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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"

共 4198 条记录，以下是1791-1800 订阅

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A Housing Price Prediction Method Based on Stacking Ensemble learning Optimization Method 10

A Housing Price Prediction Method Based on Stacking Ensemble...

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10th IEEE international conference on Cyber Security and Cloud Computing and 9th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yang, Zhenyu Zhu, Xinghui Zhang, Yangcong Nie, Peng Liu, Xinbo Hunan Agr Univ Coll Informat and Intelligence Changsha China Guangdong Univ of Science and Technology Guangzhou China

ISBN: (纸本)9798350312461

the growth of real estate sector has been significantly influenced in recent years by ongoing regulation of real estate acquisition policy and the effects of the COVID-19 epidemic on economy. the fluctuation in housing prices is one of the most concerning factors for prospective homeowners. Whether property prices can sustain a comparatively constant level for an extended period of time is a crucial factor for prospective homeowners. Numerous modeling and application techniques for prediction algorithms, together with the promotion of machine learning algorithms, offer fresh approaches to forecast residential real estate values. this work proposes a novel stacking ensemble learning method (DStacking) which is based on the diversity of learners including XGBoost and BP neural network. through the application of ensemble learning algorithm, D-Stacking method can successfully predict the possible promotion of housing price. Housing price datasets from China and the USA were used in the experiments to guarantee the generalizability of findings. Experimental findings indicate that the diversity of base learners significantly affects the predictive power of D-Stacking method. Furthermore, the more diverse the models are, the more precise the predictions can be with proposed D-Stacking method. Compared with classical Stacking ensemble learning models, the proposed D-Stacking method demonstrates an excellent feasibility in reducing the RMSE to 0.869 and 1.029 across various datasets. © 2023 IEEE.

关键词： Forecasting

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Criminal Tendency Identification Using Deep learning Approaches: A Novel Approach for Security Protection 4th

Criminal Tendency Identification Using Deep Learning Approac...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Stephen Neal Joshua, Eali Tirupati Rao, N. Bhattacharyya, Debnath Joys, Nakka Marline Department of Computer Science and Engineering Gandhi Institute of Technology and Management AP Gandhi Nagar Rushikonda Visakhapatnam India AP Visakhapatnam India Department of Computer Science and Engineering Koneru Laksmaiah Education Foundation Greenfield Vaddeswaram Guntur522502 India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology & Sciences Sangivalasa AP Bheemunipatnam531162 India

ISBN: (纸本)9789819900848

A crime is punished by the state based on its seriousness, but it may also be dealt with by other authorities if it causes bodily injury, mental suffering, or property loss to another person or property. the definition of crime is as follows: Law enforcement is under increasing pressure to provide new deterrents as criminal activity continues to grow. It is impossible for traditional criminal justice procedures to be effective in today’s quickly changing criminal environment. Investigating the methods of foreseeing or predicting crime in great detail may lead to the development of a machine that supports police personnel while also assisting them in the prevention of crime. We believe that machine learning and machine vision are excellent places to begin. Our analysis included a few instances of these strategies, which piqued our interest and piqued our want to learn more about them. In the past, authorities have used similar strategies, and they will continue to do so in the future. One of the objectives of this research is to investigate how computer vision and machine learning may be used to assist law enforcement detect and stop crimes more quickly and efficiently. the use of cutting-edge artificial intelligence and computer vision technologies may also be beneficial to law enforcement. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crime

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international workshops held at the 29th international conference on Parallel and Distributed Computing, Euro-Par 2023

International workshops held at the 29th International Confe...

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international workshops held at the 29th international conference on Parallel and Distributed Computing, Euro-Par 2023

