检索结果-内蒙古大学图书馆

computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Muthu Selvam Kishan B S College of Computing and Informatics University of North Carolina at Charlotte Charlotre North Carolina USA Department of Mechanical Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The IT profession has observed the ingenious application of Artificial Intelligence in the field of cloud computing as a solution to increase performance along with scalability within the distributed computing environment. Conventional cloud computing context has issues with management of resources, responsiveness, and finally security. The following paper will discuss an AI based architecture which will incorporate ML algorithms to optimize the usage of the resources available in the computer cloud to balance the load between different users and ensure security. Thus, the proposed model incorporates reinforcement learning for the dynamic load distribution, predictive analytics for workload forecasting, and deep learning based on anomaly detection to improve the security features of the system. The performance comparison with the baseline and the experiments conducted show satisfactory results in more than one performance indicators. For CPU usage, it rose to 26.4%, which was achieved alongside the provision of the 48% higher request throughput and the 40% better response time under the scenarios of the heavy workload. Moreover, the security framework using CNNs enhanced the detection accuracy of intrusion to 96.7% and a reduction of the false positives by sixty-six percent. These are some of the implications suggesting that the application of artificial intelligence can bring positive changes in the goals regarding cloud performance, resource utilization and overall security features. In this way, using AI-driven automation and prediction integrated in this work is offered a powerful, adaptive, and safe cloud model for expanded usage. With the proposed approach that has been developed the businesses and the service providers will be in a position to attain efficient business operational costs together with strong system reliability that will assist in developing the next generation of intelligent cloud computing.

关键词： Deep learning Cloud computing Scalability Computational modeling Predictive models Security Resource management Time factors Predictive analytics Optimization

来源：评论

学校读者我要写书评

暂无评论

Car Lock Control System Using Radio Frequency Identification(RFID)

Car Lock Control System Using Radio Frequency Identification...

引用

SmartBlock4Africa, IEEE

作者： A. Izang Aaron Erihri. O. Jonathan I. Oyerinde Emmanuel Akpovi Ominike Chukwu Esther C E. Omodara Oluwademilade Micheal Goldrich C Department of Information Technology School of Computing and Engineering Sciences Babcock University Ilishan-Remo Ogun State Nigeria Department of computer Science School of Information and Communication Technology Delta State Polytechnic Ogwashiuku Nigeria Computer Science & Information Technology Department Petroleum Training Institute Effurun Delta State Nigeria Department of Computer Science School of Computing and Engineering Sciences Babcock University Ilishan-Remo Ogun State Nigeria

ISBN: (数字)9798350376838

ISBN: (纸本)9798350376845

Car Lock controlled system addresses the critical issue of vehicle security by utilizing RFID technology to create a more secure, passive, and wireless vehicle access system. This innovation surpasses traditional key-based systems, offering enhanced convenience and security. The project involves the development of a digital door locking mechanism integrated with RFID for secure vehicle identification. The system's efficacy was validated through rigorous testing, showcasing its potential to significantly deter vehicle theft. Despite challenges such as signal interference, the outcomes suggest a promising future for RFID in vehicle security. The study concludes with recommendations for extending RF communication range and exploring emerging technologies for further enhancement. This research marks a significant step towards redefining vehicle security, demonstrating the feasibility and effectiveness of a radio frequency-controlled car lock system.

关键词： Wireless communication Radio frequency Technological innovation Control systems Security Automobiles Reliability Protection Radiofrequency identification Testing

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Network Forensics Using Hybrid Anomaly Detection with Enhanced Transformer Based ISO-Encoder and LSTM-CNN Fusion

AI-Driven Network Forensics Using Hybrid Anomaly Detection w...

