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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是1-10 订阅

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icmlsc 2023 - 2023 7th international conference on machine learning and soft computing

ICMLSC 2023 - 2023 7th International Conference on Machine L...

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7th international conference on machine learning and soft computing, icmlsc 2023

ISBN: (纸本)9781450398633

the proceedings contain 31 papers. the topics discussed include: research on location problem based on fuzzy multi-criteria decision method;decision model of ship intelligent collision avoidance based on automatic information system data and generic adversary imitation learning-deep deterministic policy gradient;research on energy consumption prediction based on fast attribute reduction of weighted neighborhood rough set with moving horizon;solving multimodal multi-objective problems with local pareto front using a population clustering mechanism;validated computation of Lipschitz constant of recurrent neural networks;neural network optimization objective vector representation based on genetic algorithm and its multi-objective optimization method;improved multilayer perceptron neural networks weights and biases based on the grasshopper optimization algorithm to predict student performance on ambient learning;and spatio-temporal deep fusion graph convolutional networks for crime prediction.

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WINS - CVMLH 2023 - Joint Proceedings of the Workshop on Intelligent Systems, WINS 2023 and the Workshop on Computer Vision and machine learning for Healthcare, CVMLH 2023, Co-located with CODD 100 - 7th international conference on computing Sciences, ICCS 2023

WINS - CVMLH 2023 - Joint Proceedings of the Workshop on Int...

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2023 Joint of the Workshop on Intelligent Systems, WINS 2023 and the Workshop on Computer Vision and machine learning for Healthcare, CVMLH 2023

the proceedings contain 10 papers. the topics discussed include: comparative analysis on enhanced image compression algorithms;military detection during close combat situations and border monitoring using adaptive customized convolutional neural network;machine learning-based rainfall prediction for various regions;methodology for the registration of images and their inclusion in medica image management systems, based on the DICOM protocol;mobile application success prediction using machine learning;a secure mobile application for speech to text conversion using artificial intelligence techniques;a comparative study of heart disease prediction using machine learning;method for teaching medical imaging diagnosis, applying virtual reality techniques;heart disease prediction using machine learning method – a review article;and web system for the management of clinical laboratory test, applied to health facilities in rural areas.

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Improved Multilayer Perceptron Neural Networks Weights and Biases Based on the Grasshopper optimization Algorithm to Predict Student Performance on Ambient learning 23

Improved Multilayer Perceptron Neural Networks Weights and B...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Michira, Mercy K. Rimiru, Richard M. Mwangi, Waweru R. School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Kenya School of Computing and Information Jomo Kenyatta University of Agriculture and Technology Kenya

ISBN: (纸本)9781450398633

the classification accuracy of a multi-layer Perceptron Neural Networks depends on the selection of its parameters such the connection weights and biases. Generating an optimal value of these parameters requires a suitable algorithm to train the multilayer perceptron neural networks. this paper presents swam based Grasshopper optimization algorithm that optimizes the connection weights and biases of Multilayer Perceptron Neural Network. Grasshopper optimization algorithm is a swarm-based metaheuristic algorithm applied for accurate learning of Multilayer Perceptron Neural Networks. the proposed Multilayer Layer Perceptron Neural Networks based on the Grasshopper Optimization Algorithm was validated using a Genetic algorithm and Backpropagation algorithm this algorithm has proved to perform satisfactorily performance by escaping local optimal and its fast convergence. © 2023 Owner/Author.

关键词： Multilayer neural networks

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LDRC: Long-tail Distantly Supervised Relation Extraction via Contrastive learning 23

LDRC: Long-tail Distantly Supervised Relation Extraction via...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Li, Tingwei Wang, Zhi Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450398633

Long-tail problem is one of the major challenges in distantly supervised relation extraction. Some recent works on the long-tail problem attempt to transfer knowledge from data-rich and semantically similar head classes to data-poor tail classes using a relation hierarchical tree. these methods, however, are based on the assumption that long-tail and head relations have a strong correlation, which does not always hold true, and the model's ability to learn long-tail relations is essentially not improved. In this paper, a novel joint learning framework that combines relation extraction and contrastive learning is proposed, allowing the model to directly learn the subtle differences between different categories to improve long-tail relation extraction. Experimental results show that our proposed model outperforms the current state-of-the-art (SOTA) model on various mainstream datasets. © 2023 ACM.

