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155 results on '"Bhattacharjee, Vandana"'

2. Kannada Continuous Speech Recognition Using Deep Learning

Author

Paul, Shubhojeet, Bhattacharjee, Vandana, Saha, Sujan Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Verma, Anshul, editor, Verma, Pradeepika, editor, Pattanaik, Kiran Kumar, editor, Dhurandher, Sanjay Kumar, editor, and Woungang, Isaac, editor

Published
2024
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4. PSO-Based Traffic Signals in a Real-World City

Author

Singh, Vandana, Sahana, Sudip Kumar, Bhattacharjee, Vandana, 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, Chakraborty, Basabi, editor, Biswas, Arindam, editor, and Chakrabarti, Amlan, editor

Published
2023
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5. Travelling Guidance Using ACO and HBMO Techniques in COVID-19 Pandemics: A Novel Approach

Author

Saket, Shashwat, Mishra, Shivam P., Bhattacharjee, Vandana, Mishra, Kamta Nath, 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, Mandal, Jyotsna Kumar, editor, and De, Debashis, editor

Published
2023
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6. Continuous Speech Recognition in Hindi for Health Care Using Deep Learning

Author

Paul, Shubhojeet, Saha, Sujan Kumar, Bhattacharjee, Vandana, 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, Mandal, Jyotsna Kumar, editor, and De, Debashis, editor

Published
2023
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17. K-NN Classification of Very Large Dataset in Spark Framework

Author

Jha, Ritesh, Bhattacharjee, Vandana, Mustafi, Abhijit, 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, 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, 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, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, J.K., editor

Published
2021
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18. Opinion Mining of Restaurant Reviews and Comparison of Different Classifiers

Author

Sinha, Ananya, Oraon, Manila, Anand, Sneha, Bhattacharjee, Vandana, 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, 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, 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, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, J. K., editor

Published
2021
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19. Classification of Big Data Using Spark Framework

Author

Jha, Ritesh, Bhattacharjee, Vandana, Mustafi, Abhijit, 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, 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, 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, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, J. K., editor

Published
2021
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20. A Fuzzy Logic Approach for Software Cost Prediction

Author

Masroor, Farha, Bhattacharjee, Vandana, Mustafi, Debjani, 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, 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, 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, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, J. K., editor

Published
2021
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21. Convolutional Neural Network-Based Visually Evoked EEG Classification Model on MindBigData

Author

Kumari, Nandini, Anwar, Shamama, Bhattacharjee, Vandana, 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, Pan, Indrajit, editor, Mukherjee, Anirban, editor, and Piuri, Vincenzo, editor

Published
2021
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22. An end-to-end deep learning based continuous speech recognition in Kannada.

Author

Kumari, Astha, Verma, Sai Aditi, Singh, Eshika, Toppo, Alokit Charles, Paul, Shubhojeet, and Bhattacharjee, Vandana

Subjects
SPEECH perception, CONVOLUTIONAL neural networks, RECURRENT neural networks, SPEECH, DEEP learning, ERROR rates, AUTOMATIC speech recognition
Abstract

Automatic Speech Recognition (ASR) is an efficient method of converting speech signals into textual format and is often referred to as Speech-to-Text System (S2T). We present an End-to-End (E2E) Deep Learning based Continuous Speech Recognition system in Kannada using Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Units (Bi-GRU) as a variant of Recurrent Neural Network (RNN). The model was built using 120 hours of manually selected continuous Kannada speech data. Experimentation has been performed on two different deep learning-based models. The accuracy with lowest Character Error Rate (CER) of 18.62% was achieved. The outcome of this experiment has been quite encouraging for developing end-to-end automatic speech recognition systems. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Application of GANs for generation of handwritten digits and letters.

Author

Mondal, Diya, Demta, Chris Calvin, and Bhattacharjee, Vandana

Subjects
GENERATIVE adversarial networks, HANDWRITING recognition (Computer science), COMPUTER vision, DEEP learning
Abstract

Deep learning models have been successfully applied in a variety of computer vision tasks. In particular, GANs (Generative Adversarial Networks) have proved very effective in generative applications involving images. This research work aims to apply GANs in the creation of handwritten digital pictures. Further, Conditional Deep Convolutional Generative Adversarial networks have been applied to generate handwritten letters. Our experiments have unambiguously underscored the extraordinary power and versatility of GANs in the arenas of character and digit generation. [ABSTRACT FROM AUTHOR]

Published
2024
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24. IoT-enhanced machine learning for precise crop disease detection and sustainable agriculture.

