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EEG data Augmentation for Emotion Recognition Using Diffusion Model 46

EEG Data Augmentation for Emotion Recognition Using Diffusio...

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46th Annual international conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhao, Yi-Dong Liu, Yan-Kai Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University School of Medicine RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital China

ISBN: (纸本)9798350371499

Electroencephalogram (EEG) signals are playing an increasingly important role in affective computing, especially in emotion recognition. However, the process of collecting EEG signals is very complex, requiring subjects to conduct long-term experiments under ideal conditions, which has high requirements for both the subjects and the experimental environment. Therefore, how to quickly obtain a large number of high-quality EEG signals has become an issue. In recent years, the success of diffusion models in the field of image generation has attracted a large number of researchers' interest. Compared to traditional generative models, diffusion models have excellent properties such as better generation performance and a more stable training process. In this paper, we apply diffusion models to emotion recognition tasks for the first time. We optimize the sampling stage of the diffusion model to make it more suitable for generating high-quality EEG signals, and then enhance the original EEG signals and apply them to emotion recognition tasks. Additionally, we explore the impact of the quantity of generated data on task performance. We use three datasets in the experiment, SEED, SEED-IV, and DEAP datasets. The experimental results indicate that our data augmentation method can significantly improve emotion recognition. © 2024 ieee.

关键词： Emotion Recognition

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Cloud Forensics Analysis Framework for Secure and Efficient data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Revathi, V. Aishwarya, B.K. Tripathi, Manju Singh, Navdeep Alabdely, Haider Kumar, Ashwani New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350373783

This study's Cloud Forensics Analysis Framework seeks secure and effective data recovery methods. The framework's five strategies make digital evidence retrieval methodical and versatile. These include adaptive evidence collecting, secure data retrieval, machine learning-based anomaly detection, intelligent automation, and context-aware metadata analysis. Each approach targets a distinct forensic process step. This makes cloud forensics investigations seem difficult. The customizable Evidence gathering Algorithm adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure data Retrieval Techniques Algorithm protects data privacy and chain of custody with advanced encryption and validation. The Machine Learning-Based Anomaly Detection Algorithm checks protected data for anomalies before sending it, making the system safer. Investigators may focus on more complex cases by simplifying and sorting basic forensic activities with the Intelligent Automation Algorithm. The Context-Aware Metadata study Algorithm weights and prioritizes cloud metadata. This completes metadata analysis. Compared to other approaches, the framework has greater Precision, Recall, F1 Score, Processing Time, Resource Utilization, and Compatibility Score. Charts convey system operation in a simple manner. This powerful and adaptable Cloud Forensics Analysis Framework improves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware metadata analysis, data retrieval, intelligent automation, machine learning-based anomaly detection, performance metrics, secure data retrieval techniques, support vector machines, and visual representation. © 2024 ieee.

关键词： Cloud security

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Sentimental Insight 2.0: Exploring the Frontiers of Opinion Mining with Deep Learning 2

Sentimental Insight 2.0: Exploring the Frontiers of Opinion ...

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2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Murugan, S.Karthick Jayachandran, S. Mageshwari, G. Latha, J. Naveen, G. R. M. D. Engineering College Kavaraipettai Department of Computer Science and Engineering Tamilnadu Thiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Tiruvallur India R. M. K. Engineering College Department of Computer Science and Engineering Tiruvallur Tamilnadu Kavaraipettai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350382952

'Sentimental Insight 2.0' emerges as revolutionary research using deep learning techniques in response to the increasing demand for more complex sentiment analysis in the age of information abundance. Traditional sentiment analysis methods sometimes fail to accurately capture the particulars of emotions, sarcasm, and context, demanding a more advanced technique. The existing system's limitations include its inability to detect minor sentiment variations and its vulnerability to noisy and distorted data. The proposed system integrates cutting-edge deep learning architectures such as recurrent neural networks (RNNs), extended short-term memory networks (LSTMs), and transformer models into an extensive structure. The result showcases the advanced sentiment analysis, outperforming existing systems with 94% accuracy, 93% precision, 91% recall, and 93% F1 score, demonstrating superior computational efficiency and emotion classification across diverse sentiments. In the information-centric world, the study's results significantly expand sentiment analysis skills, providing a vital resource for applications in social media analytics, consumer feedback, and market sentiment prediction. © 2024 ieee.

