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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68649 条记录，以下是4681-4690 订阅

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Classification of Rotten Fruits vs Fresh Fruits Using Sequential Model with Convolutional Neural Network 4

Classification of Rotten Fruits vs Fresh Fruits Using Sequen...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chouhan, Manorama Banerjee, P.S. Kumar, Amit Kushwaha, Jatin VIT Bhopal University Department of Computer Science and Engineering Bhopal India Jaypee University of Engineering and Technology Department of Computer Science and Engineering Guna India Jaypee University of Engineering and Technology Department of Computer Science and Engineering Guna India VIT Bhopal University Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350306118

This research employs Convolutional Neural Networks (CNNs) to distinguish between ripe and spoiled fruits using deep learning. The CNN model achieves an impressive accuracy rate of 98.79% in accurately categorizing these two groups. To facilitate model training and evaluation, a comprehensive dataset containing images of both fresh and rotten fruits is utilized. Libraries such as TensorFlow, OpenCV, and matplotlib are employed for efficient dataset processing. The CNN architecture is thoughtfully crafted, incorporating dropout layers to address overfitting and capture intricate fruit image characteristics. A novel approach for categorizing fruit spoilage is introduced, and the study focuses on apples, bananas, and oranges. The model's performance is evaluated using a Kaggle-sourced dataset, yielding an accuracy rate of 98.0% (0.9879), highlighting the effectiveness of the CNN model in discerning between ripe and spoiled fruits. Additionally, the research explores transfer learning techniques for fruit classification. © 2023 IEEE.

关键词： Convolutional neural networks

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TFC-net: Transfer Fusion-Compression Learning Network for Few-Shot Surface Defect Detection 3

TFC-net: Transfer Fusion-Compression Learning Network for Fe...

引用

3rd international conference on computer science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Rao, Zeyuan Zhang, Zhe Zhao, Ruixiang Lv, Wu Ma, Jie China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology Wuhan China Cssc Navigation Technology Co. Ltd Beijing China

ISBN: (纸本)9798350372052

Defect detection is a prevailing application within industry, while the scarcity of defect sample set a challenge for general deep learning method which requires enormous data for training. Considering the problem of few-shot learning in surface defect detection, inspired by transfer learning, we propose a two-stage TFC-net (Transfer Fusion and Compression few-shot learning network). For Transfer Fusion (TF) module, we fuse three networks pretrained on large scale dataset as prior knowledge to extract feature of defect samples and unify the feature distribution. For Transfer Compression (TC) module, we gradually mining representative feature of distinct defects to make classification. Experiments carried on three surface defect datasets extensively show our method achieves superior performance compared with competing methods, while the transfer speed consistent with metric learning which requires no training. © 2023 IEEE.

关键词： Surface defects

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Sentiment Analysis CNN Indonesia App Reviews on Play Store Using Naive Bayes Algorithm 1

Sentiment Analysis CNN Indonesia App Reviews on Play Store U...

引用

1st international conference on technology, Engineering, and Computing Applications, ICTECA 2023

作者： Armykav, Raka Mantoro, Teddy Ayu, Media Anugerah Asian, Jelita Nusa Putra University School of Computer Science Sukabumi Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

CCN Indonesia has become one of the most popular news apps. This study analyzes user responses to the app on the Play Store using text preprocessing, which is a set of procedures and techniques applied to raw text to clean, organize, and prepare it for processing by natural language processing (NLP) models. Then the results are used again to analyze using the Naive Bayes algorithm. This algorithm can categorize user reviews of the CCN Indonesia app into positive or negative sentiments. By analyzing user reviews, researchers can draw conclusions from users' positive or negative comments. The findings, including the analysis of user sentiment towards the CNN Indonesia app, indicate whether the CNN Indonesia app is a positive or negative app. © 2023 IEEE.

