检索结果-内蒙古大学图书馆

Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Zineb Ferhat Hamida Ahlem Drif Silvia Giordano Department of computer science University of Ferhat Abbas Sétif 1 Sétif Algeria Networking Lab Institute of Information Systems and Networking University of Applied Sciences of Southern Switzerland SUPSI Switzerland

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

The swift growth of user-created material on digital platforms has heightened the need for efficient automated methods to detect and categorize offensive language. This article presents a method for categorizing offensive Arabic content on a large scale using LLMs. We explore the challenges of understanding the Arabic language because of its intricate morphology, variations in dialects, and subtle nuances in contexts. We assess zero-shot prompting to distinguish between offensive and non-offensive Arabic language. Additionally, we consider the caliber of features extracted specifically for this task by leveraging the power of pre-trained LLMs. Our findings show that LLM-based models with zero-shot prompting on Arabic-language platforms provide rich resources for feature extraction and contribute to advancing Arabic offensive content detection.

关键词： Accuracy Social networking (online) Soft sensors Computational modeling Morphology Machine learning Feature extraction Data models Cultural differences Standards

来源：评论

学校读者我要写书评

暂无评论

LiDAR Spoofing Attack Detection in Autonomous Vehicles

LiDAR Spoofing Attack Detection in Autonomous Vehicles

引用

2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Dynamic and Private Recommendation System

Dynamic and Private Recommendation System

引用

Digital Applications, Transformation & Economy (ICDATE), International Conference on

作者： Gee Yen Lo Terence Peng Lian Tan Computer Systems and Networking Curtin University Malaysia Miri Malaysia Department of Electrical & Computer Engineering Curtin University Malaysia Miri Malaysia

The amount of data that we retrieve and use every day is increasing rapidly. As data naturally exist in different data domains, the number of data domains that we retrieve and use every day has also increased as a result. As different recommendation systems are needed for different data domains, more and more recommendation systems will need to be built. Furthermore, users will lose their privacy as many recommendation systems collect information about their users. This paper aims to solve the aforementioned issues by creating a recommendation system that will work with any data domain and upholds the privacy of its users. The Dynamic Implementation Of Privacy Based Recommendation System Application is created in this paper. The objective of this thesis is achieved as the Dynamic Implementation Of Privacy Based Recommendation System Application developed in this paper upholds the privacy of its users as it only stores users’ data in their device’s local database and the application has the ability to adapt to different data domains.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis

引用

Journal of Bionic Engineering 2024年第6期21卷 3179-3200页

作者： Chaosheng Tang Wenle Xu Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz School of Computer Science and Technology Henan Polytechnic UniversityHenanJiaozuo454003People’s Republic of China Department of Biological Sciences School of ScienceXi’an Jiaotong Liverpool UniversitySuzhou215123JiangsuChina School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University21589JeddahSaudi Arabia Department of Signal Theory Networking and CommunicationsUniversity of Granada52005GranadaSpain

Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between *** address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global ***,these approaches typically neglect the topological structure information of the graph during the global feature extraction *** paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image *** approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual *** that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global ***,we introduce a G-LFF module to fuse the local and global features effectively.

关键词： Convolutional neural networks Graph convolutional networks Graph pooling COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Transfer Learning for Intelligent Prediction of Lung Cancer Detection

A Transfer Learning for Intelligent Prediction of Lung Cance...

引用

Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

作者： Tabreer T. Al-Shouka Khattab M. Ali Alheeti Department of Computer Sciences College of Computer and Information Technology University of Anbar Anbar Iraq Department of Computer Networking Systems College of Computer and Information Technology University of Anbar Iraq

