检索结果-内蒙古大学图书馆

4th International Conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Dronalyse: Cloud-based Drone for Photovoltaic Fault Inspection and Analysis

Dronalyse: Cloud-based Drone for Photovoltaic Fault Inspecti...

引用

Cognitive engineering and Intelligent Interaction (CEII), Asia Conference on

作者： Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Tu Department of Computer Systems Engineering Faculty of Information and Communication Technology Tshwane University of Technology South Africa Faculty of Information and Communication Technology Tshwane University of Technology South Africa

ISBN: (数字)9798331508760

ISBN: (纸本)9798331508777

Photovoltaic cells otherwise known as solar cells convert sunlight into electricity. However, there are militating issues against optimal performance of this technology. Traditional methods of resolving these issues have not yielded satisfactory results. This study aims to resolve the abovementioned issues using a novel Dronalyse, a drone-based method for detecting and analyzing photovoltaic faults. The Tshwane University of technology, Soshanguve South Campus, South Africa hosted the data collection team; DJI Mavic 3 Thermal's high-resolution thermal and visual imaging capabilities were used for the data collection. The data collected comprised Hotspots images, Cracks images, and Shadings images. The following services were employed to perform the tasks; AWS IoT Core, Amazon S3, AWS Lambda, Amazon Rekognition, OpenCV on Lambda, Amazon SageMaker, Amazon QuickSight, AWS SNS (Simple Notification Service), and AWS DynamoDB Database. Extensive analysis of the thermal data reveals the anomalies as indicative of faults in the solar cells of PV module. The data we collected were processed and shared freely for scientific community use at https://***/10.17632/5ssmfpgrpc.l

关键词： Cloud computing Visualization Uncertainty Photovoltaic cells Inspection Data collection Thermal analysis Solar panels Monitoring Drones

来源：评论

学校读者我要写书评

暂无评论

Delve deep into End-To-End Automatic Speech Recognition Models

Delve deep into End-To-End Automatic Speech Recognition Mode...

引用

2023 International Seminar on Application for technology of information and Communication, iSemantic 2023

作者： Labied, Maria Belangour, Abdessamad Banane, Mouad Hassan Ii University Ben m'Sik Faculty of Sciences Laboratory of Information Technology and Modeling Casablanca Morocco Hassan Ii University Faculty of Legal Economic and Social Sciences Laboratory of Artificial Intelligence & Complex Systems Engineering Casablanca Morocco

ISBN: (纸本)9798350339215

Automatic Speech Recognition (ASR) has experienced significant advancements in recent years, with end-to-end approaches emerging as a promising paradigm shift. Unlike traditional ASR systems that rely on a pipeline of separate components, end-to-end models aim to directly transcribe speech inputs into text using deep learning architectures. In this paper, we conduct a comprehensive study on end-to-end ASR models. We review various architectures employed in end-to-end ASR, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. We explore different training methodologies, loss functions, and optimization algorithms used in end-to-end ASR. Additionally, we discuss large-scale datasets commonly used for training and evaluate the performance of end-to-end models using established evaluation metrics such as word error rate (WER). Furthermore, we analyze the strengths and weaknesses of end-to-end ASR models, highlight their applications in real-world scenarios, and discuss open challenges and future directions. By providing this comprehensive study, we aim to facilitate a deeper understanding of end-to-end ASR models and their potential for driving advancements in speech recognition technology. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

引用

computers, Materials & Continua 2023年第4期75卷 133-148页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel Mona M.Abusurrah K.Vijaya Kumar E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-Commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVisakhapatnam CampusGITAM(Deemed to be a University)India Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s *** this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class *** order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

关键词： Osteosarcoma medical imaging deep learning feature vectors computer aided diagnosis image classification

来源：评论

学校读者我要写书评

暂无评论

Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Using Supervised Machine Learning Approaches to Detect Fraud in the Banking Transaction Network 9

Using Supervised Machine Learning Approaches to Detect Fraud...

引用

9th International Conference on Web Research, ICWR 2023

作者： Khosravi, Samiyeh Kargari, Mehrdad Teimourpour, Babak Eshghi, Abdollah Aliabdi, Amin Tarbiat Modares University Industrial and Systems Engineering Tehran Iran Tarbiat Modares University Information Technology Tehran Iran Amirkabir University of Technology Computer Science Algorithm and Computational Geometry Laboratory Tehran Iran

ISBN: (纸本)9798350399691

Nowadays, criminal frauds occur in an organized manner in the banking sector. This issue is challenging since the number of organized frauds associated with such areas is estimated to range from 2% to 5% of the global gross domestic product (GDP). The people committing organized fraud use Internet-based financial services and conventional financial services. Accordingly, they use more complex plans and maps to avoid being recognized through organized fraud fighting systems. Due to the complexity and variety of fraud methods, the transaction may not seem suspicious initially. Hence, it is crucial to consider the interactions between the cards. For this purpose, the use of network theory is recommended. The current paper aims to classify each transaction as illegal or legal correctly. Therefore, extensive data analysis is used to organized fraud in the bank transaction network. Besides, a comparison between supervised learning algorithms is presented on a dataset with 46,316 transactions related to customers' card activities to distinguish between illegal and legal transactions. According to the Accuracy, Precision, Recall, and F1-Score criteria values, random forest and XGBoost could be considered suitable predictive models for fraud detection. © 2023 IEEE.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Machine Learning for Simulating Complex Learner Profiles

Probabilistic Machine Learning for Simulating Complex Learne...

引用

International Conference on information technology Based Higher Education and Training, ITHET

作者： Vamsi Krishna Nadimpalli Flemming Bugert Dominik Bittner Susanne Staufer Simon Röhrl Florian Hauser Timur Ezer Lisa Grabinger Robert Maier Jürgen Mottok Faculty of Electrical Engineering and Information Technology Laboratory for Safe and Secure Systems (LaS) Regensburg Germany

ISBN: (数字)9798331516635

ISBN: (纸本)9798331516642

Data is the foundation of all machine learning applications. In education science, especially for the learner characteristics that drive personalized learning, it is difficult to collect and often uncertain. It is challenging to model, train, evaluate, and analyze the underlying algorithms when developing AI-based systems and having small sample sizes. To address these problems, we present a synthetic data generator utilising probabilistic models. This generator can effectively model and simulate complex learner profiles. To achieve this, we collected extensive data on learning styles, learning strategies, personalities, and preferred learning paths from 593 students over several semesters at a higher education level. Then, Bayesian networks, Hidden Markov Models, and Markov Chains are used to model the relationships between learner profiles. Using the Bayesian information criterion, and cross-validation with log-likelihood scores, we compare various models to select the best fitting one for synthesizing the data. The synthetic data is then evaluated using statistical validation techniques. In addition, we developed a simulation module with the option to simulate learner profiles based on manual user-defined inputs. The data and code used in this work are available as open source 1 1 10.5281/zenodo.13768136 contributing to open science and developers for customized simulated data. In the future, this data will refine the training, evaluation, analysis, and benchmarking of algorithms for personalized learning,

关键词： Training Analytical models Machine learning algorithms Hidden Markov models Machine learning Probabilistic logic Data models Generators Bayes methods Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

引用

Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of Electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of Electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and faculty of information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sustainable Mining in the Era of Artificial Intelligence

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

来源：评论

学校读者我要写书评

暂无评论

A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

引用

14th International Conference on information and Communication systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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：