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Binning Approach based on Classical clustering for Type 2 Diabetes Diagnosis

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第3期11卷 630-637页

作者： Hai Thanh Nguyen Nhi Yen kim Phan Huong Hoang Luong Nga Hong Cao Hiep Xuan Huynh Can Tho Univ Coll Informat & Commun Technol Can Tho Vietnam FPT Univ Dept Informat Technol Can Tho Vietnam Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan

In recent years, numerous studies have been focusing on metagenomic data to improve the ability of human disease prediction. Although we face the complexity of disease, some proposed frameworks reveal promising performances in using metagenomic data to predict disease. Type 2 diabetes (T2D) diagnosis by metagenomic data is one of the challenging tasks compared to other diseases. The prediction performances for T2D usually reveal poor results which are around 65% in accuracy in state-of-the-art. In this study, we propose a method combining k-means clustering algorithm and unsupervised binning approaches to improve the performance in metagenome-based disease prediction. We illustrate by experiments on metagenomic datasets related to Type 2 Diabetes that the proposed method embedded clusters generated by k-means allows to increase the performance in prediction accuracy reaching approximately or more than 70%.

关键词： Unsupervised binning k-means clustering algorithm metagenomics metagenome-based disease prediction Type 2 diabetes diagnosis

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A k-means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2019年第7期21卷 2258-2269页

作者： Tien-Loc Le Tuan-Tu Huynh Lin, Lo-Yi Lin, Chih-Min Chao, Fei Yuan Ze Univ Dept Elect Engn Taoyuan Taiwan Yuan Ze Univ Taoyuan Taiwan Lac Hong Univ Dept Elect Elect & Mech Engn Bien Hoa Vietnam Taipei Vet Gen Hosp Dept Radiol Taipei Taiwan Xiamen Univ Dept Cognit Sci Xiamen Fujian Peoples R China

This paper proposes a new medical diagnosis algorithm that uses a k-means interval type-2 fuzzy neural network (kIT2FNN). This kIT2FNN classifier uses a k-means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the k-means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient approach. The Lyapunov theory is also used to verify the convergence and stability of the proposed method. The performance of the system is evaluated using several medical datasets from the University of California at Irvine (UCI). All of the experimental and comparison results are presented to demonstrate the effectiveness of the proposed medical diagnosis algorithm.

关键词： Classification problem Interval type-2 fuzzy neural network k-means clustering algorithm Medical diagnosis

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UAVs to the Rescue: Prolonging the Lifetime of Wireless Devices Under Disaster Situations

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORkING 2019年第4期3卷 942-954页

作者： Shakhatreh, Hazim khreishah, Abdallah Ji, Bo Yarmouk Univ Dept Telecommun Engn Irbid 21163 Jordan New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider downlink scenarios from an aerial base station to ground users. In this paper, we consider an uplink scenario under disaster situations (such as earthquakes or floods), when cellular networks are down. We formulate the placement problem of UAVs, where the objective is to determine the locations of a set of UAVs that maximize the time duration of uplink transmission until the first wireless device runs out of energy. We prove that this problem is NP-complete. Due to its intractability, we start by restricting the number of UAVs to be one. We show that under this special case the problem can be formulated as a convex optimization problem under a restriction on the coverage angle of the ground users. After that, we propose a gradient projection-based algorithm to find the optimal location of the UAV. Based on this, we then develop an efficient algorithm for the general case of multiple UAVs. The proposed algorithm starts by clustering the wireless devices into several clusters where each cluster being served by one UAV. After it finishes clustering the wireless devices, it applies the gradient projection-based algorithm in each cluster. We also formulate the problem of minimizing the number of UAVs required to serve the ground users such that the time duration of uplink transmission of each wireless device is greater than or equal to a threshold value. We prove that this problem is NP-complete and propose to use two efficient methods to determine the minimum number of UAVs required to serve the wireless devices. We validate the analysis by simulations and demonstrate the effectiveness of the proposed algorithms under different cases.

