检索结果-内蒙古大学图书馆

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zicheng Sun Yixuan Zhang Zenan Ling Xuhui Fan Feng Zhou Center for Applied Statistics and School of Statistics Renmin University of China School of Statistics and Data Science Southeast University School of EIC Huazhong University of Science and Technology School of Computing Macquarie University Center for Applied Statistics and School of Statistics Renmin University of China and Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing

ISBN: (纸本)9798331314385

Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel approach utilizing the sparse spectral representation of non-stationary kernels. This technique relaxes the constraints on kernel types and stationarity, allowing for more flexible modeling while reducing computational complexity to the linear level. Additionally, we introduce a deep kernel variant by hierarchically stacking multiple spectral feature mappings, further enhancing the model's expressiveness to capture complex patterns in data. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of our approach, particularly in scenarios with pronounced data nonstationarity. Additionally, ablation studies are conducted to provide insights into the impact of various hyperparameters on model performance. Code is publicly available at https://***/SZC20/DNSSPP.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sakanat: A Competent Mobile Application to Automate Real-Estate

Sakanat: A Competent Mobile Application to Automate Real-Est...

引用

IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Saeed Saud Sulaiman Al Qassabi N.R Wilfred Blessing Nooritawati Md Tahir College of Computing and Information Sciences University of Technology and Applied Sciences-Muscat Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia College of Engineering Universiti Teknologi MARA Malaysia Applied College Princess Nourah Bint Abdul Rahman University (PNU) Riyadh Saudi Arabia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

The traditional methods of searching for flats are often inefficient and lack necessary features, making it difficult for both landlords and tenants to find what they are looking for. The “Sakanat” mobile application is designed to bridge this gap by offering a user-friendly platform for property listing, searching, and management. Landlords can easily upload details, photos, and set rental terms, while tenants can efficiently find suitable flats based on various criteria using advanced search filters. Rent payments can be made directly through the app using a secure payment gateway, and tenants can also access a variety of additional services such as maintenance companies and flat transfer services. Sakanat leverages Flutter for cross-platform app development and Firebase for backend services and data storage. Development will prioritize user-friendliness, performance, and security. By facilitating connections between landlords and tenants, simplifying rent collection, and offering valuable additional services, Sakanat has the potential to revolutionize the flat rental experience.

关键词： Filters Scalability Memory Transforms Logic gates Control systems Mobile applications Maintenance Stakeholders Security

来源：评论

学校读者我要写书评

暂无评论

Feature Extraction and Classification Stage on Facial Expression: A Review 6

Feature Extraction and Classification Stage on Facial Expres...

引用

6th International Conference on Informatics and Computational Sciences, ICICoS 2022

作者： Shidiq, Muchamad Bachram Ernawan, Ferda Khubrani, Mousa Nugroho, Fajar Agung Universitas Nusa Mandiri Faculty of Computer Science Jakarta Indonesia Universiti Malaysia Pahang Faculty of Computing College of Computing & Applied Sciences Pahang Malaysia Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia Diponegoro University Department of Informatics Semarang Indonesia

ISBN: (数字)9781665460996

ISBN: (纸本)9781665460996

Human facial expression becomes an important technology in recent years. As information technology and networks have grown, identification and authentication have become more frequent in people's daily lives, especially using biometric technology. Human facial recognition involves face detection, feature extraction, and classification. A lot of experiments showed that there are various techniques for extracting facial features and classifying facial expressions. This paper reviews and analyze the various optimization techniques on extract feature and classification stage for human facial expression recognition. This review will compare two kinds of extract features methods and one classification method. The first technique of extracting features is the optimization technique using the K-Mean algorithm, which helps to increase recognition accuracy. The second extract feature is an optimization technique using improved Gradient Local Ternary Pattern (GLTP) which is beneficial for computational resources efficiency. Lastly, the optimization technique for image classification using a three-staged Support Vector Machine (SVM) is very helpful for increasing accuracy and eliminating error. The modified GLTP is able to obtain an accuracy of 97%. © 2022 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

引用

Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

来源：评论

学校读者我要写书评

暂无评论

Diffeomorphic Measure Matching with Kernels for Generative Modeling

arXiv

引用

arXiv 2024年

作者： Pandey, Biraj Hosseini, Bamdad Batlle, Pau Owhadi, Houman Department of Applied Mathematics University of Washington SeattleWA United States Department of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States

This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration. A theoretical analysis of the proposed method is presented, giving a priori error bounds in terms of the complexity of the model, the number of samples in the training set, and model misspecification. An extensive suite of numerical experiments further highlights the properties, strengths, and weaknesses of the method and extends its applicability to other tasks, such as conditional simulation and *** Codes 35Q68 49Q22 62F15 68T07 62R07 Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2022年第14期16卷 123-135页

作者： Maabreh, Majdi Obeidat, Ibrahim Elsoud, Esraa Abu Alnajjai, Asma Alzyoud, Rahaf Darwish, Omar Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI United States

Cyber attacks have increased in tandem with the exponential expansion of computer networks and network applications throughout the world. Fortunately, various machine/deep learning models have demonstrated excellent accuracy in predicting network attacks in the literature;nonetheless, having simple and understandable models might be a big benefit in network monitoring systems. In this study, we evaluate four feature selection algorithms to find the minimal set of predictive features of network attacks, seven classical machine learning algorithms, and the deep learning algorithm on one million random instances of the CSE-CIC-IDS2018 big data set for network intrusions. The feature selection algorithms highlighted the importance of features related to forwarding direction (FWD) and two flow measures (FLOW) in predicting the binary traffic type;benign or attack. Furthermore, the results revealed that not all features are required to build efficient ML/DL in detecting network attacks, four features unanimously selected by the feature selection algorithms were enough to build comparable ML models to those trained on all features. This might lead to models that are more suitable for deployment in terms of complexity, explainability, and scalability. Moreover, by selecting four unanimity features instead of all traffic features, the training time may be decreased by 10% to 50%. © 2022. All Rights Reserved.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms

Towards Swarm Intelligence Ontology for Formal Description o...

