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clustering and classification software for sperm subpopulation analysis

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2022年第6期10卷 585-598页

作者： Buchelly Imbachi, Francisco Zalazar, Lucia Ignacio Pastore, Juan Nicolli, Anabella Ledesma, Alba Hozbor, Federico A. Cesari, Andreina Ballarin, Virginia Univ Nacl Mar del Plata Image Proc Lab Lab Procesamiento Imagenes Icyte Unmdp Conicet ICYTE CONICETElect Engn Dept Buenos Aires DF Argentina Inst Invest Biol IIB Unmdp Conicet Mar Del Plata Buenos Aires Argentina Consejo Nacl Invest Cient & Tecn Conicet Buenos Aires DF Argentina Inst Nacl Tecnol Agr Inta Dept Prod Anim Biotecnol Reprod Balcarce Argentina

In this paper, we present the Sperm Motility Tracker v2.0 that integrates a clustering module to the typical CASA sperm motility analysis adapted to semen from different species. The novelty of the new software is the integration of a friendly tool that allows researchers and clinical staff to identify sperm subpopulations by a simple procedure. Partitioning is performed by a hierarchical clustering based on selected motility parameters that can be selected automatically or manually. To show the performance of the proposed approach, we assayed a simple cryopreservation approach to compare sperm samples by using two different extenders. After clustering, we could identify three significant subpopulations according to the automatically suggested classifier parameters. This grouping was validated over fresh semen and was in accordance with the physiological status of the cell population. We validate the results over fresh semen, obtaining clusters according to the physiological status of the cell population. Subpopulation analysis highlights the results hidden by the standard population analysis. This new software version offers users a useful and friendly tool for sperm motility subpopulation analysis in the veterinarian and biomedical fields.

关键词： Objective sperm motility sperm subpopulations clustering and classification computer-assisted semen analysis system (casa)

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A Data-driven Crowd Simulation Model based on clustering and classification 13

A Data-driven Crowd Simulation Model based on Clustering and...

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17th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

作者： Zhao, Mingbi Turner, Stephen John Cai, Wentong Nanyang Technol Univ Sch Comp Engn Parallel & Distributed Comp Ctr Singapore 639798 Singapore

ISBN: (纸本)9780769551388

In this paper, we propose a data-driven crowd behavior model that is constructed by extracting examples from human motion data describing how humans make decisions. We cluster the examples before the simulation to find similar patterns of behavior. During the simulation, at each simulation time step, we first classify the input state perceived by an agent in the simulation into one example cluster using an artificial neural network classifier. We then combine similar examples of that cluster to produce an output, a velocity vector indicating the position of the agent in the next time step. Such a two-step matching process enables the selection of the most similar example accurately and efficiently. To verify our approach, we have developed an initial prototype in which we build our model using motion data generated by a RVO2 simulator, attempting to reproduce the behavior of the RVO2 model. By comparing the position of the same agent simulated by the RVO2 model and our model respectively at the same time steps, we show that our model has the ability to reproduce the behavior of the RVO2 model accurately. As future work, we will use real human motion data as model input, so that our model may perform human-like motion behavior.

关键词： Agent-based Simulation clustering and classification Crowd Simulation Data-driven model Example-based model

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A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2020年 82卷 101743-000页

作者： Chen, Jieh-Haur Wei, Hsi-Hsien Chen, Chih-Lin Wei, Hsin-Yi Chen, Yi-Ping Ye, Zhongnan Natl Cent Univ Taoyuan Taiwan Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

A given region's volume of air passengers and cargo is frequently taken to represent its economic development. This research proposes a practical methodology for investigating the inherent patterns of the relationships between air-traffic volume and macroeconomic development, utilizing data-mining techniques, including K-means clustering and Decision Tree C5.0 classification. Using the case of Taiwan from 2001 to 2014, 32 potential macroeconomic factors ascertained from a literature review were combined with air-traffic volume data to establish a 168-month dataset. After this dataset was grouped into five clusters, decision trees were implemented to determine its critical macroeconomic characteristics. The resulting four critical factors and their thresholds were the Information and Electronics Industrial Production Index (IE Index), at 83.22;National Income Per Capita, at US$3,222;Employed Population, at 10.134 million;and the Japanese Nikkei 225 Stock Average, at 10564.44. Among these, the IE Index was found to be the first critical factor relating to air-traffic volume as well as the only characteristic to distinguish Cluster V - 58 consecutive months from March 2010 to December 2014 inclusive - among others, and the reasonableness of this finding was confirmed via examination of detailed airtraffic statistics. Besides, the effectiveness of the four identified critical factors as predictive variables were validated by comparing forecasted results with actual air traffic volume from 2015 to 2016. Understanding these four critical factors and their relative importance is of great value to policymakers seeking to allocate limited resources optimally and objectively. Therefore, as an effective and efficient means of capturing significant and explainable macroeconomic factors influencing air-traffic volume, the proposed methodology can be applied to strategy formulation, operations management, and investment planning by governments, airports, airlines, and related ent

关键词： Air-traffic volume K-means decision trees clustering and classification Macroeconomic factors

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GATE: classification and clustering of text for Semi-vowel /j/-Morphophonemic approach

GATE: Classification and Clustering of text for Semi-vowel /...

