检索结果-内蒙古大学图书馆

19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Troussas, Christos Papakostas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9789897586729

Mobile augmented reality (AR) systems offer exciting opportunities for blending digital content with the real world. However, engagement in mobile AR environments mainly relies on users' computer skills, which vary among users and impact their ability to utilize this technology. This research addresses this gap in understanding the influence of users' computer skills on interactions in mobile AR. In view of the above, this paper presents a fuzzy logic-based model to assess and refine users' computer skills in the context of mobile AR systems. Modelling the users' computer skills, the system is responsible for providing personalized assistive messages and feedback. These messages are designed to align with the fuzzy weights that have been established, enhancing users' interactions within the mobile AR environment. The presented approach is integrated in a personalized mobile AR system for spatial ability training. The evaluation results demonstrate a highly positive outlook. A major conclusion of this work is that fuzzy logic modelling has significant potential to enhance user experiences and drive advancements in mobile augmented reality technology. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Multi-type Vehicle Detection and Classification Using YOLOV5

Multi-type Vehicle Detection and Classification Using YOLOV5

引用

2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Liliana, Dewi Yanti Nalawati, Rizki Elisa Iswara, Ratna Widya Pradito, Reza Nova Heryana, Aldy Mochamad Andayani, Gresia Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350307306

In the modern era marked by technological advancement, vehicle recognition using computer vision has become increasingly important. The ability to accurately identify and classify vehicles has far-reaching implications, from efficient traffic management to better road safety. Some research on vehicle classification have been performed, but for multi-type vehicles still needs to be carried out due to the limited efforts on it, especially in vehicles variation, not only on specific vehicle such as cars, but also for two-wheeled and more. The problems of automated vehicle recognition are the variety of vehicle types on the roads;the ability to recognize many vehicles in real-time;as well as the accuracy of classifying vehicles where many obstacles and occlusions appears in the real-world. Hence, this research develops a web-based application that can recognize multi-type vehicles in real-time. The YOLOv5 method is used because of its advantages in fast recognition time and high accuracy, especially for surveillance data. Testing on dataset of multi-type vehicles including cars, trucks, motorcycles, and bicycles gave results with the accuracy rate of 93.23% and F1 score of 0.93. This research contributes to produce a web-based application for fast detecting and classifying multiple types of vehicles with the superior accuracy rate. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Deep Neural Networks 14

Forecasting Stock Market Alternations Using Social Media Sen...

引用

14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Saravanos, Christina Kanavos, Andreas University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

Forecasting stock market volatility is a challenging task, primarily due to the influence of non-financial factors such as public opinion and sentiment. Social media platforms, particularly Twitter, have the potential to sway public sentiment and impact stock market prices. In this paper, we present a novel approach for predicting the volatility of multiple indices in the U.S. stock market by integrating historical closing prices with sentiment analysis of Twitter data using deep neural networks. Our approach involves extracting features from tweet metadata and analyzing the overall presence of authors on the platform to estimate future changes in index closing prices. Through our experiments, we observed a strong correlation between the predicted closing prices of an index, the sentiment expressed in tweets, and the extracted features. Despite the sparsity of the dataset, our applied neural networks exhibited robust per-formance. This study contributes to the expanding research on predicting stock market volatility through the utilization of social media sentiment analysis and deep learning techniques. The proposed approach holds promise for applications in financial decision-making and risk management. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Sustainability factors of building information modeling (BIM) for a successful construction project management life cycle in Indonesia

引用

Journal of Building Pathology and Rehabilitation 2024年第1期9卷 1-15页

作者： Latupeirissa, Josefine Ernestine Arrang, Hermin Civil Engineering Department Faculty of Engineering Universitas Kristen Indonesia Paulus South Sulawesi Makassar Indonesia Informatics Department Faculty of Informatics Computer Universitas Kristen Indonesia Paulus South Sulawesi Makassar 90243 Indonesia

Building information modeling (BIM) is an important asset in construction projects from conceptual planning to the preconstruction and operation stages. While many construction projects in developed countries are gradually implementing BIM, the development of this technology in developing countries is still lagging. Some research shows that the application of BIM for certain uses can improve the performance of construction projects, but in Indonesia, its use is still very low. Therefore, the purpose of this study is to analyze the factors that can answer the challenges in the sustainability of BIM implementation in construction projects in Indonesia for success in its life cycle. Data were obtained from 44 respondents who answered the questionnaire questions, including owners, supervisory consultants, and contractors representing construction parties in Indonesia. Empirical analysis using multiple linear regression states that there are five factors that, if used to sustainably support the application of BIM, will affect the success of construction project management in its life cycle. The five factors rank as follows based on the results of the analysis: Understanding and Awareness of the importance of BIM;Establishment of BIM standards, codes, rules, and regulations;Competencies and Skills;Commitment and Consistency;and Monitoring and Evaluation. The development of BIM has resulted in new work processes and new ways to interact in construction projects throughout their life cycles. © 2024, The Author(s).

