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4. Natural Language Processing for Emotion Recognition and Analysis

Author

Gavhane, Jyoti, Prasad, Rajesh, Jadhav, Asavari, Parashar, Sushil, Marques, Oge, Series Editor, Chaudhury, Baishali, Editorial Board Member, Culibrk, Dubravko, Editorial Board Member, Hadid, Abdenour, Editorial Board Member, Kitamura, Felipe, Editorial Board Member, Riegler, Michael, Editorial Board Member, Schumacher, Joe, Editorial Board Member, Soares, Anderson, Editorial Board Member, Stojanovic, Branka, Editorial Board Member, Thampi, Sabu, Editorial Board Member, Van Ooijen, Peter, Editorial Board Member, Willingham, David, Editorial Board Member, Garg, Muskan, editor, and Prasad, Rajesh Shardanand, editor

Published
2024
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7. Causes of Cyber Fraud in Commercial Banks in Nigeria: A Case Study of Zenith Banks in Abuja

Author

Unwana, Ekong Eyo, Prasad, Rajesh, 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, Roy, Nihar Ranjan, editor, Tanwar, Sudeep, editor, and Batra, Usha, editor

Published
2024
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8. Automatic Text Document Classification by Using Semantic Analysis and Lion Optimization Algorithm

Author

Ranjan, Nihar M., Prasad, Rajesh S., Mane, Deepak T., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, 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, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Gunjan, Vinit Kumar, editor, Kumar, Amit, editor, Zurada, Jacek M., editor, and Singh, Sri Niwas, editor

Published
2024
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10. Contributors

Author

Abbirooban, Subramaniam, primary, Abhilash, K., additional, Abilash, K., additional, Ahuja, Kriti, additional, Al-Abyadh, Mohammed Hasan Ali, additional, Attuluri, Vamsi Kumar, additional, Chandra, Duvvi Roopesh, additional, Choudhary, Mangi Lal, additional, Choudhury, Nabanita, additional, Doewes, Rumi Iqbal, additional, Dwivedi, Divya, additional, Gangadharan, Syam Machinathu Parambil, additional, Garg, Vivek Kumar, additional, Goel, Neelam, additional, Gupta, Umesh, additional, Hoang, Vinh Truong, additional, Indu, H., additional, Jain, Vasu, additional, Jain, Paridhi, additional, Joshi, Ruchi, additional, K.K., Swathikiran, additional, Keerthika, M., additional, Krebsz, Markus, additional, Kumar, Kukatlapalli Pradeep, additional, Kumar, Mohit, additional, Kumar, Sunil, additional, Kuralamudhu, M.K., additional, Kurdhi, Nughthoh Arfawi, additional, Maheswari, G., additional, Nuryadin, Islahuzzaman, additional, Prasad, Rajesh, additional, Prasad, Jayashree, additional, Purnama, Sapta Kunta, additional, Ranjan, Nihar, additional, Singh, Manvendra, additional, Singh, Suyesha, additional, Singh, Divya, additional, Thiruthuvanathan, Michael Moses, additional, Trivedi, Gargi, additional, and Vasanth, M. Sundararaj, additional

Published
2024
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11. A Fast Algorithm for Heart Disease Prediction using Bayesian Network Model

Author

Muibideen, Mistura and Prasad, Rajesh

Subjects
Computer Science - Machine Learning, I.2, G.3
Abstract

Cardiovascular disease is the number one cause of death all over the world. Data mining can help to retrieve valuable knowledge from available data from the health sector. It helps to train a model to predict patients' health which will be faster as compared to clinical experimentation. Various implementation of machine learning algorithms such as Logistic Regression, K-Nearest Neighbor, Naive Bayes (NB), Support Vector Machine, etc. have been applied on Cleveland heart datasets but there has been a limit to modeling using Bayesian Network (BN). This research applied BN modeling to discover the relationship between 14 relevant attributes of the Cleveland heart data collected from The UCI repository. The aim is to check how the dependency between attributes affects the performance of the classifier. The BN produces a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios. The model has an accuracy of 85%. It was concluded that the model outperformed the NB classifier which has an accuracy of 80%.