关键词： Sentiment analysis

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MIRLR: An ensemble approach for predicting clinical trial enrollment rates 15

MIRLR: An ensemble approach for predicting clinical trial en...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Devi, Arti Uttrani, Shashank Singla, Aryansh Jha, Sarthak Ravula, Preethi Dasgupta, Nataraj Natarajan, Sayee Punekar, Rajeshwari S. Pickett, Larry A. Lapiello, Emilio Dutt, Varun Indian Institute of Technology Mandi Applied Cognitive Science Lab Mandi India Syneos Health Morrisville United States

ISBN: (纸本)9798350370249

The preclinical and medical sciences are greatly benefiting from clinical trials. However, participant recruiting issues continue to be a global barrier to their success. Insufficient enrollment is a persistent problem that has a large impact, leading to huge expenses and delaying scientific advances. Preventive methods are needed to find and prioritize trials that are likely to fall short of enrollment targets to address this problem. The study's main aim was to examine the importance of predictive models in predicting the probability of reaching enrollment goals before a trial starts. This study aims to provide a novel prediction framework to determine the feasibility of proposed trials using data-driven approaches and statistical analyses. Embedding algorithms like GloVe and BioBERT convert textual input into vector representations. Subsequently, numerical vectors containing the top 11 features selected through correlation algorithms are combined with the existing dataset from ***. MIRLR, a novel ensemble stacking method is developed based on five models namely, M5P, IBK, Random Forest, Logistic Regression, and REPTree. In MIRLR, each model's final predictions are combined to create a new dataset. This new dataset is input into the final Random Tree Regressor (RTR) model to forecast the enrollment rate. Results revealed that the MIRLR approach achieved a root mean square error of 0.334 patients per day on the test dataset and 0.0057 patients per day on the training dataset, following a data split of 70:30. This research showcases the MIRLR's impressive predictive capabilities, as it performed well across a range of training and test splits in accurately predicting site enrollment rates for clinical trials. © 2024 ieee.

关键词： Logistic regression

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Enhancing the Detection of Anomalous Artifact Operations in Temporal Structured Workflows 11

Enhancing the Detection of Anomalous Artifact Operations in ...

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11th international conference on Dependable Systems and Their Applications, DSA 2024

作者： Abouzeid, Mahmoud M. Huang, Pei-Shu Wang, Feng-Jian National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798331532390

Workflows are widely used for automating business processes in various modern software architecture styles, including Service-Oriented Architecture (SOA), microservices, and serverless computing. Temporal structured workflows (TSWs) involve specifying the minimum and maximum execution time intervals for each activity. TSWs facilitate expressing the temporal aspects of business processes, such as task deadlines and durations. At the design stage of TSWs, excluding erroneous operations on artifacts (data objects) can help prevent errors and unexpected behavior during execution. However, TSWs are complex to analyze due to the intricate interplay between control flow, artifact operations, and temporal aspects. Existing approaches adopt the outcome of conventional, non-temporal, algorithms for detecting continuous anomalies, resulting in inaccuracies such as false positives and undetected anomalies specific to TSWs. To improve the analysis of TSWs, this paper presents an improved methodology based on an extended SP-tree structure called TSP-tree. It presents a set of definitions for temporal-aware anomalies in TSWs. Our approach involves two steps: first, transforming a TSW into a TSP-tree, and second, applying several algorithms to the TSP-tree to detect abnormal operations on artifacts. Compared with previous methods, our approach simplifies the analysis, detects more artifact anomalies, and improves the accuracy of detecting artifact anomalies within TSWs. © 2024 ieee.

