检索结果-内蒙古大学图书馆

25th International Arab Conference on Information Technology, ACIT 2024

作者： Asassfeh, Mahmoud Al-Mousa, Mohammad Rasmi Al-Dweikat, Hasan Al-Mashagbeh, Malak Hamad Afaneh, Suha Samara, Ghassan Zaid, Ali Abu Khouj, Mohammed Al Daoud, Essam Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Department Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

Network forensics pertains to a subfield of digital forensics that deals with network security. It is utilized in conjunction with computer network traffic monitoring and analysis, which acts as an intrusion detection system and firewall and facilitates the collection of data and digital proof for network security. This platform is designed to analyze and monitor data in a real-time network environment. It is a crucial instrument for identifying and resolving instances of real-time network misuse. This research shows the differences between network forensics and digital forensics as well as the tools and techniques used in network forensics. It also discusses building forensics platforms, developing network analysis methodologies, and talking about the future of network forensics. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

A Novel Inception Deep Learning Model for Mobile AI Stroke Prediction System: AI Research and Industry Perspectives for Connected Health

A Novel Inception Deep Learning Model for Mobile AI Stroke P...

引用

Computing and Informatics (ICCI), International Conference on

作者： Bassant M. Elbagoury Rytis Maskeliuans Marwa Zaghow Nabil Kamel Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Ain Shams University Computer Science and Artificial Intelligence Programs King Salman International University KSIU Department of Multimedia Engineering Faculty of Informatics Kaunas University of Technology Lithuania Sales Business and Services Transformation Dell technologies EMEA Egypt Computer Science and Artificial Intelligence Programs Faculty of Computer Science and Engineering King Salma International university KSIU

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Double-Side Self-Tuning IPT Converter Immune to Parameters Variation Under Misalignment Condition 50

A Double-Side Self-Tuning IPT Converter Immune to Parameters...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zou, Bowei Huang, Zhicong Iam, Io-Wa Chi-Seng, Lam Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

For inductive power transfer (IPT) systems, the loosely coupled transformer (LCT) is a crucial component. Variations in the air gap can lead to fluctuations in the parameters of the LCT, such as self-inductance and mutual inductance, caused by positional deviations of the ferrite cores on both sides. However, such variable LCT parameters can damage the resonant tank, thus impacting transmission efficiency and output stability. To address this issue, this paper proposes a self-tuning S/S IPT system based on switch-controlled capacitors (SCCs). The system utilizes gradient descent methods independently on double-side to achieve decoupled tuning control. This approach effectively mitigates the impact of LCT parameter fluctuations without requiring wireless communication. Furthermore, all switches in the system operate at fixed frequencies and implement soft switching. Finally, a prototype is constructed to validate the effectiveness of the proposed system. © 2024 IEEE.

关键词： Energy transfer

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning model for Question Analysis in Low-resource Languages: A Dataset and Case Study for Persian

A Deep Learning model for Question Analysis in Low-resource ...

引用

Pattern Recognition Systems (ICPRS), International Conference on

作者： Fatemeh Ebrahimi Khaksefidi Afsaneh Fatemi Mohammad Ali Nematbakhsh Mahsa Abazari Kia Faculty of Computer Engineering Department University of Isfahan Isfahan Iran Faculty of Computer and Data Science Northeastern University London London United Kingdom

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

Question-answering systems, characterized by their fundamental functions of question classification, information retrieval, and answer selection, demand refinement to enhance precision in retrieving exact answers. Question classification, a cornerstone task, anticipates the probable answer to a posed query. However, the performance of question classification algorithms is hampered, particularly in agglutinative languages with complex morphology like Persian, where linguistic resources are limited. In this study, we propose a novel multi-layer Long-short-term memory (LSTM) Attention Convolutional Neural Network (CNN) (LACNN) classifier, tailored to extract pertinent information from Persian language contexts. Notably, this model operates autonomously, obviating the need for prior knowledge or external features. Moreover, we introduce UIMQC, the first medical question dataset in Persian, derived from the English GARD dataset. The inquiries within UIMQC are inherently intricate, often pertaining to rare diseases necessitating specialized diagnosis. Our experimental findings demonstrate a notable enhancement over baseline methods, with a 9% performance increase on the UTQC dataset, and achieving 67.08% accuracy on the UIMQC dataset. Consequently, we advocate for the adoption of the LACNN model in various morphological analysis tasks across low-resource languages, as in Question Answering systems it improves the performance for retrieving accurate answers to the users’ queries.

