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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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Next-Generation Stock Market Prediction: Integrating CNN and LSTM Model 2

Next-Generation Stock Market Prediction: Integrating CNN and...

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2nd IEEE international conference on data science and Network Security, ICDSNS 2024

作者： Agnihotri, Kuldeep Yadav, Sameer Dahiya, Suman Subramanyam, M. Patil, Harshal Kalra, Gourav Isba Group of Institutes Madhya Pradesh Indore India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India Universal Ai University Department of Management Maharashtra India Koneru Lakshmaiah Education Foundation Department of Commerce AP Vaddeswaram India Iilm University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Department of Mechanical Engineering Ambala Mullana India

ISBN: (纸本)9798350373110

Stock market forecasting is one of the most challenging tasks in the financial industry due to the non-parametric nature of the stock market's time series, which is also complex, dynamic, chaotic, volatile, noisy, and otherwise non-linear. Fortunately, with the help of modern computing, intelligent models can assist expert analysts and investors in reducing investment risk. Preprocessing, feature selection, and training the model are the three stages that make up this suggested approach. When preprocessing data, the empirical wavelet transform decomposition algorithm is employed. When it comes to feature selection, one unsupervised and competitive learning algorithm that has proven effective is the self-organizing feature map. The model was trained using a CNN-LSTM. The proposed technique outperforms CNN and LSTM with an average accuracy of 90.56 percent. © 2024 IEEE.

关键词： Marketplaces

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Enhanced Multilingual Sentiment Analysis Using Hybrid CNN-GRU Deep learning Architecture 4th

Enhanced Multilingual Sentiment Analysis Using Hybrid CNN-GR...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Jain, Vipin Mohanani, Garima Gaur, Arpit Patheja, Pushpinder Singh VIT Bhopal Univ Sch Comp Sci & Engn Bhopal MP India

ISBN: (纸本)9783031837920;9783031837937

This research investigates multilingual sentiment analysis through the lens of a hybrid deep learning model, combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU). Our study encompasses datasets in English, Portuguese, Tamil, and French, sourced from diverse online platforms and encompassing various sentiment labels. Methodologically, we detail data collection, preprocessing tailored to each language, model architecture, training techniques, and performance evaluation metrics. Results indicate superior performance of our proposed model across all languages, surpassing established benchmarks in terms of F1 score, recall, precision, and accuracy. Visual representations including confusion matrices and ROC curves further elucidate the model's efficacy. This study underscores the significance of hybrid deep learning approaches in advancing multilingual sentiment analysis, offering insights for future research and practical applications in natural language processing.

关键词： Multilingual Sentiment Analysis CNN GRU Hybrid Model NLP

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Cao Robot for Taiwanese/English Knowledge Graph Application 27

Cao Robot for Taiwanese/English Knowledge Graph Application

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27th conference on the Oriental COCOSDA international Committee for the Co-Ordination and Standardisation of Speech databases and Assessment Techniques, O-COCOSDA 2024

作者： Lee, Chang-Shing Wang, Mei-Hui Tseng, Guan-Ying Yue, Chao-Cyuan Hsieh, Hao-Chun Reformat, Marek National University of Tainan Dept. of Computer Science and Information Engineering Tainan Taiwan University of Alberta Dept. of Electrical and Computer Engineering EdmontonAB Canada

ISBN: (纸本)9798331506032

This paper proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3.1. The collected data includes lecture videos from the IEEE WCCI 2024 in Japan and the 2024 National Language Development Forum in Taiwan, along with students' learning data from the 2024 Summer School on Taiwanese/English Human and Robot Co-learning at Rende Elementary School (RDES). In addition, the fundamental concepts of Computational Intelligence (CI) and Quantum CI (QCI) learning were incorporated into the study. The generative KGs highlight important concepts, relations, and communities within the collected teaching and learning data. Additionally, we utilized data from subjects wearing braincomputer interface (BCI) devices while speaking Taiwanese/English to generate KGs. We also compared the differences in these KGs and analyzed the similarities between the transcribed texts of lectures and learners. In the future, we plan to expand the CAO robot to more validation fields across Taiwan, aiming to engage young students in speaking Taiwanese while concurrently enhancing their English language skills through interaction with the robot. © 2024 IEEE.

关键词： Knowledge graph

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SoCFlow: Efficient and Scalable DNN Training on SoC-Clustered Edge Servers 24

SoCFlow: Efficient and Scalable DNN Training on SoC-Clustere...

