检索结果-内蒙古大学图书馆

Student Assessment Factors in Study Success Using the Decision Tree Algorithm 2

Student Assessment Factors in Study Success Using the Decisi...

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Murad, Dina Fitria Dwi Wijanarko, Bambang Mayatopani, Hendra Hassan, Rosilah Inn, Arba'Iah Sunardi Bina Nusantara University Binus Online Learning Information Systems Department Jakarta Indonesia Bina Nusantara University School of Computer Science Jakarta Indonesia Pradita University Department of Information System Tangerang Indonesia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Universiti Teknologi Malaysia Pusat Pengajian Diploma Space Kuala Lumpur Malaysia

ISBN: (纸本)9798350360523

The evaluation of students is essential to comprehending and enhancing successful learning in an educational setting. In the era of information technology, the use of decision tree algorithms has become increasingly pertinent in this sphere. This study seeks to identify the student assessment factors that have the greatest impact on learning success when using the Decision Tree algorithm. The collection and analysis of student assessment data, including test scores, class participation, and other variables Modeling the relationship between these factors and student learning outcomes using decision trees. The results of the analysis permit the identification of the most influential factors in predicting the success of student learning. The findings of this study can aid educators, schools, and educational institutions in designing learning strategies that are more effective and tailored to the specific needs of each student, as well as in identifying students who are at high risk and require special attention. The implementation of the Decision Tree algorithm in student evaluation is a crucial step toward a more individualized and effective educational approach. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

F-CNN: Faster CNN Exploiting Data Re-Use with Statistical Analysis 5

F-CNN: Faster CNN Exploiting Data Re-Use with Statistical An...

引用

5th IEEE International Conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Alantali, Fatmah Halawani, Yasmin Mohammad, Baker Al-Qutayri, Mahmoud Khalifa University System-on-Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates University of Dubai College of Engineering & Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350332674

Many of the current edge computing devices need efficient implementation of Artificial Intelligence (AI) applications due to strict latency, security and power requirements. Nonetheless, such devices, face various challenges when executing AI applications due to their limited computing and energy resources. In particular, Convolutional Neural Networks (CNN) is a popular machine learning method that derives a high-level function from being trained on various visual input examples. This paper contributes to enabling the use of CNN on resource-constrained devices offline, where a trade-off between accuracy, running time and power efficiency is verified. The paper investigates the use of minimum pre-processing methods of input data to identify nonessential computations in the convolutional layers. In this work, Spatial locality of input data is considered along with an efficient pre-processing method to mitigate the accuracy loss caused by the computational re-use approach. This technique was tested on LeNet and CIFAR-10 structures and was responsible for 1.9% and 1.6% accuracy loss while reducing the processing time by 38.3% and 20.9% and reducing the energy by 38.3%, and 20.7%, respectively. The models were deployed and verified on Raspberry Pi 4 B platform using the MATLAB coder to measure time and energy. © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cybersecurity with Transformers: Preventing Phishing Emails and Social Media Scams

Enhancing Cybersecurity with Transformers: Preventing Phishi...

引用

IEEE Conference on Dependable and Secure Computing

作者： Essam Al Daoud Laith Al Daoud Mahmoud Asassfeh Ala'a Al-Shaikh Ala'a Saeb Al-Sherideh Suha Afaneh Department of Cybersecurity Zarqa University Zarqa Jordan Computer Information System Jordan University of Science and Technology Jordan

ISBN: (数字)9798331540289

ISBN: (纸本)9798331540296

In this study, the enhancement of cybersecurity through the use of transformer models is explored, specifically focusing on the prevention of phishing emails and social media scams. Two modern datasets were utilized: the Phishing_Pot dataset, which provided phishing emails across 19 languages, and a collection of legitimate emails. Additionally, a dataset from Baaz, an Arabic social media platform, was compiled. Four approaches to phishing detection were compared: zero-shot learning with large language models (LLMs), transformers for feature extraction followed by random forest classification, fine-tuning of small language models (SLMs) on newly generated datasets, and an ensemble method combining the top models from the previous approaches. The experiments demonstrated that LLMs such as GPT-4o, Gemini 1.5, and Claude 3 achieved high accuracy, while SLMs showed improved performance post fine-tuning. The ensemble methods can be used to enhance the accuracy of fine-tuned SLM for phishing detection and scam prevention, reaching an accuracy of 98.50% for the first dataset and 95.25% for the second.

