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26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Grangel-Gonzalez, Irlan Shah, Fasal Robert Bosch Corp Res Renningen Germany Robert Bosch Engn & Business Solut Private Ltd Bangalore Karnataka India

ISBN: (纸本)9781728129891

Industry 4.0 requires the integration of many actors to provide correct, personalized, and quick answers to customers. In order to meet this integration, data coming from different actors demand to be semantically integrated and harmonized. In these settings, knowledge graphs have proven to be successful in the task of semantic data integration of distinct data silos. Despite the increasing adoption of knowledge graphs in the Industry 4.0 domain for integrating and harmonizing data, still, all the power of the integrated data is not exploited. In this article, we tackle the problem of knowledge graph completion presenting an approach that applies supervised machine learning algorithms on top of the knowledge graph. In general, observed results indicate that supervised machine learning algorithms perform with an AUC of more than 88%. These outcomes suggest that knowledge graph completion enables to unveil new relations by connecting entities in the knowledge graph. Thus, the discovered relations in the knowledge graph bring added value to the Industry 4.0 domain.

关键词： Industry 4.0 smart manufacturing knowledge graphs supervised machine learning algorithms knowledge graph embeddings link prediction knowledge graph completion

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Toxicity Detection Using State of the Art Natural Language Methodologies 8

Toxicity Detection Using State of the Art Natural Language M...

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8th International Conference on Control and Robotics Engineering (ICCRE)

作者： Keskin, Enes Faruk Acikgoz, Erkut Dogan, Gulustan Turkish Aeronaut Assoc Univ Dept Comp Engn Ankara Turkiye Atilim Univ Dept Ind Engn Ankara Turkiye Univ North Carolina Wilmington Dept Comp Sci Wilmington NC USA

ISBN: (纸本)9798350345650

In this paper, the studies carried out to detect objectionable expressions in any text will be explained. Experiments were performed with Sentence transformers, supervised machine learning algorithms, and Bert transformer architecture trained in English, and the results were observed. To prepare the dataset used in the experiments, the natural language processing and machine learning methodologies of the toxic and non-toxic contents in the labeled text data obtained from the Kaggle platform are explained, and then the methods and performances of the models trained using this dataset are summarized in this paper.

关键词： language models bert transformers natural language processing supervised machine learning algorithms deep learning

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Intrusion Detection Systems using supervised machine learning Techniques: A survey

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Procedia Computer Science 2022年 201卷 205-212页

作者： Emad E. Abdallah Wafa’ Eleisah Ahmed Fawzi Otoom Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa 13133 Jordan

In this paper, we investigate the subject of intrusion detection using supervised machine learning methods. The main goal is to provide a taxonomy for linked intrusion detection systems and supervised machine learning algorithms. For this purpose, we provide a deep discussion of the concepts of intrusion detection systems, supervised machine learning techniques, and cyber-security attacks. Then, concerning the application of supervised learning for intrusion detection, we cover relevant efforts. Finally, a taxonomy is provided based on these related works. Based on this taxonomy, we can conclude that the classification performance of supervised learning algorithms is high and promising based on a study of four popular data sets in this domain: KDD’99, NSL-KDD, CICIDS2017, and UNSW-NB15. Moreover, feature selection is important and, in many cases, is needed for an enhancement in performance. Furthermore, data imbalance can be a concern, and sampling approaches can help resolve the issue. Finally, for good performance, large intrusion detection data sets necessitate a deep learning technique.

关键词： intrusion detection systems cyber security supervised machine learning algorithms

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A Pipelined Framework for the Prediction of Cardiac Disease with Dimensionality Reduction 1

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2nd International Conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Shobana, G. Subramanian, Nalini Madras Christian Coll Dept Comp Applicat Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Informat Technol Thandalam India

ISBN: (数字)9783031281839

ISBN: (纸本)9783031281822;9783031281839

Cardiac diseases are diseases that affect people across the globe, and cardiac failure occurs without any warning. Identification of cardiac diseases at an early stage becomes a challenge for researchers in the health domain. machine learning frameworks and algorithms are effectively used in the current medical field to predict and classify various diseases accurately. In this paper, we explore the traditional supervised machine learning techniques and algorithms and their cardiac disease classification accuracy. We further investigate the feature extraction technique Kernel Principal Component Analysis with a pipelined framework. The proposed framework overcomes the issue of overfitting and increases the prediction accuracy most effectively. Random Forest produced the most perfect result and Extreme Gradient Boost technique achieved an accuracy of 99.02%. Other Boosting classifiers, Gradient Boosting and Light Gradient Boosted machine produced an accuracy of 94.16% and 98.38% respectively.

