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Review of MXenes as a component in smart textiles and an adsorbent for textile wastewater remediation

Chinese Chemical Letters 2024年第2期35卷 208-222页

作者： Kaniz Farhana Kumaran Kadirgama abu shadate faisal mahamude Rajan Jose Department of Apparel Engineering Bangladesh University of TextilesDhaka 1208Bangladesh Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang26600 PekanPahangMalaysia Center of Automotive Engineering Universiti Malaysia PahangMalaysia College of Engineering Universiti Malaysia Pahang26300 GambangPahangMalaysia Faculty of Industrial Sciences & Technology Universiti Malaysia Pahang26300 KuantanMalaysia Center of Intelligent Materials Universiti Malaysia Pahang26300 KuantanMalaysia

Two-dimensional(2D)MXenes have emerged as an archetypical layered material combining the properties of an organic-inorganic hybrid offering materials sustainability for a range of *** surface functional groups and the associated chemical properties'tailorability through functionalizing MXenes with other materials as well as hydrophilicity and high conductivity enable them to be the best successor for various applications in textile industries,especially in the advancement of smart textiles and remediation of textile ***-based textile composite performs superb smartness in high-performance wearables as well as in the reduction of textile dyes from *** article critically reviews the significance of MXenes in two sectors of the textile ***,we review the improvement of textile raw materials such as fiber,yarn,and fabric by using MXene as electrodes in supercapacitors,pressure ***,we review advancements in the removal of dyes from textile wastewater utilizing MXene as an absorbent by the adsorption ***-based textiles demonstrated superior strength through the strong bonding between MXene and textile structures as well as the treatment of adsorbate by adsorbent(MXene in the adsorption process).We identify critical gaps for further research to enable their real-life applications.

关键词： Textile fibers Industrial wastewater treatment MAX phases Hybrid electrodes Adsorption

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含Mxene新型纳米流体太阳能托盘的数值研究

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Journal of Central South University 2023年第11期30卷 3656-3669页

作者： Kaniz FARHANA abu shadate faisal mahamude Kumaran KADIRGAMA Department of Apparel Engineering Bangladesh University of TextilesDhaka-1208Bangladesh Department of Mechanical Engineering College of EngineeringUniversiti Malaysia PahangGambang 26300PahangMalaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang26600 PekanPahangMalaysia

本研究通过计算研究了Mxene-大豆油纳米流体在单个太阳能托盘中的热学和流体行为。太阳能托盘具有输入口和输出口。在稳定的热通量下,将Mxene纳米流体以不同的体积浓度泵送通过太阳能板。采用计算流体动力学(CFD)流体流动辐射模型,利用... 详细信息

本研究通过计算研究了Mxene-大豆油纳米流体在单个太阳能托盘中的热学和流体行为。太阳能托盘具有输入口和输出口。在稳定的热通量下,将Mxene纳米流体以不同的体积浓度泵送通过太阳能板。采用计算流体动力学(CFD)流体流动辐射模型,利用太阳能板模拟Mxene纳米流体的热物理实验数据。研究了温度、内能、努塞尔数、传热速率、表面传热系数和表面摩擦因数等热力学和流体力学参数的影响。对于Mxene纳米流体,出口温度、内能和表面摩擦因数的最大增幅分别为3.46%、1.2%和11%。太阳能板的效率提高了30%~32%。此外,新开发的Mxene纳米流体具有很好的热应用前景,热物性能也经过方差分析得到证实。

关键词： Mxene纳米流体大豆油数值模拟热性能太阳能板

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National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma

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World Journal of Gastroenterology 2024年第9期30卷 1018-1042页

作者： faisal Saud Dar Zaigham Abbas Irfan Ahmed Muhammad Atique Usman Iqbal Aujla Muhammad Azeemuddin Zeba Aziz abu Bakar Hafeez Bhatti Tariq Ali Bangash Amna Subhan Butt Osama Tariq Butt Abdul Wahab Dogar Javed Iqbal Farooqi faisal Hanif Jahanzaib Haider Siraj Haider Syed Mujahid Hassan Adnan Abdul Jabbar Aman Nawaz Khan Muhammad Shoaib Khan Muhammad Yasir Khan Amer Latif Nasir Hassan Luck Ahmad Karim Malik Kamran Rashid Sohail Rashid Mohammad Salih Abdullah Saeed Amjad Salamat Ghias-un-Nabi Tayyab Aasim Yusuf Haseeb Haider Zia Ammara Naveed Department of Hepatopancreatic Biliary Surgery&Liver Transplant Pakistan Kidney and Liver Institute&Research CentreLahore 54000Pakistan Department of Hepatogastroenterology and Liver Transplantation Dr.Ziauddin University HospitalKarachi 75600SindhPakistan University of Aberdeen Aberdeen B243FXUnited Kingdom Department of Pathology Pakistan Kidney and Liver Institute&Research CentreLahore 54000Pakistan Department of Gastroenterology&Hepatology Pakistan Kidney and Liver Institute&Research CentreLahore 54000Pakistan Department of Radiology Aga Khan UniversityKarachi 74800Pakistan Department of Oncology Hameed Latif HospitalLahore 54000Pakistan Division of Hepatopancreatic Biliary Surgery&Liver Transplantation Shifa International HospitalIslamabad 44000Pakistan Department of Hepatopancreatic Biliary Surgery&Liver Transplant Shaikh Zayed Hospital and Post Graduate Medical InstituteLahore 54000Pakistan Department of Medicine Aga Khan University HospitalKarachi 74800Pakistan Department of Liver Transplant Pir Abdul Qadir Shah Jeelani Institute of Medical SciencesGambat 66020Pakistan Department of Medicine&Gastroenterology Lifecare Hospital and Research CentrePeshawar 25000Khyber PakhtunkhwaPakistan Department of Hepatopancreatobiliary&Liver Transplant Bahria International HospitalLahore 54000Pakistan Department of Surgery Hepatopancreatobiliary&Liver TransplantDow University of Health SciencesKarachi 74800Pakistan Department of Gastroenterology Hepatology&NutritionPir Abdul Qadir Shah Jeelani Institute of Medical SciencesGambat 66020Pakistan Department of Oncology Dr.Ziauddin HospitalKarachi 74800Pakistan Department of Radiology Rehman Medical InstitutePeshawar 25000Pakistan Army Liver Transplant Unit Pak Emirates Military HospitalRawalpindi 46000Pakistan Department of Gastroenterology Sindh Institute of Urology and TransplantationKarachi 75500Pakistan Rashid Nursing Home and Cancer Clinic Rashid Nursing Home and Cancer ClinicRawalpindi 46000Pakistan Department of Gastroentero

