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Q-learning based intelligent and Interactive Healthcare System 11

Q-Learning based Intelligent and Interactive Healthcare Syst...

引用

11th IEEE international conference on Communication Systems and Network Technologies, CSNT 2022

作者： Raina, Rashika Jha, Rakesh Kumar Suri, Ashish Smvdu School of Electronics and Communication Engineering Katra India Iiitdm Department of Electronics and Communication Engineering Jabalpur India

ISBN: (纸本)9781665480383

With the advent of the COVID-19 pandemic, the health care system has suffered a tremendous setback, especially in developing countries. Q learning is a leading and widely used Reinforcement learning scheme. Q-learning can be applied to a variety of real-time applications. this paper proposes a health care system based on the Q learning algorithm and its implementation for a set of targeted nodes. the proposed system consists of 4 phases, front-end system, RRH, BBU pool, and computing. Each phase consists of different network components that are considered nodes. We have different rewards for nodes and pathways. the AI agent will continue to change its approach for future actions. © 2022 IEEE.

关键词： Reinforcement learning

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Cande: A Model for Predicting the Risk of Campus Violence in an Edge intelligent Computing Architecture 10

Cande: A Model for Predicting the Risk of Campus Violence in...

引用

IEEE 10th international conference on Edge Computing and Scalable Cloud (IEEE EdgeCom) / 11th IEEE international conference on Cyber Security and Cloud Computing (IEEE CSCloud)

作者： Zhou, Feng Lu, Zhaojin Huang, Hai Liu, Yu Zhang, Hongbing Yang, Zelin Chen, Zhilin Zhan, Daisong Huang, Zhuo Fudan Univ Sch Comp Sci Shanghai Peoples R China Tellhow Grp Co LTD Jiangxi Tellhow Animat Coll Nanchang Jiangxi Peoples R China Shanghai Publ Secur Burea Cultural Inst Secur Branch Shanghai Peoples R China Wangsu Sci & Technol Co LTD Shanghai Peoples R China Shanghai DaoCloud Network Technol Co LTD Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

Currently, target detection technology is widely used in various research fields. For example, through the analysis of surveillance videos, it is possible to detect whether engineering personnel are wearing safety helmets and masks and whether there are illegal road occupations in open markets. Among these studies, the method of first uploading the collected videos to the central server and then conducting centralized analysis accounts for a high proportion. However, this method will bring a new server load and network transmission load. Based on the improved YOLOv5, this paper proposes a campus intelligent detection model, Cande (Campus Intelligence Detection Model), for risk prediction of campus violence incidents. the model we propose analyzes video on the edge side. the entire process does not increase the storage space capacity or bring new load to network transmission. Experimental results show that the average accuracy of our proposed model reaches 96.58%, and the average detection speed reaches 33 frames/second. this experimental result shows that the proposed model can provide adequate technical support for risk prediction of school violence incidents.

关键词： Edge computing Target detection Video classification Violence risk prediction Deep learning

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Fin-MLGAT: Line Graph Attention Network for Dialogue Emotion Recognition in Stock Market and its Evaluations 11

Fin-MLGAT: Line Graph Attention Network for Dialogue Emotion...

引用

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

作者： Feng, Xiaobing Feng, Ling Xu, Xiujuan Qiao, Jiaqi Zhang, Wenjian Shanghai University of International Business and Economics School of Finance Institute of Change Management and Artificial Intelligence Shanghai China Shanghai Ocean University School of Robotics Engineering Shanghai China Dalian University of Technology School of Software Technology Dalian China

ISBN: (纸本)9798331532390

With the rapid development of social media and digitalization, the task of emotion pattern recognition and extreme emotion detection in conversation is playing an increasingly important role in understanding emotion interaction among and between parties. Traditional methods employ probabilistic statistical models that leverage prior knowledge to construct models However when dealing with complex patterns, large scale data, and high-dimensional features, their performance becomes limited. Alternatively, deep learning methods are capable of automatically learning highly abstract feature representations from raw datasets. Our Fin-MLGAT mines the emotional information dependent on the target utterance by focusing on local con text and global context at different scales. Experiments show that our model achieves the second-highest performance among all current methods by Wa-F1. Moreover, the model can show high accuracy in a few classes without additional measures. © 2024 IEEE.

