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共 6842 条记录，以下是131-140 订阅

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Research on Economic Growth Forecasting Models Based on Big data 24

Research on Economic Growth Forecasting Models Based on Big ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Sun, Yihan Zhang, Yijian Zhejiang University Zhejiang Hangzhou China Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798400718120

Traditional economic growth forecasting methods, though somewhat successful, have limitations such as assuming a linear data generation process and ignoring the nonlinear relationships between economic variables. This study aims to integrate big data technology and machine learning methods to construct an efficient and accurate economic growth forecasting model. By systematically collecting and processing multi-source data, we apply machine learning algorithms such as linear regression, support vector machines (SVM), random forests, and gradient boosting decision trees (GBDT) for modeling and evaluation. The experimental results show that GBDT performs the best across various evaluation metrics, with the lowest mean squared error (MSE) and mean absolute error (MAE), as well as the highest R2 value. Further validation through 5-fold cross-validation and time series split methods demonstrates the robustness and generalization ability of the model, showing that the GBDT model outperforms other models in different data partitions and time series predictions. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

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Application of Deep learning-Based Malware Detection in Video Conferencing Systems 24

Application of Deep Learning-Based Malware Detection in Vide...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Wang, Weitao Lei, Lei Shao, Lisong Peng, Wang Chang, Jiale Beijing KeDong Electric Power Control System Co. Ltd Beijing China China Electric Power Research Institute Beijing China Nari Group Corporation Beijing China State Grid Electric Power Research Institute Beijing China

ISBN: (纸本)9798400718120

This paper explores the application of deep learning-based malware detection models in video conferencing systems. By constructing a large-scale dataset of malware and training deep neural network models such as CNN, RNN, and LSTM, high-precision detection of malware is achieved, outperforming traditional machine learning models. Integrating this deep learning model into the video conferencing system, a comprehensive malware defense system is built, effectively blocking a large number of attacks through real-time monitoring and intelligent analysis, significantly improving the security of video conferencing. The research results demonstrate that deep learning technology has broad prospects for application in this field, providing strong support for building a more secure video conferencing environment. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

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Drift Detection in Legacy Systems Using machine learning Techniques 3

Drift Detection in Legacy Systems Using Machine Learning Tec...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Hadge, Swapnil Shenoy, Govind Sreekar Amdocs Development Centre India Private Limited Pune India Liverpool John Moore University Liverpool United Kingdom Nitte Meenakshi Institute of Technology Information Science and Engineering Department Bangalore India

ISBN: (纸本)9798350381931

Legacy software systems represent a critical component of many organizations' technology stacks. However, they often lack documentation and comprehensive testing, making them susceptible to drift as the software environment changes over time. This thesis investigates the efficacy of supervised and unsupervised machine learning algorithms for identifying and quantifying drift in legacy software *** this study, a thorough review of existing drift detection approaches is conducted, highlighting their strengths and limitations. Subsequently, supervised machine learning algorithms, such as Support Vector machines and Random Forests, are employed to construct predictive models based on historical data. These models aim to proactively detect drift by learning from labeled instances of normal and anomalous system behaviour. Additionally, unsupervised machine learning techniques, including Principal Component Analysis, and clustering algorithms, are explored for their ability to discern patterns in software system data without the need for labeled samples. Experimental evaluations are conducted using real-world legacy software systems, demonstrating the comparative performance of supervised and unsupervised machine learning approaches in drift *** findings contribute to the growing body of knowledge in software maintenance and evolution, offering practical guidance for organizations seeking to enhance their legacy software management strategies using machine learning techniques. © 2024 IEEE.

关键词： Clustering algorithms

来源：评论

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Using machine learning-based SMOTE Analysis with the Light GBM Classification Method to Classify Diabetic Patients 3

Using Machine Learning-based SMOTE Analysis with the Light G...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Singh Gill, Kanwarpartap Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

Diabetes is a prevalent and enduring metabolic condition that impacts a significant global population. The timely identification and categorization of this ailment are of paramount importance in order to provide optimal control and therapeutic interventions. machine learning algorithms have shown encouraging outcomes in the classification of diabetes using patient data. The gradient boosting framework known as LightGBM is often favoured for this particular job because to its notable efficiency and high level of predicted accuracy. Moreover, the Synthetic Minority Over-sampling Technique (SMOTE) may be used to mitigate the issue of class imbalance in the diabetes dataset, a prevalent challenge encountered in the study of medical data. The primary objective of this work is to use the LightGBM classifier for the purpose of classifying the diabetes condition. The objective will be accomplished by the use of SMOTE Analysis, which aims to tackle the challenge of imbalanced data. Subsequently, an examination of both balanced and unbalanced datasets will be conducted, leading to the attainment of a 72 percent accuracy rate. Future researchers in this specific field of inquiry will benefit from the significant level of accuracy shown in this study. © 2024 IEEE.

