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International Conference on Computer and Information Technology (ICCIT)

作者： Md Abdus Salam Siddique Md Imran Sarker Robin Ghosh Kamal Gosh University of Arkansas at Little Rock Little Rock USA Arkansas Tech University Russellville USA Langston University Langston USA

ISBN: (纸本)9781665494366

Music lyrics have a broad scope of impacts on our day-to-day life. The connection between music and the cerebrum has been extensively studied as far as feeling and intellectual interaction. From school children to strict adherents, the audience has the right to taste great music. For example, men presented with physically rough verses tend to more generalized perspectives toward ladies. Listening to particularly toxic or nontoxic songs can affect our mood. Music recommendation system follows different features based on the user’s historical data. The listener’s mode could be improved if the recommendation system filters out toxicity. In this study, we classify lyrics in terms of toxicity and nontoxicity from different music genres and artists using machine learning (ML) algorithms. The toxicity and nontoxicity have been measured using high valence and low valence. From the results, we found that Random Forest (RF) is a much more effective toxicity classification classifier, giving an overall accuracy of 93%.

关键词： Toxicology machine learning algorithms Mood Computational modeling Motion pictures Natural language processing Libraries

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Family Expenditure and Income Analysis using machine learning algorithms

Family Expenditure and Income Analysis using Machine Learnin...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Y. Bhavya Sri Y. Sravani Y. Bhargava Sai Surendra S. Rishitha M. Sobhana Velagapudi Ramakrishna Siddhartha Engineering College India

ISBN: (纸本)9781665482981

Expenditure analysis should be done by every household to manage all the expenses of the family. Income prediction gives an overview of the income earned by the household to manage all the family’s financial needs. Based on the expenses and other required data from the user, the system will predict the user’s annual income to meet the expenses. The predicted annual income can be used by the government for initiating policies for the poor people. This prediction task is performed using Decision Tree and Random Forest Regression algorithms, as the data used for this model is continuous. Our proposed Random Forest model predicts with an accuracy of 74.35%. Based on accuracy metrics, our model is compared with Decision Tree, accuracy of 48%. Clearly, the our proposed model is more suitable for classifying than the Decision Tree model. As decision trees are best suitable for predictions based on non-linear data, we cannot depend on a single decision tree for the prediction of income. Bagging technique-based Random forest Regression is made use for the prediction of the income.

关键词： Measurement machine learning algorithms Supervised learning Government Predictive models Prediction algorithms Task analysis

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Diabetes Prediction using machine learning algorithms with Feature Selection and Dimensionality Reduction

Diabetes Prediction using Machine Learning Algorithms with F...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： S Sivaranjani S Ananya J Aravinth R Karthika Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

In today's world diabetes has become one of the most life threatening and at the same time most common diseases not only in India but around the world. Diabetes is seen in all age groups these days and they are attributed to lifestyle, genetic, stress and age factor. Whatever be the reasons for diabetics, the outcome could be severe if left unnoticed. Currently various methods are being used to predict diabetes and diabetic inflicted diseases. In the proposed work, we have used the machine learning algorithms Support Vector machine (SVM) & Random Forest (RF) that would help to identify the potential chances of getting affected by Diabetes Related Diseases. After pre-processing the data, features which influences the prediction are selected by implementing step forward and backward feature selection. The Principle Component Analysis (PCA) dimensionality reduction method is analyzed after the selection of specific features and the accuracy of the prediction is 83% implementing Random Forest (RF) which is significant in comparison with Support Vector machine (SVM) with accuracy of 81.4%.

关键词： Support vector machines Radio frequency Dimensionality reduction machine learning algorithms Feature extraction Diabetes Random forests

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Comparative Intrepretation Of machine learning algorithms In Predicting The Cardiovascular Death Rate For Covid-19 Data

Comparative Intrepretation Of Machine Learning Algorithms In...

