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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Karthikeyani, B. Yuvaraj, D. Parimalam, T. Parthasarathi, P. Nandha Arts and Science College India CollegeGobi India Department of Computer Science and Design Kongu Engineering College Perundurai India Department of Computer Science Nandha Arts and Science College Perundurai India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (纸本)9798350383867

Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps. © 2024 IEEE.

关键词： Deep learning

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Cloud Computing’s Effect on Video Games Streaming 2

Cloud Computing’s Effect on Video Games Streaming

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Komathi, A. Lenin, J. Asha, S. Suresh, A. Suguna, M. Srinivasan, C. Department of Computer Science Nadar Saraswathi College of Arts and Science College Tamilnadu Theni India Department of ComputerScience and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Information Technology S.A. Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Andhra Pradesh Puttur India Tamil Nadu Salem India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

The disruptive power of cloud computing is altering the gaming industry. Real-time gaming is now feasible because to advancements in streaming algorithms, further blurring the lines between the digital and physical worlds. Understanding the potential of Cloud computing to change the gaming industry is the emphasis which investigates the complicated relationship between numerous technologies. By using cutting-edge streaming protocols and techniques like Adaptive Bitrate Streaming (ABS), buffering, caching, and compression, users may have uninterrupted, high-quality gaming experiences. The study's findings herald a new era of interactive entertainment and shed light on the transformative possibilities of these technologies. Asynchronous Bit-Rate (ABR) encoding, buffering, caching algorithms, video compression standards, and streaming protocols are just few of the numerous digital components that work together to provide uninterrupted data transmission. The rapid progress of ABR algorithms has made it possible to dynamically alter video quality to maximize the user experience across a broad variety of network scenarios. Buffering and caching algorithms play crucial roles by proactively managing data flow and storage to avoid disruptions in the streaming experience. © 2023 IEEE.

关键词： Autism buffering algorithms caching methods Cloud Computing Impact Interactive Entertainment Standards for video compression Streaming Algorithms streaming protocols Video Games

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Cybersecurity in Internet of Things Networks using Deep Learning Models 2

Cybersecurity in Internet of Things Networks using Deep Lear...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Anitha, V. Naveen Kumar, C.G. Kuchipudi, Ramu Sharma, Khushboo Muralidharan, J. Rajagopal, R. Imayam College of Engineering Department of Computer Science and Engineering Kannanur Thuraiyur Tamil Nadu Trichy India Karnataka State Open University Department of Studies and Research in Computer Science Karnataka Mysuru06 India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India Kpr Institute of Engineering and Technology Department of Ece Tamil Nadu Coimbatore India Alliance College of Engineering & Design Alliance University Department of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9781665491990

The Internet of Things and global grid that connects millions of sensors and devices have made distributed software and services ubiquitous. IoT's vast reach and economic value make it a natural target for hackers, making security the industry's top worry. Privacy has always been studied, but IoT design and new intimidations make most standard safeguards ineffective. Deep learning's data, odd case technique, and ability to detect unexpected threats may help detect IoT intrusions. The 'Internet of Things' includes computers, sensors, network infrastructure, software, networks, users, gateways, and services (IoT). Due to the increasing importance of Internet of Things (IoT) innovations, growth and management are huge. Data protection and privacy have recently caught users' attention. As social media grows, more people connect. As connections grow, more safe spaces are needed. This article discusses data protection, including deep learning models, Machine Learning (ML) concepts, safety and security, and cybersecurity management. To illustrate IoT network vulnerability, convolutional neural networks (CNN), Long Short-Term Memory (LS TM), and CNN combinations have been studied and compared. We can detect IoT cyberattacks with over 99% accuracy using Modbus network traffic data. © 2023 IEEE.

关键词： Internet of things

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Emotion Analysis of English-Translated Tamil Literature 15

Emotion Analysis of English-Translated Tamil Literature

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swathi, K. Kamalam, G.K. Suganya Baby, N. Aadhishri, A. Kawethaa Shree, D.K. Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Kongu Engineering College Department of Information Technology Tamil Nadu India Kongu Engineering College Department of Mathematics Tamil Nadu India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu India Ssm College of Engineering Department of Management Studies Tamil Nadu India

ISBN: (纸本)9798350370249

Focus of this study is to investigate the nuanced world of emotions in English-translated Tamil literature through the lens of sentiment analysis. The dataset consists of poems expressing a range of emotions including joy, sadness, love, anger, pride, courage, and fear. To effectively analyze and classify these emotions, we utilized cutting-edge transformer-based models, like BERT, DistilBERT, and RoBERTa, to conduct our analysis. The outcomes of our study showcase the exceptional capabilities of these models in effectively analyzing emotions within poetic expressions. RoBERTa, in particular, emerged as a standout choice, consistently achieving high accuracy, precision, recall, and F1 scores across all emotion categories. Its extensive pre-training and dynamic masking techniques allowed itto excel in capturing the intricate emotional nuancesembedded in the translated poems. DistilBERT demonstrated commendable efficiency without compromising accuracy, making it a practical option for real-time sentiment analysis. BERT, although a strong performer, showcased marginally reduced efficiency compared to RoBERTa and DistilBERT. © 2024 IEEE.

