检索结果-内蒙古大学图书馆

Data-Driven Decision Support by Utilizing Machine Learning to Predict Passenger Flow for Route and Station Optimization

引用

Journal of The Institution of Engineers (India): Series B 2025年 1-20页

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Department of Computer Engineering Pandit Deendayal Energy University Gandhinagar India Department of Information Technology L D College of Engineering Ahmedabad India

A smart public transportation system with reliable services addresses urban challenges like traffic congestion, infrastructure maintenance, travel costs, and pollution. As part of smart city initiatives, urban public transport systems have adopted advanced data collection methods, enabling data analytics and predictive model development. This study evaluates optimal machine learning models for predicting passenger flow across routes and stations within the Thane Municipal Transport (TMT) network using real historical data from March 2021 to May 2022. Key spatial and temporal features such as weekday patterns, route distance and direction, public holidays, COVID-19 impacts, and weather conditions were identified through exploratory data analysis and incorporated into the models. Several algorithms, including Decision Trees, XGBoost, Extra Tree Regressor, and Random Forest, were implemented to predict daily passenger commute on an hourly basis. Random Forest emerged as the most accurate model, achieving an MAE of 114.55, RMSE of 236.71, and an R2 of 0.9516, leveraging its ensemble approach to capture complex, non-linear patterns and ensure robustness to variability, including disruptions caused by the pandemic. These findings provide actionable insights for optimizing route design, bus allocation, maintenance scheduling, and overall transport system efficiency, enabling stakeholders to achieve sustainable and adaptive public transport management. © The Institution of Engineers (India) 2025.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

AI-Based Security Protocols for IoT Applications: A Critical Review

引用

Recent Advances in computer Science and Communications 2023年第5期16卷 2-18页

作者： Chadha, Harshita Gupta, Shruti Khanna, Ashish Kumar, Neeraj Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Punjab Chandigarh India

The Internet Of Things (IoT) is a network of heterogeneous nodes that exchange data and critical information amongst themselves with minimum human intervention. The utility of this technology is large, thus it is used today in most modern applications. With billions of nodes connected across multiple applications, the area for potential security attacks is ever increasing. In addition to this, the nature of the data being transmitted also becomes more sensitive to the latest applications and this calls for effective security provisions. Due to its unique nature, traditional security provisions are not as successfully applicable in IoT networks. This leaves these networks vulnerable to malicious intruders. In such a scenario, Artificial Intelligence (AI) comes out as a powerful solution. This article serves to provide an overview of previously proposed AI-based solutions that can be applied to IoT networks to secure them. An industry-wide overview of security provisions is provided by categorizing IoT applications into three broad sectors, namely, healthcare, smart grid, and smart city. The survey strives to give a clear industry-oriented vision of the available AI solutions and address the requirement of an application-ready security survey in the field. © 2023 Bentham Science Publishers.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

引用

computer Systems Science & engineering 2023年第1期44卷 613-628页

作者： L.K.Shoba P.Mohan Kumar Department of Information Technology Jeppiaar Engineering CollegeChennai600119India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal *** this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application *** approach,which was focused on image quality,improves medical image *** enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be *** total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

关键词： OCT CSCR macula segmentation boosted anisotropic diffusion with unsharp maskingfilter two class support vector machine classifier and shallow neural network with powell-beale classifier

来源：评论

学校读者我要写书评

暂无评论

Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

引用

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8639-8658页

作者： Karthika, K. Rangasamy, Devi Priya Anna University Chennai India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. © 2024 – IOS Press. All rights reserved.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

引用

Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Study Influencing Factors of Maternal Health and the Role of Internet of Things (IoT) to Improve Maternal Care

引用

SN computer Science 2024年第6期5卷 778页

作者： Rani, Deepa Kumar, Rajeev Chauhan, Naveen Department of Computer Science and Engineering National Institute of Technology

