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共 20255 条记录，以下是541-550 订阅

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intelligent data Management in Cloud Using AI 3

Intelligent Data Management in Cloud Using AI

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Khan, Mohd Amaan Walia, Ranjan Chandigarh University Mohali Punjab Gharaun India

ISBN: (纸本)9798350381931

The rapid growth of data in various domains has necessitated the need for efficient and intelligent data management techniques in cloud storage. This research paper explores the application of AI-driven approaches to enhance data accessibility, security, and performance in cloud storage ***, the paper investigates the challenges faced in traditional data management systems, such as limited accessibility, potential security vulnerabilities, and performance bottlenecks. These challenges are addressed by leveraging the power of artificial intelligence *** research presents an overview of the role of AI in data management, including machine learning algorithms, natural language processing, and computer vision. These AI-driven approaches enable intelligent data classification, indexing, and retrieval, facilitating improved data accessibility in cloud storage ***, the paper discusses how AI can enhance security measures in cloud storage. It explores the use of AI-based anomaly detection algorithms to identify and mitigate potential security threats, such as unauthorized access, data breaches, and malicious activities. AI-driven encryption techniques are also explored to strengthen data protection in cloud *** addition to accessibility and security, the research paper delves into AI-driven techniques to optimize the performance of cloud storage systems. It investigates the application of machine learning algorithms for intelligent resource allocation and workload balancing, leading to enhanced system performance and reduced ***, this research paper highlights the significance of intelligent data management in cloud storage and showcases the potential of AI-driven approaches to address the challenges associated with data accessibility, security, and performance. The findings of this study can assist cloud service providers, researchers, and practitioners in developing more efficient and robust data management solu

关键词： Information management

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intelligent Decision Optimization for Industrial Supply Chain Based on Reinforcement learning and Deep Q Network 3

Intelligent Decision Optimization for Industrial Supply Chai...

引用

3rd international Signal Processing, Communications and engineering Management conference, ISPCEM 2023

作者： Han, Xin Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350307023

The management of industrial supply chains involves a series of complex decision-making processes, where the uncertainty of demand, supply, and inventory poses significant challenges. Understanding the essence of these challenges, this paper seeks to introduce an avant-garde approach to address them. At the heart of our proposal is the application of an intelligent decision optimization framework constructed on the foundational principles of reinforcement learning (RL). This approach, distinct from traditional methods, leverages the power of machine learning to guide decision-making, offering notable advantages in adaptability and precision. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

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Exploring IoT and Machine learning Integration for Soil Nutrients Monitoring and Crop Recommendation: A Survey 3

Exploring IoT and Machine Learning Integration for Soil Nutr...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khemnar, Vijaya R. Bendre, Mininath R. Kolekar, Vikas K. Pravara Rural Engineering College Department of Computer Engineering Loni India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331509675

The integration of machine learning (ML) and Internet of Things (IoT) technologies has a scope of improvement in precision farming techniques and revolutionise the agriculture sector. This research paper examines the integration of IoT and ML in soil nutrient monitoring and crop recommendation systems. The aim is to provide a thorough overview of the most recent developments, applications, challenges, and potential future directions. IoT sensors collect and give immediate soil health information, while ML algorithms analyses these data to suggest nutrient deficits and recommend the most suitable crops. This research work evaluates advancements in IoT and Machine learning applications, emphasising their benefits for precision agriculture. This integration aims to improve agricultural production and sustainability via data driven decision making and resource optimisation. At the end research gaps are discussed, along with future research directions. © 2025 IEEE.

关键词： Smart agriculture

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Application of an Improved Convolutional Neural Network-Based Method in Network Intrusion Detection 3

Application of an Improved Convolutional Neural Network-Base...

