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146 results on '"Biswas, Saroj Kumar"'

9. Smart Farming Monitoring Using ML and MLOps

Author

Akkem, Yaganteeswarudu, Biswas, Saroj Kumar, Varanasi, Aruna, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Castillo, Oscar, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2023
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10. A Review on Rainfall Prediction Using Neural Networks

Author

Mandal, Sudipta, Biswas, Saroj Kumar, Purayastha, Biswajit, Chakraborty, Manomita, Kundu, Saranagata, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep, editor, Singh, Deepak, editor, Tiwari, Vivek, editor, and Misra, Sanjay, editor

Published
2023
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14. An Improvised Particle Swarm Optimization Using Balanced Local Best

Author

Doley, Bubul, Boruah, Arpita Nath, Rahman, Sazidur, Biswas, Saroj Kumar, Chakraborty, Manomita, Sarkar, Sunita, Purkayastha, Biswajit, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Singh, Brahmjit, editor, Coello Coello, Carlos A., editor, Jindal, Poonam, editor, and Verma, Pankaj, editor

Published
2021
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15. Comparative Analysis of Intelligent Systems using Support Vector Machine for the Detection of Diabetic Retinopathy

Author

Sri Venkateswara Reddy, G., Das, Dolly, Biswas, Saroj Kumar, Sai Prashanth, B., Praful Bhargav, B., Vinay Kumar, T., Bordoloi, Monali, Purkayastha, Biswajit, Rehman, Tohida, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Singh, Brahmjit, editor, Coello Coello, Carlos A., editor, Jindal, Poonam, editor, and Verma, Pankaj, editor

Published
2021
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17. Performance Analysis of Recursive Rule Extraction Algorithms for Disease Prediction

Author

Chakraborty, Manomita, Biswas, Saroj Kumar, Purkayastha, Biswajit, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Panigrahi, Chhabi Rani, editor, Pati, Bibudhendu, editor, Mohapatra, Prasant, editor, Buyya, Rajkumar, editor, and Li, Kuan-Ching, editor

Published
2021
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18. Removal of Eye-Blink Artifact from EEG Using LDA and Pre-trained RBF Neural Network

Author

Ghosh, Rajdeep, Sinha, Nidul, Biswas, Saroj Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Elçi, Atilla, editor, Sa, Pankaj Kumar, editor, Modi, Chirag N., editor, Olague, Gustavo, editor, Sahoo, Manmath N., editor, and Bakshi, Sambit, editor

Published
2020
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21. Keyword Extraction from Tweets Using Weighted Graph

Author

Biswas, Saroj Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Mallick, Pradeep Kumar, editor, Balas, Valentina Emilia, editor, Bhoi, Akash Kumar, editor, and Zobaa, Ahmed F., editor

Published
2019
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22. Graph-Based Sentiment Analysis Model for E-Commerce Websites’ Data

Author

Bordoloi, Monali, Biswas, Saroj Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Mallick, Pradeep Kumar, editor, Balas, Valentina Emilia, editor, Bhoi, Akash Kumar, editor, and Zobaa, Ahmed F., editor

Published
2019
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23. The Role of Social Media in Crisis Situation Management: A Survey

Author

Goel, Akanksha, Chakraborty, Manomita, Biswas, Saroj Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, Bhattacharya, Abhishek, editor, and Dutta, Soumi, editor

Published
2019
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24. Infusing Weighted Average Ensemble Diversity for Advanced Breast Cancer Detection.

Author

Abhisheka, Barsha, Biswas, Saroj Kumar, and Purkayastha, Biswajit

Subjects
*METASTATIC breast cancer, *DEEP learning, *BREAST cancer, *EARLY detection of cancer, *DEATH rate
Abstract

Breast cancer is a widespread health threat for women globally, often difficult to detect early due to its asymptomatic nature. As the disease advances, treatment becomes intricate and costly, ultimately resulting in elevated fatality rates. Currently, despite the widespread use of advanced machine learning (ML) and deep learning (DL) techniques, a comprehensive diagnosis of breast cancer remains elusive. Most of the existing methods primarily utilize either attention‐based deep models or models based on handcrafted features to capture and gather local details. However, both of these approaches lack the capability to offer essential local information for precise tumor detection. Additionally, the available breast cancer datasets suffer from class imbalance issue. Hence, this paper presents a novel weighted average ensemble network (WA‐ENet) designed for early‐stage breast cancer detection that leverages the ability of ensemble technique over single classifier‐based models for more robust and accurate prediction. The proposed model employs a weighted average‐based ensemble technique, combining predictions from three diverse classifiers. The optimal combination of weights is determined using the hill climbing (HC) algorithm. Moreover, the proposed model enhances overall system performance by integrating deep features and handcrafted features through the use of HOG, thereby providing precise local information. Additionally, the proposed work addresses class imbalance by incorporating borderline synthetic minority over‐sampling technique (BSMOTE). It achieves 99.65% accuracy on BUSI and 97.48% on UDIAT datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Early detection of Parkinson disease using stacking ensemble method.

Author

Biswas, Saroj Kumar, Nath Boruah, Arpita, Saha, Rajib, Raj, Ravi Shankar, Chakraborty, Manomita, and Bordoloi, Monali

Subjects
*EARLY diagnosis, *PARKINSON'S disease, *LOGISTIC regression analysis, *NEURAL transmission, *RANDOM forest algorithms, *HUMAN error
Abstract

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures. [ABSTRACT FROM AUTHOR]

Published
2023
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