Author

Suman, Sonu Kumar, Singh, Vivek, Prasad, Umesh, Chakravarty, Soumitro, and Bhattacharjee, Vandana

Subjects
SUSTAINABLE agriculture, PLANT diseases, MACHINE learning, SUSTAINABILITY, CONVOLUTIONAL neural networks
Abstract

Crop diseases present a substantial threat to worldwide food security, emphasizing the critical importance of timely and precise disease detection to mitigate yield losses and support sustainable agriculture. This study introduces an innovative approach that harnesses the power of Machine Learning (ML) and the Internet of Things (IoT) to revolutionize the detection of crop diseases. Within this framework, IoT sensors strategically deployed across agricultural fields gather real-time environmental data, while high-resolution crop images are captured using drones and on-site cameras. ML models, including Convolutional Neural Networks (CNNs), harness this data to enable early and accurate disease detection. By integrating IoT capabilities, we can correlate disease outbreaks with environmental factors, thereby enhancing the decision-making process. The results are promising, consistently demonstrating high accuracy in disease detection and the potential for reducing the need for chemical treatments. This research paper signifies a significant step towards promoting sustainable farming practices and enhancing global food security. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Graph convolutional neural networks for traffic forecasting and prediction: A review.

Author

Singh, Vandana, Sahana, Sudip Kumar, and Bhattacharjee, Vandana

Subjects
TRAFFIC estimation, CONVOLUTIONAL neural networks, TRANSPORTATION management, URBAN planning, ROAD interchanges & intersections
Abstract

Traffic forecasting and prediction are crucial in urban planning, transportation management, and decision-making. Traditional methods often struggle to capture traffic data's complex spatial dependencies and temporal dynamics. In recent years, Graph Convolutional Neural Networks (GCNs) have emerged as a powerful tool for addressing these challenges. GCNs leverage graph structures to model relationships between traffic nodes, such as road intersections or segments, and have shown promising results in various traffic forecasting and prediction tasks. Within this paper, an extensive exploration unfolds, delving into the utilization of GCNs in the realm of traffic forecasting and prediction. It meticulously illuminates their inherent strengths, navigates through limitations, and envisions potential future trajectories. Additionally, a pioneering framework for traffic forecasting takes centre stage within these pages. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Contributors

Author

Agrawal, Shriyansh, primary, Akila, C., additional, Ali, Rashid, additional, Bhattacharjee, Vandana, additional, Bisht, Sankalp Singh, additional, Chawla, Priyanka, additional, Chopra, Meher, additional, Dutt, Varun, additional, Ganguly, Chirantan, additional, Guglani, Arshpreet Singh, additional, Gupta, Sarishty, additional, Gupta, Anil Kumar, additional, Gupta, Vaibhav, additional, Jahan, Roshan, additional, jain, Megha, additional, Jain, Saurabh, additional, Jain, Shikha, additional, Jain, Veenus, additional, Kaushik, Keshav, additional, Kavitha, V., additional, Kumar, Adarsh, additional, Nanda, Aparajita, additional, Nanta, Bharti, additional, Nayak, Sagnik, additional, Pandey, Dhiraj, additional, Pandey, Kavita, additional, Racherla, Akhila Sahiti, additional, Rawat, Tara, additional, Sahu, Raj, additional, Shandilya, Herumb, additional, Sharma, Kewal Krishna, additional, Sharma, Neha, additional, Skariah, Senu Mary, additional, Sood, Rishik, additional, Sriram, Degala, additional, Syaputra, Reno Ardian, additional, Tripathi, Manish Madhav, additional, Tuteja, Sonal, additional, Uttrani, Shashank, additional, and Varma, Hrishav, additional

Published
2022
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28. IoT in Healthcare: A Big Data Perspective

Author

Jha, Ritesh, Bhattacharjee, Vandana, Mustafi, Abhijit, Kacprzyk, Janusz, Series Editor, Jain, Lakhmi C., Series Editor, Pattnaik, Prasant Kumar, editor, Mohanty, Suneeta, editor, and Mohanty, Satarupa, editor

Published
2020
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30. A Takagi-Sugeno Based Fuzzy Inference System for Predicting Electrical Output in a Combined Cycle Power Plant

Author

Kumari, Nandini, Anwar, Shamama, Bhattacharjee, Vandana, 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, 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, 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, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, J. K., editor

Published
2020
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32. A New Neighborhood-Based Outlier Detection Technique

Author

Gupta, Umang, Bhattacharjee, Vandana, Bishnu, Partha Sarathi, 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, Liang, Qilian, Series Editor, Martin, 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, 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, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, Jyotsna Kumar, editor

Published
2019
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36. Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach.

