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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Optimization Simulation of Computational Model of Particle Swarm Optimization Algorithm Based on machine learning 5

Optimization Simulation of Computational Model of Particle S...

引用

5th international conference on Applied machine learning, ICAML 2023

作者： Liu, Shuying Xian Yang Normal University Department of Computer Xian Yang712000 China

ISBN: (纸本)9798350341416

Particle Swarm Optimization (PSO) algorithm is a heuristic algorithm based on bionic technology, which has the advantages of simple concept and fast convergence. Although PSO algorithm has good exploration ability, it is easy to lose population diversity and lead the algorithm to fall into local optimum. In this article, the mutation idea in genetic algorithm (GA) is used for reference, and mutation operation is introduced into PSO algorithm to expand the population search space, and the diversity of particle population is maintained. At the same time, according to the parameters of artificial neural network (ANN), the initial spatial position of particles similar to that in PSO algorithm is randomly selected. the process of constantly adjusting the network parameters from the calculation error is similar to the optimization process of PSO algorithm. In this article, the improved PSO algorithm is used to replace the gradient correction method to correct the network parameters, so that the network output is close to the expected output, and an ANN based on the improved PSO algorithm is established, and its application in spatial pattern optimization is discussed. © 2023 IEEE.

关键词： Heuristic algorithms

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Ensemble learning Approaches for Cardiovascular Diseases Prediction: A Comparative Evaluation 4

Ensemble Learning Approaches for Cardiovascular Diseases Pre...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ezhilan, R. Babu, N. Kannan, S. Vinod Kumar, D. Murali, G. Karan, G. Tamil Nadu Salem India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Cardiovascular diseases (CVDs) continue to be the major cause of mortality worldwide, emphasizing the critical need for early detection methods. Identifying heart disease in its initial stages poses a considerable challenge for healthcare providers. However, recent advancements in diagnostic technologies provide encouraging opportunities for early diagnosis and intervention. this article evaluates the performance of four machine learning algorithms-AdaBoost, XGBoost, LightGBM, and CatBoost-in predicting heart disease across two classes: normal (0) and abnormal (1). the models were assessed based on precision, recall, and F1-score to determine their effectiveness. For class 0, LightGBM achieved the highest F1-score (0.89), with a precision of 0.93 and recall of 0.85, indicating a strong balance between precision and recall. In class 1, AdaBoost, XGBoost, and LightGBM all achieved an F1-score of 0.9, with LightGBM demonstrating higher recall (0.94) and strong overall performance. AdaBoost excelled in recall for class 1 (0.96), while CatBoost had the lowest F1-scores across both classes. the results suggest that LightGBM is the most robust model for heart disease prediction, consistently delivering high precision and recall, making it an effective algorithm for this application. © 2024 IEEE.

关键词： Contrastive learning

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machine learning based Crop Suitability Prediction and Fertiliser Recommendation System 4

Machine Learning based Crop Suitability Prediction and Ferti...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Gayathiri, B. Brindha, P. Karthika, I. Saranya, E. Rajeshkumar, G. Rajesh Kanna, P. Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Builders Engineering College Nathakadaiyur Tiruppur India M. Kumarasamy College of Engineering Dept of Cse Karur India Sri Eshwar College of Engineering Dept of Cse Coimbatore India

ISBN: (纸本)9798350300093

As the world population grows demand for sustainable agriculture increases. It causes major impacts like rising supply chain costs, not being able to maintain the safety and quality of your products, insufficient communication between parties failure to track and control inventory in warehouses and stores on the food supply chain. Yield forecast which is important for agriculture stakeholders can be obtained by using machine learning models and data from multiple sources. In the previous years, prediction of yield was done manually with the experience of farmers and it was not so accurate. the yield prediction being a major can be solved by the advanced technologies and with the available data. this study offers a system that advises crops appropriate for the region and fertilizers that are suited for crops based on soil measurements to farmers, all based on the yield history of previous years. this agricultural yield forecast and fertilizer recommendation uses machine learning methods. the Random Forest method is used to propose crops since it produces better results than other algorithms, while the K-means clustering technique is used to suggest fertilizers depending on the NPK level of the soil. © 2023 IEEE.

关键词： Forecasting

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application of Fuzzy Comprehensive Evaluation Method based on AHP in the Selection of Medical Air Compressor Types 4

Application of Fuzzy Comprehensive Evaluation Method based o...

