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Application of machine learning algorithms in quality assurance of fermentation process of black tea- based on electrical properties

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JOURNAL OF FOOD ENGINEERING 2019年 263卷 165-172页

作者： Zhu, Hongkai Liu, Fei Ye, Yang Chen, Lin Li, Jingyuan Gu, Anhui Zhang, Jianqiang Dong, Chunwang China Acad Agr Sci Tea Res Inst Hangzhou 310008 Zhejiang Peoples R China Minist Agr Key Lab Tea Biol & Resource Utilizat Hangzhou 310008 Zhejiang Peoples R China Univ Copenhagen Fac Sci Dept Food Sci Rolighedsvej 26 DK-1958 Frederiksberg Denmark Sichuan Acad Agr Sci Tea Res Inst Chengdu 610066 Sichuan Peoples R China

Fermentation process directly determines the product quality of black tea. This work aimed to develop a rapid method for detecting the degree of fermentation of black tea based on electrical properties of tea leaves. An LCR meter employed to identify 11 electrical parameters of tea leaves during the fermentation process, and the content of catechins and tea pigments in tea leaves were measured by using HPLC and UV-Vis spectrometer, respectively. Principal component analysis and hierarchical clustering analysis applied to divide samples into different groups in the degree of fermentation. Correlation analysis used to characterize the responding strength of electrical parameters on the variation of catechins and pigments. Finally, multilayer perceptron, random forest, and support vector machine algorithm used to build discrimination models of fermentation degree, and the average accuracy rate on the testing set reached to 88.90%, 100%, and 76.92%, respectively.

关键词： Black tea Fermentation Electrical properties Quality components Random forest machine learning algorithms

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Using Sampling Techniques and machine learning algorithms to Improve Big Five Personality Traits Recognition from Non-verbal Cues

Using Sampling Techniques and Machine Learning Algorithms to...

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National Computing Colleges Conference (NCCC), 2021

作者： Dina Al-Hammadi Roger K. Moore The University of Sheffield Sheffield United Kingdom College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (纸本)9781728167190;9781728167206

Automatic personality recognition using the Big Five dimensions (OCEAN: extraversion, agreeableness, conscientiousness, neuroticism and openness) is capturing the attention of researchers. Personality recognition is expected to have encouraging future in Human- computer and Robot Interaction applications. Human speech conveys rich information that can be derived to recognize speaker traits. However, our focus is on the rich content of non-verbal features in human speech. We focus on how humans talk, not what they talk about. The focus in this paper is to experiment with four different machine learning techniques, and their performance in recognizing personality traits, we report our results in this regard. We use the Speaker Personality Corpus provided by the Interspeech 2012 challenge. First, we recognize three issues affecting the system's low performance: dimensionality, judges' agreement, and imbalanced data. Next, we address each issue and provide a solution to improve the system's performance. Finally, we compare our results with the baseline showing better classification results.

关键词： Human computer interaction machine learning algorithms Oceans Speech recognition machine learning Robots

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Measuring the Heart Attack Possibility using Different Types of machine learning algorithms

Measuring the Heart Attack Possibility using Different Types...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Maria Sultana Keya Muhammad Shamsojjaman Faruq Hossain Farzana Akter Fakrul Islam Minhaz Uddin Emon Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728195377

ISBN: (纸本)9781728195384

The heart seems to be a very complicated organ in human body. If some part of the heart has been seriously damaged, the remaining part of the heart will still remain functioning. But as a result of the injury, the heart can be weakened and unable to pump as much blood as normal. With timely detection of multiple possible hamstring issues, proper care, and dietary changes after a heart attack, the additional injury can be reduced or avoided. In this paper, different types of machine learning algorithms are used for measuring the possibility heart attack, they are logistic regression, random forest, bagging, MLP, and decision tree. By finding the best algorithm, this paper also shows the correlation matrices, visualizes the feature, and AUC. From this research work, it is evident that the logistic regression is the best model with an accuracy of about 80% and also gives the best AUC of about 87%.