Author

Mishra, Kamta Nath, Bhattacharjee, Vandana, Saket, Shashwat, and Mishra, Shivam Prakash

Subjects
*MACHINE learning, *MULTI-factor authentication, *FEDERATED learning, *BLOCKCHAINS, *EDGE computing, *DATA security, *DATA security failures, *PUBLIC key cryptography
Abstract

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. [ABSTRACT FROM AUTHOR]

Published
2024
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41. A machine learning approach to predict the rating of team using regression algorithms.

Author

Ramavath, Ashok, Bhattacharjee, Vandana, and Kumar, Sanjay

Subjects
*MACHINE learning, *STANDARD deviations, *ALGORITHMS
Abstract

Football is a famous sport played around the world that involves various actions from defending to attacking the ball to score goals. Various rules like half-side, No hands, Throw-ins Direct and Indirect kicks, etc. are followed. In a game, players might get yellow-cards and red-cards which serves as warning and elimination from that particular game. There are-many factors like shots, possession, pass accuracy, yellow and red cards, etc. that decides which team wins in a match. In the data that we worked on,the Rating is calculated from the parameters which are mentioned. Machine learning regression algorithms have been applied to predict the Rating feature. The goal of this paper is to know which regression algorithm performs better to predict the Rating variable. Evaluation metrics like Root mean square error (RMSE), Mean absolute error (MAE), and R square (R2) are used to identify the best algorithm among the applied algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Evaluating the Performance of Machine Learning Models for Diabetes Prediction with Feature Selection and Missing Values Handling

Author

Bhattacharjee, Vandana, Ankita Priya, and Prasad, Umesh

Subjects
Driver smoking, feature selection, logistic regression, support vector classifier
Abstract

Diabetes is a growing problem these days and many people are at risk. The effects of modern lifestyle are becoming visible in the spread of several lifestyle diseases. Obesity is one of these diseases which leads to several offshoots, diabetes being one of them. With the growth of Machine Learning (ML) techniques, researchers have been keen to apply them for predicting diabetes in the recent years. It is an important issue that the data set for applying ML techniques needs to be processed in the right way to be able to get informative results. This paper aims to study the effect of different techniques for pre-processing diabetes dataset, and then applying various ML classifiers on them. This research paper evaluates the performance of two algorithms, Logistic Regression and Support Vector Classifier based on the parameters Precision, Accuracy, Recall and F-measure. When the results were compared to other studies conducted by other researchers, it was evident that Logistic Regression performed better than all other classifiers, with the greatest accuracy of 81% utilizing only five features. The experiments have been performed using Python 3.8 and the IDE used was Jupyter Notebook.

Published
2023
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43. A Deep Learning-Based Approach for Accurate Diagnosis of Alcohol Usage Severity Using EEG Signals.

Author

Kumari, Nandini, Anwar, Shamama, and Bhattacharjee, Vandana

Subjects
DEEP learning, CONVOLUTIONAL neural networks, MACHINE learning, ELECTROENCEPHALOGRAPHY, FEATURE extraction, ERROR rates
Abstract

The majority of conventional alcohol detection approaches are based on traditional machine learning approaches and are unable to retrieve the deeply hidden attributes of alcohol-based electroencephalography (EEG) signals. The intention of this research is to evolve a deep learning-based method for detecting alcohol-related EEG signals automatically. It also investigates whether a deep learning method for alcoholism classification is effective without using any explicit feature extraction and selection steps. To assess this, the paper implements and compares deep learning-based strategies to classify alcohol-related EEG signals. A framework that uses prerecorded raw EEG data as input to a deep learning algorithm called convolutional neural network (CNN), long short-term memory (LSTM), and a cascade model called CNN + LSTM is also implemented for detecting alcoholism on an openly accessible UCI Alcoholic EEG dataset. Some conventional techniques are also implemented on prerecorded raw EEG signals, and the classification results are compared with the proposed methods. The results of the experiments show that the proposed models are capable of achieving average classification accuracies of 99.60%, 98.12%, and 95.95% on the training dataset and 92.77%, 89%, and 91% on the testing dataset along with error rates of 7.5%, 11.90%, and 8.7% from CNN, LSTM, and CNN + LSTM, respectively. [ABSTRACT FROM AUTHOR]

Published
2023
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