关键词： Spatio-temporal data

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Predictive Analysis for Sales Forecasting Using Machine Learning: Leveraging data-Driven Insights for Enhanced Business Planning and Decision-Making

Predictive Analysis for Sales Forecasting Using Machine Lear...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Koner, Jonardan Deore, Shailesh Shivaji Roja, S. Sahastrabuddhe, Kalyani Nilesh Kalshetti, Prashant Rose, Anthony Nicmar University Pune India Ssvps Bapusaheb Shivajirao Deore College of Engineering Department of Computer Engineering Maharashtra Dhule India Bharathidasan University Department of Computer Science Engineering and Applications Khajamalai Campus Trichy23 India Dr. D. Y. Patil Institute of Management and Research Pimpri Pune India Dr. D Y Patil Vidyapeeth Global Business School and Research Centre Dept. of Bba Pune India Institute of Management and Entrepreneurship Development Department of Management Studies Maharashtra Pune India

ISBN: (纸本)9798331543617

Within the modern trade scene, leveraging prescient investigation for deal estimating has developed as a basic procedure for organizations pointing to optimize their decision-making forms. This consideration centres on the application of the XG Boost calculation, a well-known machine learning method, to tackle data-driven experiences for upgraded deals determining, thus encouraging prevalent commerce arranging. Through an in-depth investigation of authentic deals information, the inquiry about illustrates the noteworthiness of consolidating machine learning strategies, especially the XG Boost calculation, for exact and solid deal forecasts. By leveraging a comprehensive dataset including different variables affecting deals, such as client socioeconomics, regular patterns, showcasing activities, and financial markets, this exhibits the algorithm's capability to capture complex connections inside the information and produce exact figures. Besides, the investigation emphasizes. interpretability of the XGBoost calculation, empowering partners to comprehend the fundamental components affecting deal expectations. This straightforwardness cultivates a more profound understanding of the driving powers behind deal patterns, encouraging educated decision-making and proactive commerce arranging. © 2024 ieee.

关键词： Adversarial machine learning

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Stress Detection from Social Media data by Graph Attention Network

Stress Detection from Social Media Data by Graph Attention N...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Selvakumar, K. Trueman, Tina Esther Ashok Kumar, J. Sai Ramesh, L. Anna University Department of Information Science and Technology Chennai India St. Joseph's Institute of Technology Department of Cse Chennai India

ISBN: (纸本)9798331543617

The changes in human body in both mentally or physical will be considered as stress. Stress can be positive or negative. Positive stress helps us to motivate, alert during critical situations. For example during exam, a positive stress can be help to stay awake longer and might help our body to work harder. But if person go continues stress without relaxation and relief then it lead to health issue such as hair loss, heart attack and physiological problem. Stress can be diagnosed through counselling or medical practice. But in restricted lab environment it is difficult to express and measure correctly. The process is complex and lab experiment is costly. Stress and physiological related clinical process required more privacy. In today world people share their thoughts, feeling, emotion, current life status through social media. Social media data reflects the particular human life status in real-time. There are lot of Deep Learning and Natural Language processing available for text processing and sentiment analysis. Most of the modelsare rules based on the positive and negative score assigned to the words or phrase. The association between the words and grammatical structure is not considered for the prediction. The main goal of this project is to identify stress detection based on the Graph Attention Networks and Relational Graph Attention Networks. The sentence is constructed as Graph with wordsas nodes and relation between the words as edges. Social media datasets(SAD and Dreaddit) are preprocessed, trained and tested by GAT and RGAT. The results are compared with SVM and Multinomial Naive Bayesian. The accuracy for SAD and Dreaddit is 95 % and 77 % respectively. The accuracy is relatively higher than the expected accuracy provided in the data set publication. © 2024 ieee.

关键词： Natural language processing systems

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A Distributed data Mining and Cloud Analysis for Predictive Gas Level, Dynamic Booking, and Smart Energy Optimization

A Distributed Data Mining and Cloud Analysis for Predictive ...

