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Multimodal sensing and decision-making for evaluating the physical fitness of university students using body area network

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WIRELESS NETWORKS 2024年第3期30卷 1465-1478页

作者： Fang, Xiangli Hao, Xiujun Langfang Normal Univ Langfang 065000 Hebei Peoples R China

These days, physical health concerns are gradually elevated to the same degree of significance as essential issues such as education, health, and protection as national living standards improve. Enhancing the physical health of university students and encouraging healthy growth are significant moments for China, due to which each university controls a physical examination every year. Utilizing wireless sensors for data capture in university students physical fitness evaluation systems has played an essential role in various areas. Therefore, this paper presents a body domain network multimodal sensor and decision algorithm-based physical health assessment system for university students. The proposed system uses multiple body domain network sensors to collect physiological indicators data by using decision algorithms to combine multiple assessment indicators for analysis and prediction, ultimately generating a health assessment report and providing customized health advice and health plans for each user. In addition, to improve the physical health evaluation of university students in the context of sports medicine integration, this paper also proposes a physical health evaluation method based on the ID3 decision tree algorithm. It constructs a physical health information mining and feature extraction model for university students in the context of sports medicine integration. It uses a decision tree optimization method for classification detection. The proposed method also uses an associative multidimensional feature detection algorithm to evaluate university students' physical fitness and health status. It establishes a decision indicator function to investigate information fusion and constrained feature decomposition through the significant difference and balanced training fusion methods. In the ID3 decision tree, the branching system of college students' physical fitness health information in the context of sports medicine fusion is designed. It examines the entro

关键词： decision tree algorithm Wireless sensor network Medical integration College students Physical health Correlation dimension Hierarchical feature detection Body area network Health evaluation system

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Automating extraction of logical domains in a web site

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DATA & KNOWLEDGE ENGINEERING 2002年第2期43卷 179-205页

作者： Ayan, NF Li, WS Kolak, O NEC USA Inc C&C Res Labs Silicon Valley Cupertino CA 95014 USA

The domain name field in a universal resource locator (URL) has been viewed as a natural choice to organize Web pages. For example, Web search results may be grouped in terms of domains and presented to users as clusters for ease of visualization. However, using this approach, large Web sites, such as Geocities, W3C, and ***, tend to yield many matches that leads to a few large, flat structured, and unorganized clusters. As a matter of fact, many pages in these sites are actually "logical domains" by themselves. For example, Web sites for projects at a university or the XML section at W3C could be viewed as "logical domains". In this paper, we propose the concept of a logical domain, which is identified by semantic relatedness, as opposed to a physical domain, which is identified simply by domain name. The identification of logical domain is important to many Web applications, such as query result reorganization, site map generation, and topic distillation. We have developed and implemented a set of rules based on link structure, path information, document metadata, and citations to identify logical domain entry pages (i.e., root pages of logical domains). The importance of these rules are automatically adjusted using a novel decision tree algorithm and training data provided by human feedback. We also develop techniques to define the boundary of each logical domain based on identified logical domain entry pages. We have conducted extensive experiments on real Web sites to evaluate the effectiveness of our proposed techniques. The experimental results show that our techniques perform very well in extracting logical domains in a Web site. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： logical domain domain boundary decision tree algorithm information gain WWW link structures

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Soft computing approach to fault diagnosis of centrifugal pump

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APPLIED SOFT COMPUTING 2012年第5期12卷 1574-1581页

作者： Sakthivel, N. R. Nair, Binoy B. Sugumaran, V. Amrita Vishwa Vidyapeetham Dept Mech Engn Amrita Sch Engn Coimbatore 641105 Tamil Nadu India Karpagam Univ Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Coimbatore 641105 Tamil Nadu India VIT Univ Dept Mech Engn Madras Tamil Nadu India

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Centrifugal pump Gene expression programming Fault diagnosis Statistical features decision tree algorithm Support vector machine Proximal support vector machine

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Enhance data availability and network consistency using artificial neural network for IoT

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第1期83卷 3111-3131页

作者： Tabassum, Mujahid Perumal, Sundresan Kashem, Saad Bin Abdul Ponnan, Suresh Chakraborty, Chinmay Chowdhury, Muhammad E. H. Khandakar, Amith Univ Sains Islam Malaysia Fac Sci & Technol Nilai 71800 Negeri Sembilan Malaysia Qatar Fdn Qatar Armed Forces Acad Bridge Program Robot & Adv Comp Fac Doha Qatar Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept ECE Chennai 600062 Tamil Nadu India Birla Inst Technol Elect & Commun Engn Jharkhand India Qatar Univ Coll Engn Dept Elect Engn Doha 2713 Qatar