引用

Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Abisha B. Sheeja Kumari V. Wilfred Blessing N. R Sutherlin Subitha G. Seldev Christopher C. Wasim Haidar SK Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Stella Mary's College of Engineering Affiliated to Anna University Tamil Nadu India Department of Computer Science and Engineering St. Xavier's Catholic College of Engineering Nagercoil Tamil Nadu India College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Traditional network forensic techniques frequently find it difficult to identify and look into sophisticated cybercrimes in today's quickly changing cyber scene. A Transformer-based Iso-Encoder and a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) fusion model are combined in this research to present an AI-driven method that makes use of a novel hybrid anomaly detection model. The LSTM-CNN fusion model performs exceptionally well in temporal and spatial feature extraction, while the Iso-Encoder is improved to capture complex patterns in network traffic. This hybrid approach is intended to accurately detect known and undiscovered anomalies. By offering practical insights into the nature of the dangers, the suggested framework not only helps with the comprehensive investigation of cyber incidents but also identifies anomalies in real-time. The model is a powerful tool for cybersecurity experts since it incorporates cutting-edge AI techniques to guarantee that it adjusts to changing cyberthreats. According to experimental data, the hybrid model maintains a low false positive rate and processing time of 0.5 ms/packet while outperforming current techniques in terms of detection accuracy (98.7%), precision (98.4%), recall (97.8%), and F1-score (98.1%). Traditional models with somewhat poorer performance include Random Forest (94.5% accuracy), Support Vector Machine (SVM) (93.7% accuracy), and LSTM (96.2 % accuracy). This research paves the way for more resilient and intelligent network forensic solutions, enhancing the ability to counteract and investigate complex cyber threats effectively.

关键词： Support vector machines Accuracy Forensics Telecommunication traffic Transformers Real-time systems Computer crime Long short term memory Anomaly detection Random forests

来源：评论

学校读者我要写书评

暂无评论

Preserving medical information from doctor's prescription ensuring relation among the terminology

引用

Computers in Biology and Medicine 2025年 188卷 109812-109812页

作者： Datta, Apurba Hasan, Md. Mehedi Mahmud, Niaz Ferdosi, Bilkis Jamal Islam, Rafiqul Rahman, Ziaur Mehedi, Sk. Tanzir Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh School of Computing Mathematics & Engineering Charles Sturt University Albury2640 Australia School of Computer Science Queensland University of Technology Brisbane4000 Australia

Healthcare is a fundamental human right, yet accessing proper healthcare remains a significant challenge. Many patients still rely on physical documents, requiring them to carry all relevant medical records during consultations. Existing methods for extracting data from medical prescriptions have primarily focused on recognizing medication names using manual image annotation or binarization techniques. These approaches often fail to capture detailed prescription information, struggle with multilingual text, and lack the ability to structure medicine-related data comprehensively. To address these limitations, we propose an advanced Electronic Health Record (EHR) system that provides a secure and accessible digital platform for storing and managing patients’ medical histories. Our study implements the best Region of Interest (ROI) detection model using the You Only Look Once (YOLO) framework, achieving 99.6% detection accuracy. The extracted ROI is processed using the best Optical Character Recognition (OCR) technique to digitize prescription data, which is then organized into a structured format within a central database. Furthermore, the system integrates a spell correction algorithm with a 96% accuracy rate to rectify misspelled medication names. Beyond text extraction, the system links corrected medication names with dosages, instructions, and manufacturer information, combining generic and brand details to ensure precise and comprehensive healthcare data management. This integrated solution enhances medication data organization, facilitates better healthcare delivery, and improves patient outcomes by streamlining prescription handling and ensuring accurate medication administration. Our EHR system bridges the gap between physical and digital records, advancing an efficient and reliable healthcare ecosystem. © 2025 The Authors

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A Review on Environmental Sustainability Using Machine Learning Applications

