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Realistic Smile Expression Recognition Approach Using ensemble classifier with Enhanced Bagging

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Computers, Materials & Continua 2022年第2期70卷 2453-2469页

作者： Oday A.Hassen Nur Azman Abu Zaheera Zainal Abidin Saad M.Darwish University Technical Malaysia Melaka Department of Information TechnologyMelaka76100Malaysia Institute of Graduate Studies and Research University of AlexandriaAlexandriaEl Shatby21526Egypt

A robust smile recognition system could be widely used for many real-world *** of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face *** this paper,an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade *** model takes advantage of the intrinsic association between face detection,smile,and other face features to alleviate the over-fitting issue on the limited training set and increase classification *** features are extracted taking into account to exclude any unnecessary coefficients in the feature vector;thereby enhancing the discriminatory capacity of the extracted features and reducing the computational ***,the main causes of error in learning are due to noise,bias,and *** helps to minimize these *** of multiple classifiers decrease variance,especially in the case of unstable classifiers,and may produce a more reliable classification than a single ***,a shortcoming of bagging as the best ensemble classifier is its random selection,where the classification performance relies on the chance to pick an appropriate subset of training *** suggested model employs a modified form of bagging while creating training sets to deal with this challenge(error-based bootstrapping).The experimental results for smile classification on the JAFFE,CK+,and CK+48 benchmark datasets show the feasibility of our proposed model.

关键词： ensemble classifier smile expression detection features extraction

Key-Attributes-Based ensemble classifier for Customer Churn Prediction

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Journal of Electronic Science and Technology 2018年第1期16卷 37-44页

作者： Yu Qian Liang-Qiang Li Jian-Rong Ran Pei-Ji Shao School of Management and Economics University of Electronic Science and Technology of ChinaChengdu 611731

Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example,the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for ***, the experimental results based on the real data collected from China Mobile show that the keyattributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.

关键词： Customer churn data mining ensemble classifier key attribute

An Improved LSTM-PCA ensemble classifier for SQL Injection and XSS Attack Detection

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Computer Systems Science & Engineering 2023年第8期46卷 1759-1774页

作者： Deris Stiawan Ali Bardadi Nurul Afifah Lisa Melinda Ahmad Heryanto Tri Wanda Septian Mohd Yazid Idris Imam Much Ibnu Subroto Lukman Rahmat Budiarto Universitas Sriwijaya Palembang30319Indonesia Universiti Teknologi Malaysia Johor81310Malaysia Universitas Islam Sultan Agung Semarang50112Indonesia Directorat General of Higher Education Research and Technology Jakarta10270Indonesia Albaha University Alaqiq65779-7738Saudi Arabia

The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been published in journals,conferences,or integrated books from the scientific repository of universities and research institutes in *** increasing popularity of the RAMA Repository leads to security issues,including the two most widespread,vulnerable attacks i.e.,Structured Query Language(SQL)injection and cross-site scripting(XSS)*** attacker gaining access to data and performing unauthorized data modifications is extremely *** paper aims to provide an attack detection system for securing the repository portal from the abovementioned *** proposed system combines a Long Short–Term Memory and Principal Component Analysis(LSTM-PCA)model as a *** model can effectively solve the vanishing gradient problem caused by excessive positive *** experiment results show that the proposed system achieves an accuracy of 96.85%using an 80%:20%ratio of training data and testing *** rationale for this best achievement is that the LSTM’s Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks’*** Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be *** addition,the LSTM’s Input Gate assists in finding out crucial information and stores specific relevant data in the memory.

关键词： LSTM PCA ensemble classifier SQL injection XSS

Evolutionary Algorithm with ensemble classifier Surrogate Model for Expensive Multiobjective Optimization

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第S1期37卷 76-87页

作者： LAN Tian College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally *** problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function *** from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for *** specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate *** extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization *** experimental results show that MOEA-EC outperforms the compared algorithms.

关键词： multiobjective evolutionary algorithm expensive multiobjective optimization ensemble classifier surrogate model

ensemble classifier-Based Features Ranking on Employee Attrition

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Journal on Artificial Intelligence 2022年第3期4卷 189-199页

作者： Yok-Yen Nguwi Nanyang Business School Nanyang Technological University639798Singapore

The departure of good employee incurs direct and indirect cost and impacts for an *** direct cost arises from hiring to training of the relevant *** replacement time and lost productivity affect the running of business *** work presents the use of ensemble classifier to identify important attributes that affects attrition *** data consists of attributes related to job function,education level,satisfaction towards work and working relationship,compensation,and frequency of business *** bagging and boosting classifiers were used for *** results show that the selected features(nine selected features)achieve the same result as the full *** selected features are age,income,working years,source of employment,years since last promotion,salary hike,and business travelling *** features were selected using ensemble *** on work and relationship do not appear to be significant attributes in attrition from ensemble classifier’s results.

