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Your search keyword '"Shelke, Nilesh"' showing total 34 results
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34 results on '"Shelke, Nilesh"'

1. Comparative Study on Different Approaches Used in Word Sense Disambiguation—NLP

Author

Bhutda, Aryan, Shelke, Nilesh, Patni, Jagdish Chandra, Mehetre, Gayatri, Malke, Pradyumna, Aggarwal, Aman, Gholse, Aryan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, 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, 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, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, 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, Tan, Kay Chen, Series Editor, Illés, Zoltán, editor, Verma, Chaman, editor, Gonçalves, Paulo J. Sequeira, editor, and Singh, Pradeep Kumar, editor

Published
2024
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2. Analysis of Object Identification and Classification Using YOLO and RCNN

Author

Mahajan, Shriniwas, Rodge, Shreyash, Kuhikar, Om, Farooqui, Sadaf, Quazi, Ziyad, Shelke, Nilesh, Patni, Jagdish Chandra, 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, Garg, Lalit, editor, Sisodia, Dilip Singh, editor, Dewangan, Bhupesh Kr., editor, Shukla, R. N., editor, Kesswani, Nishtha, editor, and Brigui, Imene, editor

Published
2024
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12. A Comprehensive Framework for Facial Emotion Detection using Deep Learning.

Author

Shelke, Nilesh, Sale, Deepali, Shinde, Sagar, Kathole, Atul, and Somkunwar, Rachna

Subjects
EMOTION recognition, FACIAL expression, EMOTIONAL state, FACIAL muscles, EMOTIONS, DEEP learning
Abstract

Because people's intentions, feelings, and emotions are reflected in their facial expressions, researchers have been drawn to create self-acting autonomous facial expression detection systems. The facial emotion expression system is difficult due to the model's complexity, the small number of training data, and the minute micro facial muscle movements, despite the advancements in deep learning frameworks for automatic facial expression detection. This study suggests a deep learning framework consisting of Fully convolutional network called FCN-Long Short-Term Memory (LSTM), to detect behavior, mood, and facial activity using fine-grained facial action unit recognition with an ERS model. Based on these distinct patterns, the framework may be used to infer an individual's emotional state. The FCN helps in extracting the useful features, which then helps to increase the accuracy of the FCN-LSTM model. The LSTM is used to recognize the facial expression and present the state of the individual based on the expression by using the features that are extracted by FCN. This combination is helpful in generating accurate results for facial emotions recognition (FER). The model is trained and tested on Emotions (Emo-DB) dataset. The accuracy is observed as 94.67%, which is higher when compared to existing deep learning models. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Domain Independent Approach for Aspect Oriented Sentiment Analysis for Product Reviews

Author

Shelke, Nilesh, Deshpande, Shriniwas, Thakare, Vilas, 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, Udgata, Siba K., editor, and Pattnaik, Prasant Kumar, editor

Published
2017
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20. Approach for Emotion Extraction from Text

Author

Shelke, Nilesh, Deshpande, Shriniwas, Thakare, Vilas, 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, Udgata, Siba K., editor, and Pattnaik, Prasant Kumar, editor

Published
2017
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21. Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques

Author

Pande, Prashant B., Dhengare, Sagar W., Raut, Jayant M., Bhagat, Rajesh M., Bahoria, Boskey V., Shelke, Nilesh, Upadhye, Sachin D., and Vairagade, Vikrant S.

Abstract

The need for sustainable and resilient composite construction materials that can cope with increasing environmental and structural demands of modern construction is becoming urgently critical. The proposed model will handle the lack of sustainability and mechanical performance of the existing approaches. Specifically, they are not capable of dynamically adapting up to the changing environmental conditions and the intrinsic complexity of optimizing the material properties for the composites. To overcome these constraints, the present study develops an innovative multimethod framework by integrating the implementation of several state-of-the-art optimization and machine-learning techniques in order to enhance the sustainability and resilience of composite materials. The work is initialized by proposing a Multiple Objective Genetic Algorithm (MOGA) with dynamic fitness functions for the optimization of material designs, by balancing environmental impacts with mechanical performance in real time. This approach, hence, fits different environmental conditions and material requirements at the same time while importantly enhancing the design stage itself. At the same time, Gaussian Process Regression is the method that enables future LCA outcome prognoses undertaken using RL; it is possible to deal with the sustainability prediction as uncertain, and hence it is incorporated in the ongoing process of material optimization. In this way, RL will adaptively optimize processing parameters for the manufacturing of composites: both material resilience and goals regarding sustainability are realized through self-learning. Finally, a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm is introduced to probe and polish the solution space for composite material designs to leap over local optima hurdles. The overall improvement in the integrated attributes is 15% of the carbon footprint decrease, 20% in the tensile strength, and 12% decrease in energy consumption during processing. This study exemplifies one of the outstanding novel designs of composite materials, offering dynamism, adaptiveness, and robustness in enhancement of sustainability and resilience parameters in the process.

Published
2024
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