检索结果-内蒙古大学图书馆

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

作者： Banumathi, S. Sivajothi, E. Indumathi, G.S. Kefyalew, Enku Yosef Sivakumar, J. Priya, V. Banu Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India Technical and Vocational Training Institute Department of Electrical and Electronics Technology Addis Ababa Ethiopia St.Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Thiruvallur District India

ISBN: (纸本)9798331512965

Cloud computing has transformed global data infrastructure with unparalleled scale, flexibility, and accessibility. This study examines how cloud computing innovations have changed data storage, processing, and management worldwide. By examining the progression of cloud services from simple storage and computing to sophisticated platforms and SaaS, we examine how they have transformed corporate operations, economic growth, and societal development. Virtualization, containerization, serverless architectures, and edge computing are the main cloud computing advancements, which we investigate through a literature study and case studies. data security, privacy, and regulatory frameworks are also considered while adopting the cloud. Cloud computing promotes digital transformation in banking, healthcare, education, and government, according to study. The industry's sustainability and green computing efforts and data centers' environmental impacts are also covered. We found that cloud computing continues to spur innovation by enabling new business models, global connectivity, and data-driven application growth. Cloud technologies' integration with AI, IoT, and 5G networks is likely to change the global data infrastructure, opening new research and development options. This report covers the cloud computing landscape, its advancements, and its potential to shape global data management and digital ecosystems. © 2025 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Blockchain Optimized Routes for Efficient Short distance donation Services in Intelligent Urban Mobility 10

Blockchain Optimized Routes for Efficient Short Distance Don...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Patil, Aabha Amey Goradiya, Uma Bhavin Patil, Pallavi Nitin Revanna, Jai Keerthy Chowlur Shree L. R. Tiwari College of Engineering Department of Computer Engg Mira Bhayandar India Harrisburg University of Science and Technology Isem HarrisburgPA United States

ISBN: (纸本)9798350384369

The aim of this research is to explore the integration of blockchain technology and the vehicle routing problem (VRP) in the context of short-distance donation services, with the primary objective being to enhance supply chain effectiveness and optimize donation logistics through the adoption of a blockchain-driven approach. The paper presents a novel methodology that incorporates blockchain principles to efficiently utilize resources and minimize trip lengths for donation transportation, thereby streamlining the VRP process. By leveraging the decentralized and transparent nature of blockchain, the proposed solution instills confidence among stakeholders and ensures secure and traceable transactions. The practical applicability of the algorithm is evaluated through simulation and case studies, considering factors such as vehicle capacity, route optimization, and cost-effectiveness. The findings highlight significant improvements in donation delivery procedures, demonstrating the transformative potential of blockchain technology in revolutionizing last-mile logistics for philanthropic activities. This study contributes significantly to the evolving field of blockchain applications by showcasing its revolutionary impact on enhancing the efficiency of donation management systems. © 2024 IEEE.

关键词： decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Multi Meta Information Fusion Graph Neural Networks for Session-based Recommendation

Journal of Network Intelligence

引用

Journal of Network Intelligence 2025年第1期10卷 366-382页

作者： Wen, Yan Sun, Hui-Zheng liu, liang Zhou, Ya-Kun Bian, Wei Yan, Ming-Hai College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Shandong Port Group Co. Ltd Qingdao266000 China Geography and Environment Management Faculty of Environment Universoty of Waterloo WaterlooN2L3G1 Canada

Session-based recommendation aims to predict the anonymous user’s next click action based on his/her latest click sequence. Most state-of-the-art approaches use graph neural networks to model sessions as graphs to capture dynamic transitions between items within a session. Inspired by recent progress on hypergraph-based neural networks, we think it is a good candidate to model the commonly related items within a session, and it can provide a sound complement to the normal graph models. In addition, cross session information may also be helpful. But the excessive noise introduced by other sessions is a big challenge and is still not be well resolved by existing works. To this end, we propose a new approach, called Multi Meta Information Fusion Graph Neural Networks (MM-GNN) to infer user preferences for the current session in a more comprehensive way for the anonymous *** fuse information from normal graph and hypergraph to fully exploit their capabilities of capturing sequential behavior and group intention within a session. In order to better use cross session information, we propose a globallevel subsequent item injection module, which injects information of the most possible subsequent item of the last item in the current session in the global scope. Besides, to learn richer information on the hypergraph structure, we model the whole session as a hyperedge to complement sliding windows-based hyperedges when creating the hypergraph. We have evaluated the method on several benchmark datasets and the results show that our method outperforms state-of-the-art methods. © 2025, Taiwan Ubiquitous Information CO lTd. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Machine learning and deep Transfer learning approaches were used to create a Face Mask Identification model for COVId-19

Machine Learning and Deep Transfer Learning approaches were ...