关键词： Building Information Modeling (BIM) Implementing in Indonesia Successful construction project management life cycle Sustainability factors of BIM

来源：评论

学校读者我要写书评

暂无评论

Shared Parking Concept in the Smart City Environment 8th

Shared Parking Concept in the Smart City Environment

引用

8th International Congress on Information and Communication Technology, ICICT 2023

作者： Špitálová, Zuzana Mandová, Lucia Opatovský, Martin Faculty of Informatics and Information Technology Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9789819930906

With the modern city infrastructure and increasing number of subjects in the traffic environment, there is a need for increased number of parking places. As the parking place is usually not used for a whole day, it could be shared with the others in the certain community. As it eliminates the useless driving due to the finding of free parking place, the traffic becomes more effective and produces less emissions. We designed the application for smart city environment, which helps to achieve the described situation. It consists of two parts, the frontend and backend. The data processed by the backend part can be further used for the city infrastructure improvements. The front-end part, designed in React Native, represents the graphical interface. The back-end part, designed in PostgreSQL, represents the database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Genetic Algorithm Augmented Inception-Net based Image Classifier Accelerated on FPGA

引用

Multimedia Tools and Applications 2023年第29期82卷 45097-45125页

作者： Kaziha, Omar Bonny, Talal Jarndal, Anwar Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Deep learning models for computer vision applications specifically and for machine learning generally are now the state of the art. The growth of size and complexity of neural networks has made them more and more reliable, yet in greater need of computational power and memory as is evident from the heavy reliance on graphical processing units and cloud computing for training them. As the complexity of deep neural networks increases, the need for fast processing neural networks in real-time embedded applications at the edge also increases and accelerating them using reconfigurable hardware suggests a solution. In this work, a convolutional neural network based on the inception net architecture is first optimized in software and then accelerated by taking advantage of field programmable gate array (FPGA) parallelism. Genetic algorithm augmented training is proposed and used on the neural network to produce an optimum model from the first training run without re-training iterations. Quantization of the network parameters is performed according to the weights of the network. The resulting neural network is then transformed into hardware by writing the register transfer level (RTL) code for FPGAs with exploitation of layer parallelism and a simple trial-and-error allocation of resources with the help of the roofline model. The approach is simple and easy to use as compared to many complex existing methods in literature and relies on trial and error to customize the FPGA design to the model needed to work on any computer vision or multimedia application deep learning model. Simulation and synthesis are performed. The results prove that the genetic algorithm reduces the number of back-propagation epochs in software and brings the network closer to the global optimum in terms of performance. Quantization to 16 bits also shows a reduction in network size by almost half with no performance drop. The synthesis of our design also shows that the Inception-based classifier is cap

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Challenges of Integrating Blockchain Technology into Educational Institutions

Challenges of Integrating Blockchain Technology into Educati...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Ali, Abbas Hussein Yavanoǧlu, Uraz Graduate School of Informatics Gazi University Dept. Of Computer Science Ankara Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350364910

The latest advancements in blockchain technology have significantly influenced several sectors, such as banking, healthcare, and supply chain networks. Because of its distinct attributes, like decentralization, trustworthiness, and security, this technology has recently been employed in the field of education. While the blockchain is bright, several problems, such as legal, immutability, and scalability questions, continue to hinder its adoption in education. In this paper, we look at educational application system in terms of blockchain-based solutions that have been created for educational purposes, as well as the potential challenges of blockchain technology in the field of education. © 2024 IEEE.

关键词： Blockchain technology educational application

来源：评论

学校读者我要写书评

暂无评论

Leveraging Artificial Intelligence for Active Cyber Defense Against to Advanced Persistent Threats to Homeland Security

Leveraging Artificial Intelligence for Active Cyber Defense ...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Özbay, Recep Yavanoǧlu, Uraz Graduate School of Informatics Gazi University Dept. Of Computer Forensics Ankara Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350364910

Increasing cyber threats have rendered traditional security solutions such as firewalls and intrusion-detection/prevention systems insufficient in providing adequate protection against advanced persistent threats to homeland security. To enhance protection, the implementation of active cyber defense is recommended, offering alternative yet effective measures against these threats. This paper examines the phases of the cyber kill chain, one of the cyber defense techniques, which has been enhanced to analyze and understand advanced persistent threats. It explains the use of deception, slowdown, and counterattacks for active cyber defense. Additionally, the utilization of artificial intelligence in active cyber defense techniques is discussed. Finally, the paper elucidates how these techniques can be employed within the scope of active cyber defense to enhance security. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

引用

computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

来源：评论

学校读者我要写书评

暂无评论

End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 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：