检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Aspects of Data Transfer and Synchronization for Vulnerable Road Users Emergency Scenarios Based on Blockchain Technology in ITS

Aspects of Data Transfer and Synchronization for Vulnerable ...

引用

2024 systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO 2024

作者： Pomogalova, A.V. Donskov, E.A. Elagin, V.S. Vladyko, A.G. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Infocommunication Systems Department Saint Petersburg Russia St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences Laboratory of Computer Security Problems Department St. Petersburg Russia Saint-Petersburg State University of Telecommunications Faculty of Infocommunication Networks and Systems the Bonch-Bruevich Saint Petersburg Russia Saint-Petersburg State University of Telecommunications Faculty of Fundamental Training the Bonch-Bruevich Saint Petersburg Russia

ISBN: (纸本)9798350373448

The paper presents a study of response scenarios in an intelligent transport system, in which pedestrians are participants, to emergency situations and their prevention. Pedestrians are included in the VRU (Vulnerable Road Users) category within the general ITS architecture and are most susceptible to accidents due to the lack of movement control. Scenarios for interaction with such traffic participants may include not only rules for responding and providing assistance as soon as possible, but also identifying the initiator of the incident for the purpose of further control and prevention of situations of a similar nature. The authors of the work propose the use of blockchain technology to ensure data safety, as well as the option of building an ITS architecture with a module for monitoring incident data and timely response. The paper provides mathematical calculations of the possibility of using blockchain technology within the framework of the proposed concept and modeling. © 2024 IEEE.

关键词： blockchain distributed ledger technology emergency scenario Intelligent Transport System ITS V2X Vehicle-to-Everything VRU

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Intelligent IoT systems for Real-Time Food Quality Monitoring and Analysis 2

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnan, Prabhakar Ebenezar, U. Samson Ranitha, R. Purushotham, N. Balakrishnan, T. Suresh Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University Department of Computer Science and Engineering Mr.palliAP India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring. © 2024 IEEE.

关键词： Artificial Intelligence Food Quality Analysis Intelligent IoT systems Real-Time Monitoring

来源：评论

学校读者我要写书评

暂无评论

Knowledge Driven Cyber-Convergent systems Based on Situational Agents

Knowledge Driven Cyber-Convergent Systems Based on Situation...

引用

International computer Sciences and Information Technologies (CSIT)

作者： Oleksiy Kovalenko Department of Computer Systems Networks and Cyber-security National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Cyberization of different kinds of activity, as a base for providing Industry 5.0 services, is concerned with the formation of needed capabilities for the target system or system of systems in a specific domain. The formation of needed system capabilities is achieved by merging particular capabilities of constituent components (systems) within cyber-convergent systems. Convergence can be achieved on the basis of system orderliness with the appropriate value of structural entropy of the target system according to the situation in the subject area. Conceptual factors of architectural components for constructing knowledge-driven cyber-convergent systems are discussed in the paper.

关键词： Industries Stochastic systems Merging Control systems Entropy Risk management Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Optimized Data Storage Algorithm of IoT Based on Cloud Computing in Distributed System

Optimized Data Storage Algorithm of IoT Based on Cloud Compu...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Deepthi, K.Jaya Balakrishnan, T.Suresh Krishnan, Prabhakar Ebenezar, U.Samson Nageshwari Mohan Babu University Department of Aiml AP Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy620009 India

ISBN: (纸本)9798350373783

IoT and cloud technology are vital for large systems. An improved data storage technique for IoT applications and cloud distribution is described in this research. Scalability, reliability, latency, and resource usage are internal and external to the algorithm. The approach above scales well with work portion or dataset size. The first experiment shows the algorithm scaling perfectly even as IoT devices generate additional data. Experiment 2 proves linear scalability works for 100,000 IoT devices, while Experiment 3 efficiently organizes 1 million data points. It proved the algorithm by tolerating manufactured faults. To ensure data service availability, Experiment 1 tests the system's operational dependability on node failures and network disturbances. With 0.1% downtime even under challenging conditions, experiment 2 showed the algorithm's incredible robustness in real-world applications. To improve data organization, this experiment exhibits data consistency when nodes fail. Lower and constant time improves operation safety and reliability. Algorithm 1 shows how the algorithm can quickly answer data queries and keep up with the massive amount of data transfers, supporting low-latency IoT applications. Average data retrieval latency of experiment 2 is 10 ms, which is real-time for vital information. Trial 3 processes data online with a latency © 2024 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Enhanced Analysis of Genomic Data for Precision Medicine

Machine Learning-Enhanced Analysis of Genomic Data for Preci...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sasikala, R. Deepthi, K. Jaya Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy620009 India Mohan Babu University Department of Aiml Ap Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350373783

Precision medicine employs genetic data, which is enhanced with machine learning algorithms, to determine the most relevant treatment method per patient's genotype. We have zoned in on the area of a machine learning-guided genetic information analysis in this study to assist in clinical decisionmaking of precision medicine while improvising. Genetic information for 500 cancer patients cases taken from the public database and cleaned to lean out the desired features were furnished. Different machine learning algorithms were trained, including SVM, random forest, gradient boost, deep learning (CNN) and ensemble (ensemble method). Their performance was investigated for predicting cancer subtypes. The ensemble model reached the top accuracy level (88%), the best sensitivity (85%), ever specificity (91%) and AUC score (0.95). By comparison a group had better result than an individual algorithm could across all metrics. In further, deep learning methods were reported to attain more accuracy and F1 scores which is more than traditional machine learning methods. These findings hence prequalify the usefulness of ML in investigating genotype data in precision medicine, and enable physicians with useful information to select treatment options, make prognosis, and indicate the medical care. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-UAV Routing for Critical Iinfrastructure Monitoring Considering Failures of UAVs : Reliability Models, Rerouting Algorithms, Industrial Case

Multi-UAV Routing for Critical Iinfrastructure Monitoring Co...