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International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

作者： D. R. Krithika K. Rohini Vels Institute of Science Technology and Advanced Studies Chennai India

Every year 31% of people die from cardiovascular disease worldwide. The big data analytics technique is very useful to Identify Heart disease and COVID-19. To control the COVID-19 spread around the world and many of the companies adapting this technology and also remote places patient reports doctors view easily to analyze health condition of the patient using IOT based big data. In 2019 COVID-19 (Novel coronavirus Disease) was recognized. COVID-19 signs of CT scan include pleural thickening and vascular enlargement. Nucleic acid detection and epidemiological tracing are using Chest CT scans counteract. To understanding of the disease COVIDE-19 the Researchers are using ML, AI (Artificial Intelligence) and natural language processing. We are using big data analytics to track the spread of this coronavirus. In this paper we discuss about Comparison of Tools in Big data Analytics using machine learning Algorithm.

关键词： COVID-19 Heart machine learning algorithms Computed tomography Medical services Big Data Tools

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Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model

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JOURNAL OF FOOD ENGINEERING 2019年 263卷 288-298页

作者： Nyalala, Innocent Okinda, Cedric Nyalala, Luke Makange, Nelson Chao, Qi Chao, Liu Yousaf, Khurram Chen, Kunjie Nanjing Agr Univ Coll Engn Nanjing 210031 Jiangsu Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

A prediction method of mass and volume of cherry tomato based on a computer vision system and machine learning algorithms were introduced in this study. The relation between tomato mass and volume was established as M = 1.312V(0.9551), and was used to estimate mass on a test dataset at an R-2 of 0.9824 and RMSE of 15.84g. Depth images of tomatoes at different orientations were acquired and features extracted by image processing techniques. Five regression prediction models based on 2D and 3D image features were developed. The RBF-SVM outperformed all explored models with an accuracy of 0.9706 (only 2D features) and 0.9694 (all features) in mass and volume estimation respectively. The model predicted mass or volume can then be applied to the established mass-volume power function. This introduced system can be applied as a non-destructive, accurate and consistent technique to in-line sorting and grading of cherry tomatoes based on mass, volume or density.

关键词： Computer vision Cherry tomato Mass estimation machine learning algorithms Volume estimation Fruit sorting and grading

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Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 669卷 844-855页

作者： Chen, Di Chang, Naijie Xiao, Jingfeng Zhou, Qingbo Wu, Wenbin Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China Minist Agr Key Lab Agr Remote Sensing Beijing 100081 Peoples R China Univ New Hampshire Earth Syst Res Ctr Inst Study Earth Oceans & Space Durham NH 03824 USA

As an important indicator of soil quality, soil organicmatter (SOM) significantly contributes to land productivity and ecosystemhealth. Accuratelymapping SOMat regional scales is of critical importance for sustainable agriculture and soil utilization management and remains a grand challenge. Many studies used soil sampling data and machine learning algorithms to predict SOM at regional scales for a given year, while few studies mapped SOM formultiple years and examined its temporal dynamics. We compared the performance of fourmachine learning algorithms: decision tree (DT), bagging decision tree (BDT), randomforest (RF), and gradient boosting regression trees (GBRT) in mapping SOM in Hubei province, China over the 18-year period from 2000 to 2017. Our results showed that RF and DT had the highest coefficient of determination (R-2) (0.61) and the lowest potential bias (9.48 g/kg), respectively, while GBRT had the lowest mean error (ME) (1.26 g/kg), root mean squared error (RMSE) (5.41 g/kg) and Lin's concordance correlation coefficient (LCCC) (0.72). The SOM map based on GBRT better captured the distribution of the soil sample data than that based on RF. The trained GBRT model and the spatially explicitly data on explanatory variables (e.g., climate, terrain, remote sensing) were used to predict SOM for each 500 m x 500 m grid cell in Hubei for the period from 2000 to 2017. Our results showed that the SOM content of cropland was relatively high in the southeast and relatively low in the north. The SOM content in the topsoil varied from 0.89 to 58.86 g/kg and was averaged at 20.52 g/kg. The mean cropland SOM content of the province exhibited an increasing trend from 2000 to 2017 with an increase of 0.26 g/kg and a growth rate of 1.28%. Spatially, the SOMcontent increased in southernHubei and decreased in central and northern parts of the province. A large portion of the areas with decreasing SOM content in northern Hubei was reclaimed cropland, while a large part of t