IoT networks have become famous and utilized in many industries such as agriculture, medical, manufacture, and others due to their efficiency and productivity. WSN is an IoT network used for smart farming and smart health monitoring. WSNs can self-manage, self-configure, self-diagnose, and self-heal, making them ideal for agricultural monitoring. A wireless sensor network collects data from numerous sensor nodes scattered across the physical world. WSN data processing is critical when a node fails for unknown reasons. Data handling is an essential aspect of WSN;once any node fails due to unknown reasons, data reliability and availability become crucial. Hence, limited battery energy, low bandwidth, limited computing capacity, and link failure affect network performance. Therefore, an effective cluster-based data aggregation with an appropriate routing must be designed in the media access control. The proposed hybrid artificial neural network and decision tree algorithm with cognitive radio is developed to select the cluster head. The higher amount of residual energy increases the number of packets received at the base station and aggregate the data from the normal sensor nodes. The on-demand routing protocol is designed to keep data in local storage for retransmission during link failure to obtain reliable data transmission. The proposed method performance is analyzed as residual energy, end to end delay, normalized overhead, packet delivery ratio, packet drop, and throughput. This proposed method is evaluated with the cluster-based data aggregation scheme to prove its efficiency. The proposed method residual energy is 8.3Joules for 50 nodes;it is high compared to the cluster-based data aggregation scheme.

关键词： Artificial Neural Network Cluster base network Wireless Sensor Network Data aggregation decision tree algorithm On-demand routing protocol

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Plastics waste identification in river ecosystems by multispectral proximal sensing: a preliminary methodology study

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WATER AND ENVIRONMENT JOURNAL 2021年第2期35卷 569-579页

作者： De Giglio, Michaela Dubbini, Marco Cortesi, Irene Maraviglia, Michela Parisi, Erica Isabella Tucci, Grazia Univ Bologna Geog Sect DiSCi Piazza San Giovanni Monte 2 I-40124 Bologna Italy Univ Florence DICEA Florence Italy

A considerable amount of the plastics produced around the world is now dispersed throughout the environment, and in particular in aquatic ecosystems. This can have damaging consequences for plants, animals and human beings. This study investigates some approaches for detection and monitoring of plastics waste in river habitats through multispectral image classification. The data are acquired using a proximity sensor in the electromagnetic spectrum range that includes the ultraviolet, visible and near infrared bands, as for the WorldView-2 satellite. The in-depth analysis of the spectral signatures obtained shows typical plastics trends and reflectance values in the near infrared bands. Different classification methods were compared to test their effectiveness for the isolation of plastics samples dispersed in a river habitat. This project represents the first step within a wider research programme, with the aim to define a new approach for future river pollution monitoring.

关键词： decision tree algorithm multispectral proximity sensor plastic spectral signature plastic waste remote sensing river ecosystem

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Suggesting teaching methods by analyzing the behavior of children with special needs

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BIO-algorithmS AND MED-SYSTEMS 2020年第1期16卷 1-20页

作者： Shrawankar, Urmila Shireen, Azra GH Raisoni Coll Engn Dept Comp Sci & Engn Nagpur MS India

The behavioral pattern of children with special needs depends on their emotional and developmental disability. Any abnormal and incorrect pattern of behavior which is below the level of development as the expected norm can be considered as "the challenging behavior." For supporting children with behavioral problems, many interventions and strategies alone, or in combination, are used. Behavioral and developmental problems, if not treated well, in childhood, may cause a problem and have negative long-term and short-term effects on a child's personal life, education, family, and professional life. Detailed knowledge about the child's behavior is important to define the problem. Hence, the software contains the questionnaire pattern, which is divided into categories like parents, teachers, doctors, and friends. Child behavior checklist is used to define the questionnaire. Images and video analysis are used to detect the current emotion in the child. By analyzing the behavioral pattern and current emotion, the teaching method will be suggested.

关键词： behavioral disorder behavioral recognizing data analytics data preprocessing decision tree algorithm Microsoft emotion/face API mood detection teaching interventions video frames detector video processing

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Ground visibility prediction using tree-based and random-forest machine learning algorithm: Comparative study based on atmospheric pollution and atmospheric boundary layer data

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ATMOSPHERIC POLLUTION RESEARCH 2024年第11期15卷

作者： Wang, Fuzeng Liu, Ruolan Yan, Hao Liu, Duanyang Han, Lin Yuan, Shujie Chengdu Univ Informat Technol Coll Elect Engn Chengdu 610225 Peoples R China Chengdu Meteorol Off Pengzhou Meteorol Adm Chengdu 611930 Peoples R China Chengdu Univ Informat Technol Sch Atmospher Sci Chengdu 610225 Peoples R China Nanjing Joint Inst Atmospher Sci Key Lab Transportat Meteorol China Meteorol Adm Nanjing 210041 Peoples R China Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai 200135 Peoples R China

To mitigate haze impacts, three visibility simulation schemes were designed using decision tree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.