A Review on Environmental Sustainability Using Machine Learn...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： E. Nirmala Manish Yadav Palanichamy Naveen M. Maragatharajan M. Suresh Pranjal Bhargava School of Computing Science and Engineering VIT Bhopal University Kothri Kalan Madhya Pradesh India School of Computing Science engineering and Artificial Intelligence VIT Bhopal University Kothri Kalan Madhya Pradesh India Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Environmental sustainability reduces the destruction of natural resources in the hands of mother earth for next generation. Due to the development of cities and its social and economic factors leads the major role of any such destruction. It affects huge volume of biological system in terms of environmental pollution, change in climate and respective population growth. Henceforth, the general population is severely affected with their quality of life in the present scenario. This research study discusses about various resources that are affected severely due to the environmental factors have stated with the proven evident. Along with this various prediction technology using machine learning procedures are discussed in detail to prevent any such destruction by improving the present style of work. The prediction of any such destruction exploiting resources like land, water, mineral and planetaries creates its impact in the form of climatic changes, as a result of this we are facing drought, earthquakes and landslide. Apart from preventing techniques over plantation of greeneries, it is necessary to implement techniques towards efficient use of resources Monitoring is essential features. All these things are now essentially managed with the help of computer aided technology. Further the effective usage of environmental factors results into safe future for the next generation. This can be achieved by using AI with SUV to increase the prediction accuracy. Further this article is directed towards future direction to prevent and use the natural resources is discussed for the benefit of population around the world.

关键词： Natural resources Pollution Reviews Green products Urban areas Plantations Machine learning Terrain factors Next generation networking Water resources

来源：评论

学校读者我要写书评

暂无评论

Noisy intermediate-scale quantum computers

引用

Frontiers of physics 2023年第2期18卷 1-62页

作者： Bin Cheng Xiu-Hao Deng Xiu Gu Yu He Guangchong Hu Peihao Huang Jun Li Ben-Chuan Lin Dawei Lu Yao Lu Chudan Qiu Hui Wang Tao Xin Shi Yu Man-Hong Yung Junkai Zeng Song Zhang Youpeng Zhong Xinhua Peng Franco Nori Dapeng Yu Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China International Quantum Academy Shenzhen 518048China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Physics Southern University of Science and TechnologyShenzhen 518055China Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Hefei National Laboratory University of Science and Technology of ChinaHefei 230088China CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of ChinaHefei 230026China Quantum Computing Center and Cluster for Pioneering Research RIKENWakoshiSaitama 351-0198Japan Physics Department University of MichiganAnn ArborMI 48109-1040USA

Quantum computers have made extraordinary progress over the past decade,and significant milestones have been achieved along the path of pursuing universal fault-tolerant quantum *** advantage,the tipping point heralding the quantum era,has been accomplished along with several waves of *** hardware has become more integrated and architectural compared to its toddler *** controlling precision of various physical systems is pushed beyond the fault-tolerant ***,quantum computation research has established a new norm by embracing industrialization and *** joint power of governments,private investors,and tech companies has significantly shaped a new vibrant environment that accelerates the development of this field,now at the beginning of the noisy intermediate-scale quantum ***,we first discuss the progress achieved in the field of quantum computation by reviewing the most important algorithms and advances in the most promising technical routes,and then summarizing the next-stage ***,we illustrate our confidence that solid foundations have been built for the fault-tolerant quantum computer and our optimism that the emergence of quantum killer applications essential for human society shall happen in the future.

关键词： quantum computer quantum algorithm quantum chip

来源：评论

学校读者我要写书评

暂无评论

Detection of Leaf Diseases in Agricultural Plants Using Machine Learning

Detection of Leaf Diseases in Agricultural Plants Using Mach...

引用

2023 International Conference on Computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Kholiya, Deepak Mishra, Amit Kumar Dumka, Ankur Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University School of Agriculture Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Women Institute of Technology Uttarakhand Dehradun India Dept. of Computer Science & Engineering Uttarakhand Dehradun India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350338003

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important study area is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plant illnesses. Choosing a classification method is always challenging because the quality of the results can differ depending on the input data. A few examples of several classification techniques include K-Nearest Neighbour Classifier (KNN), Probabilistic Neural Network (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis, Artificial Neural Network (ANN), and Fuzzy Logic. Classifications of plant leaf diseases have many uses in various industries, including agriculture and biological research. Pre-symptomatic diagnosis and crop health information can aid in the ability to manage pathogens through proper management approaches. Convolutional neural networks (CNNs) are the most widely used DL models for computer vision issues since they have proven very effective in tasks like picture categorization, object detection, image segmentation, etc. The experimental findings demonstrate the proposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range of categorization accuracy is 76% to 100%, based on. © 2023 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Cooperative and Competitive Multi-Agent Systems:From Optimization to Games

