检索结果-内蒙古大学图书馆

Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On Differential Controllability and Observability Functions

arXiv

引用

arXiv 2025年

作者： Kawano, Yu Besselink, Bart Scherpen, Jacquelien M.A. Graduate School of Engineering Hi-roshima University Higashi-Hiroshima739-8527 Japan Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Jan C. Willems Center for Systems and Control University of Groningen Groningen9700 AK Netherlands Faculty of Science and Engineering Jan C. Willems Center for Systems and Control University of Groningen Groningen9700 AK Netherlands

Differential balancing theory for nonlinear model reduction relies on differential controllability and observability functions. In this paper, we further investigate them from two different perspectives. First, we establish novel connections between these differential energy functions and their incremental counterparts by assuming the existence of the corresponding optimal state feedback for each controllability function. Specifically, an upper bound on the incremental controllability/observability function is provided by the corresponding differential energy function. Conversely, an upper bound on the differential controllability function can be estimated from the incremental controllability function. Furthermore, the differential observability function can be constructed from the incremental observability function. Second, we explore the positive definiteness of the differential controllability/observability function in the context of controllability/observability and stability. © 2025, CC BY-NC-ND.

关键词： Observability

来源：评论

学校读者我要写书评

暂无评论

Automated Visceral and Subcutaneous Fat Segmentation in Computed Tomography

Automated Visceral and Subcutaneous Fat Segmentation in Comp...

引用

IEEE International Symposium on Biomedical Imaging

作者： Rui Castro Inês Sousa Fábio Nunes Jennifer Mancio Ricardo Fontes-Carvalho Carlos Ferreira João Pedrosa Faculty of Engineering of the University of Porto (FEUP) Porto Portugal Faculty of Medicine of the University of Porto (FMUP) Porto Portugal Vila Nova de Gaia and Espinho Hospital Center (CHVNGE) Vila Nova de Gaia Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Cardiovascular diseases are the leading causes of death worldwide. While there are a number of cardiovascular risk indicators, recent studies have found a connection between cardiovascular risk and the accumulation and characteristics of visceral adipose tissue in the ventral cavity. The quantification of visceral adipose tissue can be easily performed in computed tomography scans but the manual delineation of these structures is a time consuming process subject to variability. This has motivated the development of automatic tools to achieve a faster and more precise solution. This paper explores the use of a U-Net architecture to perform ventral cavity segmentation followed by the use of threshold-based approaches for visceral and subcutaneous adipose tissue segmentation. Experiments with different learning rates, input image sizes and types of loss functions were employed to assess the hyperparameters most suited to this problem. In an external test set, the ventral cavity segmentation model with the best performance achieved a 0.967 Dice Score Coefficient, while the visceral and subcutaneous adipose tissue achieve Dice Score Coefficients of 0.986 and 0.995. Not only are these competitive results when compared to state of the art, the interobserver variability measured in this external dataset was similar to these results confirming the robustness and reliability of the proposed segmentation.

关键词： Image segmentation Computed tomography computer architecture Manuals Biomedical measurement Feature extraction Robustness

来源：评论

学校读者我要写书评

暂无评论

The applied principles of EEG analysis methods in neuroscience and clinical neurology

引用

Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Liver Disease Induced by Hepatitis Virus Using Machine Learning Methods 8

Diagnosis of Liver Disease Induced by Hepatitis Virus Using ...

引用

8th International Conference on Information Technology Trends, ITT 2022

作者： Hafeez, Muhammad Ahmad Imran, Azhar Khan, Muhammad Imran Khan, Ali Haider Nawaz, Asif Ahmed, Sheeraz Air University Department of Creative Technologies Islamabad Pakistan Air University Faculty of Computing and A.I Department of Creative Technologies Islamabad Pakistan School of Systems and Technology University of Management and Technology Department of Computer Science Lahore Pakistan Faculty of Electrical Engineering Higher Colleges of Technology Dubai United Arab Emirates Iqra National University Department of Computer Sciences Peshawar Pakistan

