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9th International Conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Ramani, K. Jahnavi, M. Reddy, P. Jagadeesh Venkatachakravarthi, P. Meghanath, P. Imran, S.K. Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School of Computing Tirupati India Sree Vidyanikethan Engineering College B.Tech-Information Technology Tirupati India

ISBN: (纸本)9798350397376

The most popular cryptocurrency in the world is Bitcoin, which enables users to perform secure online transactions. When carrying out quick transactions, including cash transactions, this aids in keeping your money secret. Most of Consumers have been interested in the Bitcoin ecosystem in recent years. Predicting the bitcoin price accurately is a difficult task due to its high volatility. In this paper, we used deep learning and machine learning algorithms namely Long Short-Term Memory, Autoregressive Integrated Moving Average , XGBoost, Prophet and Sentiment analysis were performed on bitcoin *** algorithms were trained on live streaming finanacial data, and results are compared based on predicted metrics like Root mean Square Error,Mean Absolute Error and R2. The results show that Sentiment analysis combined with LSTM provide better performance in bitcoin price prediction of all other algorithms. © 2023 IEEE.

关键词： Long short-term memory

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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A Transformer-Based Approach for Fake News and Spam Detection in Social Media Using RoBERTa

A Transformer-Based Approach for Fake News and Spam Detectio...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Ankit Abhijit Pal Sudin Mondal C. Ashok Kumar C. Jothi Kumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The spread of misinformation and spam on social media has become a critical challenge, undermining information integrity and online security. Addressing this pressing issue, this study introduces an advanced solution utilizing RoBERTa, a transformer-based deep learning model, for the effective detection of fake news and spam. By employing sophisticated Natural Language Processing (NLP) techniques, the proposed method demonstrates exceptional accuracy in classifying unreliable content, making it a valuable tool in today's digital communication landscape. The approach is designed to provide real-time detection capabilities, ensuring swift identification and mitigation of misinformation and spam. Its adaptability allows seamless application across different domains, enhancing its versatility. Furthermore, the solution emphasizes sustainability by incorporating continuous monitoring and retraining mechanisms, which enable the model to maintain its performance even as the nature of misinformation evolves. This work underscores the transformative potential of deep learning models like RoBERTa in combating the widespread dissemination of online threats. By ensuring precise, scalable, and timely detection, the study contributes to creating a safer, more trustworthy digital ecosystem. In doing so, it addresses a critical need in the current era, where the reliability of information is paramount for societal and individual well-being.

关键词： Support vector machines Deep learning Logistic regression Adaptation models Accuracy Social networking (online) Text categorization Transformers Natural language processing Fake news

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Drug-Drug Interaction: An Improved Prediction Approach Based on Convolutional Neural Networks

Drug-Drug Interaction: An Improved Prediction Approach Based...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sharmila, K.Soni Thanga Revathi, S. Kiran Sree, Pokkuluri School of Computing Srm Institute of Science and Technology Kattankulathur India Shri Vishnu Engineering College for Women Department of Cse Bhimavaram India

ISBN: (纸本)9798350313987

Drug-Drug Interaction (DDI) is a significant challenge in modern healthcare as they have the potential to cause adverse side effects and hinder patient well-being. Accurate DDI prediction is critical for ensuring the efficacy and safety of medication management. This article proposes a novel method for detecting DDIs that makes use of convolutional neural networks (CNNs) for performing feature extraction and prediction augmentation. CNNs are used to automatically extract useful characteristics from drug combinations by using the underlying data patterns within drug interaction datasets. These collected features are then incorporated into a prediction model, which allows for more accurate detection of possible DDIs. To compare the performance of the proposed method, traditional classifiers used for drug-drug prediction, such as support vector machines (SVM), adaptive boosting (AB), and gradient boosting decision tree (GBDT), were preferred. The simulation results clearly demonstrated the efficacy of the proposed approach, emphasizing its potential for significantly enhancing the prediction accuracy. Notably, when multiple characteristics were interacted, the proposed strategy improved drug feature extraction by 24.4% when compared to the utilization of single features. This improvement indicates the method's robustness and capacity to capture complex interactions between pharmacological features, resulting in more accurate predictions. © 2023 IEEE.

