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Attribute Weighted naïve bayes Classifier

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Computers, Materials & Continua 2022年第4期71卷 1945-1957页

作者： Lee-Kien Foo Sook-Ling Chua Neveen Ibrahim Multimedia University Cyberjaya63100Malaysia

The naïve bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.

关键词： Attribute weighting naïve bayes Kullback-Leibler information gain classification

naïve bayes Algorithm for Large Scale Text Classification

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Instrumentation 2021年第4期8卷 55-62页

作者： Pirunthavi SIVAKUMAR Jayalath EKAnaYAKE Department of Information and Communication Technology Faculty of TechnologyRajarata University of Sri LankaMihintale 50300Sri Lanka Department of Computer Science and Informatics Faculty of Applied SciencesUva Wellassa University of Sri LankaBadulla 90000Sri Lanka

This paper proposed an improved naïve bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry important information.Organizing large amounts of data and extracting useful information is a challenging task.The extracted information can be considered as new knowledge and can be used for deci sion-making.We extract comments from YouTube on videos and categorized them in domain-specific,and then apply the naïve bayes classifier with improved techniques.Our method provided a decent 80%accuracy in classifying those comments.This experiment shows that the proposed method provides excellent adaptability for large-scale text classification.

关键词： naïve bayes Text Classification YouTube Sentimental Analysis

Improved Bearing Fault Diagnosis by Feature Extraction Based on GLCM, Fusion of Selection Methods, and Multiclass-naïve bayes Classification

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Journal of Signal and Information Processing 2021年第4期12卷 71-85页

作者： Mireille Pouyap Laurent Bitjoka Etienne Mfoumou Denis Toko Energy Signal Imaging and Automation Laboratory (LESIA) University of Ngaoundere Yaounde Cameroon Division of Applied Research & Innovation Nova Scotia Community College Dartmouth Canada Department of Mechanical Engineering College of Technology University of Buea Buea Cameroon

The presence of bearing faults reduces the efficiency of rotating machines and thus increases energy consumption or even the total stoppage of the machine. It becomes essential to correctly diagnose the fault caused by the bearing. Hence the importance of determining an effective features extraction method that best describes the fault. The vision of this paper is to merge the features selection methods in order to define the most relevant featuresin the texture of the vibration signal images. In this study, the Gray Level Co-occurrence Matrix (GLCM) in texture analysis is applied on the vibration signal represented in images. Features selection based on the merge of PCA (Principal component Analysis) method and SFE (Sequential Features Extraction) method is done to obtain the most relevant features. The multiclass-Na?ve bayesclassifier is used to test the proposed approach. The success rate of this classification is 98.27%. The relevant features obtained give promising results and are more efficient than the methods observed in the literature.

关键词： GLCM PCA SFE naïve bayes Relevant Features

bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

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Intelligent Automation & Soft Computing 2023年第5期36卷 2425-2440页

作者： D.Palanikkumar R.Ramesh Kumar Mehedi Masud Mrim M.Alnfiai Mohamed Abouhawwash Department of Computer Science and Engineering Dr.NGP Institute of TechnologyCoimbatore641048India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical waste.Medical wastage is not properly managed it will become a hazard to the environment and humans.Managing medical wastage is a major issue in the city,municipalities in the aspects of the environment,and logistics.An efﬁcient supply chain with edge computing technology is used in managing medical waste.The supply chain operations include processing of waste collec-tion,transportation,and disposal of waste.Many research works have been applied to improve the management of wastage.The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and transparency.To over-come these issues,in this paper we implement an efﬁcient naive bayes classiﬁer algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical waste.To minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is used.The accuracy obtained for the naïve bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

关键词： Binary bat algorithm naïve bayes supply chain edge medical wastage

DDoS Detection in SDN using Machine Learning Techniques

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Computers, Materials & Continua 2022年第4期71卷 771-789页

作者： Muhammad Waqas nadeem Hock Guan Goh Vasaki Ponnusamy Yichiet Aun Faculty of Information and Communication Technology(FICT) Universiti Tunku Abdul Rahman(UTAR)Jalan UniversitiBandar Barat31900 KamparPerakMalaysia