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Sugumar, D. Ramadevi, R. Prasad, J. Mohankumar, N. Joy Winnie Wise, D.C. Velmurugan, S. Care College Of Arts And Science Department Of Commerce Tamil Nadu Trichy India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha Nagar Department Of Biomedical Engineering Tamil Nadu Chennai India Kpr Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Tamil Nadu Coimbatore India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Rajalakshmi Institute Of Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

The state-of-the-art Internet of Things (IoT) innovation known as the Autonomous Gas Reservoir Management System (AGaRMS) totally changes the most common way of overseeing gas supplies. AGaRMS uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. complex AI calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. This, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMS appeals to the savvy progression of energy, which empowers strong gas usage. The framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. The AGaRMS system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMS optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design. © 2024 ieee.

关键词： Internet of things

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Modified AI-based learning Approach to Secure data using Deep learning algorithms

Modified AI-based learning Approach to Secure Data using Dee...

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2024 ieee international conference on Intelligent computing and Emerging Communication Technologies, ICEC 2024

作者： Nallapareddy, Venkata Sai Swaroop Reddy Sony Interactive Entertainment Department of Software Engineering Austin United States

ISBN: (纸本)9798331508432

Intelligent cyber security systems and policies of today rely on deep learning, a technology derived from artificial neural networks (ANNs). Cyber risk analytics that use artificial intelligence (AI) to boost business resilience and understand cyber security risk: pros and cons. Make use of deep learning tools like generative adversarial networks, deep belief networks, deep transfer learning, and deep reinforcement learning to intelligently combat cyber threats. There is also a set of tools that includes generic adversarial networks, auto-encoders, multilayer perceptrons, convolutional neural networks, recurrent neural networks (also known as long short-term memory), and so on. It is also possible to use these networks in ensembles or hybrid techniques. The goal of the backpropagation method is to convert inputs into outputs by making the most of the network weights. When training, a number of optimisation methods are employed, including Adam, Stochastic Gradient Descent, and Limited Memory BFGS. A wide range of cybersecurity issues could be addressed by these neural networks. Building reliable Internet of Things (IoT) systems, detecting harmful botnet traffic, analyzing malware, and conducting security threat analyses are all possible with MLP-based networks. Finding a computationally expensive solution to a complex security model may be difficult with MLP since the model is hyperparameter sensitive and needs adjustment of many different parameters, including the number of hidden layers, neurons, and iterations. © 2024 ieee.

关键词： Adversarial machine learning

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Dynamic Graph Neural Networks with Neighborhood Constraints 21

Dynamic Graph Neural Networks with Neighborhood Constraints

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21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Ren, Jiankang Lv, Xian Zhang, Jun Li, Xu Ni, Zheng Zhang, Haijun School of Computer Science and Technology Dalian University of Technology Dalian China Dalian University of Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian China School of Computer Science and Technology Xinjiang Normal University Xinjiang China Graduate School of Education Dalian University of Technology Dalian China

ISBN: (纸本)9798350365221

Graph data are well suited to represent complex interactions and dependencies owing to its inherent relational structure. Graph neural networks (GNNs) can effectively explore the topological structure and node feature information of graph data to perform perceptual inference tasks. In GNNs, the state of each node is iteratively updated by aggregating information from its neighbors. Note that some noise may be introduced due to the information aggregation of irrelevant neighbor nodes, leading to poor performance with higher computational overhead. This paper proposes a dynamic graph generation method based on neighborhood constraints, which learns the degrees of the nodes and imposes the neighborhood constraints to limit the neighbor-hood space of the nodes, resulting in a sparse graph with dynamic neighborhoods. Based on the sparse graph, the dynamic graph neural network selectively aggregates the information of the most important neighbors to improve the performance of the model. Furthermore, to ensure neighborhood constraints satisfaction during the learning process, a loss function is designed to penalize the nodes whose degrees exceed a specific threshold. The evaluation results demonstrate that our proposed method achieves a higher inference accuracy with the same or higher sparsity compared to state-of-the-art methods. © 2024 ieee.

关键词： Graph neural networks

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