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IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Reddy, K. Sairam Sasanka, K. Prasanna, S. Venkatesan, R. SRC Sastra Univ CSE Kumbakonam India

ISBN: (纸本)9781509012770

In recent years, many successful machine learning applications have been developed. classification & clustering is one such. This application is cross-disciplinary, now that it is based on data mining algorithms on the technical side and on graphemes and morphophonemic on the linguistic side. It will thus map the correspondence between grapheme < y > and related phonemes via morphemes in a given context. The grapheme < y > is often realized as an approximant /j/ (a consonant phoneme), as in < yacht >;or as vowel / I / as in < racy >, or a diphthong / aI / as in < sky >, etc. The objective, that is to say, of this text analysis is to map the various articulators//phonemic realizations of the grapheme < y >. This experiment will thus help the study the occurrence of < y > in different positions in words, word initially, finally or elsewhere, as in these examples. The training data (or corpus) chosen for this experimentation is a set of English literary texts [Harry Potter part 1, part 2, The complete works of William Shakespeare By William Shakespeare, The Adventures of Sherlock Holmes By Arthur Conan Doyle, (A Christmas carol) By Charles Dickens's. As for the tools, the alphabet recognizer is used for retrieving information and categorizing the data as noun, adjective, etc. Then, GATE Developer, the Text engineering tool is used to analyze the dataset and to derive the output with statistical data of global distribution < y > as in all the input documents. This project is particularly relevant because it offers, as will be demonstrated, a solution to problems due to lack of one-to-one correspondence between spelling and pronunciation in English, as for instance, in the context of language pedagogy. Glossary of technical terms (of both linguistics & computing) is appended, now that the paper is inter-disciplinary.

关键词： clustering and classification machine learning Text mining GATE graphane morphme phoneme morphophonemic mapping NLP

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High-resolution Self-Organizing Maps for advanced visualization and dimension reduction

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NEURAL NETWORKS 2018年 105卷 166-184页

作者： Saraswati, Ayu Van Tuc Nguyen Hagenbuchner, Markus Tsoi, Ah Chung Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Self-Organizing Map Artificial neural network clustering and classification

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Intelligent clustering techniques for prediction of sugar production

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MATHEMATICS AND COMPUTERS IN SIMULATION 2002年第3-5期60卷 159-168页

作者： Kaburlasos, VG Spais, V Petridis, V Petrou, L Kazarlis, S Maslaris, N Kallinakis, A Aristotle Univ Thessaloniki Dept Elect & Comp Engn Automat & Robot Lab GR-54006 Thessaloniki Greece Hellen Sugar Ind SA GR-54110 Thessaloniki Greece

The accurate, and timely prediction of the annual sugar-beet crop yield is important to Sugar Industry because, based on it, the "harvest campaign" can be scheduled efficiently. This work presents intelligent clustering techniques for effecting efficient, small error prediction of the annual sugar-beet crop yield for the Hellenic Sugar Industry based on production and meteorological data acquired during a period of 11 years. The experiments here demonstrate that intelligent clustering techniques can provide with better estimates of sugar production than alternative prediction methods including an "energy conservation" system model. (C) 2002 IMACS. Published by Elsevier Science B.V. All rights reserved.

关键词： prediction of sugar production Hellenic Sugar Industry mathematical models computational intelligence clustering and classification

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Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines

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ROCK MECHANICS AND ROCK ENGINEERING 2022年第3期55卷 1499-1516页

作者： Yang, Haiqing Song, Kanglei Zhou, Jiayuan Chongqing Univ Sch Civil Engn State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400045 Peoples R China Natl Joint Engn Res Ctr Geohazards Prevent Reserv Chongqing 400045 Peoples R China

When a tunnel boring machine (TBM) is applied to the tunnel constructed in the mixed-face ground, the ground conditions ahead of tunnel face have a key impact on the operation performance and safety. Aiming to establish an automatic prediction model for geological conditions based on the operational data of TBM, the first step is to conduct clustering analysis using Canopy and K-means algorithms to recognize ground types based on geological data. Then, the ground type obtained by clustering analysis and corresponding operational parameters of tunneling machine are combined to construct a sample set. The outlier detection and synthetic minority oversampling technique (SMOTE) were used to preprocess the sample set. To obtain the best prediction effect, three different classifiers were applied for the model selection. By comparing the prediction performance of these three classifiers models, the gradient boosting decision tree (GBDT) model with accuracy of 0.804 shows the best performance as the geological prediction model. The test results of the prediction model show a low sensitivity when training set is small (set as 20%). The analysis of the importance of the model inputs showed that among the six machine parameters used in this study, the total thrust force, penetration rate and ratio of thrust to torque, are the three most influential inputs on the ground condition prediction results. Hence, the proposed prediction procedure can be applied to characterized and predicted ground conditions to ensure the safety and efficiency of tunneling.

