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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是521-530 订阅

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A Novel machine learning Approach for Fast and Efficient Detection of Mango Leaf Diseases 3

A Novel Machine Learning Approach for Fast and Efficient Det...

引用

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

作者： Swapno, S. M. Masfequier Rahman Nobel, Sm Nuruzzaman Islam, Md Babul Haque, R. Meena, V. P. Benedetto, F. Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka Bangladesh UNICAL Dept Comp Modeling Elect & Syst Engn Arcavacata Di Rende Italy Appl Bahari Big Data Res House Dhaka Bangladesh Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept EEE Bengaluru India Univ ROMA TRE Dept Econ Lab SP4TE Via Silvio Amico 77 Rome Italy

ISBN: (纸本)9798350372977;9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99%. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Food Safety EfficientNetv2L Leaf disease Classification dataset Mango Leaf

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The Application of Intelligent database System in Material Support 24

The Application of Intelligent Database System in Material S...

引用

3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Zhai, Chenggong Xu, Hongsi Lv, Huifang Zhou, Liyong Wan, Fei Zhang, Pinghua Army Logist Acad Dept Quartermaster Procurement Chongqing Peoples R China Army Logist Acad Dept Logist Command Chongqing Peoples R China Army Logist Acad Mil Logist Dept Chongqing Peoples R China

ISBN: (纸本)9798400709777

Material support is a crucial part of the daily operations of many organizations and enterprises. With the continuous development of information technology, the application of intelligent database systems in the field of material support has become an important tool for improving efficiency, reducing costs, and providing better decision support. It can be used for material inventory management, real-time monitoring of inventory levels, and automatic triggering of ordering or replenishment processes. In addition, the intelligent database system can also predict future material needs, optimize the supply chain, assist in emergency response and material allocation, and conduct quality control and tracking. These characteristics can improve the efficiency, accuracy, and reliability of material management, ensure timely supply and quality safety of materials, and meet people's basic living needs and emergency rescue needs

关键词： Intelligent database Material support data analysis and prediction Supply chain optimization

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Analysis of Various machine learning Approaches for Predicting Sun Irradiation Levels 3

Analysis of Various Machine Learning Approaches for Predicti...

引用

3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Tomar, Priyanshu Yadav, Anurag Bhatt, Samiksha Sharma, Priyanka School of Allied Science Dev Bhoomi Uttarakhand University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798350387193

The present paper examines how well various machine learning approaches predict solar irradiation levels. Applications like solar energy generation and climate modeling rely on accurate predictions of sun irradiation. Several machine learning approaches, such as Gaussian Process Regression (GPR), Artificial Neural Network (ANN), Decision Tree (DT), Linear Regression (LR), and Support Vector machine (SVM), are investigated and compared in the present study. The investigation entails comparing the approaches' predicted accuracy and assessing their effectiveness using historical data on solar irradiation for the Dehradun region of India (30°19'27.95"N, 78°2'2.11 'E). Also, this shows how well each method scales and how efficient it is computationally, with reference to the lower error in sun irradiance prediction, especially for the GPR technique, as the root mean square error (0.797098637), mean square error (0.635366237), mean absolute error (0.430421925), and regression value (0.999992228). © 2024 IEEE.

关键词： Solar irradiance

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Advanced Deep learning Framework for Multi-Stage Bird Song Classification 3

Advanced Deep Learning Framework for Multi-Stage Bird Song C...

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3rd international conference on Electronics and Information Technology, EIT 2024

作者： Wang, Haoran Zhang, Chun Zang, Guohui Wang, Wenzhuo Wang, Nana College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Hainan Acoustics Laboratory Institute of Acoustics Chinese Academy of Sciences Haikou China

ISBN: (纸本)9798350369151

Birdsong classification is vital for ecological monitoring, particularly in island wetlands. This study introduces the Multi-Stage (MS) model to address complex and mixed birdsong signals, comparing its performance against ResNet50, GoogLeNet Inception v1, and GoogLeNet Inception v4. Using a self-tested dataset of six bird species, the MS model achieved the highest recognition rate of 95.17%. The findings confirm the effectiveness of deep learning in birdsong recognition, while also highlighting the strengths and limitations of various models for specific tasks. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Cyber Attacks Utilizing Deep learning Model using Network/Web Traffic data 3

Prediction of Cyber Attacks Utilizing Deep Learning Model us...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kannan, Balaji Sakthivanitha, M. Jayashree, S. Maruthi, R. School of Computing Science Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350375190

Nowadays, cyber-attacks are growing predominantly due to the development of technologies. It will lead to financial losses to a company and the other problems related to attacks. It is very important to predict such attacks from outsiders to safeguard our networking systems to provide effective security. The Artificial Intelligence (AI), machine learning (ML) and Deep learning (DL) techniques leverage enormous amounts of data to identify the cyber attacks. These learning approaches are used to identify a broad range of cyber-attacks by analyzing the web traffic or network traffic to identify potential threats such as malware, network intrusions and other types of attacks. The study demonstrates the various deep learning methods to predict the anomalies and other potential threats with more accuracy in real time. © 2024 IEEE.

关键词： Forecasting

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Research on Performance Prediction Model of Metal Structural Materials Based on machine learning Algorithm 3

Research on Performance Prediction Model of Metal Structural...