引用

3rd international conference on intelligent Communications and Computing, ICC 2023

作者： Chen, Huan You, Guirong Shiue, Yeouren College of Information Engineering Fujian Business University Fuzhou China Fujian Prov. Universities Eng. Research Center of Big Data Analytics for Business Intelligence Fujian Business University Fuzhou China National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu Taiwan

ISBN: (纸本)9798350308327

This study investigates the use of convolutional neural network (CNN) technology to solve the problem of network intrusion detection, specifically studying the impact of convolutional layer count. Experiments revealed that two convolutional layers yield optimal results. An AdaBoost+CNN model was proposed using this CNN with optimization strategies such as ensemble learning. Eight classical learning algorithms were compared with the proposed methods, and the results were evaluated using parameters such as accuracy, precision, and F-measurement. Compared with traditional machine learning, deep learning, and other methods, the method proposed in this study achieved higher classification accuracy and successful results in detecting network attacks. © 2023 IEEE.

关键词： Adaptive boosting

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Who Is to Err? A Limited Effect of data Labeling in Prediction of Users' Subjective Impressions of Web Designs 3

Who Is to Err? A Limited Effect of Data Labeling in Predicti...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Bortnikova, Anna V. Bakaev, Maxim A. Novosibirsk State Technical University Department of Automated Control Systems Novosibirsk Russia Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia

ISBN: (纸本)9798331516321

User Experience (UX) researchers and designers who seek to predict users' subjective impressions nowadays turn to Machine learning (ML) models trained on marked-up data. Labeling of graphical user interfaces (UIs) is a rather time-consuming process that is also prone to errors and inaccuracies, particularly if motivation of the human labelers is low. Correspondingly, the size of datasets in Human-Computer Interaction (HCI) rarely exceeds the order of 10,000, and the field largely relies on feature-based models. In our paper, we compare two principal ways of obtaining the features, with respect to their effect on the quality of the models that predict users' impressions of web pages' Complexity, Aesthetics and Orderliness. First, we calculated several layout-related metrics - balance, equilibrium, symmetry - for 368 website screenshots labeled by 40 human volunteers. Second, we obtained the similar metrics from ML-based online services working with raw web UI images. Our results suggest that despite the limitations of the current automated web UI parametrization tools, their metrics can actually yield superior models (having R2s higher by 107% on average). Our key implication for UI/UX designers and ML researchers is that the expected ROI should be carefully weighted before committing to the tedious manual web UI data labeling. © 2024 IEEE.

关键词： Web Design

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Efficient Detection of Runways, Taxiways, and Aprons in Commercial Airports Using YOLOv8 and High-Resolution Satellite Imagery 3

Efficient Detection of Runways, Taxiways, and Aprons in Comm...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Panneerselvam, Ramesh Kumar Yasen Shaik, Mohammed Sundarapalli, Hanish Venkat Prakash Gandham, Kumar N S L Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331523893

This study presents an advanced satellite-based detection system designed for the comprehensive identification of multiple facilities within both commercial and civilian airports. The system utilizes cutting-edge remote sensing technologies and machine learning algorithms to accurately detect and categorize various essential elements crucial for airport operations. These elements include runways, taxiways, and aprons. Using high-resolution satellite imagery, the system aims to improve airport infrastructure management, optimize safety protocols, and improve overall operational efficiency on a global scale. Leveraging deep learning techniques, specifically object detection models, we explore the suitability of different architectures such as Faster R-CNN, YoLO V8, detectors for accurately identifying these facilities. The project involves extensive data collection, preprocessing, model training, and evaluation to ensure robust performance. Considering the potential deployment on edge devices, the chosen models are optimized for efficiency without compromising accuracy. © 2025 IEEE.