ISBN: (纸本)9783031488023

the proceedings contain 40 papers. the special focus in this conference is on Parallel and Distributed Computing. the topics include: Towards Resource-Efficient DNN Deployment for Traffic Object Recognition: From Edge to Fog;the Implementation of Battery Charging Strategy for IoT Nodes;subMFL: Compatible subModel Generation for Federated learning in Device Heterogeneous Environment;towards a Simulation as a Service Platform for the Cloud-to-things Continuum;cormas: the Software for Participatory Modelling and Its application for Managing Natural Resources in Senegal;Malleable APGAS Programs and their Support in Batch Job Schedulers;task-Level Checkpointing for Nested Fork-Join Programs Using Work Stealing;making Uintah Performance Portable for Department of Energy Exascale Testbeds;Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm++, C++, HPX, Go, Julia, Python, Rust, Swift, and Java;Parallel Auto-Scheduling of Counting Queries in machine learning applications on HPC Systems;Energy Efficiency Impact of Processing in Memory: A Comprehensive Review of Workloads on the UPMEM Architecture;enhancing Supercomputer Performance with Malleable Job Scheduling Strategies;a Performance Modelling-Driven Approach to Hardware Resource Scaling;Adaptive HPC Input/Output Systems;dynamic Allocations in a Hierarchical Parallel Context;designing a Sustainable Serverless Graph Processing Tool on the Computing Continuum;diorthotis: A Parallel Batch Evaluator for Programming Assignments;Experiences and Lessons Learned from PHYSICS: A Framework for Cloud Development with FaaS;improved IoT application Placement in Fog Computing through Postponement;high-Performance Distributed Computing with Smartphones;blockchain-Based Decentralized Authority for Complex Organizational Structures Management;Transparent Remote OpenMP Offloading Based on MPI;DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines;exploring Factors Impacting Data Offloading Performance in

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application Research Based on computer 3D Digital Photovoltaic Cable Engineering Statistics System 4

Application Research Based on Computer 3D Digital Photovolta...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Runbiao, Liu Enzhi, Cao Jihong, Guo Qichao, Tang Zhiqiang, Fan Yanxia, Li Wind Power Engineering Design Consulting Co. Ltd Beijing China Earth View Image Inc Beijing China

ISBN: (纸本)9798350342710

in this paper, mobile geographic information system (GIS) technology is applied to photovoltaic cable lines. Combined with the characteristics of mobile phone geographic information system, a new method of photovoltaic power cable workload calculation is proposed. It is intended to use GPS real-time positioning technology to intelligently collect and display the wiring situation of solar cells and the surrounding environment. the number of photovoltaic cell wiring is quickly estimated by image approximation method. Using Dynamo's data link technology, you can quickly build a mathematical model of the cable. Dynamo visualization program was used to establish the calculation model of cable quantity. And the number of cables is calculated. the results of measurement and manual calculation are compared and analyzed with concrete examples. the research results of this paper verify the feasibility of the technical scheme. © 2023 IEEE.

关键词： Cables

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Audio Source Separation using Wave-U-Net with Spectral Loss 4

Audio Source Separation using Wave-U-Net with Spectral Loss

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4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Patkar, Varun Parmar, Tanish Narvekar, Parth Pawar, Vedant Gomes, Joanne Department of Information Technology St. Francis Institute of Technology Mumbai India

ISBN: (纸本)9781665459877

Existing Audio Source Separation models usually operate using magnitude spectrum and neglect the phase information which results in long-range temporal correlations because of its high sampling rates. Audio source separation has been a problem since long and only a handful of solutions have been presented for it. this research work presents a Wave-U-Net architecture with Spectral Loss Function which separates input audio into multiple audio file of different instrument sounds along with vocals. Existing Wave-U-Net Architecture with Mean Square Error (MSE) loss function provides poor quality results due to lack of training on only specific instruments and use of MSE as an evaluation parameter. While commenting about the loss functions, shift invariance is an important aspect that should be taken into consideration. this research work makes use of Spectral Loss Function in coordination with Wave-U-Net architecture, which automatically syncs the phase even if two audio sources are asynchronised. Spectral Loss Function solves the problem of shift invariance. the MUSDB18 Dataset is used to train the proposed model and the results are compared using evaluation metrics such as Signal to Distortion Ratio (SDR). After successful implementation of the Wave-U-Net Architecture with Spectral Loss Function it is observed that the accuracy of the system has been improved significantly. © 2023 IEEE.

