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Ensemble Machine Learning Model to Predict the Waterborne Syndrome

algorithmS 2022年第3期15卷 93页

作者： Gollapalli, Mohammed Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Informat Syst POB 1982 Dammam 31441 Saudi Arabia

The COVID-19 epidemic has highlighted the significance of sanitization and maintaining hygienic access to clean water to reduce mortality and morbidity cases worldwide. Diarrhea is one of the prevalent waterborne diseases caused due to contaminated water in many low-income countries with similar living conditions. According to the latest statistics from the World Health Organization (WHO), diarrhea is among the top five primary causes of death worldwide in low-income nations. The condition affects people in every age group due to a lack of proper water used for daily living. In this study, a stacking ensemble machine learning model was employed against traditional models to extract clinical knowledge for better understanding patients' characteristics;disease prevalence;hygienic conditions;quality of water used for cooking, bathing, and toiletries;chemicals used;therapist's medications;and symptoms that are reflected in the field study data. Results revealed that the ensemble model provides higher accuracy with 98.90% as part of training and testing phases when experimented against frequently used J48, Naive Bayes, SVM, NN, PART, Random Forest, and Logistic Regression models. Managing outcomes of this research in the early stages could assist people in low-income countries to have a better lifestyle, fewer infections, and minimize expensive hospital visits.

关键词： waterborne disease classification algorithm machine learning data mining retrospective analysis artificial intelligence public health prevalence and management

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A Chinese Text Classier Based on Strong Class Feature Selection and Bayesian algorithm

A Chinese Text Classier Based on Strong Class Feature Select...

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作者： Yanqiu Chen Peili Sun Department of Computer Science Dalian Neusoft Institute of Information Dalian P.R.China Economic Management Institute Dalian Ocean University

For improve the efficiency and accuracy of Chinese text categorization,this paper presents a new Chinese text classier,in which word segmentation system is based on forward scan corpus,and word frequency statistics method is different in the training stage and the test stage,after that,a novel feature selection is proposed according to word frequency,mutual information and classificatory information,Finally a fast Bayes theory classier is *** prove this classier is simple and effective.

关键词： Chinese text categorization feature selection feature weighting classification algorithm

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The Application of Negative Selection algorithm in Multi-angle Infrared Vehicle Images Recognition

The Application of Negative Selection Algorithm in Multi-ang...

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International Conference on Telecommunications and Signal Processing

作者： Xi Yu Dongmei Fu Tao Yang Kamil Riha School of Information and Engeering University of Science and Technology Beijing Department of Telecommunications Brno University of Technology

ISBN: (纸本)9781479984992

At present, the objects of the vehicle recognition based on image processing are mostly visible images with rich details and limited camera angles. Few studies have been done on the recognition of multi-angle infrared vehicle images with less detail. However, it has important implications for transportation and especially public security. This paper extracts object contour of the vehicle from infrared vehicle images. After image correction and normalization, the geometric features and moment invariants are extracted. Based on the tolerance process of biological immune cells, this paper proposes a new Multi-classification Artificial Immunity Negative Selection (MAINS) algorithm. This algorithm can recognize vehicle types by the objects' geometric features and moment invariants. It has a stable performance and global search ability, also a high correct recognition rate in infrared vehicle image recognition.

关键词： classification algorithm Infrared Images Negative Selection Vehicle Recognition

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Wearable Pressure Sensor Suit for Real-Time Detection of Incorrect Exercise Techniques

Wearable Pressure Sensor Suit for Real-Time Detection of Inc...

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IEEE Sensors

作者： Ivin Kuriakose Shirley Chauhan Anis Fatema Aftab M. Hussain FeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd IndiFeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd IndiFeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd IndiFeCS Lb Cener for VLSI nd Ebedded Syses Technoogy (CVEST) Inernion Insiue of Inforion Technoogy Hyderbd Indi

ISBN: (数字)9781665484640

ISBN: (纸本)9781665484657

In recent years, the popularity of weight training has increased significantly. However, incorrect technique or physical form while performing an exercise can not only slow down progress but also cause serious injuries. Deadlift is a famous weight training exercise, which if done incorrectly, can lead to chronic back pain. In this paper, we present a wearable pressure sensor system that checks the posture of the user while performing deadlift. The suit employs a set of flexible pressure sensors, made using velostat (piezoresistive) material, to get the amount of bending for specific muscles. We have developed an algorithm to process the data in real-time so that feedback about incorrect posture can be provided to the user immediately. The algorithm has been configured using data from a single subject, however, it provides accurate classification for multiple subjects in the same weight class, thus eliminating the need for subject-wise configuration. The algorithm classifies a repetition (rep) of the exercise into a good rep or bad rep with an overall accuracy of 95.5%, across three subjects. With some enhancements, the system can also be configured to classify reps of other exercises.

关键词： classification algorithm Deadlift Flexible system Pressure sensor Wearable suit

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Transformer Fault Diagnosis Based on Support Vector Machine

Transformer Fault Diagnosis Based on Support Vector Machine

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Yan Zhang,Bide Zhang,Yuchun Yuan Institute of Electrical and Information Xihua University Chengdu,China (610039) Zichun Pei,Yan Wang Institute of Electrical and Information Xihua University Chengdu,China (610039)

Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power *** of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training *** vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small ***,SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power *** this study,support vector machine is proposed to analysis dissolved gases content in power transformer oil,among which cross-validation is used to determine free parameters of support vector *** experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM *** experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small ***,the SVM model is a proper alternative for diagnosing power transformer fault.