关键词： Airport runways

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An Identification of Guava Fruit Disease Using ML 3

An Identification of Guava Fruit Disease Using ML

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Nandi, Subarna Hasan, Nazmul Rashid, Omor Faruk Al Miraz, Shafiun Reza, Md. Shafi Rahman, Farjana International University of Business Agriculture and Technology Uttara Model Town Miyan Research Institute Dhaka Bangladesh Kazla Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh London South Bank University Dept. of Data Science 103 Borough Rd LondonSE1 0AA United Kingdom Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jeonbuk National University Graduate School of Integrated Energy of AI North Jeolla Jeonju Korea Republic of

ISBN: (纸本)9798350388282

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers' socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used. © 2024 IEEE.

关键词： Fruits

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AI-Enhanced HR Interview Simulation for Realistic Candidate Assessment 3

AI-Enhanced HR Interview Simulation for Realistic Candidate ...

引用

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

作者： Sarumathi, S. Gowthaman, R.L. Sabareesh, M.B. Sultana, A. Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research study proposes an AI application for a HR interview simulation system to improve candidate assessment. The proposed system is based on the recent AI technologies that generate questions out of the candidate’s resume and job description as in the case of a real interview with the employer. AI technology not only do the questioning but also get the emotional analysis of the candidate through the interview by analyzing the tone of the candidate’s voice and other factors. AI-based assessment captures both cognitive and affective performance, where the candidate’s merit and quality are given a true measure. This means that the proposed system greatly enhances the capabilities of the HR teams to investigate massive numbers of candidates while reducing the biases associated with conventional pure human interviews. Further, this research study aims to explain the system’s architecture, where facial expression analysis and voice sentiment analysis remain as the main components alongside reinforcement learning for question adaptation. With LLM-based approach, organizations can increase the efficiency of the hiring process, enrich candidates’ experience, and make better decisions. © 2025 IEEE.

关键词： Reinforcement learning

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Technical Evaluation of the Impact of Industry-Education Integration on College Students’ Employment Rate Based on Machine learning Models 24

Technical Evaluation of the Impact of Industry-Education Int...

引用

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

作者： Zhuang, Yan Su, Ruiqian Xiamen Institute of Technology Fujian Xiamen China

ISBN: (纸本)9798400718120

This study explores the impact of industry-education integration on college students’ employment rate using machine learning models. The original data was preprocessed through feature engineering, and models such as logistic regression, decision tree, and random forest were used, with hyperparameters optimized using techniques like grid search. The experimental results show that, among 58,342 graduate data, the neural network model performs best in evaluation metrics such as accuracy, recall, precision, and F1 score. The model is most accurate in predicting medium employment quality, with errors stemming from feature noise, nonlinear relationships, and data imbalance. Analysis based on major categories and school types indicates that the model performs better on data from science and engineering disciplines and key universities. This study contributes to the formulation of relevant educational policies. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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MetaLIRS: Meta-learning for Imputation and Regression Selection 25th

MetaLIRS: Meta-learning for Imputation and Regression Select...

引用

25th international conference on intelligent data engineering and automated learning

作者： Erez, Ill Baysal Flokstra, Jan Pod, Mannes van Keulen, Maurice Univ Twente EEMCS NL-7500 AE Enschede Netherlands

ISBN: (纸本)9783031777301;9783031777318

Missing data is a prevalent problem in data science for many fields such as natural, social, and health sciences. Since most regression methods can not handle missing data directly, imputation methods are used in data pre-processing. Finding the best imputation method is non-trivial, however. Moreover, our results show that an independent choice for a best imputation method does not always result in the best predictive performance in the end;the combination matters. Furthermore, search-based approaches for finding a best-fitting imputer/regressor-pair can be computationally intensive. In this paper, we propose the MetaLIRS (Meta learning Imputation and Regression Selection) framework for developing resource-friendly ML-based recommendation models for method selection. With MetaLIRS, we constructed a proof-of-concept recommendation model based on 12 meta-features that achieves an accuracy of 63% for selecting the best-fitting imputer/regressor-pair. A data scientist can use this model for a quick resource-friendly recommendation on which imputation and regression method to use for their particular data set and task without the need for an expensive grid search among methods.

关键词： Missing data Imputation Regression

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