引用

2021 International Conference on Information and Digital Technologies, IDT 2021

作者： Kliushnikov, Ihor Kharchenko, Vyacheslav Fesenko, Herman Zaitseva, Elena National Aerospace University 'KhAI' Networks and Cyber Security Department of Computer Systems Kharkiv Ukraine University of Zilina Faculty of Management Science and Informatics Zilina Slovakia

ISBN: (纸本)9781665436922

Route-based reliability models of the unmanned aerial vehicle (UAV) fleet carrying out monitoring of critical infrastructure facilities and comprising main and redundant UAVs are developed. A nuclear power plant (NPP) and monitoring stations (MSs) are considered as a critical infrastructure and critical infrastructure facilities, respectively. These models are used when multi-UAV routing for NPP monitoring and allow calculating the probability of the successful fulfilment of the plan (SFP) for the UAV fleet to cover the whole target MSs of the NPP. The dependencies showing the relationship of the probability of the SFP to both the UAV reliability function and used route-based reliability models are obtained and explored. An example of the proposed models application for routing main and redundant UAVs of the fleet to cover the whole target MSs of the Zaporizhzhia NPP is given. © 2021 IEEE.

关键词： Nuclear power plants

来源：评论

学校读者我要写书评

暂无评论

A ROS-2 based Path Planning and Maze Solving of Autonomous Robots

A ROS-2 based Path Planning and Maze Solving of Autonomous R...

引用

Advances in Computing,Communication, Electrical, and Smart systems (iCACCESS), International Conference on

作者： Ahsan Kabir Nuhel Sazzad Hossain Md.Tonim Hassan Iftekhar Haider Ankur Saha Adnan Sabith Taha Asma-UI Husna Ony Electrical and Electronics Engineering American International University-Bangladesh (AIUB) Computer Networks and Systems Security University of Hertfordshire Electrical and Electronics Engineering University of Brahmanbaria (UOB) Computer Science and Engineering American International University-Bangladesh (AIUB) Computer Science and Engineering University of Information Technology and Sciences

The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launch files are used to initialize the sensor and control nodes. Attaining optimal path planning and accurate localization are the main objectives. The combination of novel motion planning based on the orientation of the robot with graph search algorithms like DFS, A*, and Dijkstra's leads to effective maze discovery and traversal. Thorough testing shows that the system can successfully navigate unfamiliar maze configurations, demonstrating the dependability of ROS2 and modern robotics techniques. By completing this study, a foundation for future advancements in perception and planning to manage more complex settings is laid.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

引用

Telecommunications Conference (ITC-Egypt), International

作者： Krithika L. B Nallakaruppan MK Mohamed Baza Mahmoud Badr Yuvraj Singh Deora School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Deparment of Computer Science College of charlsron charlsron SC USA Department of Networks and Computer Security College of Engineering State University of New York (SUNY) Polytechnic Institute Utica NY USA

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as “I have Fever” or “Shoulder pain.” Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Temperature sensors Deep learning Temperature measurement Shoulder Medical services Gesture recognition Sensors

来源：评论

学校读者我要写书评

暂无评论

Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Singh, Shailendra Pratap Kumar, Naween Kumar, Gyanendra Balusamy, Balamurugan Bashir, Ali Kashif Al Dabel, Maryam M. Department of Computer Science and Engineering MMMUT UP Gorakhpur India SCSET Bennett University Greater Noida India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India India Networks and Security Manchester Metropolitan University United Kingdom Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The demand for real-time, high-quality services (QoS) is increasing with the proliferation of the resource-constrained nature of edge devices that facilitate the Internet of Things (IoT) and wireless sensor network (WSN) applications. Several existing multi-objective algorithms, such as MOPSO, Elitism MOGA, MODE, and others, are capable of balancing exploration and exploitation;they assist in efficient QoS management for WSN-IoT applications, address resource limitations, and align with the objectives of the applications. However, they suffer from showing robustness in the solution and efficient convergence rates on benchmark functions impacting overall QoS. This paper proposes a multi-objective optimization and edge-intelligent adaptation-based strategy to address QoS management issues, jointly optimize several competing objectives, like energy and latency, and maximize localization and coverage rates while considering the limitations of edge devices. The proposed work uses a novel Grey-wolf optimizer (GWO) Algorithm with an innovative bird-edge-computing adaptation approach to analyze the complex connections between input parameters, edge resources, and QoS indicators to generate Pareto-optimal solutions. The evaluation of the proposed edge intelligence technique with IoT applications demonstrates its effectiveness compared to conventional heuristic-based approaches. This approach enhances the QoS in IoT applications and improves resource utilization and scalability in edge computing environments. © 2025 IEEE.

关键词： Quality of service

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：