Cancer is a widespread and potentially lethal illness. There are many different types of cancer. The most common type of cancer is lung cancer. High mortality rates from lung cancer are major public health concerns. Therefore, early detection of cancer reduces the possibility of dying from the disease. A CT scan is used to determine the best course of treatment. Screening with CT has been shown to reduce mortality from lung cancer by detecting the disease at its earliest stages when it manifests as pulmonary nodules. Artificial Intelligence (AI) systems, which employ transfer-learning models, have shown great potential in enhancing the precision and speed of lung cancer diagnosis and treatment. Transfer learning enables adapting pre-existing models to new tasks, facilitating more efficient and effective medical data analysis. Additionally, these systems can assist clinicians in identifying the specific type and stage of lung cancer, which can guide treatment decisions. Our study explores the use of CNN transfer learning with RESNET, MobileNetV2, Xception, and VGG16 models for medical image analysis tasks; res-net achieved the highest testing accuracy in our research, at 0.94, with a testing loss of0.16, which has demonstrated the potential to enhance the accuracy and efficiency of healthcare AI systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligent Detection System for Multi-Step Cyber-Attack Based on Machine Learning 15

Intelligent Detection System for Multi-Step Cyber-Attack Bas...

引用

15th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Jasim, Omar Hammad Al-Dosary, Duaa Ahmed, Hamsa M. Al-Ani, Muzhir Shaban University of Anbar Computer Networking Systems Department Ramadi Iraq Al Baha University Faculty of Computer Science and Information Technology Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq

ISBN: (纸本)9798350335149

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms. © 2023 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

引用

Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

来源：评论

学校读者我要写书评

暂无评论

Integrated Sensing and Communications for Statistical-QoS Provisioning Over 6G M-MIMO Mobile Networks Using FBC

Integrated Sensing and Communications for Statistical-QoS Pr...

引用

IEEE International Conference on Communications (ICC)

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Since the 6G mobile wireless networks require the high-performances on both channel state estimations and information transmissions, the technique of integrated sensing and communication (ISAC) has attracted considerable research attention due to its ability to sense and communicate by sharing the same frequency band and hardware. However, how to jointly optimize the sensing and communication functions of the ISAC to support the 6G traffic transmissions over a time-varying wireless fading channel has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose the ISAC scheme to jointly sense the channel state and transmit the wireless-streaming data using massive multiple-input and multiple-output (massive MIMO) communications over the Rician fading channel. First, we establish the system models for the ISAC scheme under the Rician fading wireless channel and the channel state estimation scheme using the radar sensing feedback. Second, we define the channel state estimation distortion and the capacity-distortion function of a massive MIMO channel to jointly measure the performances of sensing and communication in our ISAC scheme. Third, we integrate the capacity-distortion function with the finite blocklength coding (FBC) scheme by developing the concept of the ISAC-based E-effective capacity to implement the statistical delay and error-rate bounded provisioning for supporting the 6G traffic under our ISAC scheme. Finally, we use numerical analyses to validate and evaluate our proposed ISAC scheme with massive MIMO in the non-asymptotic regime.

关键词： 6G mobile communication Wireless sensor networks Channel estimation Rician channels Massive MIMO Radar Quality of service

来源：评论

学校读者我要写书评

暂无评论

Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Methods to Detect Autism Among Children: Review

Machine Learning Methods to Detect Autism Among Children: Re...

引用

Communication and Information Technology (AICCIT), Al-Sadiq International Conference on

作者： Anas D. Sallibi Khattab M. Ali Alheeti Department of Computer Sciences College of Computer and Information Technology University of Anbar Anbar Iraq Department of Computer Networking Systems College of Computer and Information Technology University of Anbar Anbar Iraq

Autism Spectrum Disorder (ASD) is a neurological condition that permanently affects a person’s ability to learn languages, speak, think clearly, and interact with others. In the embryonic stages, its symptoms typically emerge. The conditions of ASD can be improved by identifying and treating them early. ASD is either genetic or caused by environmental factors. Currently, the only methods utilized to diagnose ASD are clinically based-tests. In addition to requiring extended diagnostic time, this will also result in a sharp increase in medical expenses. Machine Learning (ML) approaches are being used to supplement traditional methods to increase diagnoses’ accuracy and response time. The paper presents a review of the recent literature on different ML approaches for the classification and identification of ASD in children and the study of the characteristics of ASD. The concept of ASD and the techniques used to discover it are explained; at the end of the paper. The most prominent techniques used in ML to identify children with ASD were discussed in detail. Finally, The main objective of our study is to focus on whether the child is at risk of developing ASD spectrum disorder in its early stages And highlight the importance of early diagnosis using ML methods to reduce the symptoms of the disease.

关键词：

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：