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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Bhatt, Nirav Sanjay Nath Pandey, Trilok Reddy, Suravaram Rishabh Jayasurya, B. Dash, Bibhuti Bhusan Shekhar Patra, Sudhansu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350308167

The market for used cars is a dynamic and ever-expanding industry, with various factors influencing the pricing of pre-owned vehicles. Accurate pricing is crucial for both buyers and sellers, as it ensures fair transactions and minimizes the risk of financial loss. This paper introduces a machine learning algorithms developed for the purpose of predicting used car prices based on a comprehensive set of features. Various machine learning (ML) techniques such as multiple linear regression, ridge regression, lasso regression, Bayesian regression, Decision tree regression, random forest regression, eXtreme Gradient Boost (XGB) regression, Light Gradient Boosting Machine (LGBM) regression, Gradient boosting regression and Multilayer Perceptron (MLP) regression. To assess the algorithm's performance, rigorous evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) score has been employed. © 2023 IEEE.

关键词： ANN Bayesian Regression Decision Tree Regression Lasso Regression LGB Linear Regression MLP Random Forest Regression Ridge Regression XGB

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Analyzing the social conduct of users to detect and prevent cybercrime across social networks 3

Analyzing the social conduct of users to detect and prevent ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Gupta, Atika Matta, Priya Pant, Bhasker Department of Computer Applications Graphic Era Deemed to Be University Dehradun India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India

The online social network features have made it possible to reach cybercriminals in previously unreachable countries and locations. Machine Learning techniques allow us to predict and detect harmful forms of human behaviour such as cyberbullying. Cyberbullying can be easily committed;it is fast-spreading and dangerous, aggressive behaviour. The criminal only requires a laptop and an internet connection and no confrontation with the victim. Anonymous users on social media websites are also the primary cause of aggressive behaviour. Big-data analysis unfolds the otherwise hidden information through deep learning and can help forecast the future when combined with machine learning techniques. The rise of cybercrime poses a significant threat to individual users and the community. For that, effective mitigation techniques are urgently required. This study proposes an innovative model IdentityLinkerNet (ILN) designed to detect cybercriminals by analyzing the social conduct of users across social networks. There are some ethical concerns regarding this study which are privacy concerns related to data collection and sharing, consent and data security. The limitation of this study lies in collecting datasets for user behaviour analysis across multiple social media platforms. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Tweets

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images 1

Improved UNET Architecture for Effective COVID-19 Severity P...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Ramla, M. Jasmine, R. Anita Ramesh, M. Bai, Femilda Josephin Joseph Shobana SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350376517

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%. © 2024 IEEE.

关键词： COVID-19

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Meme Expressive Classification in Multimodal State with Feature Extraction in Deep Learning 2

Meme Expressive Classification in Multimodal State with Feat...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Barveen, A. Geetha, S. Faizal, M.K.Mohamed M.I.E.T. Engineering College Department of Computer Science and Engineering Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350397635

Memes are a socially interactive way to communicate online. Memes are used by users to communicate with one another on social networking sites and other forums. Memes essentially focus on speech recognition and image macros. While a meme is being created, it focuses on the semiotic type of resources that the internet community interprets with other resources, which facilitates the interaction among the internet and meme creators. Memes recreate based on various approaches, which fall under various acts such as existing speech acts. Based on the expressive face with captioned short texts, even the short text is exaggerated. Every year, meme mimicking applications are created that allow users to use the imitated meme expressions. Memes represent the shared texts of the younger generations on various social platforms. The classifications of sentiment based on the various memetic expressions are the most efficient way to analyse those feelings and emotions. HOG feature extraction allows the images to be segmented into blocks of smaller size by using a single feature vector for dimension, which characterizes the local object appearances to characterize the meme classification. The existence of specific characteristics, including such edges, angles, or patterns, is then analyzed by combining HOG features using multi-feature analysis on patches. Based upon the classification methodology, it classifies the sentiments, which tend to improve the learning process in an efficient manner. By combining a deep learning approach with a recurrent neural network, the extended LSTM-RNN can identify subtle nuances in memes, allowing for more accurate and detailed meme classification. This proposed method effectively evaluates several classification techniques, including CNN and Extended LSTM-RNN for meme image characterization. Through training and validation, Extended LSTM-RNN achieved 0.98% accuracy with better performance than CNN. © 2023 IEEE.

关键词： Feature extraction

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Coupling Mobile Brain Imaging and Virtual Reality Omnidirectional Treadmill to Explore Attenuated Situational Awareness during Distracted Walking

