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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

共 7166 条记录，以下是491-500 订阅

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Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method 3

Predicting House Price Model : A Comprehensive Analysis with...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Sharma, Mansi Sharma, Deepak Burle, Rushikesh Patil, Pawan Joge, Isha Puri, Chetan Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Wardha India Department of Commerce & Management Sciences Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Machine Learning Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798350381931

This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the *** dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset. © 2024 IEEE.

关键词： Decision trees

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Stability Analysis of 3C Electronic Industry Robot Grasping Based on Visual-Tactile Sensing 3

Stability Analysis of 3C Electronic Industry Robot Grasping ...

引用

3rd international conference on Robotics, Automation and Artificial intelligence (RAAI)

作者： Yan, Teng Sun, Yuxiang Zhang, Yang Yu, Zhenxi Li, Wenxian Zhang, Kailiang Shenzhen Univ Shenzhen Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (纸本)9798350307573;9798350307566

This study addresses the stability of robotic grasping and assembly in the 3C electronics industry, an area often plagued by issues such as slippage and twisting of target objects during grasping, which negatively impacts production efficiency and product quality. Previously, solutions primarily relied on conventional visual detection techniques, which suffered from low recognition rates in complex environments, strong dependence on light and background, and suboptimal performance with transparent or reflective objects. Although advances in sensor technology allowed traditional tactile detection methods to effectively gauge grasp strength and prevent objects from being damaged, these approaches were constrained by the objects' material, shape, and friction, affecting accuracy. Unfortunately, both of these require considerable datasets to train. We propose a new grasping solution with small datasets. It integrates Visual-Tactile sensing to counter these limitations via a Dual-Stream neural network-based model to synthesize data. The merged data are then fed into a Vision Transformer (ViT) network to determine the grasping stability, with an achieved accuracy of 98.86%. This approach enhances grasping stability and exhibits superior adaptability in complex environments.

关键词： robotic grasping stability visual-tactile sensing small datasets multi-source data fusion vision transformer

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Federated Learning Framework for Plant Leaf Disease Diagnosis 3

A New Federated Learning Framework for Plant Leaf Disease Di...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Aadarsh Kumar Chattopadhyay, Pratik Singh, Lokesh Pattern Recognition Lab Dept. of Cse Varanasi India Reinventbio.io Varanasi India

ISBN: (纸本)9798331528201

Several Deep Learning-based approaches have recently been proposed for plant leaf disease detection. Traditional learning-based models necessitate centralizing large image datasets, requiring huge volumes of storage space. It appears that a better predictive model can be developed if the available data at multiple data sites can be effectively utilized using a collaborative learning strategy. Federated Learning (FL) offers a decentralized alternative, enabling collaborative model training by allowing multiple participants to update a global model using their local data without sharing high volumes of raw image datasets. Our work presents an FL framework to train a Vision Transformer-based model with an improved Federated Averaging (FedLossAvg) algorithm. Experimental results demonstrate our model's capability to handle large, varied datasets. Using four clients, our global model achieved an F1-score of 95.62% and an accuracy of 99.89% on the PlantVillage data. © 2024 IEEE.

关键词： Federated learning

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Advances in Artificial intelligence, Big Data and Algorithms - Proceedings of the 3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

Advances in Artificial Intelligence, Big Data and Algorithms...

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

ISBN: (纸本)9781643684444

The proceedings contain 149 papers. The topics discussed include: effects of AI on smart agriculture: a case study of digital agriculture base;image retrieval-based product identification for automatic checkout systems;application of lightweight emotion recognition model in intelligent construction site monitoring;research on the application of smart wearable in the emotional management of the elderly;artificial intelligence technology in the field of broadcasting and hosting;intelligent inspection method for photovoltaic modules based on image processing and deep learning;application of AI algorithms in power system load forecasting under the new situation;research on the intelligent operation and maintenance control system of power distribution network based on big data technology;research progress of intelligent rail transit train control system;and design and implementation of intelligent potted plant management system based on Internet of Things.

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Bi-Static Sensing in OFDM Wireless Systems for Indoor Scenarios 3

Bi-Static Sensing in OFDM Wireless Systems for Indoor Scenar...

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3rd international conference on 6G Networking

作者： Yajnanarayana, Vijaya Geuer, Philipp Ericsson Res Stockholm Sweden

ISBN: (纸本)9798350378603;9798350378597

The sixth generation (6G) systems will likely employ orthogonal frequency division multiplexing (OFDM) waveform for performing the joint task of sensing and communication. In this paper, we design an OFDM system for integrated sensing and communication (ISAC) and propose a novel approach for passive target detection in an indoor deployment using a data driven AI approach. The delay-Doppler profile (DDP) and power delay profile (PDP) is used to train the proposed AI-based detector. We analyze the detection performance of the proposed methods under line of sight (LOS) and non-line of sight (NLOS) conditions for various training strategies. We show that the proposed method provides 10 dB performance improvement over the baseline for 80% target detection under LOS conditions and the performance drops by 10 - 20 dB for NLOS depending on the usecase scenarios.

