检索结果-内蒙古大学图书馆

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Thamilselvan, R. Natesan, P. Rajalaxmi, R.R. Prasanth, D. Nithish Swaminathan, S. Yuvapriya, R. Kongu Engineering College Computer Science and Engineering Perundurai Erode India Kongu Engineering College Department of Computer Applications Perundurai638 060 India

ISBN: (纸本)9798350372748

In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural production by providing water to crops in regions where rainfall alone is insufficient to meet their needs. This abstract provides an overview of crop irrigation, its importance, and various irrigation methods used in agriculture. Irrigation is a critical component of agricultural production, and advancements in machine learning algorithms have the potential to optimize irrigation practices for crop cultivation. Additionally, machine learning has developed in several industries to improve quality. Machine learning algorithms like Random Forest, K-Nearest Neighbor, Support Vector Machine and Decision Tree are used to find the various results. Internet of Things also plays a vital role in irrigation by making use of the sensors. This review's investigation was conducted using a variety of machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

MAIDS: malicious agent identification-based data security model for cloud environments

引用

Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Predicting Risky Environment for Child Inside House using Deep Learning 5

Predicting Risky Environment for Child Inside House using De...

引用

5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Ahmad, Naeem Arya, Sumedha Singh, Deepak Department of Computer Applications Raipur India Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9781665475242

We place a high priority on making sure kids are safe. Yet in the modern world, there are a lot of threats to children's safety and challenges to achieving other safety goals. Parent can't always supervise their kids and leave them alone inside the house. So this is a big problem of our time as the small kids is unable to assess the risk due to any harmful activity or objects. The purpose of this study is to provide safety to the child by alerting the parent about the child's position and surroundings. This proposed model can be used to monitor child's movements. We proposed a children's video data set. The video data set contains every moment of the child in the form of frames, and 30 frames are created every second. Additionally, we train a baseline computer vision module that determines the level of dangerousness for a child using deep learning. The baseline consists of well-established methods like conditional and logistic regression for child's pose estimation, as well as cutting-edge deep learning image classifiers. We then integrate the stumbled-condition baseline into various learning platforms and evaluate performance in terms of accuracy and loss. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

引用

computer Modeling in engineering & sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China The Center for Intelligent Systems and Applications Research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency ***,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this ***,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

来源：评论

学校读者我要写书评

暂无评论

Document Classification and Data Extraction 3

Document Classification and Data Extraction

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Khurana, Madhav Chaturvedi, Ravi Prakash Mishra, Annu Gupta, Devika Negi, Ishika Mishra, Ambuj Sharma, Hirdesh Jemtec Department of Computer Science & Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science & Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350384598

T- Data classification and extraction are fundamental tasks in the field of computer vision and data analysis. This abstract presents an overview of these concepts along with the utilization of Python and OpenCV, a popular computer vision library, in building data extraction tools. Data classification involves organizing data into categories or classes based on certain characteristics or features. It enables efficient data management and analysis by grouping similar data together. In the context of computer vision, data classification often involves categorizing images or videos based on their content, such as identifying objects, scenes, or patterns within them. Data extraction, on the other hand, involves retrieving specific information or features from a dataset. This process is crucial for tasks like data mining, information retrieval, and automated document processing. In computer vision applications, data extraction often entails locating and extracting relevant information from images or videos, such as text, objects, or numerical data. © 2024 IEEE.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

Real-Time Face Detection on Security Surveillance Footage

Real-Time Face Detection on Security Surveillance Footage

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Mohd, Noor Dham, Shradhay Bisht, Jai Singh Mehta, Aryan Joshi, Akshita Martha, Sheshikala Department of Computer Science and Engineering Dehradun India Department of Computer Applications Dehradun India Sr University Department of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350379716

Recent years have witnessed notable progressions in facial recognition technology which have been led by the inception of deep learning models-primarily Siamese Neural Networks. This article delves into the use of Siamese Neural Networks for one-shot face classification tasks, giving priority to extracting discriminative features and training deep learning models. Among the pillars of our methodology are: data collection;preprocessing;model building and real-time verification using deep learning abilities, especially Convolutional Neural Networks (CNNs). We hope that through this approach we can significantly improve the accuracy of facial recognition in real-time surveillance applications with what CNNs have to offer. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Embedded Deep NN with Convolution NN-ID to Binominal Sentiment Prediction 9

Embedded Deep NN with Convolution NN-ID to Binominal Sentime...