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

作者： Zhan, Jianning Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (纸本)9798331519032

The traditional performance evaluation of metal materials depends on trial and error, which is expensive and time-consuming. machine learning algorithm, especially deep learning, has brought revolutionary changes to this field because of its powerful ability in data processing and pattern recognition. In this paper, a Deep Random Forest Regression Network (DRFRN) model is proposed, which combines the feature extraction ability of the deep neural network with the high robustness and interpretability of the RF algorithm to realize the accurate prediction of the properties of metal materials. In the data preprocessing stage, abundant data of metal materials are obtained from databases such as Materials Project and NIST Inter-Laboratory Materials database, and cleaned, standardized and principal component analysis are carried out to reduce the data dimension. In the model construction stage, the compound loss function is designed, and the strategy of phased training, early stop and dynamic learning rate adjustment is adopted to optimize the model performance. The experimental analysis shows that DRFRN model shows high prediction accuracy and good data fitting ability when predicting the key performance indexes of stainless steel, aluminum alloy and titanium alloy. Compared with support vector machine (SVM), decision tree (DT) and convolutional neural network (CNN) models, DRFRN model has obvious advantages in prediction accuracy, robustness and generalization ability. This study not only provides a new solution for the performance prediction of metal materials, but also provides a reference for the performance prediction in other fields. © 2024 IEEE.

关键词： Titanium alloys

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Research on data reliability and storage security verification for cloud platform based on machine learning 3

Research on data reliability and storage security verificati...

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wen, Chengjun Liu, Guohong Ding, Fang Li, Zhendong Lin, Haitang Institute of Automation and Information Technologies Satbayev University Almaty Kazakhstan

ISBN: (纸本)9781510680449

At present, the data security and reliability of cloud storage are increasingly threatened by unreliable factors, resulting in major data security problems. As the data security of cloud platforms becomes increasingly prominent, this study starts with the current cybersecurity situation and the challenges of cloud data storage, and emphasizes the urgency of developing an efficient and reliable data verification mechanism. Subsequently, we propose a novel type of machine learning model that leverages advanced algorithms to identify and defend against potential threats of data breaches and corruption. In addition, we have verified the effectiveness of the model through a series of rigorous experiments, and the experimental results show that the model can not only accurately identify various security vulnerabilities, but also adapt to different cloud platform environments, and provide a strong guarantee in terms of data integrity and confidentiality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Cloud storage

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Global Self-Supervised Graph learning for Recommendation 3

Global Self-Supervised Graph Learning for Recommendation

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Liu, Xinyue Software Engineering Shandong University Jinan Shandong250000 China

ISBN: (纸本)9798350380989

In recent years, Comparative learning (CL) has driven a series of effective research in the recommendation domain, and it excels in dealing with the data sparsity problem in recommender systems since no data labeling is required in CL. The typical model flow of the approach consists of structurally perturbed augmentation of the user-item bipartite graph, and model optimization by maximizing the consistency of node representations across different graph augmentations. Although the model has been shown to be effective, we believe that existing contrastive learning research and applications are not yet sufficient. To enhance the global nature of contrast learning, we introduce global graph view self-supervised graph learning (GSGL). This method adds an auxiliary self-supervised task to the traditional recommendation supervision task to realize multi-view contrast learning. Specifically, each node builds enhanced views by three data enhancement methods (node loss, edge loss, and random noise) and performs two-by-two comparison learning on these views. Through empirical studies on three benchmark datasets, the effectiveness of the GSGL algorithm is demonstrated, which not only leads to a more uniform view embedding spatial distribution and improves the recommendation accuracy, but also exhibits strong robustness to interaction noise. The code and used datasets are released at https://***/LX-Moon/GSGL. © 2024 IEEE.

关键词： Comparative learning graph augmentations Recommending System Self-supervised learning

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Application of machine learning Approaches for Power Estimation of Digital VLSI Circuits at Register Transfer Level 3

Application of Machine Learning Approaches for Power Estimat...

引用

3rd IEEE international conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Parmar, Chinmay Patel, Manish I. Institute of Technology Nirma University Electronics and Communication Engineering Department Ahmedabad India

ISBN: (纸本)9798350374643

In VLSI, estimation of power, performance, and area at appropriate steps during the design process is necessary to meet required specifications. In digital VLSI circuit design, early-stage power estimation helps in making decisions, such as selecting architecture options and design choices. Recently, machine learning is being used for power estimation. In this work, machine learning-based power estimation at the RTL level is proposed. datasets are crucial for machine learning;however, they are relatively scarce in VLSI applications. A dataset for power estimation was generated using Cadence EDA tools with the help of Python and TCL scripts for automation. Various machine learning models were then applied, and the best-performing six models were evaluated based on R-squared and Root Mean Square Error (RMSE) as performance metrics. © 2024 IEEE.

关键词： Adversarial machine learning

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Online Vocational Education Personalized Course Content Push Algorithm Based on Big data 3

Online Vocational Education Personalized Course Content Push...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lan, Feipeng Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (纸本)9798350380989

A collaborative filtering based online teaching recommendation system for higher vocational colleges is proposed. UserCF system for user oriented personalized recommendation and object-oriented personalized recommendation system are established. The concept of multiprocessing is proposed to improve the learning performance of the network. By means of subject modeling, subject dictionary modeling, data preprocessing and behavior dictionary modeling, the dynamic information ontology is extracted. The dynamic ontology of network behavior is weighted by six factors, namely time, content, object, place, behavior, and number of reentries. This results in the decay model of user data in the network. Through the scoring matrix of user data, the degree of similarity between users and the filtering of data is to achieve the best recommendation. And the filtered data has less similarity and better identification, as well as less push delay. Practice has proved that this improved scheme is effective. © 2024 IEEE.

关键词： mathematical model personalized learning push algorithm Recommendation system search engine vocational education

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