关键词： supervised machine learning algorithms Kernel principal component analysis Cardiac disease Light gradient boosting Random forest

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Ensemble based machine learning Approach for Predicting Customer Churn in E-Commerce 26

Ensemble based Machine Learning Approach for Predicting Cust...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Masroor, Mohammad Irtiza Sadman Sakib, K.M. Hasan, Mahmudul Al Arman, Md Abdullah Dey, Pronay Sanam, Tahsina Farah Ahsanullah University of Science & Technology Dhaka Bangladesh Northern University Bangladesh Dhaka Bangladesh Bangladesh University of Engineering & Technology Dhaka Bangladesh

ISBN: (纸本)9798350359015

Due to the intense competition among E-Commerce industries, customers have a large selection of products to choose from and as a result, these industries experience customer churn. Since retaining existing customers than acquiring new ones is cost effective, customer churn prediction is key for the E-commerce industry. The goal of this paper is to forecast customer churn for a ride sharing application by training appropriate supervised ensemble-based machine learning models including Gradient Boost, Cat Boost and XG Boost on a known dataset containing 50,000 known samples of many such customer attributes as well as their loyalty to the application. A variety of manual preprocessing approaches, such as removing missing data, duplicate data, outliers, etc., were employed to reduce the high-class imbalance in the raw dataset while also preventing useful information loss in the already small dataset, enabling the models to predict customer churn with neutrality. Standardization techniques was also utilized to normalize the data so as to restrict the wide distribution of each feature and improve model performance. The AUC-ROC performance metric was used, which met our objectives while also working well with imbalanced datasets. Gradient Boost and Cat boost algorithms performed the best in identifying potential churning customers, with AUC-ROC scores of 0.85 and computation times of 58 seconds and 80 seconds respectively, while XG boost was not far behind with an AUC-ROC score of 0.84 and the fastest computation time of 45 seconds among all models assessed. © 2023 IEEE.

关键词： Class imbalance Customer Churn supervised machine learning algorithms

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Detection of Adulteration in Fruits Using machine learning 6

Detection of Adulteration in Fruits Using Machine Learning

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6th International Conference on Wireless Communications, Signal Processing and Networking (IEEE WiSPNET)

作者： Brighty, S. Prince Sahaya Harini, G. Shri Vishal, N. Sri Ramakrishna Engn Coll Dept CSE Coimbatore Tamil Nadu India

ISBN: (纸本)9781665440868

Food is essential for life. The food we take should be pure, nutritious and free from any type of adulteration for proper maintenance of human health. In this paper, an IoT based food and formalin detection technique is developed to detect the presence of formalin using machine-learning approaches. Volatile compound HCHO gas sensor connected with Raspberry pi3 were used to extract the concentration of the formalin as a function of output voltage of any fruit or vegetable and different machine learning algorithms were used to classify the fruit or vegetable based on their extracted features. supervised machine learning algorithms have been incorporated in our system to accurately predict the correct concentration of formalin at all temperatures which is also able to correctly classify between artificially added and naturally formed formalin.

关键词： Adulteration formalin naturally occurring formalin formaldehyde machine learning IoT HCHO gas sensor Raspberry Pi3 supervised machine learning algorithms

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Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma

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BRIEFINGS IN BIOINFORMATICS 2019年第3期20卷 985-994页

作者： Shahriyari, Leili Ohio State Univ Math Biosci Inst Columbus OH 43210 USA

Motivation: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes). Results: Regardless of normalization methods, a support vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the vital status of the patients. However, the fitting time of SVM depended on the normalization methods, and it reached its minimum fitting time when files were normalized to the unit length. Furthermore, among all 12 learning algorithms and 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files had the best performance in terms of maximizing the accuracy as well as minimizing the fitting time. We also investigated the effect of dimensionality reduction methods on the performance of the supervised ML algorithms. Reducing the dimension of the data set did not increase the maximum accuracy of 78%. However, it leaded to discovery of the 7SK RNA gene expression as a predictor of survival in patients with colon adenocarcinoma with accuracy of 78%.