引用

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Gokila, S. Rajeswari, S. Helen, D. Lakshna, A. Hindustan Institute of Technology And Science Department of Computer Applications Chennai India Shrimathi Devkunvar Nanalal Bhatt Vaishnav College For Women Pg Department of Computer Science Chennai India Srm Institute of Science And Technology Department of Computer Applications Chennai Kattankulathur India Jeppiaar Engineering Collge Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350365092

Objective: The objective is to develop a generalized deep learning system for sentiment analysis in a feedback system. This implies an intention to create a model that can effectively analyze sentiment across different domains or datasets. Methodology: It is proven that the deep learning systems are playing a major role in NLP, but still there is a need of enhancement to make the prediction system a perfect one. The Convolution Neural Network (CNN) could emphasize its ability to capture spatial dependencies in textual data and the Deep Neural Network(DNN) fetches the feature in text with varying semantics. In this context the proposed work Embed the Deep Neural Network with Convolution Neural Network in a three-layered ID architecture(EDNN with CNN-ID.) The model is enhanced in generalizing input-shape, kernel, channel, and filter parameters of the convolution layer. It produces remarkable results in sentimental analysis. The work is experimented with a feedback system from three different domains like Movie Review, Doctor Treatment Review, Product Review. Result: The EDNN with CNN-ID produces accuracy 93%, 91% and 91% for the three datasets © 2024 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

A Prediction Model for Student Academic Performance Using Machine Learning

引用

Informatica (Slovenia) 2023年第1期47卷 97-108页

作者： Kaur, Harjinder Kaur, Tarandeep Garg, Rachit Scool of Computer Applications Lovely Professional University Phagwara144401 India COS School of Computer Science and Engineering Lovely Professional University Phagwara144401 India

Academic data mining impacts a large number of educational institutions, significantly, playing a prime role in accumulating, studying, and analyzing the academic data. The accumulated academic data can be processed and analyzed for various purposes. It can be used for predicting the student academic performance and thereby broadening the retention rate of academic institutions. The prediction of students’ academic performance at the initial stage helps the students to identify their lacking subjects such that they can focus more on their deficient subjects and improvise their academic performance. Currently, numerous machine learning techniques are being used by the academic institutions to extract, analyze and predict the students’ academic performance and identify the fast and slow learners. This paper proposes an ensemble model, using the voting method for preclusive prediction of the student academic performance. The predicted results are being further utilized by the poor performers to concentrate more on their deficit courses. Accordingly, the instructors can focus on creating and implementing novel strategies or amending the existing pedagogical tools and approaches to aid the slow learners in improvising their performance. The proposed model has been tested on the academic data of an educational institution using the RapidMiner tool. The results depicts that how the number of E grades proportionally affects the performance of the students in academics. The proposed ensemble model generates the predicted results with an accuracy of 90.83%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Career Prospect Prediction System for Non-computer Stream Students in Software Companies 1st

An Enhanced Career Prospect Prediction System for Non-comput...

引用

1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Abraham, Biku Ambili, P.S. Department of Computer Applications Saintgits College of Engineering Kottayam India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9789811984921

The stakeholders of the present education system have a key focus on the chances of on-campus employability of their wards. Since software companies play prominent role as campus recruiters, there is a low demand for the traditional engineering branches. The need to enhance skills for software related employment is a challenge to the private sector professional institutions for the existence of non-computer streams in this scenario. In this study, the authors being academic experts in the similar field, attempt to support institutions, students, curriculum designers, and the faculty by providing insight to better employment possibility in campus. Data set for the study is from the same institution for which the placement prediction was completed. The methodology models the problem as a sequential event prediction problem and employs deep learning techniques. The system extracts data from data set with 19 attributes. The results indicate that this predictive approach provides enhancement methods by which a non-computer stream student can ensure full-time employment in software related jobs through in-campus selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：