Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.

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Decision Support System for Diabetes Classification Using Data Mining Techniques

Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering ◽

10.4018/978-1-7998-9023-2.ch053 ◽

2021 ◽

pp. 1091-1113

Author(s):

Ahmad M. Al-Khasawneh

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Mining Algorithms ◽

Use Of Data ◽

Predictive Data Mining ◽

Severity Of The Disease ◽

Using Data

The use of data mining algorithms in health information systems has played a significant role in developing applications that help to diagnose different diseases. The type of the disease determines the selection of the algorithm, parameters to be used, and dataset pre-processing steps, etc. In this chapter, diagnosing diabetes mellitus is the target since it has gained significant attention in the last few decades due to the increased severity of the disease. Four predictive data mining approaches are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset: k-nearest neighbor, support vector machine, multilayer perceptron neural network, and naive Bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

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Decision Support System for Diabetes Classification Using Data Mining Techniques

Advances in Healthcare Information Systems and Administration - Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics ◽

10.4018/978-1-5225-5460-8.ch012 ◽

2018 ◽

pp. 281-303

Author(s):

Ahmad M. Al-Khasawneh

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Mining Algorithms ◽

Use Of Data ◽

Predictive Data Mining ◽

Severity Of The Disease ◽

Using Data

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Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

Plants ◽

10.3390/plants10010095 ◽

2021 ◽

Vol 10 (1) ◽

pp. 95

Author(s):

Heba Kurdi ◽

Amal Al-Aldawsari ◽

Isra Al-Turaiki ◽

Abdulrahman S. Aldawood

Keyword(s):

Data Mining ◽

Plant Size ◽

Support Vector ◽

Classification Algorithms ◽

Palm Tree ◽

Rhynchophorus Ferrugineus ◽

Red Palm Weevil ◽

Palm Weevil ◽

Using Data ◽

F Measure

In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.

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Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

Engenharia Agrícola ◽

10.1590/s0100-69162012000100019 ◽

2012 ◽

Vol 32 (1) ◽

pp. 184-196 ◽

Cited By ~ 2

Author(s):

Rubens A. C. Lamparelli ◽

Jerry A. Johann ◽

Éder R. dos Santos ◽

Julio C. D. M. Esquerdo ◽

Jansle V. Rocha

Keyword(s):

Data Mining ◽

Expectation Maximization ◽

Spectral Behavior ◽

Use Of Data ◽

Spectral Profiles ◽

Coffee Plants ◽

Using Data ◽

Hyperion Image ◽

Noise Fraction ◽

Behavior Profiles

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

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Osteoporosis Risk Prediction Using Data Mining Algorithms

Journal of Community Health Research ◽

10.18502/jchr.v9i2.3401 ◽

2020 ◽

Author(s):

Efat Jabarpour ◽

Amin Abedini ◽

Abbasali Keshtkar

Keyword(s):

Data Mining ◽

Personal Information ◽

Disease Diagnosis ◽

Support Vector ◽

Data Mining Algorithms ◽

Industry Standard ◽

Disease Information ◽

Increased Risk ◽

Using Data ◽

Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for (TAN) when the precision of TAN is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.

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Using Data Mining for Forecasting Data Management Needs

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch124 ◽

2008 ◽

pp. 2088-2104

Author(s):

Qingyu Zhang ◽

Richard S. Segall

Keyword(s):

Data Mining ◽

Data Management ◽

Forest Cover ◽

Human Lung Cancer ◽

Production Environment ◽

Data Set ◽

Biotechnology Research ◽

Use Of Data ◽

Using Data ◽

Tools And Techniques

This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of data mining with multidimensional databases for determining data management needs for the selected biotechnology data of forest cover data (63,377 rows and 54 attributes) and human lung cancer data set (12,600 rows of transcript sequences and 156 columns of gene types). The data mining is performed using four selected software of SAS® Enterprise MinerTM, Megaputer PolyAnalyst® 5.0, NeuralWare Predict®, and Bio- Discovery GeneSight®. The analysis and results will be used to enhance the intelligence capabilities of biotechnology research by improving data visualization and forecasting for organizations. The tools and techniques discussed here can be representative of those applicable in a typical manufacturing and production environment. Screen shots of each of the four selected software are presented, as are conclusions and future directions.

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For Better Healthcare Mining Health Data

Advances in Data Mining and Database Management - Intelligent Multidimensional Data Clustering and Analysis ◽

10.4018/978-1-5225-1776-4.ch006 ◽

2017 ◽

pp. 135-158

Author(s):

Güney Gürsel

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Healthcare Management ◽

Support Vector ◽

Privacy And Security ◽

Healthcare Data ◽

Management Fraud ◽

Use Of Data ◽

Customer Relation Management ◽

Vector Machines

Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. The most used task is classification. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. In addition, privacy and security of patient data is a big issue to deal with because of the sensitivity of healthcare data. There exist additional serious challenges. This chapter is a descriptive study aimed to provide an acquaintance to data mining and its usage and applications in healthcare domain. The use of Data mining in healthcare informatics and challenges will be examined.

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Data-Driven Modelling of Smart Building Ventilation Subsystem

Journal of Sensors ◽

10.1155/2019/3572019 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Grigore Stamatescu ◽

Iulia Stamatescu ◽

Nicoleta Arghira ◽

Ioana Fagarasan

Keyword(s):

Data Mining ◽

Data Streams ◽

Data Driven ◽

Support Vector ◽

Commercial Building ◽

Monitoring And Control ◽

Smart Building ◽

Building Ventilation ◽

Using Data ◽

Rich Data

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

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Email Worm Detection Using Data Mining

Techniques and Applications for Advanced Information Privacy and Security ◽

10.4018/978-1-60566-210-7.ch002 ◽

2011 ◽

pp. 20-34

Author(s):

Mohammad M. Masud ◽

Latifur Khan ◽

Bhavani Thuraisingham

Keyword(s):

Data Mining ◽

Feature Selection ◽

Principal Component ◽

Classification Model ◽

Support Vector ◽

Two Phase ◽

Feature Selection Technique ◽

Worm Detection ◽

Phase Selection ◽

Using Data

This chapter applies data mining techniques to detect email worms. Email messages contain a number of different features such as the total number of words in message body/subject, presence/absence of binary attachments, type of attachments, and so on. The goal is to obtain an efficient classification model based on these features. The solution consists of several steps. First, the number of features is reduced using two different approaches: feature-selection and dimension-reduction. This step is necessary to reduce noise and redundancy from the data. The feature-selection technique is called Two-phase Selection (TPS), which is a novel combination of decision tree and greedy selection algorithm. The dimensionreduction is performed by Principal Component Analysis. Second, the reduced data is used to train a classifier. Different classification techniques have been used, such as Support Vector Machine (SVM), Naïve Bayes and their combination. Finally, the trained classifiers are tested on a dataset containing both known and unknown types of worms. These results have been compared with published results. It is found that the proposed TPS selection along with SVM classification achieves the best accuracy in detecting both known and unknown types of worms.

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