关键词： Marketplaces

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Classifying Alzheimer Disease using VGG19 18

Classifying Alzheimer Disease using VGG19

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Fathima, Arshia Taranum, Fahmina Hijab, Maniza Hashmi, Syed Mohammed Akbar Ahmad, Syed Shabbeer Gupta, Gaurav Mufakham Jah College of Engineering and Technology Hyderabad India Shoolini University HP India

ISBN: (纸本)9789380544519

A disease called as Alzheimer, always put health agencies in alarming situation and thus necessitating targeted interventions. this study employs the VGG19 convolutional neural network (CNN) in conjunction with the ADNI dataset to enhance the precision of Alzheimer's detection. the ADNI dataset, a comprehensive repository of neuroimaging data spanning various cognitive states, serves as the foundation for robust Alzheimer's detection. Leveraging the VGG19 CNN architecture renowned for its image classification capabilities, we analyze three-dimensional MRI scans to discern subtle patterns indicative of Alzheimer's disease. through fine-tuning and transfer learning, our research adapts VGG19 to accurately detect Alzheimer's disease and its several subclasses. Further this study tries to illuminate rapid progression of the concerned disease. Despite challenges such as Alzheimer's complexity, high-dimensional MRI data, and ethical considerations, our findings represent a significant advancement in Alzheimer's disease identification. By integrating technology and medicine, this research not only enhances diagnostic accuracy but also contributes to the broader understanding of Alzheimer's disease. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Alzheimer Disease Alzheimer Disease Neuroimaging Initiative Cognitive Impairments Visual Geometry Group

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Network Security Situation Prediction Based on Sequential Extreme learning Machine 11

Network Security Situation Prediction Based on Sequential Ex...

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2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Yin, Chunwu Xu, Lin Xi'An University of Architecture and Technology School of Information and Control Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350314014

To improve the prediction accuracy of Extreme learning Machine (ELM), this paper establishes a sequential ELM algorithm with a forgetting factor by applying exponential weighting to new and old sample data, and obtains the optimal forgetting factor through simulation analysis. First, considering the different contribution rates of new and old data to the accuracy of the prediction model, this paper presents an ELM external weight online identification algorithm with a forgetting factor for downgrading old information. then, based on the actual situation of network security attacks in a certain enterprise, the optimal forgetting factor for ELM external weight online learning is obtained through simulation analysis. Finally, the network security situation prediction accuracy of three ELM prediction models is compared. the simulation results show that ELM with forgetting factor has higher accuracy than traditional ELM and online sequential ELM. © 2023 IEEE.

关键词： Forecasting

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RT DL Tasks Distribution for Sensitive data Protection and Resource Optimization 11

RT DL Tasks Distribution for Sensitive Data Protection and R...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Zeidan, Farah Elhayani, Mostafa Soubra, Hassan German University in Cairo Computer Science and Engineering Cairo Egypt Informatics Technische Universität München Cairo Egypt

ISBN: (纸本)9798350322101

With the rapid growth of data-intensive applications and the proliferation of the Internet of things (IoT) devices, traditional cloud architectures face significant challenges in enabling real-time decision-making. Centralized cloud infrastructures have limitations in handling the increasing volume of data generated at network ends. In response, edge computing has emerged as a promising paradigm that brings computation closer to the data source, offering advantages over traditional cloud architectures. this paper presents an edge computing system that incorporates a load balancer to determine whether real-time tasks should be offloaded to the edge or to the cloud. To enhance security, a data retention measure is introduced to ensure sensitive data remains within the edge device, irrespective of other load-balancing conditions. this approach minimizes the risk of attacks and unauthorized access by containing sensitive data within the edge device. Furthermore, the impact on system performance is evaluated by monitoring CPU utilization before and after task execution. this research contributes secure and efficient task offloading strategies in edge computing, with implications for real-time processing and data privacy sensitive applications. © 2023 IEEE.

关键词： Decision making

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Multi-Modal data Fusion Based Cardiac Disease Prediction using Late Fusion and 2D CNN Architectures 11

Multi-Modal Data Fusion Based Cardiac Disease Prediction usi...

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11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Patel, Kunvar Kant Kanodia, Ayush Kumar, Basant Gupta, Rajeev Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj India

ISBN: (纸本)9798350308433

this paper presents a multimodal data based late fusion technique used in the classification of cardiac normality and abnormality integrating 12-lead ECG image data and electronic health records (EHR) data. Cardiac diseases are big problems worldwide, and need better ways to diagnose to help patients. Only electrocardiograms (ECG) report may not be conclusive for predicting cardiac normality and abnormality. In recent studies, the integration of ECG image and EHR has emerged as a promising approach for improving cardiac disease detection and diagnosis. the EHR data includes critical parameters such as Blood Pressure (BP), Oxygen Saturation (SpO2), Body Mass Index (BMI), and blood sugar level etc. these parameters are obtained along with 12-lead ECG images, from our own prepared multimodal database (CardioHTDC database). the proposed multimodal late fusion technique employs a deep learning architecture. the late fusion technique involves concatenating the outputs of individual deep learning architecture to create a combined feature vector that incorporates both visual and structured information. A 2D CNN (Convolutional Neural Network) architecture is considered as model I, responsible for feature extraction from 12-lead ECG images while an MLP (Multi-Layer Perceptron) architecture is designated as model II, tasked with processing EHR data, to capture patterns, that help predicting early cardiac disease. the experimental results demonstrate that the proposed late fusion technique provides significant important by achieving a maximum accuracy for cardiac multimodal dataset, while an accuracy of only 72.2% has achieved using multimodal late fusion techniques. However, lower accuracy of the algorithms is multi-modal cardiac dataset and non-Availability of the other existing dataset. © 2024 IEEE.