引用

4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Gu, Wang Wang, Sang Huang, Dongmei Central South University College of Civil Engineering Changsha China Hunan Genewell Medical Technology Co. Ltd R&d Department Changsha China

ISBN: (纸本)9798350373233

this paper adopts the Analytical Hierarchy Process (AHP) to decompose the indicators for the selection of medical air compressors, calculates the weights of each indicator according to its importance, and uses the Fuzzy Comprehensive Evaluation Method (FCEM) to select the type of air compressor that best fits the hospital conditions. the AHP and the FCEM are widely used and applicable, and they can quantitatively analyze many qualitative objectives and arrive at the optimal solution. this paper also demonstrates how to use YAANP software for AHP calculation and validate the results. © 2023 IEEE.

关键词： Fuzzy set theory

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Integrating machine learning into an SMT-Based Planning Approach for Production Planning in Cyber-Physical Production Systems

Integrating Machine Learning into an SMT-Based Planning Appr...

引用

26th European conference on Artificial Intelligence (ECAI)

作者： Heesch, Rene Ehrhardt, Jonas Niggemann, Oliver Helmut Schmidt Univ Hamburg Germany

ISBN: (纸本)9783031504846;9783031504853

Cyber-Physical Production Systems (CPPS) are highly complex systems, making the application of AI planning approaches for production planning challenging. Most AI planning approaches require comprehensive domain descriptions, which model the functional dependencies within the CPPS. though, due to their high complexity, creating such domain descriptions manually is considered difficult, tedious, and error-prone. therefore, we propose a novel generic planning approach, which can integrate mathematical formulas or machine learning models into a symbolic SMT-based planning algorithm, thus shedding the need for complex manually created models. Our approach uses a feature-vector-based state-space representation as an interface of symbolic and sub-symbolic AI, and can identify a solution to CPPS planning problems by determining the required production steps, their sequence, and their parametrization. We evaluate our approach on twelve planning problems from a real CPPS, demonstrating its ability to express complex dependencies within production steps as mathematical formulas or integrating ML models.

关键词： AI Planning Cyber-Physical Production System machine learning SMT

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Fourth international conference on computer Vision and Data Mining, ICCVDM 2023

Fourth International Conference on Computer Vision and Data ...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

ISBN: (纸本)9781510674448

the proceedings contain 106 papers. the topics discussed include: people recognition with foot pressure based on neural network;MVS-STRNet: cross-view space target recognition from multi-view stereo;video visibility prediction method for foggy weather based on spatio-temporal variation feature;comparison and improvement of some attention mechanisms in the video field;enhancing video quality assessment through brightness information;ERBNet: an automatic polyp image segmentation method to enhance receptive field and boundary;exposition and implementation of linear equation system reconstruction method in electromagnetic wave CT imaging;deep learning-based 3D face reconstruction method for video stream;and video anomaly detection via improved future frame prediction.

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AN INTEGRATED application OF BUILDING INFORMATION MODELING, computer-AIDED MANUFACTURING, machine learning, AND thE INTERNET OF thINGS: A Hybrid Stadium as a Case Study 28th

AN INTEGRATED APPLICATION OF BUILDING INFORMATION MODELING, ...

引用

28th international conference on computer-Aided Architectural Design Research in Asia, CAADRIA 2023

作者： Qin, Bowen Wang, Tianyu Dubbeldam, Winka Korhammer, Justin Huang, Cong Wu, Dengguo Jiang, Hongming Archi-Tectonics NYC LLC United States Huadong Engineering Corporation Ltd China

ISBN: (纸本)9789887891796

Using building information modeling (BIM), design teams can maximize decision-making efficiency by using one data-sharing platform and one integrated digital model. We present a novel builtproject that integrates computer-aided manufacturing (CAM), machine learning, and internet of things (IoT) with a BIM process. the goal is to solve design and manufacturing problems and tightly connect virtual design data with the real-world construction process. Introducing CAM in the early phase reduces the cost of reverse engineering and optimizes material and construction costs. machine learning has been used for curtain wall panel standardization and to solve manufacturing problems. the IoT concept focuses on directing the data stream onto real-world objects and updating the digital model using feedback from real-world monitoring. Using the integrated application in this project, a hybrid Olympic stadium, helped to accelerate the process of design and reduced the time and cost for manufacturing and construction. © 2023 and published by the Association for computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： Internet of things

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TPU v4: An Optically Reconfigurable Supercomputer for machine learning with Hardware Support for Embeddings 23

TPU v4: An Optically Reconfigurable Supercomputer for Machin...

