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Automatic sorting system based on image classification 3

Automatic sorting system based on image classification

引用

3rd international conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Xiong, Zihui School of Intelligent manufacturing Nanjing University of Science and Technology Nangjing21009 China

ISBN: (纸本)9781510681880

In the traditional logistics and distribution process, a large number of tasks cannot be completed accurately and in a timely manner due to errors in manual operations, low efficiency, and high requirements for the working environment. The automated sorting system, by introducing advanced technologies such as image recognition, radio frequency identification (RFID), deep learning, and computer vision, can achieve rapid, accurate, and intelligent item classification and sorting. This ultimately improves delivery efficiency, reduces service costs, and minimizes human errors. This article introduces an automated sorting system based on image recognition and robotic arm grasping. In terms of model selection, we experimented with various deep learning models and ultimately chose InceptionV2 as the final model. To improve the model's accuracy, we adjusted relevant parameters and introduced transfer learning to optimize the model. Experimental results demonstrate that the introduction of transfer learning significantly enhances the model's accuracy, performing well on both the training and testing datasets. Finally, we utilized the CoppeliaSim robot simulation platform to realize the robotic arm's grasping and classification of items on a conveyor belt, completing the closed loop of the automated sorting system. © 2024 SPIE.

关键词： Belt conveyors

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Speaker Gender Recognition Based on Semi-Supervised learning 3

Speaker Gender Recognition Based on Semi-Supervised Learning

引用

3rd international conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Zhang, Zheyan Li, Renwei Chen, Kewei Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China

ISBN: (纸本)9798331528386

Determining a speaker's gender from speech signals is essential for intelligent human-machine interaction. Significant progress has been made in supervised learning for speaker gender recognition. However, effective performance relies heavily on large labeled datasets. To address this, a semi-supervised learning framework is introduced to fully leverage vast amounts of unlabeled speech data for gender recognition. First, contrastive learning is employed to enhance and augment a relatively small set of labeled audio data, training an encoder capable of distinguishing speech genders. Next, a fuzzy clustering algorithm is utilized to generate gender pseudo-labels from a large pool of unlabeled data. Finally, the speaker gender recognition model is iteratively trained by using these pseudo-labels. Experiments and analyses on the Common Voice English dataset demonstrate that the model attains a mean accuracy of 97.4% in gender recognition tasks. © 2024 IEEE.

关键词： Self-supervised learning

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2023 3rd international conference on intelligent Technologies, CONIT 2023

2023 3rd International Conference on Intelligent Technologie...

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

ISBN: (纸本)9798350338607

The proceedings contain 337 papers. The topics discussed include: comparative study of BERT models and Roberta in transformer based question answering;development of low cost automated test system for RF power amplifiers using open sourced python programming;web mining: opportunities, challenges, and future directions;enhancing business intelligence through business analytics and data mining techniques using python;diagnosis of liver diseases using machine learning algorithms and their prediction using logistic regression and ANN;a machine learning based melanoma skin cancer using hybrid texture features;trump card: an user engagement based mobile application with citizens daily work reminder, instant help, and financial assistance facilities;an algorithmic state machine design approach for digital divider controller;soil data analytics using machine learning techniques – a survey;and the machine learning model with hybrid pooling approach for transmission line insulator classification and faults detection.

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Design of a Sports intelligent learning Platform Based on Computer Virtual Technology 3

Design of a Sports Intelligent Learning Platform Based on Co...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Shi, Mingna Institute of Physical Education Sichuan University of Arts and Sciences Dazhou China

ISBN: (纸本)9798350376173

In today's highly developed era of informatization and intelligence, computer virtual technology has emerged and been widely applied in various fields, creating various forms of intelligent systems. In the field of sports, by utilizing advanced computer virtual technology and intelligent equipment to provide support and services for sports activities, it combines traditional sports with modern technology to promote the intelligent development of various fields of sports. The topic adopts the method of integrating multiple disciplines such as computer science and sports science, and explores the design of a sports intelligent learning platform based on computer virtual technology. The platform construction ideas are analyzed from the design basis, logical architecture, functional modules, database design, etc. The platform workflow is explained. Finally, the design of a sports intelligent learning platform based on computer virtual technology is summarized, and it also looks forward to the existing problems and development directions, in order to promote the greater role of intelligent engineering in the field of sports and provide strong support for smart sports learning. © 2024 IEEE.

