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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems

SplitVAEs: Decentralized scenario generation from siloed dat...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Islam, H. M. Mohaimanul Vo, Huynh Q. N. Ramanan, Paritosh Oklahoma State University School of Industrial Engineering and Management StillwaterOK74078 United States

ISBN: (纸本)9798350362480

Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to the existence of data silos resulting from computational and logistical bottlenecks. In this paper, we present SplitVAE, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of experiments on distributed memory systems, we demonstrate the broad applicability of SplitVAEs in a variety of domain areas that are dominated by a large number of stakeholders. Furthermore, these experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Lastly, the experiments demonstrate that SplitVAEs deliver robust performance compared to centralized, state-of-the-art benchmark methods while significantly reducing data transmission costs, leading to a scalable, privacy-enhancing alternative to scenario generation. © 2024 IEEE.

关键词： decentralized learning scenario generation stochastic optimization variational autoencoders

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Game Theory-Based Fastest Route Plan Method for Transportation Network Applications 6

Game Theory-Based Fastest Route Plan Method for Transportati...

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Liu, Zheng-Tan Wu, Ling Chen, Chi-Hua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350306613

This paper presents a vehicle route planning method based on game theory principles and innovative utility functions. By addressing the complexities of real-Time traffic congestion, the proposed framework offers a dynamic allocation strategy for rational decision-making. The utility function, which inte-grates traffic flow volume, road capacity, and congestion effects, provides accurate travel time estimations. Mathematical analysis and validation, including genetic algorithms, underscore the framework's robustness. Equilibrium solutions reveal allocation strategies responsive to varying road conditions. Comparative scenarios demonstrate the utility function's effectiveness in guiding enterprises' decisions. This research extends beyond static models, envisioning a future of data integration, multi-objective optimization, adaptive learning, and eco-friendly navigation. By converging these routes, the paper sets the stage for a smarter, more sustainable transportation landscape. © 2024 IEEE.

关键词： game theory Nash equilibrium route plan transportation network applications

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE international conference on computing and data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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A Named Entity Recognition Method in the Winter Olympics Domain Based on the Fusion of BERT and Domain-specific Static Word Embeddings 3

A Named Entity Recognition Method in the Winter Olympics Dom...

引用

3rd international conference on Cloud computing, Big data Application and Software Engineering, CBASE 2024

作者： Han, Xiaole Liu, Pingshan Liang, Qi Cao, Yuan Business School Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9798331506582

Aiming at the shortcomings of BERT in identifying domain-specific entity boundaries under limited data and computational resources, this paper explores an entity recognition model named BWBLC, which integrates BERT's dynamic contextual information with static word embeddings in the Winter Olympics domain and applies the fused vectors to the BiLSTM-CRF model for entity recognition. This fusion method can comprehensively utilize both global and local information to achieve more comprehensive information utilization, effectively compensating for BERT's shortcomings in identifying domain-specific entity boundaries under limited data and computational resources, thereby improving the performance of named entity recognition tasks in the Winter Olympics domain. The comparative experimental results on the dataset show that the proposed model outperforms other models in terms of macro-average precision, macro-average recall, and macro-average Fl score. © 2024 IEEE.

关键词： Deep learning

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Performance Comparison of machine learning and Deep learning Algorithms for Liver Disease Detection 8th

Performance Comparison of Machine Learning and Deep Learning...

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8th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Bhusnurmath, Rohini A. Betageri, Shivaleela Department of Computer Science Karnataka State Akkamahadevi Women’s University Karnataka Vijayapura586108 India

ISBN: (纸本)9789819976218

The main cause of death worldwide, which has a significant negative influence on the vast majority of people, is liver disease. Various factors that affect the liver are the cause of this disease. For instance, alcoholism, obesity, and untreated hepatitis. The cost and complexity of this disease's diagnosis are enormous. The proposed work aims to reduce the high cost of liver disease diagnosis through detection by comparing the efficiency of machine learning (ML) and deep learning (DL) algorithms. In the proposed study, variety of algorithms have been employed that include Convolution Neural Network (CNN), Artificial Neural Network (ANN), Gaussian Naive Bayes (GNB), Random Forest (RF), and Logistic Regression (LR). The effectiveness of the performance is assessed using a variety of metrics that include accuracy, precision, recall, F-1 score, train time, and test time. Proposed work primarily focuses on the use of medical data for the detection of disease related to liver. It has been noted that the proposed work performed better than state-of-the-art work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classifier ILPD Liver disease Preprocessing

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EEG Signal-Based Human Emotion Recognition Using Power Spectrum Density and Discrete Wavelet Transform 8th

EEG Signal-Based Human Emotion Recognition Using Power Spect...

引用

8th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Shaila, S.G. Anirudh, B.M. Nair, Archana S. Monish, L. Murala, Prathyusha Sanjana, A.G. Department of Computer Science Engineering Dayananda Sagar University Bangalore India

ISBN: (纸本)9789819976218

Emotion recognition has been a problem in the field of brain–computer interface. Numerous ways are available for recognizing human emotions and one such technique is through Electroencephalogram (EEG) signals. EEG signals are recordings of the subject’s electrical activity in the brain. Feature extraction approaches such as Power Spectrum Density (PSD) and Discrete Wavelet Transform (DWT) are fed as features to various machine learning (ML) and deep learning (DL) models. This work aims to develop models that predict emotions from EEG data. In addition, the results of the above-mentioned feature extraction approaches are compared in this work. The proposed feature extraction methods and models are applied on the DEAP dataset. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Brain–computer interface Deep learning Discrete Wavelet Transform EEG signals Emotion recognition LSTM machine learning Power Spectrum Density

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TailClip: Mitigating Tail Latency in Cloud Gaming via Smart Video Frame Generation

TailClip: Mitigating Tail Latency in Cloud Gaming via Smart ...