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Liver, the largest vital organ in the human body, helps in digestion, energy storage, and toxic removal processes. Hepatitis A, B, C, fatty liver, cirrhosis, and liver cancer all cause swelling of the belly and legs, changes in the color of urine, and jaundice. Early identification is necessary to save the lives of those infected with the Hepatitis virus. Although clinical tests such as Abdominal Ultrasound, Chloride Blood Test, Liver Biopsy, Liver Function Tests (LFT), Magnetic Resonance Cholangio Pancreatography (MRCP), Total Protein and Albumin/Globulin (A/G) Ratio, and others are used in the diagnosis of Hepatitis virus, but high-level expertise of the doctors are also required. With continual advancements in Artificial Intelligence (AI) and Machine Learning algorithms, medically intelligent systems for early detection of fatal liver disease can be developed for prediction of deadly liver disease in the early stage. The goal of this paper is to examine the accuracies of several Machine Learning algorithms such as Linear Regression, Decision Tree, Random Forest, KNN, and SVM. For this work, author used the data set accessible at the UCI database and proved that the Support Vector Machine perform better than all other methods with.91.84% accuracy. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Collaborative Hybrid Human AI Learning through Conceptual Exploration 3

Collaborative Hybrid Human AI Learning through Conceptual Ex...

引用

3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2024

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes Obiedkov, Sergei Ernst-Schröder-Zentrum Karolinenpl. 5 Darmstadt64289 Germany TU Dresden Dresden01062 Germany Intelligent Information Systems Universisty of Hildesheim Universitätsplatz 1 Hildesheim31141 Germany Knowledge & Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Faculty of Computer Science cfaed ScaDS.AI TU Dresden Dresden01062 Germany

Conceptual Exploration is a sophisticated method for the interactive and structured acquisition of knowledge from experts. It is therefore particularly suitable for the use in hybrid settings where both humans and AIs act as experts. This article provides a brief summary of how conceptual exploration can be used in the context of Hybrid Human AI systems, as discussed within a tutorial during the third HHAI conference in Malmö, Sweden. We will recapitulate two small experiments that were carried out with the participants of the tutorial and their results. Finally, we give some pointers on how this promising link can be further researched. © 2024 Copyright for this paper by its authors.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation and Proposed Enhancement of Latest Image Watermarking Schemes Against Specific Forgery Attacks

Performance Evaluation and Proposed Enhancement of Latest Im...

引用

International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Adnan Nadeem AlHassan Muhammad Ashraf Amir Mehmood Hani Almoamari Saad Said Alqahtany Muhammad Sarim Qammer H. Abbasi Faculty of Computer and Information Systems Islamic University of Madinah Madinah Kingdom of Saudi Arabia Department of Physics Federal Urdu University of Arts Science and Technology Karachi Pakistan Department of Computer Science Al-Kawthar University Karachi Pakistan Department of Computer Science Federal Urdu University of Arts Science and Technology Karachi Pakistan James Watt School of Engineering (of Affiliation) University of Glasgow Glasgow United Kingdom

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

Detection. Watermarking techniques can be classified based on various criteria, such as visibility, fragility, application domain, and extraction methods. However, their performance degrades against certain forgery attacks due to specific algorithmic loopholes. To deal with this issue, an in-depth performance evaluation and gap analysis of the latest invisible image watermarking schemes is presented in this paper. Then we propose an enhancement in terms of defining and using secret keys for the process of watermarking to improve temper detection and its localization against forgery attacks. First comprehensive testing is conducted regarding the imperceptibility and temper detection capability of these watermarking schemes against copy-paste, object deletion, text addition, and collage attacks. This testing results in finding the security weaknesses against collage attacks. Finally, we implemented the proposed enhancement and illustrated improved results up-to 99% in terms of localizing forgery detection in certain scenarios of collage attacks enhancement.

关键词： Performance evaluation Location awareness Protocols Watermarking Forgery Internet Classification algorithms Testing

来源：评论

学校读者我要写书评

暂无评论

Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge Control

SSRN

引用

SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument Maintenance support Engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Student Perceptions of AI-Enhanced Adaptive Learning systems: A Pilot Survey

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

引用

International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Klara Ida Katonane Gyonyoru Jozsef Katona Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.

关键词： Surveys Adaptive learning Power engineering Data security Real-time systems Mathematics Problem-solving Artificial intelligence Faces

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：