关键词： 7SK RNA gene expression colon adenocarcinoma normalization methods supervised machine learning algorithms TCGA HTSeq-FPKM-UQ data sets

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Classifying design-level requirements using machine learning for a recommender of interaction design patterns

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IET SOFTWARE 2020年第5期14卷 544-552页

作者： Silva-Rodriguez, Viridiana Edith Nava-Munoz, Sandra Castro, Luis A. Martinez-Perez, Francisco E. Perez-Gonzalez, Hector G. Torres-Reyes, Francisco Univ Autonoma San Luis Potosi Fac Ingn Dr Manuel Nava 8 San Luis Potosi San Luis Potosi Mexico Inst Tecnol Sonora Dept Computac & Diseno Obregon Mexico

Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design-level requirements analysis. The success of the software system is mostly dependent on how well users' requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human-computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design-level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers' decision making when designing user interfaces.

关键词： learning (artificial intelligence) user interfaces human computer interaction pattern classification object-oriented methods interactive systems decision making software quality user interface design design-level requirements analysis software system interactive system design process human-computer interactions design task interaction design patterns recommendation model design-level requirements classification supervised machine learning algorithms

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Analysis of COVID-19 cases and comorbidities using machine learning algorithms: A case study of the Limpopo Province, South Africa

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SCIENTIFIC AFRICAN 2023年 21卷

作者： Boateng, Alexander Maposa, Daniel Mokobane, Reshoketswe Darikwa, Timotheus Gyamfi, Charles Univ Free State Dept Biostat Bloemfontein South Africa Univ Limpopo Dept Stat & Operat Res Polokwane South Africa Kwame Nkrumah Univ Sci & Technol Dept Stat & Actuarial Sci Kumasi Ghana

This study examined the biological, social, and clinical risk factors for mortality in coronavirus of the year 2019 (COVID-19) hospitalised patients. The population of the study is prone to COVID-19, thus understanding the most common traits and comorbidities of people who were affected is crucial in reducing its consequences. In this study, four supervised machine learning algorithms were implemented and compared to predict the mortality rate based on the explanatory variables across the five districts of Limpopo Province in South Africa. The data was obtained from Lim-popo Department of Health. Prediction about the chances of dying from COVID-19 disease was made using logistic regression, random forest, support vector machine, and decision tree algo-rithms on the dataset of 20,592 records with twenty-one attributes. Due to the imbalanced nature of the data, Random Over-Sampling Examples (ROSE) were employed to balance our data for more accurate classification effectively. The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. We used 70% of the data for training, while 30% was selected for testing the predictive algorithms. A technique called Step Akaike's Information Criterion (StepAIC) was deployed to reduce the insignificant variables from the full model of the logistic regression. According to the findings of the study, among the four algorithms tested, random forest had the highest recall rate for predicting mortality at roughly 79 percent compared to the other three algorithms. Accordingly, we conclude that random forest algorithm is appropriate for predicting the chances of patients dying from COVID-19 based on the attributes of the five districts of Limpopo Province. In terms of the features and their importance, a function called Variable Importance (VarImp) was used to check which of the attributes have predictive power on the outcome variable (discharged status). The findings revealed that

关键词： Comorbidities supervised machine learning algorithms Random forest Logistic regression COVID-19 Limpopo province South Africa

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HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm

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IEEE ACCESS 2021年 9卷 146797-146809页

作者： Ashri, Sarria E. A. El-Gayar, M. M. El-Daydamony, Eman M. Mansoura Univ Fac Comp & Informat Sci Dept Informat Technol Mansoura 35516 Egypt

supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifiers using the ensembled model with a majority voting technique to improve prediction accuracy. Furthermore, a proposed preprocessing technique and features selection based on a genetic algorithm is suggested to enhance prediction performance and overall time consumption. In addition, the 10-folds cross-validation technique is used to overcome the overfitting problem. Experiments were performed on a dataset for cardiovascular patients from the UCI machine learning Repository. Through a comparative analytical approach, the study results indicated that the proposed ensemble classifier model achieved a classification accuracy of 98.18% higher than the rest of the relevant developments in the study.

关键词： Diseases Heart Support vector machines Predictive models Data models machine learning algorithms Genetic algorithms Cardiovascular disease supervised machine learning algorithms simple genetic algorithm ensembled model majority voting technique

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