检索结果-内蒙古大学图书馆

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Khulaidi, Nesmah A. Zahary, Ammar T. Hazaa, Muneer A.S Nasser, Adel A. Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen University of Science and Technology Faculty of Computing and It Department of Computer Science Sana'a Yemen Thamar University Faculty of Computer and Information System Department of Computer Science Thamar Yemen Sa'adah University Faculty of Sciences Department of Information Systems and Computer Science Sa'adah Yemen

ISBN: (纸本)9798350305333

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

引用

computers, Materials & Continua 2023年第10期77卷 665-680页

作者： Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Ahmad Almadhor Tai-hoon Kim Department of Computer Science COMSATS UniversityIslamabadPakistan College of Computer Studies De La Salle UniversityManila1004Philippines Faculty of Information and Communication Studies University of the Philippines Open UniversityLos Banos4031Philippines College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia School of Electrical and Computer Engineering Yeosu CampusChonnam National UniversityYeosu-si59626Korea

Cardiac disease is a chronic condition that impairs the heart’s *** includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart *** conditions can lead to serious complications and even be life-threatening if not detected and managed in *** have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and *** approaches have been applied to predict and treat heart disease utilizing ML and *** paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease *** approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart *** models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart ***-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed *** reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

关键词： Deep neural network heart disease healthcare machine learning stacking

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

引用

computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman Qasem Mohammed Al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health *** huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been *** COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social *** methods have been applied for detecting rumors and tracking their sources for COVID 19-related ***,very few studies have been conducted for this purpose for the Arabic language,which has unique ***,this paper proposes a comprehensive approach which includes two phases:detection and *** the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the *** experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

引用

computers, Materials & Continua 2023年第2期74卷 3315-3332页

作者： Mesfer Al Duhayyim Saud S.Alotaibi Shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed Rizwanullah Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Arts at MuhayelKing Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing ***,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management *** study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human *** goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous *** HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and *** addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 ***,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

关键词： Hazardous waste image classification ensemble learning deep learning intelligent models human health weighted voting model

来源：评论

学校读者我要写书评

暂无评论

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

引用

computer Modeling in Engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

来源：评论

学校读者我要写书评

暂无评论

Sensor-based identification to detect counterfeit smartphones using Blockchain

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16521-16538页

作者： Almuhaideb, Abdullah M. Alsunaidi, Shikah J. Department of Networks and Communications College of Computer Science and Information Technology SAUDI ARAMCO Cybersecurity Chair Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia

The rapid growth of the technology sector and the increasing need to own smartphones contributed to the increase in the rate of smartphones theft, as well as the sale of counterfeit or substandard devices. The Internet provides many tools and tutorials that show the criminal how to manipulate the identity of a stolen device to prevent tracking or detection, as well as to help him/her unlock/unblock the device. Consequently, the likelihood of recovering or finding stolen smartphones is significantly reduced. There are many challenges during the development of a security solution to this problem, the most important of which is the limited smartphone resources. In this paper, we propose a new device identification approach considering the challenges of the smartphone identification process. It utilizes the sensor calibration-based identity as a device fingerprint and the Blockchain technology to manage the fingerprint. The comparisons with related solutions showed that our proposed method can balance between all solution requirements including compatibility with resource-constrained devices, fulfilling of the device identification, and the security requirements to detect counterfeit smartphones. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Enhancing Student's Performance Classification Using Ensemble Modeling

Iraqi Journal for Computer Science and Mathematics

引用

Iraqi Journal for computer science and Mathematics 2023年第4期4卷 204-214页

作者： Nafea, Ahmed Adil Mishlish, Muthanna Shaban, Ali Muwafaq Shaban AL-Ani, Mohammed M. Ali Alheeti, Khattab M. Mohammed, Hussam J. Department of Artificial Intelligence College of Computer Science and IT University of Anbar Ramadi Iraq Department of Mathematics Faculty of Education for pure science University of Anbar Ramadi Anbar Iraq Department of Information System University of Anbar Ramadi Iraq Selangor Bangi Malaysia Department of Computer Science University of Anbar Ramadi Iraq

A precise prediction of student performance is an important aspect within educational institutions to improve results and provide personalized support of students. However, the predication accuracy of student performance considers an open issue within education field. Therefore, this paper proposes a developed approach to identify performance of students using a group modeling. This approach combines the strengths of multiple algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM). Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robust and reliable predictive model because it considers The experiments were evaluated using the Open University Learning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive dataset containing various characteristics related to the student’s activities thereby five cases based on the utilized dataset were investigated. The experiment results showed that the proposed ensemble model presented its ability with accurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed model exceeded the accuracy of individual basic models by using the strengths of various algorithms to improve the generalization by reducing the potential weaknesses of individual models. Consequently, the education institutes can easily identify students who may need additional support and interventions to improve their academic performance. © The Author(s), 2023.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Using an Evolutionary Algorithm Such as Genetic Algorithm (Ga) to Optimize Linear Equations 5

Using an Evolutionary Algorithm Such as Genetic Algorithm (G...

引用

5th International Conference on Neural networks and Neurotechnologies, NeuroNT 2024

作者： Wdaa, Abdul Sttar Ismail Al-Barzinji, Shokhan M. Tarish, Hiba A. College of Education for Pure Science University of Anbar Ramadi Iraq College of Computer Science and Information Technology University of Anbar Department of Computer Networks Systems Ramadi Iraq University of Technology Civil Engineering Departments Baghdad Iraq

ISBN: (纸本)9798350363739

An Evolutionary algorithm is a subset of evolutionary computations. Cloning, mutation, recombination, and selection are some of the methods used by the evolutionary algorithm that were inspired by biological evolution. The problem that was addressed in this study is how to find the coefficient values of the variables in linear equations that contain a set of variables, it must equal the right side. After reviewing previous research looking for optimization, genetic algorithm, which will use in this paper, so how are going to use this strategy to solve our problems? Select the best chromosomes from this population. Perform random chromosomal hybridization. Then, within the genes of our chromosomes, produce random mutations. Will send these chromosomes for selection again. The best chromosomes among them are selected again. It crosses and turns again, and the process is repeated until a certain number of generations have passed or find the best option. After applying the genetic algorithm through the Matlab program to the linear equation, which contains six variables, for example, the results were very excellent, as the parameter values were found in a very short time and with high accuracy, completely matching the right side of the equation. © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

引用

computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences 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 Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

来源：评论

学校读者我要写书评

暂无评论

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

引用

computer systems science & Engineering 2023年第4期45卷 737-751页

作者： Saud S.Alotaibi Eatedal Alabdulkreem Sami Althahabi Manar Ahmed Hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social ***,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process *** OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into ***,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification ***,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM *** major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual *** addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction ***,BLSTM model is employed for the effectual detection and classification of ***,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

关键词： Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning

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：