait
Routledge
Saikat Gochhait
Routledge
Saikat Gochhait
Routledge
Igor Shirolapov, Alexander Zakharov, Saikat Gochhait, Vasiliy Pyatin, Mariya Sergeeva, Natalia Romanchuk, Yuliya Komarova, Vladimir Kalinin, Olga Pavlova, and Elena Khivintseva
World Scientific and Engineering Academy and Society (WSEAS)
Background: In the last decade, the concept of the Glymphatic system as a complexly organized perivascular transport has been formed, it “connects” the cerebrospinal fluid with the lymphatic vessels of the meninges through the extracellular space of the brain. The exact molecular mechanisms of the functioning of the glymphatic pathway have not been fully characterized, but its key role in the cerebral clearance of metabolites and neurotoxic substances is noted. Neurodegenerative diseases affect millions of people around the world, and the most common pathologies from this heterogeneous group of diseases are Alzheimer's disease and Parkinson's disease. Their pathogenesis is based on abnormal protein aggregation, formation of neurofibrillary insoluble structures, and inefficient removal of neurotoxic metabolites. Aim: This article reviewed the evidence linking glymphatic system dysfunction and the development of human neurodegenerative diseases, and noted the key role of aquaporin-4 in the clearance of metabolites from the brain. Setting and Design: The actual sources of data were compiled and reviewed from PubMed, Scopus, and Web of Sciences from 2012 to 2023. Result and Discussion: Glial-dependent perivascular transport promotes the clearance of interstitial solutes, including beta-amyloid, synuclein, and tau protein, from the parenchymal extracellular space of the brain in normal and pathological conditions. An increase in the proportion of metabolites and pathological proteins in the dysfunction of the glymphatic pathway enhances the progression of cognitive impairment and neurodegenerative processes. In turn, the aging process, oxidative stress, and neuroinflammation in Alzheimer's disease and Parkinson's disease contribute to reactive astrogliosis and may impair glymphatic clearance. Conclusion: This review describes in detail the features of the glymphatic system and discusses that its dysfunction plays a fundamental significance in the pathological accumulation of metabolites during the progression of neurodegeneration and neuroinflammation. Understanding these processes will make it possible to take new steps in the prevention and treatment of neurodegenerative diseases.
Saikat Gochhait, Miriam Cano Rubio, Rocío Martínez Jiménez, and Sabiha Fazalbhoy
Inderscience Publishers
Saikat Gochhait and Manisha Paliwal
IEEE
Developing a GIOT-enabled surveillance cobot is the objective of this study. The cobot transmits real-time video footage from a preset environment to a base control station via internet or Wi-Fi. In this methodology, the cobot is controlled in real time by a human controller, who uses the data to operate the cobot. The cobot is small and independent, and it transmits data wirelessly. An application that monitors and controls a cobot's movements using a wireless network and a Raspberry Pi board can help detect and monitor terrorist attacks around the world.
Saikat Gochhait
Inderscience Publishers
Aritra Mitra, Saikat Gochhait, Ahmed J. Obaid, and Mohammed Ayad Alkhafaji
IEEE
Since revolutionary digitization has taken hold in all industries and companies, the excessive growth of data is overtaking the world around us. With this explosion of data comes an increased responsibility to protect it from external threats, exploitation and misuse of information. The healthcare industry is expanding its horizons with the latest cutting-edge technologies such as robotic process automation, cloud transformation and digitization, generating several zettabytes of data every year. With this excessive data growth, the responsibility to protect the data from external threats, exploitation and information misuse is also increasing. The steep rise in data breaches, disclosure of important public and corporate data, fraudulent activities such as threatening phone calls, false insurance claims, and even illegal monetary claims have rocked the world. This in turn increases the urgency and need for an advanced, standardized data protection strategy. In this research study, the Scopus database has been used as a source for a bibliometric analysis to discuss recent research activities on big data protection. The expected outcome of this research is a broader understanding of how organizations operating in the healthcare sector are addressing overall data management by shaping existing organizational policies and adapting new security standards.
Mayuri Puri and Saikat Gochhait
IEEE
Cyberattacks are used to steal money, data, or intellectual property, but the goal is increasingly to produce overt disruption or political influence. Healthcare is more vulnerable to cyberattacks than other industries due to inherent weaknesses in its security posture. In addition to medical equipment and other systems connected to IT networks, cybersecurity threats and vulnerabilities can pose a threat to the confidentiality, resilience, and veracity of those systems. As a result of the rich supply of valuable data, Healthcare makes a good target for cybercriminals. Additionally, while Cybersecurity is critical for patient safety, it has an unreliable track record. Breach of infrastructure has resulted in millions of health records being stolen, potentially putting patients' lives at risk. This necessitates the integration of Cybersecurity into patient safety. Before these attacks, many security experts struggled to persuade corporate executives of the necessity of cyber security; significantly, a great deal can be gained, in the long run, from risk mitigation, through both cost savings and reputation protection. A holistic solution to prioritizing Cybersecurity in the healthcare business necessitates cultural transformations, enhanced leadership communication, and changes in how practitioners conduct their roles in the clinical setting.
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