关键词： Federated learning

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Tourism Image Recognition and Scenic Spot Recommendation System Based on Deep learning 3

Tourism Image Recognition and Scenic Spot Recommendation Sys...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Zhang, Feifei Li, Jing Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798331519032

The purpose of this article is to explore the application of DL (Deep learning) in the intelligent development of tourism, and realize end-to-end tourism service by constructing a tourism image recognition and scenic spot recommendation system based on DL. In terms of methods, this article first collects and preprocesses large-scale tourism image data sets, and then designs a DL model for tourism image recognition, which is effectively trained and optimized. Then, combined with DL technology, a personalized scenic spot recommendation system is designed. Through system integration, image recognition and recommendation system are closely combined to provide users with all-round tourism services from image recognition to scenic spot recommendation. The experimental results show that the proposed image recognition method has achieved excellent performance on tourist images, and the evaluation indexes such as accuracy and recall are better than the existing algorithms. At the same time, the recommendation system also shows good personalized recommendation ability, which improves the accuracy by about 10% and the user satisfaction by about 15%. This research provides a complete set of intelligent solutions for tourism, which is expected to inject new vitality into the transformation and upgrading of tourism. © 2024 IEEE.

关键词： Contrastive learning

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Enhancing Solar Power Forecasting Through Hybrid Machine learning Models, Satellite data Integration, and Transfer learning Techniques 3

Enhancing Solar Power Forecasting Through Hybrid Machine Lea...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Aneesh, M. Nischal, U. Shendige, Sathvik N. Ashwini, A. Bnm Institute of Technology Department of Cse Bangalore India Bnm Institute of Technology Department of Eee Bangalore India

ISBN: (纸本)9798331515911

This paper introduces an innovative method for improving solar power prediction accuracy by integrating realtime weather forecasting, advanced machine learning techniques, and hybrid modelling frameworks. The proposed methodology incorporates satellite-based solar irradiance data, time-series prediction leveraging Long Short-Term Memory (LSTM) networks, and transfer learning for cross-regional model adaptation. These innovations aim to address the limitations of traditional models and provide a more resilient framework for forecasting solar power in diverse geographic regions. © 2025 IEEE.

关键词： Adversarial machine learning

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Using Machine learning to Predict Student Success in Undergraduate engineering Programs 3

Using Machine Learning to Predict Student Success in Undergr...

引用

3rd international conference on Computing and Machine Intelligence (ICMI)

作者： DeJong, Brian P. Karadogan, Ernur Cent Michigan Univ Sch Engn & Technol Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Undergraduate engineering programs are typically considered some of the most challenging as their curricula require students to have an aptitude for math, science, and engineering. The resources (time, effort, funds) required to finish an engineering degree is substantial. Therefore, it is imperative that the engineering students are supported with well-informed academic guidance as early in their education as possible so that these resources can be used most effectively. Analytical and data-driven methods such as machine learning techniques can be used to inform this guidance process by predicting student success based on features such as individual traits and academic performance. In that direction, we investigated the effectiveness of using machine learning in predicting engineering student success based on academic performance in core math, physics, and engineering courses in three undergraduate engineering programs. The data categories selected for training and testing of the machine learning models in this study are common to most engineering programs nationwide and can be customized in a straightforward manner for other engineering disciplines. The methodology and results outlined in this preliminary study shows promise for predicting degree and cumulative GPA in our three engineering programs.