关键词： Electrocardiograms

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BiBE: A Self-supervised Contrastive learning Architecture for Malware Detection 11

BiBE: A Self-supervised Contrastive Learning Architecture fo...

引用

11th IEEE international conference on Computer Science and Network Technology, ICCSNT 2023

作者： Wang, Yu Xu, Mingdi Luo, Kexin Tong, Hui Jin, Chaoyang Xie, Bo Wuhan Digital Engineering Institute Dept. of Computing Environment Wuhan China Wuhan Digital Engineering Institute Dept. of Systems Engineering Wuhan China

ISBN: (纸本)9798350311594

Malware detection has become significantly more difficult due to the constant evolution of malware and the emergence of various variants. While machine learning-based malware detection method solves the limitations of traditional methods to a certain extent, the vast majority of current work uses supervised learning methods, which require large sets of labeled data, which means a huge amount of manpower and time cost. Self-supervised learning can solve these problems by extracting information from unlabeled data for learning. In this paper, we introduce BiBE, a malware detection architecture based on self-supervised learning framework. We first preprocess the samples with normalization, complete the missing information of features, and perform vectorization processing. then, after learning and encoding the sample features by setting proxy tasks, we use comparative learning to distinguish similar samples, achieve the division of positive and negative samples and assign pseudo-labels. Finally, the labeled samples and pseudo-labeled samples are put into the Bi-LSTM network to train the classifier to detect malware. Moreover, we also evaluate the model on a public dataset. the evaluation shows that the BiBE model has good convergence and generalization in training, achieves excellent detection performance, and outperforms other existing methods, verifying the feasibility of the BiBE model. © 2023 IEEE.

关键词： Supervised learning

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GRACE-C: GENERALIZED RATE AGNOSTIC CAUSAL ESTIMATION VIA CONSTRAINTS 11

GRACE-C: GENERALIZED RATE AGNOSTIC CAUSAL ESTIMATION VIA CON...

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11th international conference on learning Representations, ICLR 2023

作者： Abavisani, Mohammadsajad Danks, David Plis, Sergey M. Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Halicioglu Data Science Institute Department of Philosophy University of California San Diego San DiegoCA92093 United States TReNDS Center Department of Computer Science Georgia State University AtlantaGA30302 United States

Graphical structures estimated by causal learning algorithms from time series data can provide misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data. Existing algorithms provide limited resources to respond to this challenge, and so researchers must either use models that they know are likely misleading, or else forego causal learning entirely. Existing methods face up-to-four distinct shortfalls, as they might a) require that the difference between causal and measurement timescales is known;b) only handle very small number of random variables when the timescale difference is unknown;c) only apply to pairs of variables;or d) be unable to find a solution given statistical noise in the data. this paper addresses these challenges. Our approach combines constraint programming with both theoretical insights into the problem structure and prior information about admissible causal interactions to achieve multiple orders of magnitude in speed-up. the resulting system maintains theoretical guarantees while scaling to significantly larger sets of random variables (> 100) without knowledge of timescale differences. this method is also robust to edge misidentification and can use parametric connection strengths, while optionally finding the optimal solution among many possible ones. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Random variables

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Mobile Robot Path Planning Using Deep reinforcement learning 11

Mobile Robot Path Planning Using Deep reinforcement learning

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11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Abedi, Ali Anari, Reza Ghaderizadeh Mohammadi, Hossein Shiraz University Department of Mechanical Engineering Shiraz Iran

ISBN: (纸本)9798350308105

this article provides an overview of a robotics project that explores path planning techniques using deep learning and reinforcement learning. the goal of the project is to enable map-less navigation for mobile robots. the project utilizes the Robot Operating System (ROS) and Gazebo simulation tools along with TurtleBot3 robots. data is collected by moving the robot in different simulated environments and recording images, velocities, and positions. this data is used to train Vision Transformer (ViT) and convolutional neural network models to predict robot velocities based on visual input. the trained models are then utilized within a Soft Actor-Critic reinforcement learning algorithm to learn optimal navigation policies. Key challenges addressed include data collection, network architecture design, synchronization with the ROS framework, reward definition, and training adjustments to improve goal achievement. the project provides insights into deep learning techniques for robotic vision and control. It demonstrates the potential of combining imitation learning and reinforcement learning for map-less navigation. Further work could explore deploying the trained models to physical robots and enhancing generalizability across diverse environments. this research contributes to advancing vision-based autonomous robot navigation. © 2023 IEEE.

关键词： Robot Operating System

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