关键词： BERT DistilBERT Emotion Analysis Natural Language Processing poem RoBERTa

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Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network & Supervised Approach

Sarcasm Detection followed by Sentiment Analysis for Bengali...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Pal, Moumita Prasad, Rajesh Pune India Alliance University Aced Bangalore India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350333817

As www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it's hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%). © 2023 IEEE.

关键词： Sentiment analysis

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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Revolutionizing Healthcare With Cloud Computing: The Impact of Clinical Decision Support Algorithm 1

Revolutionizing Healthcare With Cloud Computing: The Impact ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Lenin, J. Komathi, A. Vijayan, Hima Rathinam, Anantha Raman Kasthuri, A. Srinivasan, C. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India S.A. Engineering College Department of Information Technology Tamil Nadu Chennai India Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350330915

The integration of cutting-edge technology, such as cloud computing and Clinical Decision Support (CDS) algorithms, is radically altering the healthcare system. This research digs into how Inference Engines, Bayesian Networks, Machine Learning Algorithms, and Natural Language Processing (NLP) have all played critical roles in reshaping the healthcare industry. Medical professionals may more efficiently use CDS algorithms thanks to cloud-based technologies that streamline data storage, sharing, and processing. Decision-making is aided by Inference Engines because they provide organized insights based on preset criteria. This is where Bayesian Networks come in, with their ability to represent complicated, probabilistic interactions among variables for exact diagnoses and risk assessment. Machine learning algorithms improve predictive analysis by identifying trends in large datasets, paving the way for more individualized approaches to medical care. Chatbots and sentiment analysis are only two examples of how NLP is transforming the doctor-patient relationship by teaching computers to understand human language. Those in the medical field may greatly increase their diagnostic precision, treatment efficiency, and patient outcomes by adopting these innovations. © 2023 IEEE.

关键词： Bayesian Networks Clinical Decision Support Cloud Computing Data-driven Healthcare Healthcare Transformation Inference Engines Machine Learning Algorithms Natural Language Processing Patient-Centric Solutions Personalized Medicine

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Prevention of Security Attacks at Wireless Network Layers using Machine Learning Techniques 18

Prevention of Security Attacks at Wireless Network Layers us...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ramisetty, Sowjanya Ghantasala, G. S. Pradeep Gupta, Gaurav Sonia Kg Reddy College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

Wireless sensor networks have great potential for use in flood control, weather forecasting systems, the military, and the healthcare industry. A WSN's nodes are connected to one another and share information. When sending data between nodes, data security is essential. In the world of networking, security is a top priority. Data travels along a path when it is transferred from one location to another, increasing the likelihood that a rogue node would enter the network. Detecting malicious nodes within a network presents a formidable challenge, particularly due to external assaults on data packets during their transmission between nodes. Hackers take advantage of weaknesses to manipulate and alter data as it travels from the source node to the destination node. This paper looked at machine learning algorithm-based preventive measures and layer-level security challenges. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine Learning Security Security attacks Wireless Sensor Networks

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Heuristic Pneumonia and Tuberculosis Detection in X-Ray Images Using Convolutional Neural Networks

Heuristic Pneumonia and Tuberculosis Detection in X-Ray Imag...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Bantelay, Lidia Mekuanint Abebe, Mesfin Sharma Rajendran, Rajesh Sungheetha, Akey Sengottaiyan, N. College of Engineering Wolaita Sodo University Department of Electrical and Computer Engineering Ethiopia Adama Science and Technology University Adama Ethiopia Aced Alliance University Department of Computer Science and Engineering India

ISBN: (纸本)9798350303452

Pneumonia and tuberculosis are major public health problems worldwide. These diseases affect the lungs, and if not diagnosed properly in time, they can become fatal. Chest x-ray images are widely used for Pneumonia and TB disease detection and diagnosis. Detection of Pneumonia and TB from chest x-ray images is difficult and needs experience due to similar pathological features of the diseases. Sometimes a misdiagnosis occurs due to this. Several researchers used deep learning and machine learning techniques to solve this problem. These studies used chest x-ray images exclusively to develop Pneumonia and TB disease detection models. But using only chest x-ray images can not necessarily lead to accurate disease detection. Medical records are required to interpret chest x-ray images in the appropriate clinical context. This work develops a multi-input Pneumonia and TB detection model using chest x-ray images and medical records. Convolutional neural network for the chest x-ray image data and Multilayer perceptron for the medical record data are applied to develop the model. We performed feature level concatenation to join the output feature vectors from Convolutional neural network and Multilayer perceptron for disease detection model development. For comparison, we also developed an image-only and medical record-only model. Consequently, the image-only model gives an accuracy of 92.68%, the medical record-only model results in 98.72% accuracy, and the joint model accuracy is recorded at 99.61 %. © 2023 IEEE.

关键词： Convolutional neural networks

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