检索结果-内蒙古大学图书馆

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh 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 Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

来源：评论

学校读者我要写书评

暂无评论

Amino Acid Encryption Method Using Genetic Algorithm for Key Generation

引用

computers, Materials & Continua 2022年第1期70卷 123-134页

作者： Ahmed S.Sakr M.Y.Shams Amena Mahmoud Mohammed Zidan Department of Information System Faculty of Computers and InformationMenofia UniversityEgypt Department of Machine learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computers and InformationKafrelshiekh UniversityEgypt Hurghada Faculty of Computers and Artificial Intelligence South Valley UniversityEgypt

In this new information era,the transfer of data and information has become a very important *** data must be kept secured from unauthorized persons using *** science of cryptography depends not only on complex mathematical models but also on encryption *** acid encryption is a promising model for data *** this paper,we propose an amino acid encryption model with two encryption *** first key is generated randomly using the genetic *** second key is called the protein key which is generated from converting DNA to a protein ***,the protein message and the first key are used in the modified Playfair matrix to generate the cypher *** experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only *** addition,the proposed model has been tested over different types of characters including white spaces and special characters,as all the data is encoded to 8-bit *** performance of the proposed model is compared with other models using encryption time and decryption *** model also balances all three principles in the CIA triad.

关键词： Cryptography amino acid genetic algorithm playfair deep learning DNA computing

来源：评论

学校读者我要写书评

暂无评论

Reshaping Automated Writing Evaluator for English as Foreign Language Users 3

Reshaping Automated Writing Evaluator for English as Foreign...

引用

3rd IEEE International Conference on Social sciences and Intelligence Management, SSIM 2023

作者： Prawati, Maria Tamarina Hasani, Muhammad Fikri Christian, Lay Ivana, Jane Bina Nusantara University Faculty of Humanities English Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Jakarta Indonesia School of Information System Bina Nusantara University Information System Department Jakarta Indonesia

ISBN: (纸本)9798350327786

To understand the role of the human rating and automatic writing evaluator (AWE), we compared their scores. There was a notable score difference in Focus and Purposes as a metric. This result indicated the need to validate and evaluate the metrics. Regarding AWE, more data is needed to improve the inference performance. Another improvement needs to include word embedding by pre-training the evaluator with a bigger corpus to represent better results. © 2023 IEEE.

关键词： academic writing automated writing evaluation English as foreign language

来源：评论

学校读者我要写书评

暂无评论

Cocktail Party Effect Using Parallel Intra and Inter Self-attention 3rd

Cocktail Party Effect Using Parallel Intra and Inter Self-at...

引用

3rd International Conference on Advances in information and Communication Technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Engagement Level Detection Using Facial Extraction and Multi-Stacked Convolutional Neural Network in e-Learning Settings 12

Engagement Level Detection Using Facial Extraction and Multi...

引用

12th Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2024

作者： Bachtiar, Fitra Abdurrachman Mutiar Mahesa, Al'Ravie Cooper, Eric Wallace Intelligent System Laboratory Universitas Brawijaya Computer Science Faculty Malang Indonesia Affective Engineering and Arts Laboratory Ritsumeikan University College of Information Science and Engineering Ibaraki Japan

ISBN: (纸本)9798350379914

Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing student learning success as students are regulating themselves during learning one-on-one using computer devices. Failing to detect student engagement may result in unsuccessful learning. In extreme cases, students may disengage and learning purpose cannot be achieved. This research aims to detect user engagement in e-Learning settings by using a deep learning approach with a feature extraction method. The dataset used in this study is the DAiSEE dataset. The dataset is in the form of videos and is extracted into frames and specific facial features. The facial area was extracted using the Mediapipe Library. The features include 1434 3D landmark features, 52 action unit features, one depth estimation feature, and three head pose estimation features. This research also conducts image selection, which selecting frames with an interval of 15 in the train, validation, and test folders. The multi-stacked Convolutional Neural Network model was used to classify four levels of engagements. The results were compared to the existing approach in engagement detection. The untuned proposed model achieved an accuracy of 48.95%, while the tuned proposed model achieved an accuracy of 52.94%. The proposed model is able to compete with state-of-the-art methods using a less complex model. Even so, the model still cannot learn labels perfectly due to a very imbalanced dataset and a possibility of model overfitting. This research is expected to be useful for educational institutions and the e-Learning field in the future. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Developing Engagement in the Learning Management system Supported by Learning Analytics

引用

computer systems science & Engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

来源：评论

学校读者我要写书评

暂无评论

Named Entity Recognition for Identifying Entities Related to Illegal Migration in Libya: An Analysis of Twitter Textual Data 4

Named Entity Recognition for Identifying Entities Related to...

引用

4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Badr, Hajar Awahida, Zamzam Essgaer, Mansour Ajaal, Asma Ahessin, Abbas Sebha University Faculty of Information Technology Infromation System Department Sebha Libya Sebha University Faculty of Sciences Computer Science Department Sebha Libya Colledge of Techniqal Sciences Department of Computer Technology Sebha Libya

ISBN: (纸本)9798350372632

The issue of illegal migration dominates political and media discourse, particularly on social media, generating interest in collecting and analyzing textual data from platforms like Twitter, and Facebook. Named Entity Recognition (NER) is a natural language processing technique used to extract information from unstructured text, including identifying named entities such as people, organizations, and locations. This study aims to contribute to the identification of entities within collected textual data from Twitter related to illegal migration associated with Libya. The study will employ data mining tools, particularly pre-trained NER models to achieve its objective. The research is significant as there is currently no Libyan study that has addressed this specific subject using this technique. NER can help researchers, policymakers, law enforcement agencies, and humanitarian organizations to better understand the scope, dynamics, and impact of this complex issue. © 2024 IEEE.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

引用

computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

来源：评论

学校读者我要写书评

暂无评论

Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

引用

computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

来源：评论

学校读者我要写书评

暂无评论

Arabic Abstractive Summarization Using the Multilingual T5 Model 6

Arabic Abstractive Summarization Using the Multilingual T5 M...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Karim, Abdelrhman A. Usama, Mohammed Ibrahim, Manar A. Hatem, Youssef Wael, Waleed Mazrua, Ahmad T. El-Monayer, Ghada K. Elbanhawy, Magy Foad, Khaled Moawad, Ibrahim F. Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Natural language processing (NLP) is a branch of artificial intelligence (Al) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual TS (MTS) model AASMTS to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MTS architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MTS. © 2024 IEEE.

关键词： Deep neural networks

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：