关键词： Heart machine learning algorithms Cardiac arrest Injuries Regression tree analysis Random forests Logistics

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An Energy-Efficient Programmable Mixed Signal Accelerator for machine learning algorithms

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IEEE MICRO 2019年第5期39卷 64-72页

作者： Kang, Mingu Srivastava, Prakalp Adve, Vikram Kim, Nam Sung Shanbhag, Naresh R. IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Univ Illinois Urbana IL USA Univ Illinois Comp Sci Urbana IL USA

We propose PROMISE, the first end-to-end design of a PROgrammable Mixed-Signal accElerator from Instruction Set Architecture to high-level language compiler for acceleration of diverse machine learning algorithms by e... 详细信息

关键词： Computer architecture Hardware Instruction sets Kernel machine learning algorithms Neural networks Task analysis

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Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 1-9页

作者： Yuvaraj, N. SriPreethaa, K. R. KPR Inst Engn & Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Health care systems are merely designed to meet the needs of increasing population globally. People around the globe are affected with different types of deadliest diseases. Among the different types of commonly existing diseases, diabetes is a major cause of blindness, kidney failure, heart attacks, etc. Health care monitoring systems for different diseases and symptoms are available all around the world. The rapid development in the fields of Information and Communication Technologies made remarkable improvements in health care systems. Various machine learning algorithms are proposed which automates the working model of health care systems and enhances the accuracy of disease prediction. Hadoop cluster based distributed computing framework supports in efficient processing and storing of extremely large datasets in cloud environment. This work proposes the novel implementation of machine learning algorithms in hadoop based clusters for diabetes prediction. The results show that the machine learning algorithms can able to produce highly accurate diabetes predictive healthcare systems. Pima Indians Diabetes Database from National Institute of Diabetes and Digestive Diseases is used to evaluate the working of algorithm.

关键词： Healthcare systems machine learning algorithms Hadoop clusters Predictive analysis Cluster computing

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A Wearable Sensor for Arterial Stiffness Monitoring Based on machine learning algorithms

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IEEE SENSORS JOURNAL 2019年第4期19卷 1426-1434页

作者： Miao, Fen Wang, Xurong Yin, Liyan Li, Ye Chinese Acad Sci Key Lab Hlth Informat Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China Northeastern Univ Shenyang Shenyang 110819 Liaoning Peoples R China Chinese Acad Sci Joint Engn Res Ctr Hlth Big Data Intelligent Anal Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China

Arterial stiffness is strongly associated with cardiovascular events. Existing devices for evaluating arterial stiffness based on ultrasound or pulse wave velocity suffer a lot from complexity and inconvenience in home-care settings. This paper proposed a wearable sensor for arterial stiffness monitoring via machine learning techniques. The proposed sensor is comprised of one electrocardiogram (ECG) and one photoplethysmogram (PPG) module. The ECG and PPG signals were first simultaneously collected by the wearable sensor, and 21 features were extracted from two signals for arterial stiffness evaluation. A genetic algorithm-based feature selection method was then used to select the important indicators. Multivariate linear regression (MLR), decision tree, and back propagation (BP) neural network were employed to develop the model. Vascular age and 10-year cardiovascular disease risk from OMRON arteriosclerosis instrument were deemed as the gold standard to evaluate arterial stiffness. Experimental results based on 501 diverse subjects showed that the MLR approach exhibited the best accuracy in vascular age estimation (correlation coefficient, 0.89;mean of the residual, 0.2136;and standard deviation of the residual, 6.2432). While the BP neural networks-based approach was hest in cardiovascular disease risk estimation (correlation coefficient, 0.9488;mean of the residual, - 0.3579%;and standard deviation of the residual, 3.7131%). The results indicate that the proposed learning-based sensor has great potential in arterial stiffness monitoring in home-care settings.

关键词： Arterial stiffness machine learning algorithms pulse wave analysis

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Evaluating machine learning algorithms to Detect and Classify DDoS Attacks in IoT

Evaluating Machine Learning Algorithms to Detect and Classif...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Amardeep Chopra Sunny Behal Vishal Sharma SBS State Technical Campus Ferozepur Punjab India

A Distributed Denial of Service (DDoS) attack is a lethal threat to web-based services and applications. These attacks can cripple down these services in no time and deny legitimate users from using these services. The problem has further prevailed with the massive usage of unsecured Internet of Things (IoT) devices across the Internet. Moreover, many existing rule-based detection systems are easily vulnerable to attacks. In this paper, we performed a comparative analysis of machine learning (ML) algorithms to detect and classify DDoS attacks. As part of the work, various machine learning algorithms such as Naïve Bayes, J48, Random Forest and ZeroR ML classifiers are compared. Principal Component Analysis (PCA) method has been used to select the optimal number of features. WEKA tool has been used to implement ML algorithms.

