检索结果-内蒙古大学图书馆

Hybrid active shape model and deep neural network approach for lung cancer detection

International Journal of information Technology (Singapore) 2024年 1-12页

作者： Othmani, Mohamed Issaoui, Brahim El Khediri, Salim Khan, Rehan Ullah Computer Science Department Faculty of Sciences Gafsa University Gafsa Tunisia Unit of Scientific Research Applied College Qassim University Buraydah Saudi Arabia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia

Lung cancer is consistently ranked as the primary cause of cancer-related fatalities worldwide. The timely identification and effective treatment of lung cancer play a pivotal role in patient survival rates. Generally, higher rates of lung cancer mortality have been observed in men compared to women, largely attributable to smoking levels. This article proposes a new hybrid approach to lung cancer detection using the Computed Tomography (CT) scan images. Our objective is two folds: first, the development of a robust and accurate segmentation approach based on the Active Shape Model (ASM), and second, the implementation of a fully automatic lung cancer detection system employing the Deep Neural Networks (DNN). Given the diverse nature of cancer growth within the lung, it can appear in any location, showing a wide range of shapes, sizes, and contrasts. The proposed approach thus lays the foundation for precise segmentation, enabling a comprehensive understanding of the structural nuances. The experimental evaluation shows that the proposed approach achieves good precision and accuracy and can help practitioners as an enhanced tool for fast and reliable cancer detection. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Active shape model Deep neural network, Cancer detection Detection Segmentation

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Segmentation of MRI Images Based on K-Means and White Shark Optimizer 24

Brain Tumor Segmentation of MRI Images Based on K-Means and ...

引用

24th International Arab Conference on information Technology, ACIT 2023

作者： Braik, Malik Al-Hiary, Heba Al-Betar, Mohammed Azmi Al-Balqa Applied University Department of Computer Science Al-salt Jordan Al-Balqa Applied University Department of Computer Information Systems Al-salt Jordan Department of Information Technology Ajman United Arab Emirates Al-Balqa Applied University Irbid Jordan

ISBN: (纸本)9798350384307

Due to the intricacy of the process involved in segmenting and extracting the tumor regions in magnetic resonance imaging (MRI), it is quite difficult to successfully detect the diseases. Due to its critical significance in subsequent image processing phases, this creates a requirement for a suitable clustering algorithm. In this study, a clustering method is presented that may address a variety problems for MRI brain tumor segmentation. One of the most well-known conventional clustering methods that is simple to use, quick to process, and ensures convergence is k-means (KM). Its weakness, which is defined by its susceptibility to the initial center's random initialization, is present. Because of this, the KM was optimized in this study using the white shark optimizer (WSO) to address this weak side of the KM. The KM would set the starting point for WSO's final position. Evaluation results of WSO-based KM were judged on a publicly available datasets in terms of several relevant evaluation methods and contrasted with outcomes of KM, fuzzy c-means (FCM), and other meta-heuristics when practiced to cluster the same datasets. The WSO-based KM clustering approach outperformed the standard KM and other competing algorithms-based KM. © 2023 IEEE.

关键词： FCM Image segmentation K-means White shark optimizer

来源：评论

学校读者我要写书评

暂无评论

Scheduling Tool for Deterministic Communication in Distributed Real-Time systems 10

Scheduling Tool for Deterministic Communication in Distribut...

引用

10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Mach, Jan Tašková, Valéria Kohútka, Lukáš Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

A network with critical data streams, where the timing of incoming and outgoing data is a necessity, is called a deterministic network. These networks are mostly used in association with real-time systems that use periodic hard-real-time data which need to be scheduled. The algorithm proposed in this paper is focused on achieving the lowest possible latency within the specified network topology depending on the physical parameters of the network and the periodic requirements of the communicating nodes. The algorithm allows to add priorities to the data flow between nodes and also the option to specify if preemption is allowed within the network, since not all devices support it. The result is a schedule of hard-real-time data communication within a specified network topology where all requirements of communicating nodes are met if such a solution is possible. © 2023 Copyright for this paper by its authors.

关键词： deadline deterministic scheduling Ethernet scheduling algorithm time-sensitive networking

来源：评论

学校读者我要写书评

暂无评论

From Data to Diagnosis: Investigating Approaches in Mental Illness Detection 2

From Data to Diagnosis: Investigating Approaches in Mental I...