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29th ACM international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Xu, Daliang Xu, Mengwei Lou, Chiheng Zhang, Li Huang, Gang Jin, Xin Liu, Xuanzhe Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China State Key Lab Networking & Switching Technol Beijing Peoples R China Natl Key Lab Data Space Technol & Syst Beijing Peoples R China

ISBN: (纸本)9798400703720

SoC-Cluster, a novel server architecture composed of massive mobile system-on-chips (SoCs), is gaining popularity in industrial edge computing due to its energy efficiency and compatibility with existing mobile applications. However, we observe that the deployed SoC-Cluster servers are not fully utilized, because the hosted workloads are mostly usertriggered and have significant tidal phenomena. To harvest the free cycles, we propose to co-locate deep learning tasks on them. We present SoCFlow, the first framework that can efficiently train deep learning models on SoC-Cluster. To deal with the intrinsic inadequacy of commercial SoC-Cluster servers, SoCFlow incorporates two novel techniques: (1) the group-wise parallelism with delayed aggregation that can train deep learning models fast and scalably without being influenced by the network bottleneck;(2) the data-parallel mixed-precision training algorithm that can fully unleash the heterogeneous processors' capability of mobile SoCs. We have fully implemented SoCFlow and demonstrated its effectiveness through extensive experiments. The experiments show that SoCFlow significantly and consistently outperforms all baselines regarding the training speed while preserving the convergence accuracy, e.g., 1.6x-740x convergence speedup with 32 SoCs. Compared to commodity GPU (NVIDIA V100) under the same power budget, SoCFlow achieves comparable training speed but reduces energy consumption by 2.31x-10.23x with the same convergence accuracy.

关键词： SoC-Cluster distributed machine learning mixed precision training

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st international conference on machine learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Artificial Intelligence aware Knowledge Graphs and Deep learning-Based Diagnostic Prediction Model in Healthcare 7

Artificial Intelligence aware Knowledge Graphs and Deep Lear...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Babu, Ramenani Hari Keshta, Ismail Soni, Mukesh Chafik, Berdjouh Byeon, Haewon Rai, Vertika Teerthanker Mahaveer University Teerthanker Mahaveer College of Pharmacy Department of Pharmacy Practice Uttar Pradesh Moradabad India College of Applied Sciences AlMaarefa University Computer Science and Information Systems Department Riyadh Saudi Arabia Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune India University of El Oued Liap Laboratory PO Box 789 El Oued39000 Algeria Inje University Department of Ai and Software Gimhae50834 Korea Republic of Galgotias University School of Allied Health Sciences Department of Medical Laboratory Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Diagnostic prediction experts use a patient's medical history to anticipate their future diagnosis. Attention mechanisms and recurrent neural networks are typically utilized for this purpose;however, data shortages often cause complications. Additionally, healthcare information can help diagnostic prediction algorithms. This study links healthcare ideas using AI-aware, intelligent healthcare ontologies and patient visit records as an intelligent graph. Next, utilize a graph convolution module to collect geographical attributes that show patient visit healthcare code linkages. Using a multihead attention technique, visit information and healthcare knowledge are compared to predict the patient's future health. On two open-source healthcare datasets, the suggested solution outperforms state-of-the-art diagnostic prediction algorithms and better uses IMG data. © 2024 IEEE.

关键词： Deep learning Diagnostic Prediction Healthcare Healthcare Knowledge Historical Health Records

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Integrating Artificial Intelligence for Sustainable and Adaptable Architecture 2

Integrating Artificial Intelligence for Sustainable and Adap...

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2nd international conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Lalitha, Valapula Suneetha, Regidi Basi Reddy, A. Mate, Nilesh R. Mujeebunnisa, Syeeda India Sanketika Vidya Parishad Engineering College Department of Ece Visakhapatnam India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Centre for Distance and Online Education Pune India Cmr University School of Science and Computer Studies Bengaluru India

ISBN: (纸本)9798350364699

In nature, flexibility is essential, and AI may enhance it in architecture. The article integrates AI to develop self-learning buildings that can adapt to future obstacles. This research aims to create structures that use sensors to gather environmental data and respond. Phases comprise this study's strategy. In Phase 1, AI in architecture is extensively researched. That study in Phase 1 guided Phase 2's building design. The parametric building design in phase 2 uses machine learning and computational design techniques. Grasshopper scripting and Rhino modelling were used to create an algorithm that would create shapes that would both biomimetic the process of coral growth and be tailored to the project's chosen location. The selection procedure for the produced experimental investigations is shown in Phase 3. To determine which type used the least amount of energy, many evaluations were conducted, including measurements of sunshine, radiation, and shadows. Phase 4 involves developing the shape to improve the building's efficiency. Phase 5 involves conducting performance assessments to demonstrate that the final form is highly adaptable and climatic or ecologically sensitive. This building's radiation exposure, according to the research, is between 200 and 300 kWh/m2. According to the shadow study, the building shape casts a shadow for eight hours. According to the analysis, the building's shape makes it more adaptive by reducing its energy use. An AI engine technology is used in the last stage to forecast the building's future growth. By incorporating technology into building design, we may create flexible structures that reduce building energy use and contribute to the creation of a sustainable world. © 2024 IEEE.