关键词： Analytical models Accuracy Refining Morphology Manuals Data augmentation Question answering (information retrieval)

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

引用

computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

来源：评论

学校读者我要写书评

暂无评论

Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

引用

2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

来源：评论

学校读者我要写书评

暂无评论

The Sharma-Tasso-Olver-Burgers equation:its conservation laws and kink solitons

引用

Communications in Theoretical Physics 2022年第2期74卷 1-8页

作者： K Hosseini A Akbulut D Baleanu S Salahshour Department of Mathematics Near East University TRNCMersin 10Turkey Department of Mathematics-Computer Art-Science FacultyEskisehir Osmangazi UniversityEskisehirTurkey Department of Mathematics Faculty of Arts and SciencesCankaya UniversityAnkara 06530Turkey Institute of Space Sciences Magurele-BucharestRomania Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbulTurkey

The present paper deals with the Sharma-Tasso-Olver-Burgers equation(STOBE)and its conservation laws and kink *** precisely,the formal Lagrangian,Lie symmetries,and adjoint equations of the STOBE are firstly constructed to retrieve its conservation *** solitons of the STOBE are then extracted through adopting a series of newly well-designed approaches such as Kudryashov and exponential *** graphs in 2 and 3D postures are formally portrayed to reveal the dynamical features of kink *** to the authors’knowledge,the outcomes of the current investigation are new and have been listed for the first time.

关键词： Sharma-Tasso-Olver-Burgers equation conservation laws Kudryashov and exponential methods kink solitons dynamical features

来源：评论

学校读者我要写书评

暂无评论

EmotionRF: Enhancing Emotion Classification from EEG Signals Using Machine Learning with Explainable AI

EmotionRF: Enhancing Emotion Classification from EEG Signals...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Kamrul Golder Anindya Nag Fahim Faisal Tarun Hirak Mondal Md. Mehedi Hassan Asif Karim Department of Computer Science and Engineering North Western University Khulna Bangladesh Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Faculty of Science and Technology Charles Darwin University Casuarina Australia

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study investigates the application of machine learning techniques for classifying human emotions using electroencephalography (EEG) data. Focusing on the categorization of positive, negative, and neutral emotional states, we utilize the ‘Feeling Emotions’ EEG brainwave dataset from Kaggle to evaluate the efficacy of various classification *** machine learning algorithms are employed in this analysis: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), ExtraTrees (ET), and Decision Tree (DT). Our proposed model, ‘EmotionRF’, based on RF, achieves 99% accuracy, significantly outperforming other algorithms. To enhance the interpretability of the high-performing Random Forest model, we apply Explainable AI techniques, specifically LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). These methods provide valuable insights into the model’s decision-making processes and feature importance, thereby increasing the transparency of its predictions and confirming its *** findings highlight the potential of EEG data in developing reliable tools for psychological assessment and adaptive interfaces. This research contributes to the advancement of machine learning applications in neuroscientific data analysis and underscores the importance of interpretable AI in clinical and research settings.

关键词： Radio frequency Support vector machines Accuracy Explainable AI Computational modeling Predictive models Brain modeling Electroencephalography Data models Random forests

来源：评论

学校读者我要写书评

暂无评论

Enhancing Underwater Images with Color Correction and UW-Net Technology 3

Enhancing Underwater Images with Color Correction and UW-Net...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chandrasekaran, Hariharasudhan Bhatt, Pooja Tiwari, Vinita Rastogi, Shailesh Chennai Institute of Technology Department of Civil Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Vadodara India Pune India Department of Finance Pune India

ISBN: (纸本)9798331508456

The profound importance of effective underwater image restoration is well-recognized across a variety of domains including underwater exploration, marine biology, environmental monitoring, and autonomous underwater vehicle operations. Current methodologies, however, struggle to adequately address the inherent challenges of underwater imaging, such as severe color distortion and poor visibility due to light absorption and scattering. This paper introduces UW-Net, an innovative end-to-end neural network model specifically tailored for underwater image restoration. UW-Net ingeniously integrates discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) to enhance the extraction and reconstruction of underwater image features. A pivotal aspect of UW-Net is its novel color correction strategy which specifically targets the red and blue spectral distortions, typical of underwater imaging. Additionally, the network incorporates a U-Net architecture with DWT for effective down-sampling and IDWT for precise up-sampling, alongside a chromatic adaptation transform layer aimed at boosting image contrast and color fidelity. Extensive training and evaluation on prominent datasets reveal that UW-Net significantly outperforms existing methods, offering substantial improvements in various performance metrics. This paper delineates the design, implementation, and testing of UW-Net, showcasing its superior capability to restore high-quality visuals from degraded underwater images. © 2025 IEEE.

关键词： Underwater photography

来源：评论

学校读者我要写书评

暂无评论

GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

引用

arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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：