引用

2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Rao, B. Srinivasa Aarthi, d. Revathi, R. Verma, Shanti Nandankar, Praful V. Radhakrishnan, P. Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Karpagam College of Engineering Coimbatore Department of Computer Science and Engineering Tamil Nadu Tiruppur India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India L J University Department of Computer Applications Gujarat Ahmedabad India Government College of Engineering Department of Electrical Engineering Maharashtra Nagpur India Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

COVId-19 pandemic has led to an international health emergency the WHO considers wearing a face mask an appropriate form of public health protection. This work will describe a face mask identification model that incorporates both deep and traditional machine learning techniques. Parts of the suggested model can be divided into two. Using Resnet50, the initial part of the system is set up for feature extraction. The second component classifies face masks using decision trees, support vector machines (SVMs), and the ensemble approach. The research will focus on three face-masked datasets. The Real-World Masked Face dataset Includes three datasets: real-world masked faces, simulated faces, and wild faces (lFW). 99.64% of RMFd's SVM classifier is accurate throughout testing.. It achieved a 99.49% accuracy rate in SMFd and a 100% accuracy rate in lFW. © 2022 IEEE.

关键词： COVId-19

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Packet delivery Ratio Improvement Protocol design over 5G Assisted Wireless Sensor Network Environment 2

An Enhanced Packet Delivery Ratio Improvement Protocol Desig...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Balaji, T.S. Charaan, R.M.dilip Rajalakshmi, J. Samuthira Pandi, V. Shobana, d. Priya, J. lakshmi Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India S.A.Engineering College Department of Cse Tamilnadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

Sensors are pervasive in modern life, and Wireless Sensor Networks (WSNs) will play an increasingly important role in the years to come. Broadband, energy, coverage, and route optimization resource shortages are WSN's main drawbacks. Optimizing the packet delivery ratio is a crucial aspect of wireless sensor network (WSN) architecture. In order to enhance WSNs over the long term, previous approaches used a secure zone-based routing mechanism. For packet transmission, a new criterion was developed in multi-hop communication. It was built into the routing metric to protect against flooding, message manipulation, and dropping attacks. Congestion in WSN increases and reduces packet delivery ratio, even while energy conservation and attack resilience are accomplished. Our proposed solution, Advanced Path Selection with Traffic Estimation (APSTE), is a new way of routing data packets that takes flow differentiation into account. This should help increase the packet delivery ratio and solve this issue. In order to determine how effective, the suggested method is, it is cross-validated using the standard routing model known as link Consistency with Path Evaluation (lCPE). In order to improve the WSN's QoS parameters and extend the network's lifespan, the APSTE approach reduces the energy consumption of sensor nodes while maintaining or improving their logical throughput and packet acquisition rates. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Intelligent Energy Management Energy Storage Systems Using Machine learning

Intelligent Energy Management Energy Storage Systems Using M...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj dayana, T.R Nisha Adkar, Siddhika dilip Prakash, d. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Cardiac Vascular disease Prediction Using Stacked Ensemble Algorithm for Type-2 diabetes Mellitus 4

Cardiac Vascular Disease Prediction Using Stacked Ensemble A...

引用

4th International Conference on Sentiment Analysis and deep learning, ICSAdl 2025

作者： Selvarathi, C. Manikandan, T. Madhu Mitha, G. Sharmila, S. Thanasingh, Sengolrajan Pandiaraja, P. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Department of Ece Tamilnadu Thiruchengode637205 India Vivekanandha College of Engineering for Women Department of Electrical and Electronics Engineering Tamilnadu Thiruchengode637205 India Vivekanandha College of Engineering for Women Department of Ece Tamilnadu Thiruchengode637205 India Department of Electrical and Electronics and Engineering Tamilnadu Trichy621 215 India

ISBN: (纸本)9798331523923

Cardiac vascular disease (CVd) is a most serious disease associated with highest humanity and complaint rates in the global population. The risk of developing CVd consciously increases in patients having long-term diabetes difficulties. Consequently, it is necessary to predict the association between CVd and type-2 diabetes Mellitus (T2dM) for effective decision making to initiate appropriate treatment of T2dM patients. First, the proposed research work to examine the purpose of machine learning (Ml) techniques to predict the risk of death rate in CVd patients with T2dM. Then the proposed method is mainly focusing to predict the survival rate of regularity of CVd patient's incidence with T2dM. However, the dataset is unbalanced and handling such unbalanced dataset is one of the major challenges addressed using cost sensitive learning algorithms for reducing the misclassification rate. This proposed method stacked ensemble with cost-sensitive learning that combines the prediction results of the three base classifiers to predict the survival rate of patients with cardiovascular disease and T2dM. The proposed stacked ensemble algorithm uses a 10 fold cross validation method with network search optimization to tune the hyper-parameter of the meta classifier model. The proposed algorithm is used to measure the performance of dataset being evaluated by splitting the data into 80% training and 20% testing. The analysis of outcomes displays that the survival rate of heart failure patients is better predicted using the cost-sensitive stacked ensemble learning technique to achieves precision of 93% and higher accuracy. The unbalanced class and cost sensitive learning may help to address the class imbalance issue. The incorrect weight assignment may lead to overfitting to the minority class while underperforming on the majority class. © 2025 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Urban Mobility Optimization with IoT and deep Q Networks for Traffic density Management 3