关键词： Sentiment analysis

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FPGA Implementation of Floating Point Linear Programming

FPGA Implementation of Floating Point Linear Programming

引用

2023 international conference on System science and Engineering, ICSSE 2023

作者： Hsu, Chih-Chung Lee, Pei-Jun National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798350322941

Linear programming is often applied in real-life situations and computer science, particularly in various resource allocation problems, such as optimizing the production of machines that produce different-priced products within the same unit of time or integrated circuit (IC) layout optimization. The Simplex algorithm is the most commonly used core algorithm, known for its robustness and high scalability. However, since linear programming models usually store variables in floating-point format to maintain accuracy, processing them on an FPGA platform is difficult. This paper proposes a logic circuit design for the Simplex algorithm model that uses the IEEE-754 single-precision format. The proposed method significantly accelerates the solving time by taking advantage of the high-speed computation of FPGA. Since FPGA IP Cores are not used, these algorithms can be implemented on any FPGA platform. © 2023 IEEE.

关键词： Linear programming

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Unsupervised Domain Adaptation Methods for Causal Correlation

Unsupervised Domain Adaptation Methods for Causal Correlatio...

引用

2023 international Annual conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Zhou, Liyuan Dou, Quansheng Tang, Huanling Li, Xiujuan Liu, Yanchao College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Computer Science and Technology Kashi University Xinjiang China

ISBN: (纸本)9798350309003

Unsupervised domain adaptation methods enhance the ability of models to generalize by minimizing the distributional disparities between the source and target domains through the transfer of knowledge from different domains and tasks, utilizing both labeled and unlabeled data. However, a challenge arises due to the presence of pseudo-correlations between certain 'confounders' and category labels, leading to a decrease in model prediction accuracy. To tackle this issue, this paper introduces the Causal Correlation Reweighting Module (CCRM). The CCRM samples features related to the labels and features independent of the labels, evaluates their independence, applies reweighting to the label-related features, and reduces the pseudo-correlation between the label-independent features and category labels. This approach results in improved model generalization performance and enhances the model's ability to handle unsupervised domain adaptation tasks. The proposed method is validated on widely-used domain adaptive datasets such as Digits, Office-Home and VisDA-2017, demonstrating higher accuracy compared to previous domain adaptation models. © 2023 IEEE.

关键词： Knowledge management

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A Proposed AI Method for Tracking College Students' Academic Progress 9

A Proposed AI Method for Tracking College Students' Academic...

引用

9th international conference on Information technology Trends, ITT 2023

作者： Allam, Hesham Khan, Zahoor Letsholo, Keletso Pandya, Bharti Shah, Kavita Ahamed, Jinesh Higher Colleges of Technology Computer and Information Science Dubai United Arab Emirates Higher Colleges of Technology Computer and Information Science Fujairah United Arab Emirates Higher Colleges of Technology Computer and Information Science Abu Dhabi United Arab Emirates Higher Colleges of Technology Business Division Dubai United Arab Emirates

ISBN: (纸本)9798350327502

Educators face significant challenges in monitoring the academic progress of their students in a particular course. Identifying students who are struggling academically can be a daunting task, but teachers can take proactive steps to provide them with additional support once these students come to their attention. Educational institutions today collect vast amounts of data on students from various sources, but they are constantly seeking innovative ways to leverage this information to enhance their reputation and the quality of their instruction. This research delves into the potential of utilizing machine learning algorithms to effectively track students' academic progress and notify teachers about those who may be at risk of failing a class. The translated prediction model presents an easily accessible visual representation that enables teachers to promptly implement appropriate interventions. Our proposed approach employs various machine learning techniques to create a predictive model that can identify students who may be at risk early on, thus mitigating factors that could hinder their learning. This could serve as a foundation for future predictive models to aid university educators in optimizing the overall learning process. © 2023 IEEE.

关键词： Decision trees

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Combined Adaptive Backstepping and LQR Control for Inverted Pendulum 3

Combined Adaptive Backstepping and LQR Control for Inverted ...