关键词： machine learning

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Skin Lesion Classification Using machine learning 4th

Skin Lesion Classification Using Machine Learning

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4th international conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Vindhya, J. Pooja, C. Dongre, Manisha H. Gowrishankar, S. Srinivasa, A.H. Veena, A. Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Karnataka Bengaluru560056 India

ISBN: (纸本)9789811974014

the proliferation of abnormal cells that are capable of invading or spreading to other parts of the body causes skin cancer. Early and accurate detection of skin cancer can be treated successfully. therefore, we look into the different machine learning models that are implemented to classify the skin lesion images accurately and in a short amount of time. It is classified into benign or malignant skin lesions. machine learning techniques have great potential in classifying the medical images for detecting the disease. the main features for this task include color, texture, and shape which differentiate malignant from benign lesions. Considering these serious issues, a variety of initial recognition methods for malignant lesions has been developed by researchers. this paper provides a comprehensive evaluation of different machine learning algorithms for skin cancer detection. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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international workshops held at the 29th international conference on Parallel and Distributed Computing, Euro-Par 2023

International workshops held at the 29th International Confe...

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international workshops held at the 29th international conference on Parallel and Distributed Computing, Euro-Par 2023

ISBN: (纸本)9783031506833

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Hybrid SORN Hardware Accelerator for Support Vector machines 1

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4th international conference for Next Generation Arithmetic

作者： Huelsmeier, Nils Baerthel, Moritz Rust, Jochen Paul, Steffen Univ Bremen Inst Electrodynam & Microelect ITEM Me Bremen Germany DSI Aerosp Technol GmbH Bremen Germany

ISBN: (数字)9783031321801

ISBN: (纸本)9783031321795;9783031321801

this paper presents a new approach for support vector filtering to accelerate the training process of support vector machines (SVMs). It is based on the Sets-of-Real-Numbers (SORN) number format, which provides low complex and ultra fast computing. SORNs are an interval based binary number format, showing promising results for complex arithmetic operations, e.g. multiplication or fused multiply-add. To apply SORNs to high dimensional vector arithmetic, a combination of SORN arithmetic and fixed-point adder trees is used. this Hybrid SORN approach combines the advantages of SORNs, concerning reduction of computational costs and time, and fixed point adders in terms of precision. A Hybrid SORN support vector filtering architecture is implemented on an FPGA board with Zynq 7000 XC7Z100 SoC and evaluated for the MNIST dataset. It can be considered as hardware accelerator, reducing the training time by factor 1.38 for one-versus-rest and 2.65 for oneversus-one SVM implementation.

关键词： SORN Hybrid SORN support vector machine hardware accelerator FPGA MNIST machine learning

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machine learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features 4th

Machine Learning-Based Tomato Leaf Disease Diagnosis Using R...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

作者： Ahmed, Faisal Naim Uddin Rahi, Mohammad Uddin, Raihan Sen, Anik Shahadat Hossain, Mohammad Andersson, Karl Department of Computer Science and Engineering Premier University Chattogram Bangladesh University of Chittagong Chattogram Bangladesh Luleå University of Technology Skelleftea Sweden

ISBN: (纸本)9789811994821

Tomato leaves can be infected with various infectious viruses and fungal diseases that drastically reduce tomato production and incur a great economic loss. therefore, tomato leaf disease detection and identification are crucial for maintaining the global demand for tomatoes for a large population. this paper proposes a machine learning-based technique to identify diseases on tomato leaves and classify them into three diseases (Septoria, Yellow Curl Leaf, and Late Blight) and one healthy class. the proposed method extracts radiomics-based features from tomato leaf images and identifies the disease with a gradient boosting classifier. the dataset used in this study consists of 4000 tomato leaf disease images collected from the Plant Village dataset. the experimental results demonstrate the effectiveness and applicability of our proposed method for tomato leaf disease detection and classification. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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proceedings of 2023 IEEE 5th international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

Proceedings of 2023 IEEE 5th International Conference on Civ...

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5th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

ISBN: (纸本)9798350310603

the proceedings contain 280 papers. the topics discussed include: research on cloud detection system of multi-source satellite remote sensing image based on computer deep learning technology;an online inductance current modeling method for SS-compensated WPT system;improving misalignment tolerance by variation of self-inductance for EV-WPT with small air gap;pedestrian detection algorithm based on improved YOLOv4;smooth switching control strategy for microgrid off-grid and grid-connected based on droop control;research on high speed train speed tracking anti-disturbance controller with finite-time prediction compensation;a dynamic feedforward adaptive controller for flight simulator motion platforms using RELS method;research on application of multi-navigation technology fusion to improve vehicle positioning accuracy in complex scene;and state estimation method for few-measurements power distribution network based on improved deep graph convolutional network.

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