关键词： Green buildings

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Experimental Analysis of learning Based Approach to Evaluate the Integration of Social Networking Platform with Internet of Things Technology

Experimental Analysis of Learning Based Approach to Evaluate...

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

作者： Karunakaran, A. Venkatraman, C.B. Suganthi, P. Santhi, G.B. Alok, Narkhede Shalout, Imad S. A. Engineering College Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Sri Sai Ram Institute of Technology Department of Cse Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of engineering Department of Civil Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Enterprising entrepreneurs are forging a new paradigm for consumer-business interactions by leveraging the convergence of social networking platforms-such as Facebook and Twitter-and the Internet of Things (IoT). This paper aims to assess an integration pipeline's performance using a learning based approach. Our aim is to understand the complex interplay between data that has been collected from IoT and how social media users act using advanced methods in data analytics. Our approach involves gathering data from both SNPs and the Internet of Things (IoT) and pre-processing it as needed before building a model that can be predicted and its performance evaluated. As opposed to the proposed method Elevated Social Networking Integration (ESNI) is a better strategy than the existing Social Networking Integration Analyzer (SNIA), which permits collecting less information through connected devices to enhance decision-making on consumer behavior and encounters. By offering a methodical approach to evaluating the value proposition of combining IoT with SNP, this study adds to the corpus of knowledge. The framework facilitates seamless interaction across diverse social media platforms, optimizing data flow and user experience. Empirical testing demonstrates ESNI's effectiveness in boosting engagement metrics and user satisfaction compared to conventional integration methods. This research highlights ESNI's potential to redefine social networking strategies, providing a robust foundation for future innovations in digital communication and marketing. © 2024 IEEE.

关键词： data assimilation

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An Attention-Based, Dual-Domain Adaptive Convolutional Neural Network for Fast MRI Reconstruction 24

An Attention-Based, Dual-Domain Adaptive Convolutional Neura...

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10th international conference on computing and Artificial Intelligence, ICCAI 2024

作者： Hu, Rui Chen, Shufan Tao, Jinxu Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei23007 China Department of Automation University of Science and Technology of China Hefei23007 China

ISBN: (纸本)9798400717055

In recent years, deep learning has demonstrated great potential in accelerating Magnetic Resonance Imaging (MRI). However, most existing works simply consider MRI reconstruction as an image-to-image task, or typically use a single and identical network for both frequency and image domain reconstruction simultaneously, without taking into consideration the data characteristics and internal interactions of the two domains, which leads to ineffective learning of the image reconstruction models. In this study, we proposed an attention-based, dual-domain reconstruction network to facilitate fast and accurate MRI reconstruction by adaptively learning different data characteristics in the frequency domain and image domain. In response to the inhomogeneous energy distribution in K-space, we designed a densely connected multi-attention network for frequency domain reconstruction. For image aliasing at low sampling rates, we developed a residual encoder-decoder network with an attention module for the image domain reconstruction. The overall network contains two parallel and interactive branches that simultaneously perform on K-space and image data, with the data fusion modules to fuse dual domain information in each reconstruction layer, and the long skip connections to transfer inter-layer information. We have evaluated our method on two datasets. A series of comparative and ablation experiments demonstrate that our method effectively reconstructs images from undersampled K-space data, achieving better image reconstruction performance and restoring significant textures of brain slices. © 2024 ACM.

关键词： Magnetic resonance imaging

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learning Multi-Granularity Task Primitives from Construction Videos for Human-Robot Collaboration

Learning Multi-Granularity Task Primitives from Construction...

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ASCE international conference on computing in Civil Engineering (I3CE)

作者： Pan, Zaolin Yu, Yantao Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Human-robot collaboration (HRC) is an emerging solution for the construction industry's productivity and safety challenges. The seamless HRC requires robots to understand the structure of construction tasks. Nevertheless, the implicit, dynamic construction task flow poses a nontrivial challenge. To address this challenge, a vision-based multi- granularity task's primitive learning method is proposed. This study seeks to enhance the mutual understanding between workers and robots by determining which granularity level is best for the tasks' understanding. Results show that the intermediate level has the best compromise between classification performance and embedded task knowledge. The outcomes will improve the smoothness of a HRC team.

关键词： Robots

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