关键词： Adaptation models Accuracy Social networking (online) Phishing Prevention and mitigation Zero shot learning Transformers Data models Electronic mail Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

引用

4th International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

引用

computer systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： IbrahimM.Alwayle Badriyya B.Al-onazi Mohamed K.Nour Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Amal S.Mehanna Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’*** gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or *** practice is called review *** the last few years,various techniques have been recommended to solve the problem of spam *** spam detection study focuses on English reviews,with a lesser interest in other *** review detection in Arabic online sources is an innovative topic despite the vast amount of data ***,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion *** presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and ***,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible ***,unigram and bigram feature extractors are *** SGRU model is employed in this study to identify and classify Arabic spam *** the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU *** experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC *** extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

来源：评论

学校读者我要写书评

暂无评论

K-Banhatti Sombor Invariants of Certain computer Networks

引用

computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

来源：评论

学校读者我要写书评

暂无评论

Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

引用

computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

来源：评论

学校读者我要写书评

暂无评论

Interdependency patterns of potential seaport risk factors in relation to supply chain disruption in Indonesia

引用

Journal of Shipping and Trade 2023年第1期8卷 6页

作者： Do. Bagus, Muhammad Reza Hanaoka, Shinya Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8550 Japan Department of Information System College of Informatics and Computer Management Tidore Mandiri (STMIK Tidore Mandiri) Tidore Indonesia

As an integral part of the global supply chain network, Indonesian supply chain entities should understand conditional seaport risk factors that could lead to seaport threats that affect supply chain continuity. This study aims to provide a procedure for evaluating the interdependencies, implications, and correlations among various seaport risk factors for supply chain threats, specifically by investigating current practices in the developed economic region of Indonesian seaport operations. The study uses a rough set method to solve feature selection problems and multivariate analysis of variance to assess the correlation between dependent and independent variables. We find 39 conditional seaport risk factors that are potentially influenced by about 21 dependent factors related to seaport-fulcrum supply chain entities. Furthermore, threats from the planning process, infrastructure, seaport service process, distribution process, financial costs of nuclear enterprises and security existed and affluent highest potential risk in Indonesia. © 2023, The Author(s).

关键词： Association rule mining Genetic algorithm Interdependency risk analysis Rule rough set theory Seaport risk factors

来源：评论

学校读者我要写书评

暂无评论

The three dimensional dosimetry imaging for automated eye cancer classification using transfer learning model 14

The three dimensional dosimetry imaging for automated eye ca...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mohammed, Murtuza Ali Mohammed, Vazeer Ali Ramakrishnan, Rakesh Mohammed, Mehmood Ali Logeshwaran, J. Maheswaran, S. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

computer vision methods utilizing transfer learning can offer a promising approach for automated eye cancer diagnosis with three-dimensional dosimetry imaging. The latest advances in AI have encouraged researchers to use deep learning models for automated cancer classification. This paper presents a novel approach using three-dimensional dosimetry imaging for automated ocular cancer classification using transfer learning. The approach uses a combination of dataset augmentation methods. The approach is adapted from the popular Inception V3 CNN architecture. Two different feature extraction approaches, namely mean and maximum pooling, are compared while extracting features from the output of the CNN layers. The extracted features are fed to the classifier which uses the SVM with a linear kernel as its decision system. The effectiveness of this method is evaluated on a set of 20 healthy ocular images and 26 suspicious ocular images. The overall classification accuracy achieved with this approach is 94.85%. The results obtained demonstrate that this approach has a promising potential for automated ocular cancer classification with an appropriate transfer learning model. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

An innovation prediction of DNA damage of melanoma skin cancer patients using deep learning 14

An innovation prediction of DNA damage of melanoma skin canc...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ramakrishnan, Rakesh Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. Maheswaran, S. University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

There are now a variety of intriguing options for the study of genetic data thanks to recent developments in artificial neural networks and deep learning. In this study, we use a deep learning-based prediction model to find possible DNA damage in individuals with melanoma skin cancer. We create a convolutional neural network (CNN) model to forecast the DNA damage susceptibility of melanoma cancer cells using a publically available genome sequencing dataset. This model preprocesses the genomic data, extracts features, and categorises them. Comparing the results of our CNN model with those of a traditional logistic regression model, we find that our CNN reported superior performance in identifying differences between healthy and cancerous samples with an accuracy of nearly 96%. The model can be used to augment the standard clinical diagnosis of melanoma, which only uses visual assessment and histology. By intervening sooner, clinicians can put forward more personalized and informed plans of care and surveillance for each melanoma patient, reducing medical costs and improving the quality of patient diagnosis. © 2023 IEEE.

关键词： Neural network models

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：