引用

50th Annual international Symposium on computer Architecture (ISCA)

作者： Jouppi, Norman P. Kurian, George Li, Sheng Ma, Peter Nagarajan, Rahul Nai, Lifeng Patil, Nishant Subramanian, Suvinay Swing, Andy Towles, Brian Young, Cliff Zhou, Xiang Zhou, Zongwei Patterson, David Google Mountain View CA 94043 USA

ISBN: (纸本)9798400700958

In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes) dynamically reconfigure its interconnect topology to improve scale, availability, utilization, modularity, deployment, security, power, and performance;users can pick a twisted 3D torus topology if desired. Much cheaper, lower power, and faster than Infiniband, OCSes and underlying optical components are <5% of system cost and <3% of system power. Each TPU v4 includes SparseCores, dataflow processors that accelerate models that rely on embeddings by 5x-7x yet use only 5% of die area and power. Deployed since 2020, TPU v4 outperforms TPU v3 by 2.1x and improves performance/Watt by 2.7x. the TPU v4 supercomputer is 4x larger at 4096 chips and thus nearly 10x faster overall, which along with OCS flexibility and availability allows a large language model to train at an average of similar to 60% of peak FLOPS/second. For similar sized systems, it is similar to 4.3x-4.5x faster than the Graphcore IPU Bow and is 1.2x-1.7x faster and uses 1.3x-1.9x less power than the Nvidia A100. TPU v4s inside the energy-optimized warehouse scale computers of Google Cloud use similar to 2-6x less energy and produce similar to 20x less CO2e than contemporary DSAs in typical on-premise data centers.

关键词： machine learning domain specific architecture TPU GPU IPU supercomputer optical interconnect reconfigurable embeddings large language model power usage effectiveness warehouse scale computer carbon emissions energy CO2 equivalent emissions

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Diabetes Prediction using Extreme learning machine: application of Health Systems 5

Diabetes Prediction using Extreme Learning Machine: Applicat...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Reddy, S. Nava Bharath Reddy, K.V. Narasimha Rao, S.N. Tirumala Kumar, K.S.M.V. Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

Recently, the health sector is widely adopting artificial intelligence models such as machine learning (ML), deep learning for data analysis, disease prediction, and disease classification. However, the conventional models failed to analyze the data. therefore, this work is focused on analysis of diabetes prediction using extreme learning machine (DP-ELM) model. Initially, Pima Indian diabetes is considered, which is pre-processed for missing data symbols identification. then, the statistical features from pre-processed dataset are extracted using principal component analysis (PCA). then, ELM model is trained with the PCA features and forms the trained feature dataset. then, a random test combination is applied for ELM testing, which classifies the positive and negative status of diabetes. the simulations proved that;the proposed DP-ELM outperformed in terms of accuracy as compared to existing methods. © 2023 IEEE.

关键词： Forecasting

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Comparison of EfficientNet and VGGNet Using RGB Photos for Human Action Recognition 4

Comparison of EfficientNet and VGGNet Using RGB Photos for H...

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4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： An, Ran Beijing - Dublin International College Beijing University of Technology Beijing China

ISBN: (纸本)9798331528676

In the field of computer vision, human action recognition, is a key technology, underpinning numerous application scenarios such as video surveillance, human-computer interaction, sports analysis, and more. Convolutional Neural Network (CNN)-based models have demonstrated impressive performance in human action detection difficulties due to the swift advancement of deep learning techniques. the subject matter focuses on the research of human action identification using two advanced CNN models, VGGNet and EfficientNet, on RGB image datasets. It also compares the differences in feature extraction and classification capabilities between the VGGNet and EfficientNet models. this paper selects an RGB image dataset containing information on 15 categories of human actions as the experimental basis, providing strong support for model training. During the evaluation phase, accuracy is adopted as the metric, and each model's capabilities undergo evaluation. the results indicate that the EfficientNet model exhibits higher accuracy and better generalization capabilities in human action recognition tasks, primarily due to its efficient network structure and optimized model design. the categorization of various postures aids in the advancement of technology for recognizing human behavior and helps to highlight the diversity and complexity of human behavior. ©2024 IEEE.

关键词： Convolutional neural networks

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