引用

2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Montasser, Reem Kadry Mazen, Sherif A. Helal, Iman M. A. October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt

ISBN: (纸本)9798350362633

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Geospatial Analysis of COVID-19 Lockdown Effects on Air Quality in Jordan and Surrounding Countries 11

Geospatial Analysis of COVID-19 Lockdown Effects on Air Qual...

引用

11th International Conference on information Technology, ICIT 2023

作者： Al-Tawil, Marwan Al-Zoubi, Moh'D Belal Majdalawi, Yousef Kh. Almarabeh, Tamara Al Dahoud, Ali The University of Jordan Dept. Computer Information Systems Amman Jordan AL Zaytoonah University of Jordan Faculty of Science and IT Jordan

ISBN: (纸本)9798350320060

In spite of the adverse effects of the COVID-19 pandemic on the global economy, it has had a positive impact on the environment. To combat the spread of the virus, many countries, including Jordan, imposed lockdown measures that involved the suspension of economic activities such as manufacturing operations, transportation, and construction (with some exceptions for essential services). Consequently, the levels of certain atmospheric pollutants, such as NO2 and CO, decreased by an average of 54% and 7%, respectively, during the lockdown period in Jordan. By conducting a systematic review of the lockdown measures and applying spatiotemporal analysis, this study established a methodology to analyze the influence of pandemic mitigation measures on regional air quality. The findings revealed a significant improvement in air quality throughout the study area during the lockdown period. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

引用

11th International Conference on information Technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Enhancing Task Assignment in Crowdsensing systems Based on Sensing Intervals and Location

引用

computers, Materials & Continua 2022年第6期71卷 5619-5638页

作者： Rasha Sleem Nagham Mekky Shaker El-Sappagh Louai Alarabi Noha AHikal Mohammed Elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt College of Computer and Information Systems Umm Al-Qura UniversityMecca715Saudi Arabia

The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler *** paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time *** goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task *** paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’*** process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean *** combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task *** results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,*** sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,*** the algorithms improves task assignment in MCS for both total task quality and sensing *** results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greed

关键词： Mobile crowdsensing online task assignment participatory sensing path planning sensing time intervals ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Fake Reviews Detection Using Deep Learning: A Survey. 2

Fake Reviews Detection Using Deep Learning: A Survey.

引用

2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Abdelmohsen, Yasmeen Wassif, Khaled Ramadan, Nagy Cairo University Faculty of Graduate Studies for Statistical Research Department of Information Systems and Technology Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Department of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

Online shopping and e-commerce platforms are increasingly plagued by fake reviews, which undermine consumer trust and distort purchasing decisions. This survey addresses the critical need for effective detection mechanisms by exploring the application of deep learning techniques in identifying fake reviews. It carefully examines both supervised and unsupervised deep learning models, presenting their use cases, the features they prioritize, and their effectiveness in different scenarios. Key contributions of this survey include a comprehensive review of current methodologies, a critical comparison of their practical applications, and a discussion on the challenges of implementing these models. Furthermore, the survey identifies promising future research directions aimed at enhancing the accuracy and reliability of fake review detection systems. By synthesizing insights across various studies, this paper contributes significantly to the academic and practical understanding of deep learning's potential to combat the issue of fake reviews in digital commerce. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Organizational Data Breach:Building Conscious Care Behavior in Incident Response

引用

computer systems science & Engineering 2022年第2期40卷 505-515页

作者： Adlyn Adam Teoh Norjihan Binti Abdul Ghani Muneer Ahmad Nz Jhanjhi Mohammed A.Alzain Mehedi Masud Department of Information Systems Faculty of Computer Science&Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident *** research study draws upon the literature in the areas of information security,incident response,theory of planned behaviour,and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour *** applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore’s history to-date,the 2018 SingHealth data *** single in-depth case study observed information security awareness,policy,experience,attitude,subjective norms,perceived behavioral control,threat appraisal and self-efficacy as emerging prominently in the framework’s applicability in incident *** data analysis did not support threat severity relationship with conscious care *** findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.

关键词： End user computing organizational behavior incident response data breach computer emergency response team cyber-attack

来源：评论

学校读者我要写书评

暂无评论

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

引用

computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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：