关键词： Deep learning

来源：评论

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Streamlined machine learning Workflow for Accurate Rice Yield Estimation Using Remote Sensing and Meteorological data 3

Streamlined Machine Learning Workflow for Accurate Rice Yiel...

引用

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

作者： Sinchana, H.S. Pallavi, N.R. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The goal of this project is to use machine learning (ML), meteorological data, and remote sensing data to create an effective methodology for accurate rice yield estimation. The project entails creating a customized machine learning model with the aim of forecasting rice yields while accounting for the unique agricultural characteristics of the target region. The goal of the research is to determine the best approach for estimating rice yields by utilizing sophisticated data processing techniques and carefully assessing various machine learning models. By combining these strategies, crop production forecasts should become more accurate and provide insightful information for agricultural planning and management in a variety of geographical areas. © 2025 IEEE.

关键词： Adversarial machine learning

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AI-Enhanced data Mining for Fraud Detection in Financial Transactions 3

AI-Enhanced Data Mining for Fraud Detection in Financial Tra...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Gupta, Rajan Goyal, Raghav Malik, Kanta Sahu, Indu School of IT Vivekananda Institute of Professional New Delhi India Gemini Solutions Private Limited. Application Development Haryana Gurugram India

ISBN: (纸本)9798350396157

Financial frauds are on the rise globally, causing significant financial losses. This issue has far-reaching consequences, impacting the investment industry, government, and corporate sectors alike. Manual verifications and inspections are inaccurate, expensive, and time consuming for detecting such fraudulent operations. Artificial Intelligence (AI) and machine learning (ML) techniques should be used intelligently to identify fraudulent transactions by evaluating enormous amounts of financial data. The paper outlines a process of detecting financial fraud with the application of AI and machine learning. Finally, the paper discusses how organizations can leverage AI techniques for the detection of financial frauds. © 2024 IEEE.

关键词： machine learning

来源：评论

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Police data Mining and Classification Based on PDCSC Algorithm in international Police Cooperation 24

Police Data Mining and Classification Based on PDCSC Algorit...

引用

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

作者： Du, Qian Sun, Hongjian Shandong Police Coll Foreign Language Teaching & Researching Dept Jinan Shandong Peoples R China

ISBN: (纸本)9798400709777

Aiming at the problems of low data collaboration rate and weak storage capacity of police surveillance data in national border police cooperation, the research is based on Parallel Distributed Clustering with Semantic Constraints (PDCSC) algorithm. Police surveillance data processing model is constructed. The research firstly utilizes local density clustering algorithm to mine and classify the surveillance data, and then introduces the concept of parallelism and constructs the processing model using PDCSC algorithm. The results show that the clustering purity of police surveillance data based on PDCSC method is 92.37% and the data clustering accuracy is 93.67%. Meanwhile, the surveillance data summary of PDCSC method is 7108 which is 1085 and 2241 higher than that of DBSCAN and K-means. This indicates that the PDCSC algorithm can effectively process and classify police surveillance data, providing accurate police incident identification and analysis. The research aims to provide strong support for international police cooperation and improve the efficiency and accuracy of police work.

关键词： international police cooperation PDCSC algorithm Surveillance data data mining data classification

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Analysis and Detection of Fake News Using machine learning 3

Analysis and Detection of Fake News Using Machine Learning

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： machine learning

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AI Security Continuum: Concept and Challenges 24

AI Security Continuum: Concept and Challenges

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Washizaki, Hironori Yoshioka, Nobukazu Waseda Univ Tokyo Japan

ISBN: (纸本)9798400705915

We propose a conceptual framework, named "AI Security Continuum," consisting of dimensions to deal with challenges of the breadth of the AI security risk sustainably and systematically under the emerging context of the computing continuum as well as continuous engineering. The dimensions identified are the continuum in the AI computing environment, the continuum in technical activities for AI, the continuum in layers in the overall architecture, including AI, the level of AI automation, and the level of AI security measures. We also prospect an engineering foundation that can efficiently and effectively raise each dimension.

关键词： AI Security Software engineering for AI and machine learning Metamodel Security Risk Management

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A machine learning-based trust evaluation model for wireless sensor networks 24

A machine learning-based trust evaluation model for wireless...

引用

3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Huang, Yiyang Li, Xiaoyong Yuan, Jie Yuan, Kaiguo Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

ISBN: (纸本)9798400718212

Wireless sensor networks have been widely used in many industries, where sensor nodes are used to perform specific tasks, such as information collection or data transmission. Due to the particularity of sensor nodes, the trust mechanism of their nodes has been difficult to establish. Different from the traditional trust evaluation model, this paper proposes a trust evaluation model based on machine learning principles. By combining the extracted trust features, the final trust value is obtained for decision-making. Experimental results show that the model is effective and has higher accuracy than other methods.

关键词： Computational trust Feature extraction Model classification WSN

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