关键词： Digital soil mapping Multi-year Soil organic carbon MODIS machine learning algorithms Cropland

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Geo-Object-Based Soil Organic Matter Mapping Using machine learning algorithms With Multi-Source Geo-Spatial Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2019年第4期12卷 1091-1106页

作者： Wu, Tianjun Luo, Jiancheng Dong, Wen Sun, Yingwei Xia, Liegang Zhang, Xuejian Changan Univ Dept Math & Informat Sci Coll Sci Xian 710064 Shaanxi Peoples R China Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350116 Fujian Peoples R China State Key Lab Geoinformat Engn Xian 710054 Shaanxi Peoples R China Chinese Acad Sci State Key Lab Remote Sensing Sci Inst Remote Sensing & Digital Earth Beijing 100864 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310058 Zhejiang Peoples R China Ningxia Acad Agr & Forestry Sci Inst Agr Econ & Informat Technol Yinchuan 750004 Peoples R China

Soil is a complicated historical natural continuum that presents gradual changes in its properties and geographic area. Conventional soil survey and cartography methods on a macroscopic scale based on grids with a coarse resolution are inadequate for the rapid development of precision agriculture. The demand for soil mapping content and accuracy has increased as more convenient methods of acquiring multi-source geo-spatial data have been developed, and such data are commonly employed to extract basic mapping units and environmental variables in related algorithms. We employ geo-objects as basic units of soil property mapping, which are extracted from high-resolution remote sensing images using a convolutional neural network based learning algorithm. Multi-source geo-spatial data are transferred into each geo-object as environmental variables, and the relationships between soil properties and environmental variables are mined using powerful tree-based machine learning algorithms, including regressions with random forests and XGBoost. A data set that includes soil sample points and multi-source geo-spatial data is used to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the method allows for better soil organic matter mapping than state-of-the-art interpolation-based and linear-regression-based methods. The proposed procedure has potential to be a general method for mapping other soil properties. Its advantages are embodied in the modeling of relatively miscellaneous data with implicitly associated non-linear relationships between soil properties and environmental variables. The spatial scale and accuracy of the finer maps capture more detailed characteristics of the soil properties and are applicable to the micro-domain fields required for refined soil mapping with small variations.

关键词： Environmental variables geo-object machine learning algorithms multi-source geo-spatial data soil organic matter (SOM) soil property mapping

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Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2019年 89卷 156-166页

作者： Sahu, Bhawana Shah, Sanchi Prabhudesai, Kaushiki Contini, Alessandro Idicula-Thomas, Susan ICMR Natl Inst Res Reprod Hlth Biomed Informat Ctr Mumbai 400012 Maharashtra India Univ Milan Dipartimento Sci Farmaceut Milan Italy

Background: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has also been well established. Currently, steroid preparations of estrogen and progesterone are being used for managing fertility, in spite of the harmful side effects, as there has not been much success in identification of effective FSHR modulators. Structure based drug design initiatives for identification of potent and specific FSHR modulators have been impeded due to the non-availability of the complete crystal structure of hFSHR complexed with FSH. Methods: In this study, we have modeled the 3D structure of transmembrane domain (TMD) of hFSHR and identified molecules that demonstrate good binding affinity by virtual screening of drug-like library of compounds. The 3D structural and pharmacophoric features of the binders and non-binders obtained from virtual screening were further used to develop Support Vector machine based classifier for TMD binding. Based on the observations from docking and SVM classification, a small molecule was identified for extensive MD simulations and in vitro assays for FSHR modulatory activity. Results: The molecule selected based on docking score and SVM prediction was found to inhibit FSH-induced CAMP activity by 80% at 300 mu M concentration. Conclusion: The study proposes 1,3-diphenyl-1H-pyrazole-5-carboxylate as a promising scaffold for the design of new and potent FSHR allosteric inhibitors. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Follicle Stimulating Hormone Receptor Side Effect FUEL SLEEVE HOUSING small molecule FSHR gene structural bioinformatics Tumor Progression Drug Design Binders Follicle Stimulating Hormone Modulators machine learning algorithms