关键词： TBM operational data Mixed-face ground Ground type clustering and classification Intelligent prediction

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PROJECT DISPUTE PREDICTION BY HYBRID MACHINE LEARNING TECHNIQUES

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2013年第4期19卷 505-517页

作者： Chou, Jui-Sheng Tsai, Chih-Fong Lu, Yu-Hsin Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan Natl Cent Univ Dept Informat Management Jhongli 32001 Taoyuan County Taiwan Feng Chia Univ Dept Accounting Taichung 40724 Taiwan

This study compares several well-known machine learning techniques for public-private partnership (PPP) project dispute problems. Single and hybrid classification techniques are applied to construct models for PPP project dispute prediction. The single classification techniques utilized are multilayer perceptron (MLP) neural networks, decision trees (DTs), support vector machines, the naive Bayes classifier, and k-nearest neighbor. Two types of hybrid learning models are developed. One combines clustering and classification techniques and the other combines multiple classification techniques. Experimental results indicate that hybrid models outperform single models in prediction accuracy, Type I and II errors, and the receiver operating characteristic curve. Additionally, the hybrid model combining multiple classification techniques perform better than that combining clustering and classification techniques. Particularly, the MLP + MLP and DT + DT models perform best and second best, achieving prediction accuracies of 97.08% and 95.77%, respectively. This study demonstrates the efficiency and effectiveness of hybrid machine learning techniques for early prediction of dispute occurrence using conceptual project information as model input. The models provide a proactive warning and decision-support information needed to select the appropriate resolution strategy before a dispute occurs.

关键词： machine learning clustering and classification hybrid intelligence public-private partnership project management dispute prediction

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A cognitive technology based healthcare monitoring system and medical data transmission

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MEASUREMENT 2019年 146卷 322-332页

作者： Kumar, M. Arun Vimala, R. Britto, K. R. Aravind PSNA Coll Engn & Technol Dept IT Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept EEE Dindigul Tamil Nadu India PSNA Coll Engn & Technol Dept ECE Dindigul Tamil Nadu India

In healthcare management, the volume of patient data generated from various medical devices used for patient monitoring. Increasing healthcare data is persisted in distributed database available in cloud, for sharing among medical experts. Cloud provides most excellent methods to explore the organized and unorganized data prepared from healthcare management systems. By analyzing the healthcare data, the health conditions and the treatment methods are predicted for future treatment. Yet, the existing design could not sustain both analysis and procedure for huge quantity of multi-organized healthcare data. In this paper, it is aimed to provide a cognitive technology for efficient monitoring and transmission of medical data in a healthcare industry. To do that, a Cognitive Data Transmission Method (CDTM) is proposed for monitor, record and transmit the data patient's health related data. A highest priority is assigned in data transmission, when the health condition is critical, which can be identified from the data-packet information. The cognitive nature is obtained using Simulated Annealing method. Finally, a stochastic prophesy representation is intended to predict the future health condition of the most related patients based on their present health conditions. Performance assessment of the planned procedure is recognized through general model in the cloud surroundings, that ensures 98% accurateness of prediction and sustain 99% of CPU and bandwidth consumption to decrease the analysis time (C) 2019 Published by Elsevier Ltd.

关键词： Medical data processing Big-data Healthcare monitoring system clustering and classification

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Detailed Analysis of Directional Radio Channel Measurements

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2017年第3期65卷 1281-1288页

作者： Mota, Susana Rocha, Armando Perez-Fontan, Fernando Univ Aveiro Dept Elect Telecommun & Informat P-3800193 Aveiro Portugal Inst Telecomunicacoes Campus Univ Santiago P-3800193 Aveiro Portugal Univ Vigo Dept Signal Theory & Commun Vigo 36200 Spain

Physical models describe the radio channel by characterizing the multipath propagation. Also, measurements are frequently employed in the establishment of such models, or for tuning the existing ones. This paper presents a systematic analysis of double-directional radio channel data, obtained by combining two single-input multiple-output measurements (forward and reverse), each, containing the channel frequency responses at the locations of a virtual rectangular antenna array. The measurement setup uses a vector network analyzer, which, together with the adopted data processing, represents an affordable, but very consistent, alternative to more sophisticated equipment, given that, a physical analysis of the identified channel contributions provided sensible results. The study comprised three main steps: 1) the characterization of each single-directional channel by employing a high-resolution algorithm to obtain the parameters of a chosen number of multipath components (MPCs);2) the classification of the obtained MPCs in a twofold way, first, by grouping MPCs using clustering tools, and, second, by classifying these clusters according to their type of interaction with the propagation scenario;and 3) the extraction of typical values for the number of clusters, the number of MPCs per cluster, rules for power decay, and statistical distributions for inter and intracluster delays and azimuths.

关键词： clustering and classification multipath components parameter estimation radio channel measurements

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