Maternal health during pregnancy is influenced by various factors that significantly impact pregnancy outcomes. This paper aims to highlight these critical factors, promote awareness, and advocate proactive self-care among women. By analyzing various works, a taxonomy of influencing factors is proposed to provide an understanding of their effects on maternal and fetal health. The taxonomy reveals multiple factors, including individual characteristics, social determinants, and environmental factors, that significantly affect maternal and fetal health. Therefore, by thoroughly reviewing reputed articles in the field of IoT technology in maternal care, a list of sensors and devices capable of capturing health-related parameters and various applications of IoT in maternal care is presented. IoT technology and promoting self-care can enhance maternal care and improve outcomes for both mother and fetus. Therefore, this research emphasizes the factors influencing pregnancy and the benefits of the adoption of IoT technology to enable a patient-centric approach for prompt detection of potential risks in maternal care. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： IoT Maternal care Remote monitoring Risk monitoring Self-care

来源：评论

学校读者我要写书评

暂无评论

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

引用

Intelligent Automation & Soft Computing 2023年第4期36卷 481-497页

作者： R.Jayaraj S.Lokesh Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032TamilnaduIndia

Every day,websites and personal archives create more and more *** size of these archives is *** comfort of use of these huge digital image gatherings donates to their ***,not all of these folders deliver relevant indexing *** the outcomes,it is dif-ﬁcult to discover data that the user can be absorbed ***,in order to determine the signiﬁcance of the data,it is important to identify the contents in an informative *** annotation can be one of the greatest problematic domains in multimedia research and computer ***,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image ***,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class *** pre-processed images are sent to the segmentation approach for efﬁcient labelling technique using J-image segmentation(JSEG).Theﬁnal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classiﬁer,the unlabeled images are *** proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_*** the assistance of the pro-posed methodology,the unlabeled images are labelled.

关键词： Deep learning model J-image segmentation honey badger algorithm convolutional neural network image annotation

来源：评论

学校读者我要写书评

暂无评论

An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation

引用

computer Systems Science & engineering 2023年第1期44卷 281-295页

作者： R.Prabhu S.Rajesh Department of Computer Science and Engineering AAA College of Engineering and TechnologySivakasiTamilnaduIndia Department of Information Technology Mepco Schlenk Engineering College SivakasiTamilnadu India

Cloud computing distributes task-parallel among the various *** with self-service supported and on-demand service have rapid *** these applications,cloud computing allocates the resources dynami-cally via the internet according to user *** resource allocation is vital for fulfilling user *** contrast,improper resource allocations result to load imbalance,which leads to severe service *** cloud resources implement internet-connected devices using the protocols for storing,communi-cating,and *** extensive needs and lack of optimal resource allo-cating scheme make cloud computing more *** paper proposes an NMDS(Network Manager based Dynamic Scheduling)for achieving a prominent resource allocation scheme for the *** proposed system mainly focuses on dimensionality problems,where the conventional methods fail to address *** proposed system introduced three–threshold mode of task based on its size STT,MTT,LTT(small,medium,large task thresholding).Along with it,task mer-ging enables minimum energy consumption and response *** proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme(EDS)and Decentralized Virtual Machine Migration(DVM).With a Network Manager-based Dynamic Scheduling,the proposed model achieves excellence in resource allocation compared to the other existing *** obtained results shows the proposed system effectively allocate the resources and achieves about 94%of energy efficient than the other *** evaluation metrics taken for comparison are energy consumption,mean response time,percentage of resource utilization,and migration.

关键词： Cloud computing resource allocation load balance dynamic scheduling dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

引用

computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

来源：评论

学校读者我要写书评

暂无评论

A new technology perspective of the Metaverse:Its essence,framework and challenges

引用

Digital Communications and Networks 2024年第6期10卷 1653-1665页

作者： Feifei Shi Huansheng Ning Xiaohong Zhang Rongyang Li Qiaohui Tian Shiming Zhang Yuanyuan Zheng Yudong Guo Mahmoud Daneshmand School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Jinzhong University Shanxi030619China Department of Business Intelligence and Analytics and the Department of Computer Science Stevens Institute of TechnologyHobokenNJ07030USA

The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.

关键词： Metaverse Technical framework Ubiquitous connections Space convergence Virtuality and reality interaction Human-centered communication

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：