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115 results on '"Pramanik, Pijush Kanti Dutta"'

7. Real-Time Obstacle Detection Using YOLOv8 on Raspberry Pi 4 for Visually Challenged People

Author

Upadhyaya, Bijoy Kumar, Pramanik, Pijush Kanti Dutta, Roy, Priyanka, Sen, Rituparna, 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, Senjyu, Tomonobu, editor, So–In, Chakchai, editor, and Joshi, Amit, editor

Published
2024
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15. Ubiquitous Manufacturing in the Age of Industry 4.0: A State-of-the-Art Primer

Author

Pramanik, Pijush Kanti Dutta, Mukherjee, Bulbul, Pal, Saurabh, Upadhyaya, Bijoy Kumar, Dutta, Shubhendu, Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, O. Gawad, Iman, Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, and Kumar, Akshi, editor

Published
2020
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16. Assessing the Learning Difficulty of Text-Based Learning Materials

Author

Pal, Saurabh, Mukhopadhyay, Moutan, Pramanik, Pijush Kanti Dutta, Choudhury, Prasenjit, 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, Satapathy, Suresh Chandra, editor, Bhateja, Vikrant, editor, Nguyen, Bao Le, editor, Nguyen, Nhu Gia, editor, and Le, Dac-Nhuong, editor

Published
2020
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17. Improving the Accuracy of Collaborative Filtering-Based Recommendations by Considering the Temporal Variance of Top-N Neighbors

Author

Singh, Pradeep Kumar, Setta, Showmik, Pramanik, Pijush Kanti Dutta, Choudhury, Prasenjit, 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, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Snasel, Vaclav, editor, Platos, Jan, editor, and Hassanien, Aboul Ella, editor

Published
2020
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20. A Semi-Automatic Metadata Extraction Model and Method for Video-Based e-Learning Contents

Author

Pal, Saurabh, Pramanik, Pijush Kanti Dutta, Majumdar, Tripti, and Choudhury, Prasenjit

Abstract

Video-based learning offers a learner a self-paced, lucid, memorizable, and a flexible way of learning. The availability of abundant educational video materials on the web has certainly abetted an individual's learning means. But the lack of necessary information about the videos makes it difficult for the learner to search and select the exact video as per his/her requirement and suitability in terms of the learner's learning capability and the material's relevancy, difficulty level, etc. Educational video recommendation systems also suffer from a similar problem. Extracting the required metadata, by different means, from the learning videos is a plausible solution. Despite the credible research efforts on video metadata extraction, the problem of educational video metadata extraction has been overlooked. This paper proposes a comprehensive approach to extract educational metadata from a learning video. A semiautomatic mechanism that includes manual and computational approaches is introduced for metadata extraction and to evaluate the values of these metadata. Along with identifying a set of specific metadata attributes from IEEE LOM, few additional attributes are suggested which are imperative to assess the suitability of a video-based learning object in terms of the personalized preference and suitability of a learner. The test results are validated by comparing with the manually extracted metadata by experts, on the same videos. The outcome establishes the promising effectiveness of the approach.

Published
2019
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24. A Comparative Study of Different Similarity Metrics in Highly Sparse Rating Dataset

Author

Singh, Pradeep Kumar, Pramanik, Pijush Kanti Dutta, Choudhury, Prasenjit, 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, Balas, Valentina Emilia, editor, Sharma, Neha, editor, and Chakrabarti, Amlan, editor

Published
2019
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26. Processing IoT Data

Author

Pramanik, Pijush Kanti Dutta, primary, Pal, Saurabh, additional, Brahmachari, Aditya, additional, and Choudhury, Prasenjit, additional

Published
2021
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27. Green Smart Building

Author

Pramanik, Pijush Kanti Dutta, primary, Mukherjee, Bulbul, additional, Pal, Saurabh, additional, Pal, Tanmoy, additional, and Singh, Simar Preet, additional

Published
2021
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29. List of contributors

Author

Adholeya, Radhika, primary, Ali, Abdelmegeid Amin, additional, Anwar, Eman M., additional, Armstrong, Caitrin, additional, Arora, Monika, additional, Bachhety, Shivam, additional, Bagga, Siddhant, additional, Benrimoh, David, additional, Chelvi, E. Dhiravida, additional, Chugh, Samarth, additional, Dahiya, Vandna, additional, Fratila, Robert, additional, Gabbay, Cheryl, additional, Gopi, R., additional, Gupta, Sarthak, additional, Hassan, Hassan Shaban, additional, Hemalakshmi, G.R., additional, Israel, Sonia, additional, Jain, Rachna, additional, Jayasankar, T., additional, Kapania, Shivani, additional, Khanna, Ashish, additional, Kleinerman, Akiva, additional, Krishamoorthy, Sujatha, additional, Krishnaraj, N., additional, Kumaram, Shubham, additional, Langlois-Therrien, Timothe, additional, Manjula, G., additional, Mehltretter, Joseph, additional, Mirchi, Nykan, additional, Mukhopadhyay, Moutan, additional, Pal, Saurabh, additional, Perlman, Kelly, additional, Prakash, N.B., additional, Pramanik, Pijush Kanti Dutta, additional, Rani, S. Sheeba, additional, Reddy, Shiva Shankar, additional, Ristevski, Blagoj, additional, Rollins, Colleen, additional, Rosenfeld, Ariel, additional, Saab, Bechara, additional, Savoska, Snezana, additional, Shankar, K., additional, Sharan, Swati, additional, Sharma, Deepak Kumar, additional, Sharon Roji Priya, C., additional, Singh, Simar Preet, additional, Snook, Emily, additional, Tanguay-Sela, Myriam, additional, Thoburn, Mark, additional, Venkata Lakshmi, S., additional, and Yaniv-Rosenfeld, Amit, additional