关键词： support vector machine classification algorithm cross-validation free parameters fault diagnosis

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The Study of Automatic Machine Image Monitoring of Floating Waste Bottles

The Study of Automatic Machine Image Monitoring of Floating ...

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2nd International Conference on Simulation and Modeling Methodologies,Technologies and Applications(SMTA 2015)

作者： Yong-xin Qin Rong Hu Zhao-min Ma Bo Hu Yu-qiu Shi College of Electronic & Information Engineering Guangxi University of Science and Technology

Floating bottles is one of the most common pollutant of our water. As automatic monitoring is the basis of prompt and effective waste management, a new method of automatic machine image monitoring is proposed. The original image on the surface of the water is firstly segmented, then the segmented image is processed with 5 morphological operations including dilation, closing, skeleton, skeleton after dilation and skeleton after closing. Then 6 areas characteristics are extracted from the segmented images and the 5 morphological images. Finally, the need for the salvage of the floating bottles is identified with the 6 areas characteristics based on the KNN algorithm. There are 80 images used as training samples and 50 images used as test samples in the experiments. The results of the experiments show that compared with manual identification, the accuracy rates of the training samples and test samples are always 90% higher with different KNN algorithm parameters. This means that the method is effective for the automatic monitoring of floating bottles on the water surfaces and should be helpful for the automatic salvage of waste floating bottles.

关键词： Floating bottle machine vision K Nearest Neighbour(KNN) classification algorithm

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Research on Node Detection algorithm of Tomato Seedlings Based on Digital Image

Research on Node Detection Algorithm of Tomato Seedlings Bas...

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2017 2nd International Conference on Computer, Mechatronics and Electronic Engineering(CMEE 2017)

作者： Xue-guan ZHAO Xiu WANG Jin-wei ZHANG Kai JIANG Shuai SU Beijing Research Center for Intelligent Equipment Technology in Agricultural National Engineering Research Center Information Technology in Agricultural

According to a large number of studies, the internode length is sensitive to environmental stress. In the quality testing of tomato seedlings, optimization algorithm is proposed to extract tomato seedling stem node, using line scan algorithm to extract the main stem area, improve the accuracy of the main stem region selection, and finally through the bag of words model extract main stem node, this method has good robustness. Through the test, it is found that the accuracy of main stem node detection is lowest at fourth nodes, which is 81%.These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately.

关键词： Image processing Seedling detection classification algorithm Machine vision

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Embedding naïve Bayes algorithm data model in predicting student graduation 19

Embedding naïve Bayes algorithm data model in predicting st...

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Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering

作者： Ace C. Lagman Joseph Q. Calleja Corazon G. Fernando Joseph G. Gonzales John Benedict Legaspi John Heland Jasper C. Ortega Ronel F. Ramos Maria Vicky S. Solomo Regina C. Santos FEU Institute of Technology Manila Philippines

ISBN: (纸本)9781450371803

In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： data mining machine learning algorithm big data analytics classification algorithm Naïve Bayes prediction student graduation

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Improving KNN Based Text classifications

Improving KNN Based Text Classifications

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The 2010 2nd International Conference on Future Computer and Communication(第二届未来计算机与通信国际会议 ICFCC 2010)

作者： Zongli Jiang Yi Deng School of Computer Science and Engineering Beijing University of Technology 100124 Beijing China

ISBN: (纸本)9781424458219

In a variety of text classification algorithm, KNN is a competitive one with simple implementation and high efficiency. However, with the expansion of the size of the text, the runtime of KNN will grow rapidly that cannot be afford. In this paper, we improve the KNN by introducing the kd-tree storage structure and reducing the sample space through the sample clestering methods. And experiment shows that the runtime of improved KNN algorithm reduce apparently.

关键词： classification algorithm KNN clustering

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Machine learning approach for the detection of vitamin D level: a comparative study

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 219-219页

作者： Sancar, Nuriye Tabrizi, Sahar S. Near East Univ Dept Math TR-99138 Nicosia Turkiye Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran

BackgroundAfter the World Health Organization declared the COVID-19 pandemic, the role of Vitamin D has become even more critical for people worldwide. The most accurate way to define vitamin D level is 25-hydroxy vitamin D(25-OH-D) blood test. However, this blood test is not always feasible. Most data sets used in health science research usually contain highly correlated features, which is referred to as multicollinearity problem. This problem can lead to misleading results and overfitting problems in the ML training process. Therefore, the proposed study aims to determine a clinically acceptable ML model for the detection of the vitamin D status of the North Cyprus adult participants accurately, without the need to determine 25-OH-D level, taking into account the multicollinearity *** study was conducted with 481 observations who applied voluntarily to Internal Medicine Department at NEU Hospital. The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant's metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the *** to the presence of multicollinearity, the findings showed that the performance of the SVM(RBF) is obviously negatively affected when the test is examined. Moreover, it can be obviously detected that RF is more robust than other models when the variations in the size of training data are examined. This experiment's result showed that the selected RF and ENOR showed better performances than the other two models when the size of training samples was reduced. Since the multicollinearity is more severe in the small samples, it can be concluded that RF and ENOR are not affected by the prese

关键词： Machine learning models Vitamin D Metabolic syndrome Comparative study Multicollinearity classification algorithm

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