引用

computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA), International Conference on

作者： Domagoj Tuličić Marko Horvat Sandra Lovrenčić Department of Computer System and Networks University of Applied Science in Information Technology Zagreb Croatia Department of Applied Computing University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Theoretical and Applied Foundations of Information Sciences University of Zagreb Faculty of Organization and Informatics Varaždin Croatia

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of Robotics, Artificial intelligence and Machine learning in the field of healthcare sector

引用

Materials Today: Proceedings 2022年 56卷 2323-2327页

作者： Sarath Kumar Boddu, Raja Ahamad, Shahanawaj Pradeep Kumar, K.V. Ramalingam, Mritha Kirana Pallathadka, Laxmi Peniero Tupas, Fernan Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India College of Computer Science and Engineering University of Hail Saudi Arabia Department of Mechanical Engineering Ramaiah Institute of Technology Bengaluru India Faculty of Computing College of Computing and Applied Sciences University Malaysia Pahang Malaysia Manipur International University Manipur Imphal India Department of Biology Northern Iloilo Polytechnic State College India

Technological innovation or AI is enabled to determine the human's cognitive activities. ML, AI and automation processes made rapid changes in the pharma industry. The use of different analytic techniques helped to decrease the mortality rate worldwide. Uses of the AI tools help to determine the critical disease. Identification of genetic disputes is important for diagnosis and treatment. The growing technology helps to identify the dose errors in pharma companies. Currency analysis, frequent spectral analysis and the feature attraction process are necessary for Machine Learning (ML). Industry 4.0, ML considers the effectiveness of ML. Development of the intelligence computer programme is important for machine simulation. Moreover, it increases problem-solving skills in the pharma industry. The introduction sheds light on the entire project. The literature review has discussed the role of technological advantages in the medical industry. The impact of AI, ML and automation processes enhance profitability. The advantages and disadvantages of AI, ML and automation processes have been discussed in the literature review. In the methodology section, the secondary methodology has been chosen. Some inclusion and exclusion criteria have been concerned for the collection of effective information on the research topic. Cloud computing, cognitive systems and big data analysis, R&D process make huge changes in the pharma company. Increasing the use of the automation process provides safety and consistency to develop interconnectivity between the market demands, production rate of medicines. Recommendation and conclusion will be provided to maintain accuracy throughout the research paper. © 2021

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

Metric Basis of Four-Dimensional Klein Bottle

引用

Computer Modeling in Engineering & Sciences 2023年第9期136卷 3011-3024页

作者： Ali N.A.Koam Ali Ahmad Maryam Salem Alatawi Muhammad Azeem Muhammad Faisal Nadeem Department of Mathematics College of SciencesNew CampusJazan UniversityJazanSaudi Arabia College of Computer Science&Information Technology Jazan UniversityJazanSaudi Arabia Department of Mathematics Faculty of SciencesUniversity of TabukTabukSaudi Arabia Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahorePakistan Department of Mathematics COMSATS University IslamabadLahore CampusLahorePakistan

The Metric of a graph plays an essential role in the arrangement of different dimensional structures and finding their basis in various *** metric dimension of a graph is the selection of the minimum possible number of vertices so that each vertex of the graph is distinctively defined by its vector of distances to the set of selected *** set of selected vertices is known as the metric basis of a *** applied mathematics or computer science,the topic of metric basis is considered as locating number or locating set,and it has applications in robot navigation and finding a beacon set of a computer *** to the vast applications of this concept in computer science,optimization problems,and also in chemistry enormous research has been *** extend this research to a four-dimensional structure,we studied the metric basis of the Klein bottle and proved that the Klein bottle has a constant metric dimension for the variation of all its *** the metric basis is variying in 3 and 4 values when the values of its parameter change,it remains constant and unchanged concerning its order or number of *** methodology of determining the metric basis or locating set is based on the distances of a ***,we proved the main theorems in distance forms.

关键词： Klein bottle metric basis resolving set metric dimension

来源：评论

学校读者我要写书评

暂无评论

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

引用

Computer Systems Science & Engineering 2023年第2期44卷 1419-1432页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Majed Alsanea Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the *** an auto-mated soil prediction tool helps to considerably improve the efﬁcacy of the *** the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive *** other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering *** this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classiﬁca-tion(FCMCSO-ASC)*** goal of the FCMCSO-ASC technique is to identify and categorize seven different types of *** accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature *** addition,the FCMCSO model is applied for soil classiﬁcation and the weight values of the FCM model are optimally adjusted by the use of CSO *** exam-ining the enhanced soil classiﬁcation outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

关键词： Soil classiﬁcation intelligent models fuzzy cognitive maps cat swarm optimization fuzzy logic

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：