Published
2021
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32. Green Smart Building

Author

Pramanik, Pijush Kanti Dutta, primary, Mukherjee, Bulbul, additional, Pal, Saurabh, additional, Pal, Tanmoy, additional, and Singh, Simar Preet, additional

Published
2020
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37. MAPPING LEARNER'S QUERY TO LEARNING OBJECTS USING TOPIC MODELING AND MACHINE LEARNING TECHNIQUES.

Author

SENGUPTA, SOUVIK, PAL, SAURABH, and PRAMANIK, PIJUSH KANTI DUTTA

Subjects
MACHINE learning, NATURAL language processing, INQUIRY-based learning, RECOMMENDER systems, USER-generated content, PROGRAMMING languages, CONCEPT mapping
Abstract

Inquiry-based learning supports the independent knowledge development of the learner in an e-learning environment. It is crucial for the learner to obtain the appropriate Learning Object (LO) for the intended query. Mapping a learner's query to the right LO is a challenging task, as keyword-based searching on the topics or content does not guarantee the best result for various reasons. A query that apparently connects a topic may also implicitly refer to multiple other topics. Besides, the content of an LO with the same topic name often varies over different portals. Therefore, there is always a need for a method to automatically identify the latent topics of the query and then find the most relevant LO that covers the query. This paper aims to build a recommender system that maps a given input query to a suitable LO based on the most appropriate matching of learning contents. The proposed work employs an amalgamation of different supervised and unsupervised methods of natural language processing and machine learning. The machine learning model is trained on a handcrafted dataset to map queries into predefined topics. The proposed algorithm also leverages a dynamic topic modeling technique on learning content collected from three popular e-learning portals and uses a similarity score to map the learner's (user) query to the most appropriate LO. [ABSTRACT FROM AUTHOR]

Published
2023
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40. Contributors

Author

Al-Qaheri, Hameed, primary, Banerjee, Soumya, additional, Bashir, Aadil, additional, Bhati, Jai Prakash, additional, Bhatt, Chintan, additional, Digoo, Tabish, additional, Dumka, Ankur, additional, Ebadati E., Omid Mahdi, additional, Firdous, Maria, additional, Georgieva-Tsaneva, Galya, additional, Gospodinov, Mitko, additional, Gospodinova, Evgeniya, additional, Gulzar, Arifa, additional, Hamdani, Gazanfar A., additional, Kakarla, Shirisha, additional, Kaur, Gurjit, additional, Khan, Irfan, additional, Khan, Obaid, additional, Loan, Nazir A., additional, Manzoor, Misbah, additional, Memariani, Azizollah, additional, Pal, Saurabh, additional, Pal, Tanmoy, additional, Parah, Shabir A., additional, Pramanik, Pijush Kanti Dutta, additional, Sah, Anushree, additional, Shah, Pinal, additional, Shah, Asif A., additional, Sheikh, Javaid A., additional, Thakkar, Amit, additional, Tomar, Dimpal, additional, Tomar, Pradeep, additional, Upadhyaya, Bijoy Kumar, additional, and Tabari, Mahdiyeh Yousefi, additional

Published
2019
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43. Crowd Computing

Author

Pramanik, Pijush Kanti Dutta, primary, Pal, Saurabh, additional, Pareek, Gaurav, additional, Dutta, Shubhendu, additional, and Choudhury, Prasenjit, additional

Published
2019
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46. Processing IoT Data

Author

Pramanik, Pijush Kanti Dutta, primary, Pal, Saurabh, additional, Brahmachari, Aditya, additional, and Choudhury, Prasenjit, additional

Published
2018
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47. An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms.

Author

Ganie, Shahid Mohammad, Pramanik, Pijush Kanti Dutta, Malik, Majid Bashir, Nayyar, Anand, and Kyung Sup Kwak

Subjects
CARDIOVASCULAR diseases, MACHINE learning, ALGORITHMS, DATA analysis, ELECTRONIC data processing
Abstract

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method. Results were recorded before and after the data preprocessing techniques were applied. Out of all the algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for the proposed model. The proposed model yielded better results with gradient boosting in terms of precision, recall, and f1-score. It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning. [ABSTRACT FROM AUTHOR]

Published
2023
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