Software-defined network(SDN)becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure.The SDN controller is considered as the operating system of the SDN based network infrastructure,and it is responsible for executing the different network applications and maintaining the network services and functionalities.Despite all its tremendous capabilities,the SDN face many security issues due to the complexity of the SDN architecture.Distributed denial of services(DDoS)is a common attack on SDN due to its centralized architecture,especially at the control layer of the SDN that has a network-wide impact.Machine learning is now widely used for fast detection of these attacks.In this paper,some important feature selection methods for machine learning on DDoS detection are evaluated.The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller.A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks.The experimental results show that the Random forest(RF)classifier trains the more accurate model with 99.97%accuracy using features subset by the Recursive feature elimination(RFE)method.

关键词： Machine learning software-defined network distributed denial of services feature selection protection artificial neural network decision trees naïve bayes security

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

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Computers, Materials & Continua 2023年第6期75卷 5307-5319页

作者： Khalid M.O.nahar Mohammad Alauthman Saud Yonbawi Ammar Almomani Computer Science Department Yarmouk UniversityIrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Software Engineering Department University of JeddahJeddahKSA Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,naïve bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and naïve bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

关键词： Cyberbullying social media naïve bayes support vector machine natural language processing LDA

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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Computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM.We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph.There are two kinds of path patterns in the knowledge graph:one-hop and two-hop.The one-hop path pattern maps the symptom to syndromes immediately.The two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic reasoning.Considering the different support strengths for the knowledge paths in reasoning,we design a dynamic weight mechanism.We utilize naïve bayes and TF-IDF to implement the reasoning method and the weighted score calculation.The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns.We evaluate the method with clinical records and clinical practice in hospitals.The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice.Meanwhile,the method is robust and explainable under the guide of the knowledge graph.It could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph naïve bayes syndrome differentiation

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

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Intelligent Automation & Soft Computing 2023年第2期35卷 2431-2447页

作者： Shyla Vishal Bhatnagar NSUT East Campus(Formerly Ambedkar Institute of Advanced Communication Technologies and Research) Guru Gobind Singh Indraprastha UniversityNew Delhi110031India NSUT East Campus(Formerly Ambedkar Institute of Advanced Communication Technologies and Research) New Delhi110031India

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationﬂow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based naïve bayes)algorithm toﬁnd the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as naïve bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Theﬁndings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,speciﬁcity,and a decrease in misclassiﬁcation as 9.3%,6.4%,12.5%,5.2%and 81%.

关键词： naïve bayes decision tree k-nearest neighbors logistic regression neighbors classiﬁer

Review:Heart Diseases Detection by Machine Learning Classification Algorithms

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Journal of Harbin Institute of Technology(New Series) 2022年第4期29卷 81-92页

作者： Pothala Ramya Ashapu Bhavani Sangeeta Viswanadham Department of Computer Science and Engineering Grandhi Mallikarjuna Rao Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and SciencesVishakhapatnam 531162Andhra PradeshIndia

Most human deaths are caused by heart diseases.Such diseases cannot be efficiently detected for the lack of specialized knowledge and experience.Data science is important in healthcare sector for the role it plays in bulk data processing.Machine learning(ML)also plays a significant part in disease prediction and decision⁃making in medical care industry.This study reviews and evaluates the ML approaches applied in heart disease detection.The primary goal is to find mathematically effective ML algorithm to predict heart diseases more accurately.Various ML approaches including Logistic Regression,Support vector Machine(SVM),k⁃Nearest Neighbor(k⁃NN),t⁃Distributed Stochastic Neighbor Embedding(t⁃SNE),naïve bayes,and Random Forest were utilized to process heart disease dataset and extract the unknown patterns of heart disease detection.An analysis was conducted on their performance to examine the effecacy and efficiency.The results show that Random Forest out⁃performed other ML algorithms with an accuracy of 97%.

关键词： Logistic Regression SVM k⁃NN t⁃SNE naïve bayes Random Forest