关键词： machine learning undergraduate engineering education student success prediction

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Algorithmic Trading and Machine learning: An Empirical Study of Stock Price Prediction in India 3

Algorithmic Trading and Machine Learning: An Empirical Study...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chopra, Darshan Singh, Aditya Jayapriya, J. Vinay, M. Christ University Department of Computer Science Karnataka Bangalore India

ISBN: (纸本)9798331515911

This research paper uses historical data from Ambuja Cement to compare nine machine learning algorithms for algorithmic trading in the Indian stock market. The algorithms applied include SVM, Linear Regression, Decision Tree, K-NN, Ridge Regression, Lasso Regression, Bayesian Ridge Regression, Random Forest, Elastic Net Regression, XGBoost, and reinforcement learning. MSE, MAE, and R2 are metrics used to evaluate predictive performance. The findings show that ensemble approaches and regularized regressions outperform simpler models, emphasizing the importance of model complexity and feature selection. Reinforcement learning has the potential for optimizing tactics through constant adaptation. The study provides valuable insights on enhancing algorithmic trading in emerging Indian markets. © 2025 IEEE.

关键词： Contrastive learning

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Research on Dynamic Fuzzy Testing in Securing Cloud Infrastructure based on Deep learning 3

Research on Dynamic Fuzzy Testing in Securing Cloud Infrastr...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Dan, Ningyun Zhao, Lun Pan, Wenjin Cai, Yan College of Physics and Information Engineering Zhaotong University Zhaotong657000 China Yunnan Electric Test & Research Institute Group Co. Ltd. China

ISBN: (纸本)9798331506582

As cloud infrastructure assumes an increasingly pivotal role in contemporary computing environments, ensuring its security becomes a matter of paramount importance. As a prevalent vulnerability detection technology, traditional fuzz testing encounters the challenge of scalability in the context of the intricate and evolving nature of cloud computing systems. Our approach employs deep neural networks (DNNs) to analyze the behavior of diverse software components within cloud systems. It incorporates multi-dimensional data, including system logs, network traffic, and resource utilization in virtualized environments. The neural network is capable of identifying potential vulnerability points through an automated process. To generate effective test cases, we introduce a reinforcement learning framework based on a policy gradient algorithm. This framework enables an intelligent agent to interact with the system in order to obtain feedback and subsequently update the policy in order to progressively generate more aggressive test cases. The framework is designed with an adaptive reward mechanism that not only encourages triggering crashes but also focuses on edge use cases that reveal system bottlenecks, thereby improving vulnerability coverage. Furthermore, in consideration of the dynamic nature of the cloud environment, we have developed a distributed test architecture that enables the parallel deployment of tasks in virtual machines or containers, simulates multi-tenancy and dynamic load change scenarios, and introduces a vulnerability detection mechanism at the virtualization layer. The proposed model is capable of adaptively generating highly targeted test cases and optimizing the test strategy through continuous learning, thereby markedly enhancing the detection capability of complex vulnerabilities in cloud infrastructure. © 2024 IEEE.

关键词： Cloud security

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IoT based Water Management and Monitoring System 3

IoT based Water Management and Monitoring System

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Muthulakshimi, S. Sathyamoorthy, Malathy Akash, S. Charumathi, P. Sri Suvathi, N.M. Department of Information Technology KPR Institute Of Engineering And Technology Coimbatore India

ISBN: (纸本)9798331527549

IoT (Internet of Things) has revolutionized various industries, and water management is no exception. This abstract introduces an IoT-based water management system designed to address the challenges of water scarcity, pollution, and inefficient distribution. Leveraging IoT devices like sensors, actuators, and communication modules, the system creates a networked infrastructure for real-time monitoring and management of water resources. Sensors deployed throughout the water distribution network collect data on flow, pressure, quality, and consumption patterns, which is then transmitted to a central server or cloud platform. The system instantly detects pipe leakage and promptly halts the water flow, even in the absence of human presence. This automated response helps prevent water scarcity. The system supports remote monitoring and control via web-based dashboards or mobile applications, facilitating proactive maintenance and troubleshooting. Additionally, it provides data analytics and reporting functionalities, empowering stakeholders to make informed decisions and promote water conservation initiatives. Overall, this IoT-based water management system offers a comprehensive solution to enhance sustainability, efficiency, and resilience in water distribution networks. © 2025 IEEE.

关键词： Solenoid valves

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