关键词： Adverse drug events Convolutional Neural Networks Drug-drug interactions Feature extraction Healthcare Medication management Patient safety Personalized medicine Prediction enhancement

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Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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From Buzz to Blockbuster: Predicting Movie Revenue Using a Hybrid Approach Combining Machine Learning and Sentiment Analysis

From Buzz to Blockbuster: Predicting Movie Revenue Using a H...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Senapathi Vishnu Vardhan Killemsetty Viswa Sai Balaji C. Ashok Kumar C. Jothi Kumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable opportunity to understand audience engagement and sentiment, which are critical in forecasting box office performance. By analyzing data from various social media platforms alongside movie metadata from sources like IMDb and TMDb, this study seeks to create a comprehensive model that can predict a movie's opening weekend revenue and classify it as a “Hit,” “Average,” or “Flop.” The work highlights the value of audience sentiment, pre-release engagement, and historical box office trends in predicting financial outcomes. By using a combination of quantitative metrics (likes, shares, views) and qualitative data (sentiment scores, discussion themes), the study aims to refine revenue predictions and improve classification accuracy. The hybrid model's ability to incorporate both traditional and real-time data offers several advantages, including better responsiveness to audience perceptions, the ability to identify emerging trends, and more accurate revenue forecasts. This research provides actionable insights that can aid movie studios, marketers, and investors in decision-making processes, ultimately reducing financial risks associated with movie releases. The findings contribute to the growing field of predictive analytics in the entertainment industry, offering a novel and data-driven approach to understanding and forecasting movie success.

关键词： Measurement Sentiment analysis Analytical models Accuracy Social networking (online) Predictive models Metadata Motion pictures Market research Real-time systems

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Machine Learning-Based Dynamic Signal Switching: A Promising Solution to Traffic Congestion

Machine Learning-Based Dynamic Signal Switching: A Promising...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kanchana, M. Gopirajan, P.V. Mahalakshmi, B. Vijaya Nirmala, B. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Katankulathur Tamil Nadu Chennai603203 India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Katankulathurx Tamil Nadu Chennai603203 India AMC Engineering College Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350319200

Today's growing urban population means increased revenue for individuals, translating into growing vehicles on the road. Nowadays, people use their private vehicles instead of public transportation, which results in a considerable number of personal vehicles that lead totraffic congestion everywhere. The ever-increasing number of vehicles and complex congestion leads to frequent traffic jams in major cities. In today's scenario, traditional traffic management methods have become redundant as the traffic volume has grown exponentially. The primary goal of this proposed work is to develop an algorithm that uses machine learning to control traffic effectively and ensure efficient traffic flow in the event of unforeseen events like accidents. The existing algorithm focuses on predicting shortterm traffic flow but ignores that traffic congestion may also occur due to accidents and emergencies. Our proposed model will consider all the above factors and use dynamic signal switching among lanes to predict the best and most efficient solution to reducing waiting time for vehicles and congestion on the road. To create an effective traffic flow management model with the least amount of road mayhem, our suggested model needs traffic data as input, which was then trained using various Machine Learning (ML) models. © 2023 IEEE.

关键词： IDD Image processing Machine Learning Neural Network RCNN Traffic Management YOLO

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A Classification of Inflammatory Bowel Disease using Ensemble Learning Model 1

A Classification of Inflammatory Bowel Disease using Ensembl...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Sitaraman, Surendar Rama Adnan, Myasar Mundher Maharajan, K. Krishna Prakash, R. Dhilipkumar, R. Intel Corporation CA United States The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Academy of Research and Education School of Computing Kalasalingam Department of Computer Science and Engineering Krishnankoil India Sona College of Technology Department of Information Technology Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Inflammatory Bowel Disease (IBD) is an enduring, intermittent bowel disorder with unidentified mechanism and etiology. It typically contains Ulcerative colitis (UC) and Crohn's disease (CD) that are categorized by several bowel ulcers. An impedance phase displaces dynamic development, accumulating susceptibility to exterior pathogens and theoretically leading to neurotic symptoms which encompasses bowel blockade, intestinal tear and liver lesions, with high disability ratio. The existing models have more overfitting, and class imbalance problems. To overcome that, an Ensemble Learning (EL) model such as Logistic Regression, Random Forest and Gaussian Naive Bayes is proposed in this research. Initially, the collected Gene Expression Series 106817 (GSE106817) dataset is preprocessed with Recurrent MultiArray Average (RMA) Normalization and Log2 Data Transformation to eliminate the noises. Next, Recursive Feature Elimination (RFE) technique is used to reduce high-dimensional data for selecting optimal features;and finally, the proposed EL model gives a precise rate in detecting and classifying IBD as CD, UC and Healthy controls. The proposed EL model gives better results than the other compared existing methods Random Forest-Support Vector Machine (RF-SVM) in the context of recall, accuracy, f1 score, and precision as 99.38%, 99.75%, 94.72%, and 97.94% respectively. © 2024 IEEE.

关键词： Contrastive Learning

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