引用

3rd IEEE international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Li, Xiaolin Zhang, Yongli Si, Wei Li, Xinqi Pang, Dangfeng Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China TianJin Keylaboratory of Information Sensing & Intelligent Control Tianjin China School of Information Technology Beijing Institute of Technology Guangdong Zhuhai China

ISBN: (纸本)9781665490788

In this paper, a control strategy using adaptive backstepping control combined with optimal control is proposed to solve the problem of stable control of a linear inverted pendulum in the presence of external disturbances. The kinematic model of a one-stage linear inverted pendulum is derived using Lagrange's theorem, and then a disturbance is introduced to establish a suitable Lyapunov function to design an adaptive update control rate to control the pendulum angle and attenuate the effect of the disturbance. Then the optimal controller is designed to control the trolley position, and the LQR is optimized by a genetic algorithm to form the controller of the whole linear inverted pendulum. Finally, the effectiveness and anti-disturbance capability of the method are verified by Simulink simulation. © 2023 IEEE.

关键词： Genetic algorithms

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Unsupervised Cross-Domain Adaptation through Mutual Mean Learning and GANs for Person Re-identification 3

Unsupervised Cross-Domain Adaptation through Mutual Mean Lea...

引用

3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Yao, Leethar Lin, Bo-Yu Ul Haq, Qazi Mazhar Ul Islam, Ihtesham National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350322125

Unsupervised cross-domain adaptation is a challenging task for person re-identification due to the unavailability of target domain labels. Among existing methods, pseudo-Iabels-based methods have considerable performance but most of them use target domain data without labels which are challenging difficult for the target model to learn enough features. In this paper, we use generative based models that generate more target data. In cooperation with the generative model, a mutual learning model is used to transfer knowledge of one model to another model that ultimately improves overall model performance. Ex-tensive experiments are performed on Duke and Market datasets that significantly achieve improved performance in comparison to state-of-the-art methods. © 2023 IEEE.

关键词： Generative adversarial networks

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Partitioning-based Active Trust Mechanism in Cloud-Edge-Device Collaborative Data Collection 21

Partitioning-based Active Trust Mechanism in Cloud-Edge-Devi...

引用

21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xia, Zhuoqun Wang, Ziyu Liu, Xiao Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350329223

Since the open character of the Internet of Things (IoT), IoT devices are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud. Furthermore, existing trust evaluation models struggle to balance data security needs across different regions. To address this issue, the paper proposes a partition-based active trust mechanism (PATM). This PATM scheme mainly includes the following parts: First, the region is divided into subregions, and evaluations are conducted for the indicators of each subregion. Mechanism modeling and fuzzy analytic hierarchy process are used to analyze the impact of each indicator on the sensitivity of subregions. Second, the direct estimation method is utilized to estimate the demand for active detection routing based on the sensitivity of subregions. Then, the number of operations of malicious nodes is increased through the high-frequency transmission of detection routes required by each subregion. Additionally, a trust calculation method based on node operations is proposed to evaluate the reliability of the device. The experimental results show that the PATM scheme proposed in this article can quickly identify the trust degree of nodes while balancing the needs of different regions, ensuring the credibility of data collection. © 2023 IEEE.

关键词： Internet of things

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Object Detection System in Adverse Weather Conditions Using Ann 2

Object Detection System in Adverse Weather Conditions Using ...

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2nd international conference for Innovation in technology, INOCON 2023

作者： Joshua, Jeberson A Narasiman, L. Yogeshwaran, V. Anand, M.Vijay Saveetha Engineering College Department of Computer Science and Engineering Chennai India Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798350320923

Vehicles used in the automotive supply chain and for transportation must be able to operate in all types of weather and visibility. Sudden variations in fog, snow, lighting, smoke, rain, etc. are challenging conditions for the supply chain. While normal radars and cameras will work fine in most driving situations, vehicles may malfunction in some edge cases and in situations where accidents are more likely to occur. Numerous car accidents happen in mountainous areas and on ghats because it is unable to see the road well due to strong fog, smoke, and even smog. So, the suggested system can be used to solve this issue. When a person, animal, or vehicle enters the proposed system in a risky manner, it may result in an accident, hence it offers accident prevention before it happens a warning. © 2023 IEEE.

关键词： Neural networks

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