检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Department of Computer Science HITEC University Taxila Pakistan

lectromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DBl dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained loT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data. © 2025, CC BY-NC-ND.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Edge Detection with multi-scale representation and refined Network 3

Edge Detection with multi-scale representation and refined N...

引用

3rd International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Elharrouss, Omar Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Department of Computer Science and Engineering Qatar University Qatar Center for Data Analytics and Cybersecurity University of Sharjah Ukraine

ISBN: (纸本)9781839538186

Edge detection is a representation of boundaries between objects and regions in an image. Due to the variations of types, scales, intensities as well as background, the detection of these boundaries represents a challenge for different computer vision algorithms. The exploitation of multi-scale representation using various deep learning backbones make edge detection more realistic. However, edge detection is still not effective in terms of erroneous detection in some scenarios and the quality of detected edges. In this paper, we attempted to overcome these challenges using a high-resolution and refined network by fusing the output of different scales of the network and the output of the first layers. The proposed network used horizontal and vertical blocks, while the horizontal block (HB) contains a set of convolution layers and the vertical block (VB) represent the different scale of the image using convolution and pooling layers. The output of each scale in VR is combined with the corresponding layer in HB. Then, we used an affined batch normalization layer as an erosion operation for the homogeneous region in the image before fusing these outputs for the final edge detection results. The proposed method is evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images. © IET Conference Proceedings 2022.

关键词： Edge detection

来源：评论

学校读者我要写书评

暂无评论

Martian Crater Classification Using LightGBM 74

Martian Crater Classification Using LightGBM

引用

74th International Astronautical Congress, IAC 2023

作者： Darya, Abdollah Masoud Bouridane, Ahmed Halawa, Sultan Suhail Fernini, Ilias Al-Naimiy, Hamid SAASST University of Sharjah Sharjah United Arab Emirates Center for Data Analytics and Cybersecurity University of Sharjah Sharjah United Arab Emirates

The study of Martian crater morphologies and distribution plays an important part in understanding the solar system's geological history. However, crater classification is a time-consuming process that requires manual labeling of an ever-increasing supply of images from previous and current missions. Recent works have proposed using deep learning-based techniques to automate the classification process. However, deep learning methods must be trained using vast labeled datasets to achieve acceptable accuracy. Furthermore, due to their complex architectures, deep learning methods are slower than other methods, such as gradient-boosting machine learning, which recently emerged as efficient alternatives to deep learning. This work proposes Light Gradient Boosting Machines (LightGBM) combined with Principal Component Analysis (PCA) as a lightweight alternative to deep learning to classify craters from other miscellaneous Martian surface features. The Martian surface images considered in this work were taken by the High-Resolution Imaging Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter. The balanced dataset used for training the LightGBM model consisted of 1, 500 manually labeled images, with 750 of them being craters and 750 being other features such as dunes and slope streaks. To optimize the performance of the LightGBM model an exhaustive search was conducted to find the optimal number of principal components and optimal hyperparameters that maximize classification accuracy. As a result, the optimized LightGBM model achieved a classification accuracy of 88.6% and an inference speed of 150 000 images per second. Copyright © 2023 by Mr. Abdollah Darya. Published by the IAF, with permission and released to the IAF to publish in all forms.

关键词： Machine Learning Mars PCA

来源：评论

学校读者我要写书评

暂无评论

Wearable Ultrasound devices for continuous health monitoring: Current and future Prospects 8

Wearable Ultrasound devices for continuous health monitoring...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Ottakath, Najmath Al-Maadeed, Somaya Bouridane, Ahmed Chowdhury, Muhammad E.H. Sadasivuni, Kishor Kumar Department of Computer Science and Engineering Qatar University Doha Qatar Center of Data Analytics and Cybersecurity University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering Qatar University Doha Qatar Center for Advanced Materials Qatar University Doha Qatar

ISBN: (纸本)9798350382150

Advancements in wearable electronics and portable sensors have enabled long-term and real-time health monitoring. Among these advancements, wearable ultrasound devices have garnered attention due to their portability, ease of use, and ability to penetrate deep tissues in the human body. The images generated by these devices can be harnessed for computer vision tasks, providing valuable insights into an individual’s health status even during physical activity. This paper explores the design of such wearable ultrasound imagers, surveys their current applications, and offers insights into future prospects, including visual image processing and real-time analysis. The study also addresses the challenges of using wearable ultrasound for long-term health monitoring, emphasizing the need for ongoing innovation and research. ©2024 IEEE.

关键词： Ultrasonics

来源：评论

学校读者我要写书评

暂无评论

Edge detection with multi-scale representation and refined Network

Edge detection with multi-scale representation and refined N...

引用

The 3rd International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： O. Elharrouss Y. Akbari S. Al-Maadeed A. Bouridane Department of Computer Science and Engineering Qatar University Qatar Center for Data Analytics and Cybersecurity University of Sharjah UAE

关键词： computer vision deep learning (artificial intelligence) edge detection image fusion image representation image resolution

来源：评论

学校读者我要写书评

暂无评论

Robust Perceptual Hashing for Fingerprint Template Protection 1

Robust Perceptual Hashing for Fingerprint Template Protectio...

