检索结果-内蒙古大学图书馆

12th Euro-American Conference on Telematics and Information Systems, EATIS 2024

作者： Giraldo, Luis Miguel Soler, Joaquin Perez Botella-Mascarell, Carmen Juan, Vicent Girbes Varea, Sandra Roger Torres, Julio Martos Olcina, Raimundo Garcia Electronical Engineering Department Universitat de València Burjassot Valencia46100 Spain Computer Science Department Informatics Department Universitat de València Burjassot Valencia46100 Spain

ISBN: (纸本)9798400717338

The creation and implementation of a system that bridges visible light communication (VLC) with selected identifiers, envisioned as informational beacons, are outlined. This system is adept at controlling LED panels and orchestrating communication between LED lights and the receiving unit, laying the groundwork for vehicle-to-vehicle (V2V) communication systems tailored for precise positioning. The design and assessment of test-bed requirements cater to the anticipation of future V2V optical wireless communication scenarios. Through analysis, it is demonstrated that commercial outdoor lighting can be effectively utilized for V2V positioning, capable of operating under daylight with a data transmission rate of 100 Kbps. Crucially, the system's ability to accurately detect positioning beacons facilitates the localization of vehicles within designated areas of interest, paving the way for the advancement of V2V communication technologies. This comprehensive approach not only validates the feasibility of using VLC for vehicle positioning but also sets a precedent for future developments in V2V communication systems. © 2024 Owner/Author.

关键词： Optical communication

来源：评论

学校读者我要写书评

暂无评论

Usage of Machine Learning Methods for Cause-Effect Graph Feasibility Prediction 5

Usage of Machine Learning Methods for Cause-Effect Graph Fea...

引用

5th International Conference on Machine Learning and Intelligent Systems, MLIS 2023

作者： Krupalija, Ehlimana Cogo, Emir Pozderac, Damir Ali Al Zayat, Aya Bešić, Ingmar Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo71 000 Bosnia and Herzegovina

ISBN: (纸本)9781643684468

Cause-effect graphs (CEGs) are usually applied for black-box testing of complex industrial systems. The specification process is time-consuming and can result in many errors. In this work, machine learning methods were applied for predicting the feasibility of CEG elements. All information was extracted from graphs contained in CEGSet, a dataset of CEGs. The data was converted to two different formats. The Boolean features format represents relations as separate data rows, whereas the Term-Frequency times Inverse-Document-Frequency (TF-IDF) format represents graphs as data rows. Eight machine learning models were trained on this data. The results of testing by using the 80-20 holdout method indicate that important information is lost when converting the graphs to the TF-IDF format, whereas the Boolean feature format enables 100%-accurate predictions of ensemble methods. The achieved results indicate that pre-trained models can be used as help for domain experts during the CEG specification process. © 2023 The authors and IOS Press.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

引用

IEEE Transactions on Services Computing 2025年第3期18卷 1753-1768页

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Xiamen University School of Informatics Fujian China Rmit University School of Computing Technologies MelbourneVIC Australia Tongji University Department of Control Science and Engineering Shanghai China Beijing Teleinfo Technology Co. Ltd. Caict IF-Labs China University at Buffalo-SUNY Department of Electrical Engineering NY United States Western University Department of Electrical and Computer Engineering ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. © 2025 IEEE.

关键词： Undirected graphs

来源：评论

学校读者我要写书评

暂无评论

StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5666-5675页

作者： Karim, Tasmin Shaon, Md. Shazzad Hossain Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomati Information and Communication Technology Tangail Santosh1902 Bangladesh

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention. © 2024 IEEE.

关键词： Amino acids

来源：评论

学校读者我要写书评

暂无评论

Comparison of Transformer Based and Traditional Models on Sentiment Analysis on Social Media Datasets 6

Comparison of Transformer Based and Traditional Models on Se...

引用

6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lubis, Arif Ridho Fatmi, Yulia Witarsyah, Deden Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350345162

In the field of computers and natural language processing, there is an interesting sub-field, namely sentiment analysis. Currently, the use of social media Twitter to actively communicate between individuals contains various review data, opinion data, and emotional data from discussions of topics between users on social media. So that the data generated by Twitter social media can identify patterns in the data for the field of sentiment analysis. In the sentiment analysis sub-sector, there are approaches, namely transformer-based models and traditional models, the traditional model includes the naïve Bayes algorithm, support vector machine, and regression have weaknesses in overcoming data complexity in Indonesian language tweets on Twitter social media while the transformer model includes the BERT (Bidirectional Encoder Representations from Transformers) algorithm is often applied because it has advantages in overcoming the complexity of Indonesian sentences in tweets obtained from social media and in the process this model goes through a training stage with large amounts of data then is adjusted in the concept of sentiment analysis. © 2023 IEEE.

关键词： bert naïve bayes sentiment analysis social media

来源：评论

学校读者我要写书评

暂无评论

Weighting Heart Disease Criteria Using Multi-Criteria Decision-Making

Weighting Heart Disease Criteria Using Multi-Criteria Decisi...