Urban Mobility Optimization with IoT and Deep Q Networks for...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ranganathan, Chitra Sabapathy Jayanthi, l.N. Babu, J. Jagan Manikandan, M. Sivakamy, N. Mphasis Corporation Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

Traffic density and mobility optimization in urban areas are becoming more difficult to manage to improve transportation efficiency. Urban traffic management is complicated, but this research provides a revolutionary technique using the Internet of Things (IoT) and deep Q Networks (dQN). Traffic lights, sensors, and vehicles may be monitored in real-time using IoT technology to provide a complete picture of urban mobility. As an intelligent decision-making agent, the dQN uses reinforcement learning to adapt to changing traffic circumstances and optimize traffic density management. The model is educated on historical data and refines its tactics via environmental interaction, solving urban transportation problems. A complex data fusion mechanism processes data from different IoT devices. A reinforcement learning framework uses dQN for decision-making, and a feedback loop updates and optimizes traffic management tactics. The research uses simulations and urban case studies to prove that the technique works. The findings show improved traffic flow, congestion, and mobility efficiency. It uses IoT and dQN to develop intelligent urban transport systems that scale and respond to traffic density in complex and dynamic urban environments. © 2024 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Cloud Computing through Blockchain-based Resource Allocation and Green Contracts

Energy-Efficient Cloud Computing through Blockchain-based Re...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Manikandan, G. Swaminathan, Subbiah Kanimozhi, K.V. Seethalakshmi, K. Rao, N.Venkatesvara Srm Institute of Science and Technology College of Engineering and Technology Department of Career Development Centre Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India J.N.N.Institute Of Engineering Department of Computer Science and Engineering Ushaa Garden Kannigaipair-Periyapalayam Highway Chennai India

ISBN: (纸本)9798350388602

The study that is being carried out is a blockchain resource allocation, green contract, and renewable energy integration into the cloud computing environments as a way to increase efficiency, sustainability, and stakeholders' satisfaction. Through empirical analysis, significant improvements were observed across various metrics: the resource usage rate is up to 90% from 75%, 20%-800 kWh/hour reduction in energy consumption, a 20% decrease in heat emissions to 400 kg CO2/hour, cost efficiency is improved by 20% to $8 per hour. Moreover, the renewable energy usage was at 50%, which was followed by 50% fossil fuel dependency, in contrast, and a 100% carbon offset. Integrity was ensured with security threats eliminated, reporting and audit became much more transparent and together with the standards the compliance was achieved. Stakeholders such as Providers and End Users experienced enhanced success as the level of satisfaction was made the same for both cloud service providers and consumers This research result highlights the importance of the utilization of the blockchain enabled solutions in the optimization of resources, reducing the energy consumption, promoting the wide-range use of the renewable sources of the energy, enhancing the sector security and transparency therefore improving the satisfaction levels of all stakeholders in the cloud computing operation. The cooperation between scientists and various stakeholders has remained the key factor for implementing sustainable ways that will benefit the industry. © 2024 IEEE.

关键词： Blockchain Cloud Computing Green Contracts Renewable Energy Integration Resource Allocation

来源：评论

学校读者我要写书评

暂无评论

The Intelligent Multicriteria Analysis of Asthma Using Glucocorticoid Toxicity Index (GTI) Based diagnosis Model

The Intelligent Multicriteria Analysis of Asthma Using Gluco...

引用

2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Singh, Nishant Kumar Neeraj Sharma, Ram Kumar Bansal, Shomil Kumar, dilip Srm Institute of Science & Technology Department of Computer Science & Engineering U.P. 201204 India G.L. Bajaj Institute of Technology and Management Department of Information Technology UP Greater Noida201306 India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad201003 India Its Engineering College Department of Computer Science & Engineering U.P. Greater Noida201310 India Sunder Deep Engineering College Department of Computer Science & Engineering U.P. Ghaziabad201003 India

ISBN: (纸本)9798350380880

Allergies are one of the most common respiration issues globally and are related to extensive morbidity and mortality. The correct preference for the most fulfilling remedy for allergies calls for sensitive stability in symptom management, irritation manipulation, and minimizing glucocorticoid toxicity. Sensible multicriteria analysis (IMA) strategies can assist in deciding the most advantageous route of treatment. This look provides the use of the Glucocorticoid Toxicity Index (GTI) based IMA, which combines the patient traits (demographics, asthma control degree, exacerbation danger, comorbidities, and remedy history) with a database of peer-reviewed clinical literature to decide the maximum suitable glucocorticoid remedy for the person patient. The performance of the GTI-primarily based IMA is evaluated through five medical case studies for patients with distinct stages of asthma control. The outcomes proved that the GTI-based IMA is an effective selection guide device for directing personalized bronchial asthma remedies. The GTI-primarily based IMA system can, as it should be, expect the exceptional glucocorticoid remedy for the patient and monitor and alter the mixture through the years. The findings from this study suggest that the GTI-primarily based IMA method can provide helpful steerage inside the most appropriate choice of glucocorticoid therapy for patients with asthma. © 2023 IEEE.

关键词： Toxicity

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：