引用

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 763-783页

作者： Jianrui Wang Yitian Hong Jiali Wang Jiapeng Xu Yang Tang Qing-Long Han Jürgen Kurths Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Potsdam Institute for Climate Impact Research 14473 Potsdam Institute of Physics Humboldt University of Berlin12489 BerlinGermany

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation *** a multi-agent system,agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination,which is manifested as cooperative/competitive *** survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative *** from cooperative optimization,the studies on distributed optimization and federated optimization are *** survey mainly focuses on distributed online optimization and its application in privacy protection,and overviews federated optimization from the perspective of privacy protection ***,cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs,***-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects,according to whether each player can make decisions based on the information of other ***,future directions for cooperative optimization,cooperative/non-cooperative games,and their applications are discussed.

关键词： Cooperative games counterfactual regret minimization distributed optimization federated optimization fictitious

来源：评论

学校读者我要写书评

暂无评论

Sports match prediction model for training and exercise using attention-based LSTM network

引用

Digital Communications and Networks 2022年第4期8卷 508-515页

作者： Qiyun Zhang Xuyun Zhang Hongsheng Hu Caizhong Li Yinping Lin Rui Ma Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computing Macquarie UniversitySydneyAustralia Faculty of Engineering University of AucklandNew Zealand No.1 Middle School of Pidu Chengdu Chengdu611730China e General Education Department Shandong First Medical University(Shandong Academy of Medical Sciences)TaianChina

Sports matches are very popular all over the *** prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the ***'s a challenging effort to predict a sports ***,a method is proposed to predict the result of the next match by using teams'historical match *** combined the Long Short-Term Memory(LSTM)model with the attention mechanism and put forward an ASLSTM model for predicting match ***,to ensure the timeliness of the prediction,we add the time sliding window to make the prediction have better *** the football match as an example,we carried out a case study and proposed the feasibility of this method.

关键词： Sports Prediction Long short-term memory Attention Sliding window

来源：评论

学校读者我要写书评

暂无评论

Optimizing Convolutional Neural Networks for Plant Disease Detection on ARM-M Microcontrollers

Optimizing Convolutional Neural Networks for Plant Disease D...

引用

Agrosystem engineering, technology & Applications (AGRETA), IEEE International Conference on

作者： Hermawan Nugroho Chew Jing Xan Tay Fei Siang Sivaraman Eswaran Department of Electrical and Electronic Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

Rice production in Malaysia is facing significant challenges due to plant diseases and environmental hazards, leading to a decline in the rice self-sufficiency ratio. To address these issues, this study explores the deployment of AI-driven plant disease detection models on ARM-M based microcontrollers, specifically the STM32H747I-DISCO Discovery kit. Utilizing Convolutional Neural Networks (CNNs) for image recognition, we aim to achieve real-time disease monitoring without relying on continuous internet connectivity. Our research evaluates five state-of-the-art models-FD-MobileNet, MobileNet V1, MobileNet V2, ResNet V1, and SqueezeNet V1-focusing on their suitability for resource-constrained environments. The results indicate that FD-MobileN et is the most resource-efficient model with the lowest number of Multiply-Accumulate operations (MACCs) and the shortest inference time, achieving an accuracy rate of 98.44%. However, SqueezeNet, despite having the highest accuracy at 99.22%, faces deployment challenges due to its large model size on the STM32H747I-DISCO board. This work highlights the potential of embedded AI applications in enhancing agricultural resilience and efficiency.

关键词： Plant diseases Accuracy Microcontrollers Computational modeling Crops Real-time systems Agriculture Convolutional neural networks Testing Synthetic data

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：