关键词： Unmanned aerial vehicles lifetime of wireless devices emergency response convex optimization gradient projection algorithm k-means clustering algorithm

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A categorization tool for fabric systems used in firefighters' clothing based on their thermal protective and thermo-physiological comfort performances

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TEXTILE RESEARCH JOURNAL 2019年第16期89卷 3244-3259页

作者： Mandal, Sumit Annaheim, Simon Capt, Andre Greve, Jemma Camenzind, Martin Rossi, Rene M. Empa Swiss Fed Labs Mat Sci & Technol Lab Biomimet Membranes & Text St Gallen Switzerland DuPont Int Operat Sarl Le Grand Saconnex Switzerland

Fabric systems used in firefighters' thermal protective clothing should offer optimal thermal protective and thermo-physiological comfort performances. However, fabric systems that have very high thermal protective performance have very low thermo-physiological comfort performance. As these performances are inversely related, a categorization tool based on these two performances can help to find the best balance between them. Thus, this study is aimed at developing a tool for categorizing fabric systems used in protective clothing. For this, a set of commercially available fabric systems were evaluated and categorized. The thermal protective and thermo-physiological comfort performances were measured by standard tests and indexed into a normalized scale between 0 (low performance) and 1 (high performance). The indices dataset was first divided into three clusters by using the k-means algorithm. Here, each cluster had a centroid representing a typical Thermal Protective Performance Index (TPPI) value and a typical Thermo-physiological Comfort Performance Index (TCPI) value. By using the ISO 11612:2015 and EN 469:2014 guidelines related to the TPPI requirements, the clustered fabric systems were divided into two groups: Group 1 (high thermal protective performance-based fabric systems) and Group 2 (low thermal protective performance-based fabric systems). The fabric systems in each of these TPPI groups were further categorized based on the typical TCPI values obtained from the k-means clustering algorithm. In this study, these categorized fabric systems showed either high or low thermal protective performance with low, medium, or high thermo-physiological comfort performance. Finally, a tool for using these categorized fabric systems was prepared and presented graphically. The allocations of the fabric systems within the categorization tool have been verified based on their properties (e.g., thermal resistance, weight, evaporative resistance) and construction paramete

关键词： fabric systems firefighters' clothing thermal protective performance thermo-physiological comfort performance k-means clustering algorithm fabric categorization tool

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Comparison of Business Environments in Oil-Rich MENA Countries: A clustering Analysis of Economic Diversification and Performance

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EMERGING MARkETS FINANCE AND TRADE 2019年第12期55卷 2871-2885页

作者： Aker, Sule L. Aghaei, Iman Eastern Mediterranean Univ Dept Business Adm Mersin 10 Famagusta Turkey

The purpose of present research is to cluster 11 oil-richest MENA countries;Algeria, Egypt, Iran, Iraq, kuwait, Libya, Oman, Qatar, Saudi Arabia, Sudan, and the United Arab Emirates regarding economic diversification and performance during 2010-2016 using k-means algorithm which is a versatile data mining technique. The major finding is that higher export diversity in addition to better economic performance can influence competitiveness of business environment of the countries positively and vice versa. The studied nations are partitioned into four clusters. Qatar and UAE with highest economic diversity and performance have the most favorable business environments in the region.

关键词： Business environment economic diversification economic performance k-means clustering algorithm MENA countries

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Optimizing risk budgets in portfolio selection problem: A bi-level model and an efficient gradient-based algorithm

A I I E Transactions

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A I I E Transactions 2024年第8期56卷

作者： Maryam Bayat Farnaz Hooshmand Seyed Ali MirHassani Department of Mathematics and Computer Science Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time.

关键词： Risk budgeting portfolio bi-level optimization gradient descent method k-means clustering algorithm

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Research on Falling Behavior Monitoring of Three-axis accelerometer

Research on Falling Behavior Monitoring of Three-axis accele...

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第32届中国过程控制会议(CPCC2021)

作者： Gao Fei Zhang Lu Wang Yaxin ZhangHongyuan School of Electronic Information Engineering Inner Mongolia University

Falling behavior recognition in the elderly is an emerging research direction in the field of pattern *** and foreign researchers have introduced the elderly fall behavior monitoring technologies such as acoustic detection technology,but these monitoring technologies are greatly affected by external factors and are not conducive to practical *** this paper,the three-axis accelerometer is used to identify the fall behavior of the elderly,and the k-means clustering algorithm is *** algorithm is used to identify the static state and the walking state of the ***,this paper studies the threshold judgment algorithm to identify the elderly ***,an intelligent self-rescue crutch is designed based on the threshold judgment *** experiment shows that the algorithm used in this paper has a high accuracy in the detection of the behavior and posture of the elderly,and it can effectively identify the fall behavior of the elderly through data,which has important reference significance for the research in this direction.