引用

2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al-Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Heart disease describes a group of conditions that affect the heart and there are many different factors including lifestyle, medical history, family history, and so on that may cause this complex disease. It is therefore important to consider multiple criteria, such as smoking status, blood pressure, and cholesterol levels to make informed decisions about patient priority management and treatment of HD. Multicriteria Decision Making (MCDM) allows us to identify and weigh the importance of each criterion. This approach can enhance the quality of patient care and improve clinical outcomes. Therefore, weighting HD criteria using MCDM is an essential step in achieving better healthcare outcomes in the management of HD. The FWZIC method was used in this study to weigh 11 heart disease parameters with the assistance of four cardiologists. The results of the criterion weights were without any difference in the preferences of the respective expert. Therefore, this method is flexible, robust, and useful in complex real-world problems. © 2023 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Elevating Cystic Fibrosis Detection in Lungs using HRCT Images with a Cutting-Edge CNN-Based Approach 2

Elevating Cystic Fibrosis Detection in Lungs using HRCT Imag...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Ganjam, Amrutha Sriya Reddy C, Kishor Kumar Radhika, D. Ganjam, Karthik Singh.t, Monika Akuthota, Keerthi Sai Stanley College of Engineering and Technology for Women abids Department of Computer Science Hyderabad India University of Edinburgh Department School of Informatics MSc Data Science Edinburgh United Kingdom Stanley College of Engineering and Technology for Women Deptartment. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331519056

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Survey on Privacy-Enhancing Techniques in the Era of Artificial Intelligence 4th

A Survey on Privacy-Enhancing Techniques in the Era of A...

引用

4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Dritsas, Elias Trigka, Maria Mylonas, Phivos Athena Research and Innovation Center Industrial Systems Institute Patras Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

In the era of Big Data and Artificial Intelligence (AI), the unprecedented scale and complexity of data collection, processing, and analysis pose significant privacy challenges. This paper presents a survey, providing a comprehensive and focused overview of privacy-enhancing technologies (PETs) designed to mitigate these challenges and ensure the protection of sensitive information. We explore cryptographic and non-cryptographic techniques including differential privacy (DP), homomorphic encryption (HE), secure multi-party computation (SMPC), and federated learning (FL). Each of these techniques is examined in terms of its basic principles and advantages. Also, some key challenges and ethical issues for implementing PETs are briefly discussed. Finally, we conclude the survey by denoting our future research directions in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Learning Analytics Support in Higher-Education: Towards a Multi-Level Shared Learning Analytics Framework 16

Learning Analytics Support in Higher-Education: Towards a Mu...

引用

16th International Conference on computer Supported Education, CSEDU 2024

作者： Vierhauser, Michael Groher, Iris Sauerwein, Clemens Antensteiner, Tobias Hatmanstorfer, Sebastian University of Innsbruck Department of Computer Science Innsbruck Austria Johannes Kepler University Linz Institute of Business Informatics Software Engineering Linz Austria

ISBN: (纸本)9789897586972

Assurance of Learning and Competency-Based Education are increasingly important in higher education, not only for accreditation or transfer of credit points. Learning Analytics is crucial for making educational goals measurable and actionable, which is beneficial for program managers, course instructors, and students. While universities typically have an established tool landscape where relevant data is managed, information is typically scattered across various systems with different responsibilities and often only limited capabilities for sharing data. This diversity, however, significantly hampers the ability to analyze data, both on the course and curriculum level. To address these shortcomings and to provide program managers, course instructions, and students with valuable insights, we devised an initial concept for a Multi-Level Shared Learning Analytics Framework to provide consistent definition and measurement of learning objectives, as well as tailored information, visualization, and analysis for different stakeholders. In this paper, we present the results of initial interviews with stakeholders, devising core features. In addition, we assess potential risks and concerns that may arise from the implementation of such a framework and data analytics system. As a result, we identified six essential features and six main risks to guide further requirements elicitation and development of our proposed framework. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Handwritten Text Recognition of Letters in Kyrgyz Language 24

Handwritten Text Recognition of Letters in Kyrgyz Language

引用

25th International Conference on computer Systems and Technologies, CompSysTech 2024

作者： Zhumaev, Ilgiz Shambetova, Burul Isaev, Ruslan Computer Science Department Ala-Too International University Bishkek Kyrgyzstan Faculty of Engineering and Informatics Ala-Too International University Bishkek Kyrgyzstan

ISBN: (纸本)9798400716843

This article deals with a new handwritten dataset, Kyrgyz-MNIST, and recognition of handwritten Kyrgyz letters through the Artificial Neural Network. There were collected 89 023 manuscript samples of 36 Kyrgyz letters. Since 33 of the 36 letters in the Kyrgyz language are similar to the Russian letters, the special letters "H", "Y", and "φ"were studied with special emphasis. The system of handwriting recognition works with Image processing methods and Machine Learning algorithms helped to recognize Kyrgyz manuscripts and convert them into computer letters. As a result, the CNN method recognized the handwriting letters more accurately. In total 36 letters were recognized with an average accuracy of 99.1%. Newly studied, the special letters in Kyrgyz Language are also recognized with more than 99% correct. © 2024 ACM.

关键词： Convolutional neural networks

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：