关键词： Extraction

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Research on License Plate Recognition Based on Deep learning in Complex Scenarios 23

Research on License Plate Recognition Based on Deep Learning...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Tang, Yinqing Yu, Benguo Liu, Fengning Wang, Anran Hainan Medical University China

ISBN: (纸本)9781450398633

the license plate angle is unfixed, the vehicle position is ununiform, and the picture is not sufficiently illuminated which leads to the decrease of license plate recognition accuracy. In order to improve the accuracy of license plate recognition, a deep learning-based license plate recognition method is proposed. For license plate location problem, YOLOv3 algorithm is used. the algorithm is more capable of recognizing small targets and is suitable for license plate location recognition that requires precise positioning. For the problem of license plate character recognition, the CNN plus multitask recognition method is proposed for recognition. the experimental results show that the accuracy of the license plate recognition method proposed in this paper reaches 96%, and the intelligent license plate recognition is realized. © 2023 ACM.

关键词： License plates (automobile)

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A Traffic Sign Recognition Method Based on YOLOv5 Deep learning Algorithm 23

A Traffic Sign Recognition Method Based on YOLOv5 Deep Learn...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Tang, Yinqing Yu, Benguo Wang, Anran Liu, Fengning Hainan Medical University China

ISBN: (纸本)9781450398633

Aiming at the problems of low accuracy and slow recognition efficiency of the traditional traffic sign recognition algorithm in complex environment, a deep learning traffic sign recognition method based on YOLOv5 is proposed. Firstly, the Chinese traffic sign data set TT100K is randomly divided into training set and test set. Convolutional neural network YOLOv4 and convolutional neural network YOLOv5 are used to train respectively on the training set, so as to build the prediction model of traffic signs. then the trained model is validated on the test set. through the evaluation of the experimental, it is found that compared with YOLOv4 model, YOLOv5 model has higher recognition accuracy and faster recognition speed. © 2023 ACM.

关键词： Traffic signs

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A Hybrid machine learning Method for Diabetes Detection based on Unsupervised Clustering 23

A Hybrid Machine Learning Method for Diabetes Detection base...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Liu, Junhong Peng, Bo Yin, Zezhao School of Computer and Artificial Intelligence Southwest Jiaotong University China

ISBN: (纸本)9781450398633

Diabetes is a common disease, and due to the increasing incidence year by year. But most diabetics can not be easily detected in the early stage, since the symptoms are not obvious. the objective of this study is to propose a machine-learning method based on unsupervised clustering to improve the accuracy of diabetes detection. Due to massive unlabeled data sets and the problems in the traditional K-means clustering algorithms, we adopt the Fuzzy c-means clustering algorithm with an improvement on the calculation of parameter m. Our method includes a combination of the principal component analysis(PCA), an improved Fuzzy c-means (FCM) clustering algorithm, and K-nearest neighbor(KNN) classification algorithm optimized with K value. After 10 times 10-fold cross-validation, the average accuracy of the proposed method reaches 99.31%, which is higher than that of other machine learning models. therefore, our method is proven to be more suitable for detecting diabetes. At the same time, further experiments on a new data set validate the applicability of our method in a more practical way for the diabetes detection. © 2023 ACM.

关键词： Principal component analysis

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Post Processing Selection of Automatic Item Generation in Testing to Ensure Human-Like Quality with machine learning 23

Post Processing Selection of Automatic Item Generation in Te...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Duvvuri, Venkata Lee, Gahyoung Hsu, Yuwei Makwana, Asha Morgan, Chris College of Professional Studies Northeastern University United States AITestBanks Inc. United States

ISBN: (纸本)9781450398633

Automatic Item Generation (AIG) is increasingly used to process large amounts of information and scale the demand for computerized testing. Recent work in Artificial Intelligence for AIG (aka Natural Question Generation-NQG), states that even newer AIG techniques are short in syntactic, semantic, and contextual relevance when evaluated qualitatively on small datasets. We confirm this deficiency quantitatively over large datasets. Additionally, we find that human evaluation by Subject Matter Experts (SMEs) conservatively rejects at least g1/49% portion of AI test questions in our experiment over large diverse dataset topics. Here we present an analytical study of these differences, and this motivates our two-phased post-processing AI daisy chain machine learning (ML) architecture for selection and editing of AI generated questions using current techniques. Finally, we identify and propose the first selection step in the daisy chain using ML with 97+% accuracy, and provide analytical guidance for development of the second editing step with a measured lower bound on a BLEU score improvement of 2.4+%. © 2023 ACM.