关键词： Fall Three-axis accelerometer k-means clustering algorithm

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Feature Selection algorithm Based on k-means clustering 7

Feature Selection Algorithm Based on K-means Clustering

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Tang, Xue Dong, Min Bi, Sheng Pei, Maofeng Cao, Dan Xie, Cheche Chi, Sunhuang South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9781538604908

In order to improve the performance of the feature selection algorithm, a feature selection algorithm based on k-means clustering is designed. The algorithm makes use of the idea of k-means clustering based on cosine distance to cluster the features, so that the obtained feature subset has strong correlation and no redundancy. The experimental results show that the feature selection algorithm based on k-means clustering has high efficiency for classification tasks and has short running time, so the algorithm has strong practicability for feature selection.

关键词： mechine learning feature selection k-means clustering algorithm

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Effective Big Data Retrieval Using Deep Learning Modified Neural Networks

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MOBILE NETWORkS & APPLICATIONS 2019年第1期24卷 282-294页

作者： Prasanth, T. Gunasekaran, M. Bannari Amman Inst Technol Dept Informat Technol Sathyamangalam 638401 Tamil Nadu India

In big data, the information retrieval (IR) embraces the discovery of documents from a compilation of dataset which are related to the user query. Usually, the data retrieval systems are used to scan the data. The existent approaches that employ customary IR are wearisome for big document collections. Recently, IR approaches were developed, albeit these are faster comparing to the traditional method but the quality of the document retrieved is less. To overcome such difficulties, here, effectual big data retrieval utilizing Deep Learning Modified Neural Networks (DLMNN) is proposed. Initially, the general pre-processing along with feature extraction steps are taken place. In preprocessing stage, unwanted data are removed and also used for changing the unstructured data in to structured data then in FE is done using frequency and entropy calculation of the given input data. Secondly, find the closed recurrent item dataset, after that find the weight of provided data using entropy measure and frequent item measure. In the 3rd step, the documents are clustered utilizing the k-means algorithm and then classified using DLMNN. The k-means algorithm is utilized to partition the collection of documents into several clusters then the DLMNN is used for classifying the documents into positive and negative classes. The proposed DLNN weight parameters are optimized utilizing the cuckoo search (CS) optimization algorithm. The last stage on the training process section is generating a training data-base. In the other part, the retrieval process is performed, in this section it pre-processes the user query and discovers the frequency item set then gets retrieval data. Finally, check the similarity assessment, if the information is found then it is visualized, otherwise the document is returned to the initial position. Experimental results contrasted with the previous MRT and IRI-RAS techniques concerning precision, recall, F-measure along with computation time. The proposed documen

关键词： Big data retrieval Closed frequent item set Deep learning modified neural networks (DLMNN) Cuckoo Search algorithm (CSA) k-means clustering algorithm Mutual refinement technique (MRT) Intelligent rule-based relevant information retrieval algorithm with semantics (IRI-RAS)

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Congestion management of power system with uncertain renewable resources and plug-in electrical vehicle

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第6期13卷 927-938页

作者： Prajapati, Vijaykumar k. Mahajan, Vasundhara Sardar Vallabhbhai Natl Inst Technol Dept Elect Engn Surat India

The study describes the quantifying impact of plug-in electrical vehicles (PEVs) and renewable energy sources (RES) for congestion management of power system. The proposed congestion management problem is formulated considering uncertainties of wind, solar, number of PEVs and load condition over a day. The uncertainty modelling of solar, wind and PEVs is presented using beta, Rayleigh and normal distribution functions, respectively. The PEVs uncertainty is dependent on its number and increases with escalation in number. The degree of uncertainties of RES is dependent on corresponding variable (wind and solar). These uncertainties result in large number of scenarios which increases the computational burden. The k-means clustering algorithm is applied to reduce the number of scenarios. The objective function is formulated to minimise generation cost, rescheduling cost and PEV cost for congestion management. This system is analysed by using Monte Carlo simulation. The proposed methodology relieves the congestion and reduces the generation cost, total power generation, total loss with increasing number of PEVs. The test result indicates that PEVs not only act as small storage unit but it also provides power during peak hours. The proposed approach is modelled in GAMS environment and implemented on modified IEEE 39-bus system.

关键词： power generation scheduling power system management Monte Carlo methods power generation economics renewable energy sources power markets electric vehicles normal distribution solar power stations wind power plants power system uncertain renewable resources plug-in electrical vehicle renewable energy sources RES congestion management problem uncertainty modelling normal distribution functions total power generation modified IEEE 39-bus system generation cost minimization PEV uncertainty beta function Rayleigh distribution functions k-means clustering algorithm rescheduling cost minimization PEV cost minimization Monte Carlo simulation GAMS environment

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