关键词： Joint Communication and Sensing Radar Processing Passive Target Detection Localization Artificial intelligence (AI) machine Learning (ML)

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Proceedings of the 2023 IEEE 3rd international conference on Advanced Learning Technologies on Education and Research, ICALTER 2023

Proceedings of the 2023 IEEE 3rd International Conference on...

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3rd IEEE international conference on Advanced Learning Technologies on Education and Research, ICALTER 2023

ISBN: (纸本)9798350395471

The proceedings contain 46 papers. The topics discussed include: development framework to evaluate the process of training researchers in universities;feminist thought and film: a review of the literature;school evaluation and artificial intelligence;empowering electronics learning through a hands-on approach: Arduino projects in the classroom;exploration of emotional characteristics in 2nd grade elementary students from the District of Independencia, Lima (Peru);development of a mobile application for learning sign language using machine learning in the city of Huancayo;specialist-woman interaction system for female empowerment in cases of discrimination and gender violence;and development of capabilities in engineering teachers, for greater relevance in social research.

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Proceedings - 2023 3rd international conference on Engineering Education and Information Technology, EEIT 2023

Proceedings - 2023 3rd International Conference on Engineeri...

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3rd international conference on Engineering Education and Information Technology, EEIT 2023

ISBN: (纸本)9798350326840

The proceedings contain 11 papers. The topics discuss include: design of microwave communication experimental platform for distance teaching;research on digital implementation way of international curriculum construction;research on innovative artificial intelligence education assisted by big data;role of information technology in strengthening engineering education in environmental engineering specialty;research on the training strategy of information technology enabling engineering talents training;an electronic Othello game board based on voltage divider rule;bimodal physiological emotion recognition using information contrastive learning;research on efficiency measurement of collaborative innovation between manufacturing industry and producer service industry based on two-stage DEA model;and an investigation of multilayer RNNs in sentiment analysis.

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Detection of estrus through automated classification approaches using vocalization pattern in Murrah buffaloes 3

Detection of estrus through automated classification approac...

引用

3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Sharma, Sugandha Kadyan, Virender Uttarakhand Dehradun India

ISBN: (纸本)9798350320749

The field that deals with the studying of animal sounds to detect or classify various animal conditions in an open or a closed setup. Bioacoustics has its prospective applications in speaker identification, detection of respiratory distress, estrus phases, stress induced by ecological conditions, separation and weaning and so on. In dairy cattle, especially murrah buffalo breed, inaccurate detection of estrus is a significant constraint that affects the reproductive capacity of small as well as large cattle-herds. Detecting estrus in dairy cattle at correct time assists livestock managers and farmers to augment milk production, profitability and maintaining welfare of *** studies on relationship between vocal behavior and reproductive performance is still at a very primary stage. This is due to inadequate amount of research work that has been done in order to study the association between acoustic features of animal vocal sounds and estrus owing to limited publicly available dataset. Outcomes of animal vocal sounds based studies are primarily dataset-dependent, i.e., the findings are ineffective if recording conditions are unidentified. Modern developments in deep-learning and machine-learning based approaches have led to the development of high-performance non-invasive ''animal sound classification system These systems, however, are built on scarce speech-corpora which is a preferred requisite for machine learning model training, which requires hours of good-quality recordings of sound of a single speaker in a noiseless environment. Developing such a good-quality sound corpus manually requires labour and time which makes the process expensive. In the present paper, the authors have executed machine-learning models like Random Forest, Decision Tree and SVM as well as a custom-built CNN architecture on a dataset and have carried out a comparative analysis of the above-mentioned techniques. © 2023 IEEE.

关键词： Animals

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Life Expectancy Prediction Based on Particle Swarm Optimization Support Vector machine Regression 3

Life Expectancy Prediction Based on Particle Swarm Optimizat...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Guo, Zhaodong Que, Yuanxi Li, Xiang College of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798350380989

This study delves into the application of Particle Swarm Optimization (PSO) algorithms in the parameter optimization of Support Vector Regression (SVR) models to enhance the accuracy of predicted average life expectancy. An adaptive PSO algorithm augmented with random perturbations is introduced. Through a fitness-function-guided dynamic adjustment mechanism, this method circumvents the premature convergence issue typical of conventional PSO, locating the optimal penalty parameter C and kernel parameter for the SVR model. These parameters are then incorporated into the SVR model for training and regression prediction, achieving optimal data fitting and generalization effects. Comparative analysis reveals that the PSO-enhanced SVR model outperforms the traditional SVR across various performance metrics. Notably, there is a marked improvement in the coefficient of determination, a significant re-duction in root mean square error, and mean absolute percentage error, affirming the advancements of the improved PSO-SVR model in prediction accuracy and reliability. © 2024 IEEE.

关键词： Composite Model PSO PSO-SVR SVM

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