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IEEE international conference on Multimedia and Expo (ICME)

作者： Gong, Qianyun Jiang, Kunheng Wen, Jingjing Yuan, Xinjing Shi, Jianxin Pu, Lingjun Nankai Univ Coll Comp Sci Inst Syst & Networks Tianjin Peoples R China Nankai Univ Key Lab Data & Intelligent Syst Secur Minist Educ Tianjin Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Latency is one of the most significant issues in cloud gaming, among which tail latency, mainly attributed to dynamic network environments (i.e., transmission) and limited device computing capacity (e.g., decoding), has attracted increasing attention. To mitigate the tail latency, different from existing researches considering resource adaptation such as bitrate adaptation, we propose TailClip, whose novel idea is to enable video frame generation at the client if the tail latency is about to happen. TailClip consists of two innovative components: a Deep Reinforcement learning (DRL) driven tail latency trigger that jointly decides a series of subsequent frame generation regarding multidimensional features of historical tail latency;a lightweight frame generation model derived by adaptive pruning in terms of device computing capacity at runtime. Extensive evaluations indicate the superior performance of TailClip. For example, it can remove the high-latency frames (i.e., over 100 ms) by 77% with an acceptable video quality (i.e., 0.45 dB reduction on average).

关键词： cloud gaming video frame generation tail latency WebRTC

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TDLM: A Diffusion Language Model for TCR Sequence Exploration and Generation

TDLM: A Diffusion Language Model for TCR Sequence Exploratio...

引用

2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haoyan Yang, Yuhang Huang, Yifei Zang, Tianyi Liu, Yadong Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350386226

The adaptive immune response relies on the ability of T-cell receptors (TCRs) to recognize specific antigens. The vast diversity of TCRs allows T-cells to recognize a broad spectrum of antigens, but this complexity also poses challenges for understanding and predicting TCR-antigen binding specificity. Despite the development of various machine learning and deep learning methods for prediction and clustering, there remains a need for a versatile and effective TCR language framework that can be flexibly applied to various downstream tasks, including sequence generation. Here we present TDLM, a T-cell Receptor (TCR) diffusion language model, designed to decode complex patterns within TCR sequences and apply them across various downstream tasks. Firstly, TDLM can be trained on unlabeled TCR sequence data, enabling it to utilize vast datasets to generate comprehensive embeddings. When compared to other embedding methods, TDLM embeddings enhance TCR-antigen binding prediction accuracy and enable effective TCR sequence clustering and similarity analysis, helping identify TCRs with shared antigen specificity. Furthermore, as a diffusion-based generative model, TDLM can generate highly diverse and specific TCR sequences. This ability is invaluable for the rapid screening and optimization of TCRs with target antigen specificities, offering significant potential in disease diagnosis, personalized immunotherapy, and vaccine research. The code is available at: https://***/skybluewhy/TDLM © 2024 IEEE.

关键词： T-cells

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Empowering learning: An Integrated Approach to Enhance Education

Empowering Learning: An Integrated Approach to Enhance Educa...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Sharma, Kunal Puneet Deepika Jindal, Pardeep Kumar Sharma, Preeti Indraprastha Institute of Information Technology Dept. of Computer Science and Design Delhi India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Dept. of CSE Mohali140307 India Chandigarh University Dept. of CSE Mohali India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Dept. of ECE Mohali140307 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350378092

Programming has become one of the most essential skills in virtually every industry. However, we have access to numerous resources and platforms for learning and development, but may still find ourselves inadequately prepared in this field, not reaching the level of proficiency as aspire to achieve. This has increased the demand for a dedicated phase that is allotted uncommonly for the students to support a coding society among them, right from the beginning. The initiative that is planned to settle this specific issue will also help students to overcome their limitations through continuous data-driven learning. The inevitable outcome will be a platform ALGAR which can be utilized by the students and faculty members to do any kind of code and estimation by using different programming languages like C, C++, Java, Python, etc. Moreover, they can also run their code independently inside an integrated compiler, eliminating the need for an external compiler. Along with this, the comparison of the suggested platform features with the various other existing education platforms like GitHub, Leet Code, Code Chef etc., is also discussed to empower the learning. © 2024 IEEE.

关键词： Students

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Analysis of Trojan Remote Access Malware Detection on Android Operating System using Ensemble learning Method 7

Analysis of Trojan Remote Access Malware Detection on Androi...

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7th international conference on data science and Its Applications, ICoDSA 2024

作者： Anwar, Intan Fauzia Suryani, Vera School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350365351

Malware is malicious software designed to damage or control computer systems. Malware includes various types such as computer viruses, Trojan horses, spyware, adware, crimeware and other software with malicious intent. This research performs malware detection using system calls (syscall) on the Android operating system. Malware Remote Access Trojan analysis on the Android operating system uses dynamic analysis by running each malware on the Android operating system to obtain running syscall information. The syscall results are selected using feature selection with ensemble learning. This study aims to determine the characteristics of the malware based on the syscalls obtained from the dynamic analysis and compare the accuracy, precision, recall and F1 score values of the gradient boost classifier, bagging classifier, voting classifier and stacking classifier methods. The results show that the Voting Classifier, which is the most effective of all the methods, achieves an accuracy of 95%, a precision of 92%, a recall of 100% and an f1-score of 95.85%. This accuracy value is quite good and can be applied in remote access Trojan malware detection. © 2024 IEEE.

关键词： Android malware

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