A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the *** Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this *** effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these *** coordinated effort can minimise delays and give patients a chance for curative treatment and effective *** diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance *** cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for ***,histopathologic confirmation is not always required before *** ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for *** only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette *** patients with unresectable hCCA can be considered for liver *** chemotherapy should be offered to patients with a high risk of *** use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary *** with acute cholangitis can be drained with endoscopic or percutaneous biliary *** chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA.

关键词： Hilar cholangiocarcinoma Bismuth-Corlette classification Memorial Sloan Kettering Cancer Centre Staging Preoperative biliary drainage Portal vein embolisation Surgical resection Hepatectomy

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Metaheuristics with Machine Learning Enabled Information Security on Cloud Environment

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Computers, Materials & Continua 2022年第10期73卷 1557-1570页

作者： Haya Mesfer Alshahrani faisal S.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel abu Sarwar Zamani Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud *** computing(CC)is used for storing as well as processing ***,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public *** study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-IS)technique on cloud *** proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the *** proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max *** by,the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the *** design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several *** comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures.

关键词： Information security cloud computing intrusion anomalies data mining feature selection classification

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The economic contribution of fish and fish trade in Bangladesh

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Aquaculture and Fisheries 2020年第4期5卷 174-181页

作者： MdMostafa Shamsuzzaman Mohammad Mojibul Hoque Mozumder Sabrina Jannat Mitu abu faisal Ahamad Md.Sumon Bhyuian Department of Coastal and Marine Fisheries Faculty of FisheriesSylhet Agricultural UniversitySylhet3100Bangladesh Helsinki Institute of Sustainability Science(HELSUS) Doctoral Programme in Interdisciplinary Environmental Science(DENVI)Faculty of Biological and Environmental SciencesUniversity of HelsinkiFinland

Bangladesh possesses a large wetland area comprising diversified fisheries *** and fisheries are an integral part of Bangladesh and have earned its importance due to immense export and revenue *** objective of this study was to investigate the trend of fish production,export and import of Bangladesh during the last two *** reviews of the literature were done,numerical data was obtained from the Ministry of Fisheries and Livestock(MoFL),Department of Fisheries(DOF),Bangladesh Bureau of Statistics(BBS).The results showed that fish production has increased in Bangladesh during the last two decades,starting from 17.81 lakh metric tons in 2000–01 and reaching up to 41.34 lakh metric tons in 2016–*** to the gradual decline in capture fishery,a significant percentage of total production comes from ***(Tenualosa ilisha)is the largest single-species fishery in Bangladesh which makes the highest contribution to the country's total fish *** of the fish commodity in terms of quantity and value has declined in recent years due to food quality and safety *** study has highlighted the contribution of fisheries production in Bangladesh and trade to the country's *** paying more attention to the fisheries sector,Bangladesh can boost up its economy.

关键词： Fisheries resources Inland and marine fisheries Legal issues Export and import SDGs

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An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

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Computers, Materials & Continua 2022年第4期71卷 1233-1247页

作者： Mesfer Al Duhayyim Radwa Marzouk Fahd N.Al-Wesabi Maram Alrajhi Manar Ahmed Hamza abu Sarwar Zamani Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt Department of Computer Science Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and *** mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the *** the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational ***,artificial intelligence(AI)techniques can be used for mining a large amount of *** the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization *** this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education *** presented IEAFSS-NFC model involves data pre-processing,feature selection,and ***,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification ***,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education *** IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI *** simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

关键词： Educational data mining feature selection whale optimization classification

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Intelligent Machine Learning Based EEG Signal Classification Model

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Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd N.Al-Wesabi Mohammed Abdullah Al-Hagery Anwer Mustafa Hilal abu Sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

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Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

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Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed SAlmasoud Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Mesfer Al Duhayyim Asma Abbas Hassan Elnour Manar Ahmed Hamza Abdelwahed Motwakel abu Sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop *** of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification *** this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and *** aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient *** addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected *** study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the ***,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for *** order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was *** results were assessed under different *** obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization

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Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

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Computers, Materials & Continua 2022年第7期72卷 679-693页

作者： Abdelzahir Abdelmaboud Fahd N.Al-Wesabi Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa Manar Ahmed Hamza Mohammed Rizwanullah abu Serwar Zamani Radwa Marzouk Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Science and Arts-Girls SectionKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia&Department of MathematicsFaculty of ScienceCairo UniversityGiza12613Egypt

Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification *** this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT *** proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,*** are then connected to the cloud server for further *** addition,the modelling and extraction of behaviour take ***,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal ***,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE *** order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.

关键词： Internet of things e-learning machine learning non-verbal behavior extreme learning machine classification parameter tuning

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