关键词： Semantics

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Federated learning based Object Detection using Dampened Harmonic Optimization 23

Federated Learning based Object Detection using Dampened Har...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Jain, Deepak Kumar Khanna, Akshit Gera, Bhavya Sangwan, Dhiraj Chongqing University of Posts and Telecommunications Chongqing China Birla Institute of Technology and Science Rajasthan Pilani India CSIR-CEERI Rajasthan Pilani India

ISBN: (纸本)9781450398633

Object Detection is the task of detecting and localizing objects of importance in visual media. the rapid increase in the number of powerful end devices such as mobiles and surveillance cameras have to lead to increasing in both resources and media generation. Object Detection has now become possible on end devices, but certain challenges need to be tackled to fully utilize the resources. Federated learning is one such framework that leverages the end device resources to build machine learning models while preserving data privacy. We model the federated learning framework for object detection on real-world heterogeneous datasets using a novel dampened harmonic optimizer to enhance local learning on the end device and hence reducing the communication cost during the learning process. We provide comparison of the commonly used FedAvg with our FedHarm optimization on multiple object detection models and datasets to demonstrate the merits of our proposition. Our method FedHarm with its dampened updates allows for greater local computation which reduces the overall communication rounds between edge devices and cloud and better handles heterogeneity in real-world datasets. FedHarm leads to faster convergence by 41% on average over FedAvg which is supported by extensive experiments. © 2023 ACM.

关键词： Object detection

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7th international conference on soft computing: theories and Applications, SoCTA 2023

7th International Conference on Soft Computing: Theories and...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

ISBN: (纸本)9789819720880

the proceedings contain 36 papers. the special focus in this conference is on soft computing: theories and Applications. the topics include: Detection of Duplicate Question Pairs by Applying Proposed BoW, TF-IDF, and USE Approach;the Hand Glove Enabling Voice and Text Communication;advancing Rheumatoid Arthritis Care: Exploring Technological Breakthroughs and Future Directions;twitter Trolling Detection Using machine learning;HMHDTML: Human Mental Health Detection Using Text and machine learning Model;analyzing the Growth Profile of Brain Tumor with Caputo Fractional Operator via Sumudu Transform;multi-labelled Topic Classification of Research Articles Using machine learning;security in Mobile Ad Hoc Networks: Impact of Attacks and Countermeasure Approaches;optimization of Vibrational Frequencies for Orthotropic Parallelogram Plates With Circular Variations in Tapering at Simply Supported Boundary;drowsiness Detection Using Adaboost Method and Haar Cascade Classifier to Improve Safety of Drivers;early Detection of Colorectal Cancer from Polyps Images Using Deep learning;motion Control of Underactuated Cart-Double-Pendulum System Via Fractional-Order Sliding Mode Controller;a Comparative Study of Pedestrian Detection Techniques Over the Last Decade;approximation Properties of Modified-Bernstein Operators Having Szász Weight Functions;fourier-Laguerre Expansion of Signals by Composite Summable Technique;a Multiple Linear Regression Model to Estimate Global, Direct and Diffuse Irradiance in Gurugram, India, Using Python;supervised machine learning Approaches for Customer Reviews Sentiment Analysis;stock Price Prediction on Indian Share Market Using machine learning;transparent Price Forecasting for Basic Food Commodities in a Developing Economy;Parallel Deep Convolution Neural Network (P-DCNN) Prediction of Paddy Crop Disease;Convolutional-LSTM Network for Emotion Recognition Using EEG Data in Valence-Arousal Dimension;Analysis of Multiply-Accumulate (MAC) Unit Using C

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