Published
2020

12. Cyberbullying Severe Classification Using Deep Learning Approach

Author

Mohammed, Idi, Prasad, Rajesh, 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, Mishra, Anurag, editor, Gupta, Deepak, editor, and Chetty, Girija, editor

Published
2023
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31. Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution.

Author

Sani, Zaharaddeen, Prasad, Rajesh, and Hashim, Ezzeddin Kamil Mohamed

Subjects
*CONVOLUTIONAL neural networks, *IMAGE enhancement (Imaging systems), *EARLY detection of cancer, *BREAST cancer, *DATA scrubbing, *BREAST
Abstract

Breast cancer is a prevalent and potentially life-threatening medical condition characterized by uncontrolled cell proliferation within breast tissue. This global health concern predominantly among women, although it can affect men as well. The timely and accurate detection of breast cancer is critical, as it significantly influenced treatment outcomes and survival rates. Despite advancements in human-based diagnostic methods, inherent limitations such as subjectivity, human error, and fatigue persist. To address these constraints, computer-vision-based techniques have been explored for breast cancer detection, aiming to enhance early detection, reduce diagnostic errors, and improve patient outcomes. This study advances previous approaches by integrating group convolution and the Special Euclidean motion group as a features extractor into the Faster Region Convolutional Neural Networks framework of Detectron2. This integration offers the advantage of enhancing the Convolutional Neural Network by incorporating a method to preserve invariant and equivariant features, effectively leveraging symmetries in mammography images. The INbreast dataset, comprising 410 images from 115 cases, including 90 instances with bilateral breast involvement in women, was utilized. To ensure data compatibility, DICOM to PNG and XML to JSON format conversions were performed. A data enhancement pipeline, encompassing techniques such as image cropping, truncation normalization, contrast-limited adaptive histogram equalization, and image synthesis, was employed for data preprocessing and cleaning. The proposed technique demonstrated competitive results in breast cancer detection, achieving a recall rate of 97.22. This underscores the efficacy of the integrated approach in improving diagnostic accuracy and holds promise for advancing computer-vision-based breast cancer detection methodologies. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Semantic Web Technologies

Author

Prasad, Jayashree R., Shelke, Priya M., Prasad, Rajesh S., Kacprzyk, Janusz, Series Editor, Pandey, Rajiv, editor, Paprzycki, Marcin, editor, Srivastava, Nidhi, editor, Bhalla, Subhash, editor, and Wasielewska-Michniewska, Katarzyna, editor

Published
2021
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37. Influence of Language-Specific Features for Author Identification on Indian Literature in Marathi

Author

Kale, Sunil Digambarrao, Prasad, Rajesh S., 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, Reddy, V. Sivakumar, editor, Prasad, V. Kamakshi, editor, Wang, Jiacun, editor, and Reddy, K. T. V., editor

Published
2020
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38. Authorship Identification with Multi Sequence Word Selection Method

Author

Tamboli, Mubin Shoukat, Prasad, Rajesh S., 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, Cherukuri, Aswani Kumar, editor, and Gandhi, Niketa, editor

Published
2020
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46. On uniqueness and Somigliana theorem for a thermoelastic theory.

Author

Ranjan, Amitabh Gyan, Kumar, Pravin, and Prasad, Rajesh

Subjects
RECIPROCITY theorems, THERMOELASTICITY, GENERALIZATION
Abstract

The main concern of the present article is based on unified generalized thermoelasticity in an isotropic and homogeneous medium. The uniqueness theorem and Betti type reciprocal principle are derived. Based on the reciprocal theorem, the generalization of the Somigliana and Green theorem is also established in the present context. [ABSTRACT FROM AUTHOR]

Published
2024
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47. FCM with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection.

Author

Prasad, Rajesh S., Prasad, Jayashree Rajesh, Chaudhari, Bhushan S., Ranjan, Nihar M., and Srivastava, Rajat

Subjects
*OPTIMIZATION algorithms, *DEEP learning, *REMOTE-sensing images, *CORONAVIRUSES, *DATABASES
Abstract

Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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