关键词： Machine learning ensemble classifier attrition prediction

Construction of Influenza Early Warning Model Based on Combinatorial Judgment classifier:A Case Study of Seasonal Influenza in Hong Kong

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Current Medical Science 2022年第1期42卷 226-236页

作者： Zi-xiao WANG James NTAMBARA Yan LU Wei DAI Rui-jun MENG Dan-min QIAN Department of Medical Informatics School of MedicineNantong UniversityNantong 226001China Department of Computer Science College of Engineering and Computing SciencesNew York Institute of TechnologyNew York 10023USA Department of Computer Science College of Overseas EducationNanjing University of Posts and TelecommunicationsNanjing 210023China Department of Epidemiology School of Public HealthNantong UniversityNantong 226019China Artificial Intelligence Laboratory Center De Montfort University of LeicesterLeicester LE1 9BHUnited Kingdom

Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

关键词： influenza prediction data-driven Support Vector Machine Discriminant Analysis ensemble classifier

Robust model for tunnel squeezing using Bayesian optimized classifiers with partially missing database

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Underground Space 2023年第3期10卷 91-117页

作者： Yin Bo Xing Huang Yucong Pan Yanfang Feng Penghai Deng Feng Gao Ping Liu Quansheng Liu Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province School of Civil EngineeringWuhan UniversityWuhanHubei 430072China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan UniversityWuhanHubei 430072China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanHubei 430071China

Accurately predicting and estimating the squeezing and ground response to tunneling remains ***,tunnel-squeezing hazards are much more likely to occur in deeply buried long tunnels with complex engineering-geological ***-fore,a high-performance predictive model for tunnel squeezing is necessary.A superior ensemble classifier is put forward in this study,which is composed of four individual classifiers(gradient boosting classifier,extra-trees classifier,AdaBoost classifier,and Logistic regression classifier)and two optimization algorithms(Bayesian optimization(BO)and sparrow search algorithm(SSA)).The training database covers five parameters:tunnel depth(H),rock tunneling quality index(Q),tunnel diameter(D),support stiffness(K),and strength stress ratio(SSR),about which the basic information is accessible at the early design ***,the dataset compiled from the literature is ***,the ten proposed methods are used to replace the missing *** the model training pro-cess,BO shows its strong ability to optimize seventeen *** applied to tune the classifiers’weights,SSA achieves a fast and efficient *** novel Shapley Additive Explanations–LightGBM method indicates that the K is the most important input feature,followed by SSR,Q,H,and D,*** ensemble classifier is then validated using the test set and additional his-torical case *** validation shows that the model can achieve an accuracy of 98%(i.e.,the error rate is 2%)on the test set,higher than those achieved by previous prediction ***,the predicted probability could provide warning information for timely support ***,the application results are illustrated through tests on the tunnel sections that have not yet been excavated in the line of the Sichuan–Tibet railway *** applied predictive tendencies and laws are in line with the practical *** sum-mary,the proposed model

关键词： Tunnel squeezing hazard Bayesian optimization Machine learning techniques Sparrow search algorithm ensemble classifier Incomplete database

OPT-BAG Model for Predicting Student Employability

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Computers, Materials & Continua 2023年第8期76卷 1555-1568页

作者： Minh-Thanh Vo Trang Nguyen Tuong Le Faculty of Information Technology HUTECH UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ho Chi Minh City Open UniversityHo Chi Minh CityVietnam Laboratory for Artificial Intelligence Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh CityVietnam

The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a *** on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the *** addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their *** effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of *** model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be ***,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability ***,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability *** training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation ***,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student *** empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student *** this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most *** indicates that educational institutions should focus on these factors during the learning process to improve student employability.

关键词： ensemble classifier grid search cross-validation OPT-BAG student employability

Robust Interactive Method for Hand Gestures Recognition Using Machine Learning

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Computers, Materials & Continua 2022年第7期72卷 577-595页

作者： Amal Abdullah Mohammed Alteaimi Mohamed Tahar Ben Othman Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia BIND Research Group College of ComputerQassim UniversityBuraydah51452Saudi Arabia

The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output *** devices may complicate communication with computers especially for people with *** gestures can be defined as a natural human-to-human communication method,which also can be used in human-computer *** researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable *** aims to develop a powerful hand gesture recognition model with a 100%recognition *** proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve *** majority voting method was used to aggregate accuracies produced by each classifier and get the final classification *** model was trained using a self-constructed dataset containing 1600 images of ten different hand *** employing of canny’s edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition *** experimental results had shown the robustness of our proposed *** Regression and Support Vector Machine have achieved 100%*** developed model was validated using two public datasets,and the findings have proved that our model outperformed other compared studies.

关键词： Hand gesture recognition canny edge detector histogram of oriented gradient ensemble classifier majority voting