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期16卷 1063-1076页

作者： Chuang, Chun-Hsiang Chiu, Tsai-Feng Hsu, Hao-Che Lin, Shih-Syun National Tsing Hua University Research Center for Education and Mind Sciences College of Education The Institute of Information Systems and Applications College of Electrical Engineering and Computer Science Hsinchu300 Taiwan National Tsing Hua University Research Center for Education and Mind Sciences Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300193 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Walking while engaging in distracting activities affects our perception of changes happening on the road, leading to exposure to hazardous conditions. This study aimed to evidence that pedestrians who become distracted when absorbed in their smartphones fail to maintain situational awareness. Thirty-six recruited participants performed a psychomotor vigilance task and a wayfinding task designed to simulate a multitasking scenario;meanwhile, an auditory oddball paradigm presented to participants tested their situational awareness of an external stimulus. The experiment was conducted in virtual reality (VR) environment with an omnidirectional treadmill. Participant's behavioral and electroencephalographic (EEG) responses were measured and analyzed using a newly proposed brain network measure, phase locking distance (PLD), to capture multitasking-associated couplings between brain regions. The current study supports the notion that multitasking can lead to diminished behavioral performance and changes in walking patterns. Despite the increased interregional brain connectivity, which may be an adaptive response to heightened cognitive demands, the associated reduction in event-related EEG activity suggests competition for neural resources during multitasking. Moreover, the observed EEG activities could indicate additional information processing capacity limitations and provide evidence of decreased situational awareness during multitasking, thereby increasing susceptibility to potentially hazardous situations. © 2016 IEEE.

关键词： Electroencephalography

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Granular Network Traffic Classification on Mobile Application Service for Better Parental Control 2

Granular Network Traffic Classification on Mobile Applicatio...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dhivvya, J.P. Aswini Dath, Guru Chinta, Mahesh Kumar Karki, Ankit Amrita Vishwa Vidyapeetham Amrita School of Computing Center for Wireless Networks and Applications Department of Computer Science and Engineering Kollam Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Kollam Amritapuri India

ISBN: (纸本)9798350360523

The widespread use of mobile applications among children had its limitations in excessive screen usage in entertainment. Traditional parental control techniques mostly depend on blocking applications based on their name or categories and are ineffective at identifying potentially new dangerous applications. This study addresses this issue by introducing a paradigm shift for protecting kids' digital experiences through granular network traffic classifications. This system classifies mobile app network traffic into various groups by implementing machine learning or deep learning models. Machine learning models like linear regression, Adaboost Classifier, Support Vector Classification (SVC), Ridge Classifier and deep learning models like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) were used to identify and classify mobile applications. Initially, the deep learning RNN model results had 80% accuracy in the classification of mobile apps, and the machine learning logistic SVC and Adaboost Classifier had 72% accuracy. After adding new applications to the existing dataset, the accuracy of the RNN model has remained the same 80% accuracy. Meanwhile, the machine learning model accuracy reduced to 70%, proving the flexibility of the system. This work can assist parents in monitoring their kids to use mobile applications more for education. This work has a huge scope for information technology management of social change using hybrid learning models in software-defined networking in local networks for the administrators to empower their users to wisely use the internet. © 2024 IEEE.

关键词： Learning systems

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Analysis of Educational Recommender System Techniques for Enhancing Student's Learning Outcomes 3

Analysis of Educational Recommender System Techniques for En...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Pal, Neeti Dahiya, Omdev School of Computer Applications Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9798350336238

Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future. © 2023 IEEE.

关键词： Recommender systems

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An efficient speech emotion recognition based on a dual-stream CNN-transformer fusion network

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International Journal of Speech Technology 2023年第2期26卷 541-557页

作者： Tellai, Mohammed Gao, Lijian Mao, Qirong Department of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Zhenjiang212013 China

The use of machine learning and artificial intelligence enables us to create intelligent systems. Speech emotion recognition system analyzes the speaker’s speech to determine his/her emotional state. Speech emotion recognition is a challenging pattern recognition task. This paper proposes a new robust and lightweight speech emotion recognition system based on a dual-stream CNN-Transformer fusion network that effectively captures spatial and temporal information disentangled in long-distance raw features by exploring both MFCCs and Mel-spectrograms in a parallel mechanism. Experiments are performed on the widely used emotional benchmark datasets. Our approach has shown to be quite efficient. It outperforms the best-known state-of-the-art models with an accuracy of 97.64%, 99.42%, and 97.53% on RAVDESS, TESS, and EMO-DB datasets respectively. The results demonstrate the significant advantages of the proposed model and the ability of this architecture to learn to recognize emotional data features accurately. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Predicting Customer Satisfaction in Brazil E-commerce: A Comparative Study of Machine Learning Techniques 4

Predicting Customer Satisfaction in Brazil E-commerce: A Com...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pandey, Trilok Nath Vasudev, Anirudh Sagayanathan, Dennis Anjan, Govind Arshad, Danish Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350306118

Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online industries in the world which till date is not completely understood from a customer's standpoint. Brazil being a developing country is the 12th largest market for e-commerce in the world and accounts for 30 % of e-commerce in Latin America and remains largely unexplored. In this study, we used machine learning (ML) techniques such as LSTM, XGBClassifier, Logistic Regression, Neural Network, and Random Forest to predict customer satisfaction using a retail dataset from Brazil. We contrasted the efficacy of these models using a variety of evaluation criteria, such as precision, recall, and Fl-score. The LSTM model had the highest accuracy with a score of 0.89, followed by the XGBClassifier with 0.83, Naive Bayes with 0.73, Logistic Regression with 0.74, Support Vector Classifier with 0.74, and Random Forest with 0.78. The superior performance of the LSTM model over other models suggests that it has superior predictive abilities. © 2023 IEEE.

关键词： Customer satisfaction

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