检索结果-内蒙古大学图书馆

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ripto Mukti Wibowo Bahjat Fakieh Muhammad Sher Ramzan Ahmed Saeed Alzahrani Muazzam Siddiqui Bander Alzahrani Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Luddy School of Informatics Computing and Engineering Indiana University Bloomington

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.

关键词： Technological innovation Data analysis Pandemics Voting Data visualization Big Data Data models

来源：评论

学校读者我要写书评

暂无评论

Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method 12th

Understanding the Influence of Multiple Factors on the Sprea...

引用

12th International Conference on Health information science, HIS 2023

作者： Xu, Zhenkai Lin, Shaofu Huang, Zhisheng Fu, Yu Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands Clinical Research Center for Mental Disorders Shanghai Pudong New Area Mental Health Center Tongji University School of Medicine China and Deep Blue Technology Group Shanghai China

ISBN: (纸本)9789819971077

The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

来源：评论

学校读者我要写书评

暂无评论

Task Offloading Strategy with Emergency Handling and Blockchain Security in SDN-Empowered and Fog-Assisted Healthcare IoT

引用

Tsinghua science and technology 2022年第4期27卷 760-776页

作者： Junyu Ren Jinze Li Huaxing Liu Tuanfa Qin School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510640China Guangxi University Xingjian College of Science and Liberal Arts Nanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China Faculty of Earth Sciences&Geography Trinity CollegeUniversity of CambridgeCambridgeCB21TNUK

With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of *** alleviate the pressing challenges,such as resource limitations,low-latency service provision,mass data processing,rigid security demands,and the lack of a central entity,the advanced solutions of fog computing,software-defined networking(SDN)and blockchain are leveraged in this *** the basis of these solutions,a task offloading strategy with a centralized low-latency,secure and reliable decision-making algorithm having powerful emergency handling capacity(LSRDM-EH)is designed to facilitate the resource-constrained edge devices for task ***,to well ensure the security of the entire network,a comprehensive blockchain-based two-layer and multidimensional security strategy is ***,to tackle the inherent time-inefficiency problem of blockchain,we propose a blockchain sharding scheme to reduce system time *** simulation has been conducted to validate the performance of the proposed measures,and numerical results verify the superiority of our methods with lower time-latency,higher reliability and security.

关键词： wireless body area networks(WBANs) healthcare IoT software-defined networking(SDN) fog computing blockchain task offloading blockchain sharding

来源：评论

学校读者我要写书评

暂无评论

Detecting Phishing Websites using Machine Learning Algorithm

Detecting Phishing Websites using Machine Learning Algorithm

引用

International Conference on Computing Methodologies and Communication (ICCMC)

作者： M. Kathiravan V. Rajasekar Shaik Javed Parvez V. Sathya Durga M. Meenakshi S. Gowsalya Computer Science and Engineering Hindustan Institute of Technology and Science (Deemed to be University) Chennai India Computer Science and Engineering Faculty of Engineering and Technology Vadapalani SRM Institute of Science and Technology Chennai India Department of Information Technology Hindustan Institute of Technology and Science (Deemed to be University) Chennai India

In general, malicious websites aid the expansion of online criminal activity and stifle the growth of web service infrastructure. Therefore, there is a pressing need for a comprehensive strategy to discourage users from going to these sites online. We advocate for a method that uses machine learning to categories websites as either safe, spammy, or malicious. The proposed system is limited to examining the URL itself, rather than the contents of websites. As a result, it does away with both browser-based vulnerabilities and run-time delays. The proposed method outperforms blacklisting services in terms of generality and coverage since it makes use of learning techniques. There are three distinct categories for website addresses. Neutral Web sites provide average, risk-free functionality. For a website, “spam” refers to any attempt to overwhelm the user with advertisements or sites (such as false surveys and online dating sites). Malware is defined as a website designed by hackers to cause harm to computers and steal private data. The experimental data demonstrates a dramatic improvement in performance with the new model compared to the baseline.

关键词： Uniform resource locators Machine learning algorithms Web services Phishing Support vector machine classification Pressing Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Review on DeepDream Neural Networks

A Review on DeepDream Neural Networks

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Fuad Fataftah Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Mutlimedia systems and Virtual Reality School of Computer science Universiti Sains Malaysia (USM) Penang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper provides a comprehensive review of DeepDream neural networks or the Inceptionism technique, a type of deep learning model that has gained popularity due to its ability to generate visually captivating and surrealistic images. The review begins with an introduction to deep learning and its importance in image recognition and generation tasks. It then delves into the details of the DeepDream algorithm, including its architecture, training process, and mathematical foundations. The paper also discusses various applications of DeepDream, such as artistic image generation, style transfer, and image enhancement. Additionally, the review highlights some of the challenges and limitations of DeepDream networks, including issues with overfitting and computational resources. Finally, the paper concludes with a discussion of future directions for research in this area, including the exploration of more advanced architectures and the integration of DeepDream networks with other machine-learning techniques. Overall, this review serves as a valuable resource for researchers and practitioners interested in understanding the capabilities and limitations of DeepDream neural networks.

关键词： Deep learning Training Visualization Art Reviews Shape Computational modeling computer architecture Real-time systems Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

引用

computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

来源：评论

学校读者我要写书评

暂无评论

Automated Hand Gesture Recognition for Enhancing Sign Language Communication

Automated Hand Gesture Recognition for Enhancing Sign Langua...