引用

1st International Conference on Computer Communications and Intelligent Systems, I3CIS 2022

作者： Birouk, Wafa Lahoulou, Atidel Melit, Ali Bouridane, Ahmed University of Bejaia Faculty of Exact Sciences Computer Science Department Bejaia06000 Algeria University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria LaRIA Laboratory University Mohammed Seddik Benyahia Computer Science Department Jijel18000 Algeria University of Sharjah Cybersecurity and Data Analytics Research Center Sharjah United Arab Emirates

ISBN: (纸本)9781665461887

Perceptual hashing has proven its robustness in securing biometric templates, where several methods have been proposed in order to have a robust and discriminating hash code against acceptable attacks. This paper proposes a robust perceptual hashing scheme for biometric template protection that based on SIFT and Harris functions for the feature ex-traction/selection phase and on DWT and SVD functions for the hash code generation phase. The experimental results in this paper discussed the effect of distance value and image blocks size parameters on hash performances (robustness and discrimination), which are used in the feature selection phase and the hash generation phase of our method respectively. The results show that we have could to choose the optimal parameters values in the generation of our hash code to give a best tradeoff between discrimination and robustness against acceptable image manipulations. © 2022 IEEE.

关键词： Signal reconstruction

来源：评论

学校读者我要写书评

暂无评论

Wearable Ultrasound Devices for Continuous Health Monitoring: Current and Future Prospects

Wearable Ultrasound Devices for Continuous Health Monitoring...

引用

IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Najmath Ottakath Somaya Al-maadeed Ahmed Bouridane Muhammad E.H Chowdhury Kishor Kumar Sadasivuni Department of Computer Science and Engineering Qatar University Doha Qatar Center of Data Analytics and Cybersecurity University of Sharjah Sharjah UAE Department of Electrical Engineering Qatar University Doha Qatar Center for Advanced Materials Qatar University Doha Qatar

Advancements in wearable electronics and portable sensors have enabled long-term and real-time health monitoring. Among these advancements, wearable ultrasound devices have garnered attention due to their portability, ease of use, and ability to penetrate deep tissues in the human body. The images generated by these devices can be harnessed for computer vision tasks, providing valuable insights into an individual's health status even during physical activity. This paper explores the design of such wearable ultrasound imagers, surveys their current applications, and offers insights into future prospects, including visual image processing and real-time analysis. The study also addresses the challenges of using wearable ultrasound for long-term health monitoring, emphasizing the need for ongoing innovation and research.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

引用

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 859-876页

作者： Ahmad Chaddad Qizong Lu Jiali Li Yousef Katib Reem Kateb Camel Tanougast Ahmed Bouridane Ahmed Abdulkadir School of Artificial intelligence Guilin Universiy of Electronic TechnologyGuilin 541004China Laboratory for Imagery Vision and Artificial IntelligenceEcole de Technologie SuperieureMontrealQuebec H3C 1K3Canada College of Medicine Taibah UniversityMadinah 42353Saudi Arabia College of Computer Science and Engineering Taibah UniversityMadinah 42353Saudi Arabia Laboratory of Design Optimization and Modeling SystemsUniversity of LorraineMetz 57070France Center for Data Analytics and Cybersecurity(CDAC) University of SharjahSharjah 27272United Arab Emirates Laboratoire de recherche en neuroimagerie Centre Hospitalier Universitaire VaudoisMont Paisible 161011LausanneSwitzerland

Artificial intelligence(AI)continues to transform data analysis in many *** in each domain is driven by a growing body of annotated data,increased computational resources,and technological *** medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of *** this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each *** models increase confidence if the results appear plausible and match the clinicians ***,the absence of a plausible explanation does not imply an inaccurate *** in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple *** example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health *** centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health *** narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.

关键词： Domain adaptation explainable artificial intelligence federated learning

来源：评论

学校读者我要写书评

暂无评论

Robust Perceptual Hashing for Fingerprint Template Protection

Robust Perceptual Hashing for Fingerprint Template Protectio...

引用

Computer Communications and Intelligent Systems (I3CIS), International Conference on

作者： Wafa Birouk Atidel Lahoulou Ali Melit Ahmed Bouridane Computer Science Department Faculty of Exact Sciences University of Bejaia Bejaia Algeria Computer Science Department University Mohammed Seddik Benyahia Jijel Algeria Computer Science Department LaRIA Laboratory University Mohammed Seddik Benyahia Jijel Algeria Cybersecurity and Data Analytics Research Center University of Sharjah Sharjah UAE

关键词： Codes Image matching Frequency-domain analysis Transforms Euclidean distance Fingerprint recognition Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Ensemble Learning-Based Intrusion Detection System for the Internet of Things

A Hybrid Ensemble Learning-Based Intrusion Detection System ...

引用

Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Mohammed M. Alani Ali Ismail Awad Ezedin Barka Cybersecurity Research Lab Toronto Metropolitan University Toronto Canada School of IT Administration and Security Seneca Polytechnic Toronto Canada College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain United Arab Emirates

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

The applications of the Internet of Things (IoT) have grown significantly both in scope and complexity. IoT devices are becoming an integral part of our daily lives. This significant growth in IoT adoption is accompanied by a substantial increase in the interest of malicious actors. IoT devices are a preferred target for malicious actors due to their inherent vulnerabilities and limited computational resources, which make them difficult to protect and secure. This study introduces a novel ensemble learning-based intrusion detection system (IDS) using network flow features. The goal of the proposed system is to achieve both simplicity and high detection accuracy. The novelty behind the system lies in using a new feature called “history”, extracted from flow information, combined with traditional features. The core classification engine includes bidirectional long short-term memory (BiLSTM) and multilayer perceptron (MLP) classifiers, with a decision tree (DT) classifier finalizing the decision-making process. The proposed system has been evaluated using a public IoT network dataset with an accomplished accuracy of 99.6%. The system has achieved results comparable to those of other systems that are more complex. The obtained results demonstrate the superior performance of the proposed ensemble learning-based system in comparison to conventional network-flow-based intrusion detection systems.

关键词： Accuracy Recurrent neural networks Intrusion detection Multilayer perceptrons Feature extraction Internet of Things History

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：