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Driver identification in intelligent vehicle systems using machine learning algorithms

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 40-47页

作者： Li, Zhengping Zhang, Kai Chen, Bokui Dong, Yuhan Zhang, Lin Tsinghua Univ Div Logist & Transportat Grad Sch Shenzhen Shenzhen Peoples R China Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Tsinghua Univ Div Informat Sci & Technol Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Berkeley Shenzhen Inst Data Sci & Informat Technol Res Ctr Shenzhen Peoples R China

This study proposes an applicable driver identification method using machine learning algorithms with driving information. The driving data are collected by a 3-axis accelerometer, which records the lateral, longitudinal and vertical accelerations. In this research, a data transformation way is developed to extract interpretable statistics features from raw 3-axis sensor data and utilise machine learning algorithms to identify drivers. To eliminate the bias caused by the sensor installation and ensure the applicability of their approach, they present a data calibration method which proves to be necessary for a comparative test. Four basic supervised classification algorithms are used to perform on the data set for comparison. To improve classification performance, they propose a multiple classifier system, which combines the outputs of several classifiers. Experimental results based on real-world data show that the proposed algorithm is effective on solving driver identification problem. Among the four basic algorithms, random forests (RFs) algorithm has the greatest performance on accuracy, recall and precision. With the proposed multiple classifier system, a greater performance can be achieved in small number of drivers' groups. RFs algorithm takes the lead in running speed. In their experiment, ten drivers are involved and over 5,500,000 driving records per driver are collected.

关键词： learning (artificial intelligence) intelligent transportation systems feature extraction traffic engineering computing image classification calibration statistical analysis RFs algorithm random forests algorithm multiple classifier system supervised classification algorithms data calibration method sensor installation raw 3-axis sensor data interpretable statistics feature extraction data transformation lateral accelerations longitudinal accelerations vertical accelerations 3-axis accelerometer driving data collection machine learning algorithms intelligent vehicle systems driver identification method

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Benchmark of machine learning algorithms on capturing future distribution network anomalies

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第8期13卷 1441-1455页

作者： Mohammadpourfard, Mostafa Weng, Yang Tajdinian, Mohsen Sahand Univ Technol Dept Elect & Comp Engn Tabriz Iran Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

The conventional distribution network is undergoing structural changes and becoming an active grid due to the advent of smart grid technologies encompassing distributed energy resources (DERs), aggregated demand response and electric vehicles (EVs). This establishes a need for state estimation-based tools and real-time monitoring of the distribution grid to correctly apply active controls. Although such new tools may be vulnerable to cyber-attacks, cyber-security of distribution grid has not received enough attention. As smart distribution grid intensively relies on communication infrastructures, the authors assume in this study that an attacker can compromise the communication and successfully conduct attacks against crucial functions of the distribution management system, making the distribution system prone to instability boundaries for collapses. They formulate the attack detection problem in the distribution grid as a statistical learning problem and demonstrate a comprehensive benchmark of statistical learning methods on various IEEE distribution test systems. The proposed learning algorithms are tested using various attack scenarios which include distinct features of modern distribution grid such as integration of DERs and EVs. Furthermore, the interaction between transmission and distribution systems and its effect on the attack detection problem are investigated. Simulation results show attack detection is more challenging in the distribution grid.

关键词： active control Smart grids Network security machine learning algorithms BENCHMARKS Power grids dispensing system

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