关键词： Performance evaluation machine learning algorithms Tools Denial-of-service attack Classification algorithms Internet of Things Computer crime

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Supervised machine learning algorithms to Detect Instagram Fake Accounts

Supervised Machine Learning Algorithms to Detect Instagram F...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Michael Jonathan Ekosputra Angela Susanto Ferdiana Haryanto Derwin Suhartono School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665401524

Instagram is extremely popular because many celebrities and their fan pages use Instagram as the platform for them to communicate. Instagram offers many media sharing features and has proven to be the most popular social media platform for promoting many brands. As the most popular platform, Instagram also has fake users. Regrettably, some people do malicious activities using fake accounts such as impersonating artists or influencers, hate comments and spread rumors to become viral. Hence, this research aims to detect Instagram fake users based on the user's profile. There are several stages before account authenticity detection is successful, starting from data pre-processing, selecting a classification model, and classification evaluation. The algorithms that are used to create the supervised machine learning model are Logistic Regression, Bernoulli Naive Bayes, Random Forest, Support Vector machine, and Artificial Neural Network (ANN). This paper tried two experiments. The first is that the default state of the model has no parameters, and no features are added. Second, to improve the accuracy, new features and tuning parameters were added in the experiment. Models that perform better than other models based on the second experiment with new features and parameters are Logistic Regression and Random Forest, with an accuracy of 0.93.

关键词： Support vector machines Seminars machine learning algorithms Social networking (online) Multimedia Web sites Artificial neural networks Feature extraction

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Sentiment Analysis Using machine learning algorithms

Sentiment Analysis Using Machine Learning Algorithms

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Fatma Jemai Mohamed Hayouni Sahbi Baccar Higher Institute of Computer Sciences of KEF University of Jendouba Kef Tunsia Innov'com Research Laboratory Higher School of Communications (Sup'Com) University of Carthage Ariana Tunisia CESI Graduate School of Engineering Rouen France

This work aims at building a classifier able of predicting the polarity of a comment while using machine learning (ML) algorithms. Our work is essentially divided into three tasks: data extraction, processing and modelling. In order to build our model, we use the NLTK dataset. Then, we use text mining techniques to generate and process the variables. Based on a supervised probabilistic machine learning algorithm, we tended to create a classifier to classify our tweets into positive and negative sentiments then we opt for two experiments to evaluate the performance of our model. Compered to previous reported works, we achieve greater precision.

关键词： Wireless communication Text mining Sentiment analysis machine learning algorithms Optimized production technology machine learning Probabilistic logic

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Predicting scale deposition in oil reservoirs using machine learning optimization algorithms

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RESULTS IN ENGINEERING 2024年 22卷

作者： Khodabakhshi, Mohammad Javad Bijani, Masoud Petr Univ Technol Ahvaz Fac Petr Ahvaz Iran Amirkabir Univ Technol Tehran Polytech Dept Petr Engn Tehran Iran

Scale deposition, a form of formation damage, not only affects the reservoir but also damages the well and equipment. This phenomenon occurs due to changes in temperature, pressure, and the injection of incompatible salt water, leading to ionic reactions. This study investigated permeability reduction due to scale deposition and examined how parameters such as temperature, pressure drop, and ion concentration affect the prediction accuracy. The scale deposits investigated in this study include CaSO4, 4 , BaSO4, 4 , and SrSO4. 4 . This paper uses Python to employ different machine-learning algorithms to predict the results. Each machine learning model has certain hyper-parameters that need adjustment. Failure to do so will result in reduced accuracy and incomplete interpretation of input data. The accuracy of the support vector regression (SVR) algorithm was significantly affected by the variation of the epsilon parameter in the dataset used. Therefore, before hyperparameter optimization, SVR had the lowest accuracy at 0.575. After adjusting the hyper-parameters, our findings show that SVR had the highest increase in R-squared value, which was 0.900, and the most minor growth in KNN, which went from 0.995 to 0.996. Additionally, the highest accuracy value for K-Nearest Neighbor is 0.996. Furthermore, most errors were related to SVR and XGBoost algorithms, while the most negligible errors were for the Decision Tree and KNN algorithms.

关键词： Scale deposition Permeability reduction machine learning algorithms Formation damage Sulfate scale Hyperparameter optimization

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