引用

IEEE EMBS Conference on Biomedical Engineering and sciences (IECBES)

作者： Lee Teck Junn Ashvaany Egambaram Vikneswary Jayapal Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Malaysia Department of Electrical and Electronic Engineering Centre for Intelligent Signal & Imaging Research Universiti Teknologi PETRONAS Perak Malaysia Department of Computer & Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (数字)9798350383409

ISBN: (纸本)9798350383416

This paper presents a novel approach to improving communication between individuals with hearing difficulties, with people of normal hearing capacity and unfamiliar with sign language. The work combines advanced technologies, such as artificial intelligence (AI) and computer vision to address the communication barriers faced by this group. Recent advancements in AI have made this possible. By using techniques like deep learning and computer vision, the work aims to develop a model that can turn complex sign language gestures into comprehensible/ interpretable language. Sequential deep learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are used to recognize sign language gestures using Tensor Flow, with the assistance of MediaPipe Holistic. This application aims to narrow the communication divide between individuals proficient in sign language and those unfamiliar with it. This paper highlights the potential for a beneficial outcome through the fusion of AI and computer vision, especially given the widespread availability of video accessibility on smartphones and the growing influence of AI. The result shows that GRU performs better than LSTM in recognizing the sign language gestures with an average accuracy of 97%.

关键词： Hands Deep learning Sign language computer vision Accuracy Computational modeling Real-time systems Artificial intelligence Long short term memory Smart phones

来源：评论

学校读者我要写书评

暂无评论

Fake Currency Note Recognition using Extreme Learning Machine 2

Fake Currency Note Recognition using Extreme Learning Machin...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kumar, Rajeev Lamba, Anil Kumar Mohammed, Sayed Asokan, Anju Aswal, Upendra Singh Kolavennu, Subbarao Gopal Narayan Singh University Faculty of Information Technology Bihar Jamuhar India Psna College of Engineering and Technology Ece Tamilnadu Dindigul India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Computer Science and Engineering Geeta University Delhi India Sri Eshwar College of Engineering Kinathukadavu Department of Computer and Communication Engineering Tamilnadu Coimbatore India Sphoorthy Engineering College Cse Cyber Security Department Telangana Hyderabad India

ISBN: (纸本)9781665456302

Color printing technology has made it simpler for forgers to make and copy large numbers of fake currency. The production of legible banknotes was once the sole purview of print shops, but today even a novice printer with a simple laser printer can do the job. Because of this, the prevalence of counterfeit money has increased dramatically. A number of problems, including corruption and the existence of 'black money,' have hounded India for a long time. There is also a serious problem with the circulation of counterfeit currency. The outcome is a system that can detect fake currency with greater speed and precision. It is a serious crime to create counterfeit currency, which is money that has not been authorized by the government. The vast majority of them do this professionally. The circulation of counterfeit currency retards economic development and devalues legitimate currency. In this study, it uses image processing to the problem of identifying fake Indian notes. This technique finishes the initial acquisition before the image is processed further. Pre-processing includes actions such as cropping, smoothing, and modifying the image;converting the image to greyscale;applying image segmentation;extracting features;and resizing the image. The model is then trained using an ELM. Proposed model exceeds both the CNN and SVM models in terms of accuracy, at around 97.8 percent. © 2023 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in Renewable Energy Systems 4

Machine Learning in Renewable Energy Systems

引用

4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Munir, Muhammad Sohail Shahid, Usama Nasir, Usama Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Fatima, Hoor Yaqub, Muhammad Atif Punjab Institute of Management and Professional Development Lahore Pakistan School of Business Computing and Social Sciences University of Gloucestershire Cheltenham United Kingdom Bahria University Lahore Campus Lahore Pakistan University of Central Punjab Lahore Faculty of Information Technology Lahore Pakistan Bahria University Lahore Campus Department of Computer Science Lahore Pakistan National College of Business Administration and Economics Lahore Pakistan Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350391183

From solar and wind power to hydropower and biomass, smart grids, the catalytic industry, and power storage and distribution, this paper covers the gamut of ML's present-day uses. Among the many domains covered by machine learning are classification issues and the methods used to solve them. Classification schemes have recently attracted a lot of interest as a tool for improving the deployment, administration, and optimization of Renewable Energy Systems. The integration of new data sources (such as innovative sensors) and improved algorithms for creating trustworthy data will improve the data flow between ML and systems. The growth of other important areas of data science, such big data analytics, will determine the significance of unsupervised and reinforcement learning in the energy industry. The development of sustainable uses of ML in non-industrial applications for energy management will be aided by massive implementations, specialized algorithms, and new technologies like 5G. This work aims to survey the state-of-The-Art classification algorithms for Renewable Energy situations, including both traditional and cutting-edge methods. Wind speed/power prediction, fault diagnostics in Renewable Energy Systems, power quality disturbance categorization, and other applications in alternative Renewable Energy Systems are some of the particular Renewable Energy topics covered in the paper's extensive literature analysis and discussion. Researchers and practitioners in the area may benefit from the paper's description of categorization algorithms and metrics used to Renewable Energy situations. © 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

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：