关键词： decision tree algorithm Random forest algorithm Visibility prediction Haze Artificial intelligence

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THE METHODS OF EXTRACTING WATER INFORMATION FROM SPOT IMAGE

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Chinese Geographical Science 2002年第1期12卷 68-72页

作者： DUJin－kang FENGXue－zhi 等 DepartmentofUrbanandResourceScience NanjingUniversityNanjing210093P.R.China

Some techniques and methods for deriving water information from SPOT-4(XI) image were investigated and discussed in this paper. An algorithm of decision tree (DT) classification which includes several classifiers based on the spectral responding characteristics of water bodies and other objects, was developed and put forward to delineate water bodies. Another algorithm of decision tree classification based on both spectral characteristics and auxiliary information of DEM and slope (DTDS) was also designed for water bodies extraction. In addition, supervised classification method of maximum likelyhood classification (MLC), and unsupervised method of interactive self organizing dada analysis technique (ISODATA) were used to extract waterbodies for comparison purpose. An index was designed and used to assess the accuracy of different methods adopted in the research. Results have shown that water extraction accuracy was variable with respect to the various techniques applied. It was low using ISODATA, very high using DT algorithm and much higher using both DTDS and MLC.

关键词： water body decision tree algorithm accuracy assessment

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Development of a Cloud-Based Clinical decision Support System for Ophthalmology Triage Using decision tree Artificial Intelligence

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OPHTHALMOLOGY SCIENCE 2023年第1期3卷 100231-100231页

作者： Tanya, Stuti M. Nguyen, Anne X. Buchanan, Sean Jackman, Christopher S. McGill Univ Dept Ophthalmol & Visual Sci Montreal PQ Canada McGill Univ Fac Med Montreal PQ Canada Jackman Eye Inst St John NF Canada Mem Univ Newfoundland Dept Surg Div Ophthalmol St John NF Canada

Purpose: Clinical decision support systems (CDSS) are an emerging frontier in teleophthalmology, drawing on heuristic decision making to augment processes such as triage and referral. We describe the development and implementation of a novel cloud-based decision tree CDSS for on-call ophthalmology consults. The objective was to standardize the triage and referral process while providing a more accurate provisional diagnosis and ***: Prospective comparative cohort ***: On-call referrals to a Canadian community ophthalmology ***: A web-based decision tree algorithm was developed using current guidelines and expert opinion. The algorithm collected tailored information on the patient's ophthalmic concern, and outputted a provisional diagnosis and urgency before sending an electronic referral to the on-call ophthalmology clinic. Data were described using descriptive statistics. Spearman-rho correlations and Cohen's kappa coefficient were used to characterize the observed relationships. Post hoc analysis was conducted using analysis of contingency tables and adjusted *** Outcome Measures: Diagnostic category, provisional diagnosis, and urgency for the referring pro-vider, CDSS, and ophthalmologist. Results: Ninety-six referrals were processed. Referring providers included medical doctors (76.0%, n = 73), optometrists (20.8%, n = 20), and nurse practitioners (3.1%, n = 3). The CDSS (k = 0.5898;95% confidence interval [CI], 0.4868-0.6928;P < 0.0001) performed equally well with 66.7% agreement in determining category when compared with referring providers (k = 0.5880;95% CI, 0.4798-0.6961;P < 0.0001). The CDSS (agreement = 53.1%;k = 0.4999;95% CI, 0.4021-0.5978;P < 0.0001) performed better than referring providers (agreement = 43.8%;k = 0.4191;95% CI, 0.3194-0.5188;P < 0.0001) in determining a diagnosis. The CDSS (r = 0.5014;95% CI, 0.3092-0.6935;P < 0.0001) also performed better than referring providers (r = 0.4035;95% CI, 0.2406-

关键词： Clinical decision support system decision tree algorithm Triage and referral Teleophthalmology Artificial intelligence

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Developing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using ensemble classification methods

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4161-4171页

作者： Mohammed, Parves Begum, S. Jabeen JNTUH Sree Chaitanya Coll Engn Hyderabad Telangana India Anna Univ Velalar Coll Engn & Technol Chennai Tamil Nadu India

In present scenario, Heart Disease has become the vital cause of mortality and diagnosis of heart diseases is a great confrontation in the field of medical data analysis. Data Mining is an efficient technique for processing and analyzing larger databases for deriving hidden knowledge appropriately. Hence, it is incorporated in medical data analysis for assisting in effective decision making and disease predictions. With that concern, this paper concentrates on framing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using the advanced data mining conceits. The model considers the significant features of patient data that are available in benchmark datasets. Here, the main objective of the proposed model is to enhance the classification accuracy of patient data on classes under NORMAL and ABNORMAL. For enhancing the classification accuracy, the proposed integrated model utilizes the algorithms such as decision tree algorithm, Naive Baye's Classification and Ensemble Classifiers called Random Forest and Bagging. Further, performance evaluation is performed for analyzing the proposed work. For that, images from UCI repository are utilized and the comparative analysis shows that the proposed work produces better results than the existing models compared.

关键词： Heart disease diagnosis data mining classification accuracy decision tree algorithm Naive Baye's classification random forest and bagging

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