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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 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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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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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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Computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Classifying User Experience (UX) Of The M-Commerce Application using Multinomial Naive Bayes Algorithm 7

Classifying User Experience (UX) Of The M-Commerce Applicati...

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7th International Conference on Natural Language Processing and Information Retrieval (NLPIR)

作者： Habal, Beau Gray M. Mangaba, Joel FEU Inst Technol Manila Philippines Univ Makati Makati Philippines

ISBN: (纸本)9798400709227

This research study uses the Multinomial Nave Bayes (MNB) algorithm to categorize and analyze the user experience (UX) of users of mobile commerce applications. The goal of the study is to give business owners insightful information on how well their mobile applications are performing. The study's goals are to establish evaluation standards for categorizing user experiences, use MNB to classify user experience reviews to their appropriate UX elements, analyze the results of the classification, and suggest areas for improvement to enhance the usability of m-commerce. The research plan consists of a number of sprints, including data extraction, data cleaning, classification system creation using the Multinomial Naive Bayes algorithm, and model accuracy rate evaluation. The proposed system integrates the algorithm and uses data from m-commerce applications. The results of the analysis provide insights into the different UX elements such as Value, Adoptability, Desirability, and Usability. The analysis's findings shed light on many UX components like Value, Adoptability, Desirability, and Usability. The classification model was evaluated for accuracy, achieving a result of 89.243%. This means that the model correctly classified 89.243% of the user experience reviews in the evaluation dataset, indicating a satisfactory level of accuracy. However, there were some misclassifications in the remaining 10.757% of the reviews. Therefore, the research successfully developed a system that analyzed and classifies user experiences from customer reviews using MNB. The classification model demonstrated a satisfactory level of accuracy. The findings provide valuable insights and recommendations for improving the mobile application browsing experience based on user feedback and experiences.

关键词： User Experience Elements clustering and classification

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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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Classifying User Experience (UX) Of The M-Commerce Application Using Multinomial Naive Bayes Algorithm 23

Classifying User Experience (UX) Of The M-Commerce Applicati...

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Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Beau Gray Magbanua Habal Joel B. Mangaba Computer Science Department FEU Institute of Technology Philippines Computer Science Department University of Makati Philippines

ISBN: (纸本)9798400709227

关键词： clustering and classification User Experience Elements

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Digitizing sustainable process development: From ex-post to ex-ante LCA using machine-learning to evaluate bio-based process technologies ahead of detailed design

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CHEMICAL ENGINEERING SCIENCE 2022年 250卷 117339-117339页

作者： Karka, Paraskevi Papadokonstantakis, Stavros Kokossis, Antonis Natl Tech Univ Athens Sch Chem Engn Iroon Polytechneiou 9 Athens 15780 Greece Chalmers Univ Technol Dept Space Earth & Environm Div Energy Technol Horsalsvagen 7B S-41296 Gothenburg Sweden TU Wien Inst Chem Environm & Biosci Engn Getreidemarkt 9 A-1060 Vienna Austria

Life Cycle Assessment is a data-intensive process holding great promise to benefit from advanced analytics and machine learning technologies. The present research aims at the development of a data-science based framework with capabilities to estimate LCA metrics of bio-based and biorefinery processes in early design phases. Life cycle inventories may combine experimental (pilot and lab scale) data, property and thermodynamic databases, and model-derived data from simulations and design studies. The framework applies advanced analytics such as classification trees and artificial neural networks (ANN) with a scope to produce input-output relationships through predictor variables that refer to the molecular structure of bio-chemical or bio-fuel products of interest, the feedstocks used, and the process technologies characteristics. The combined use of ANNs and trees demonstrates a coordinated level of complementarity between the approaches, while it improves robustness and streamlines LCA estimations in the early-stage design. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Ex-ante LCA Biorefineries Machine learning Artificial neural networks clustering and classification

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Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers' review

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COMPLEX & INTELLIGENT SYSTEMS 2020年第3期6卷 621-634页

作者： Yi, Shanshan Liu, Xiaofang Chengdu Univ Technol Sch Nucl Technol & Automat Engn Chengdu 610000 Peoples R China Sichuan Aerosp Vocat Coll Chengdu 610000 Peoples R China

Big data analytics plays a major role in various industries using computing applications such as E-commerce and real-time shopping. Big data are used for promoting products and provide better connectivity between retailers and shoppers. Nowadays, people always use online promotions to know about best shops for buying better products. This shopping experience and opinion about the shopper's shop can be observed by the customer-experience shared across social media platforms. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which can only be provided by the previous customer experience. The MRP and MRD are already available in the product cover or label. Several approaches have been used for predicting the product details but not providing accurate information. This paper is motivated towards applying Machine Learning algorithms for learning, analysing and classifying the product information and the shop information based on the customer experience. The product data with customer reviews is collected from benchmark Unified computing system (UCS) which is a server for data based computer product lined up for evaluating hardware, support to visualization, software management. From the results and comparison, it has been found that machine learning algorithms outperform than other approaches. The proposed HRS system has higher values of MAPE which is 96% and accuracy is nearly 98% when compared to other existing techniques. Mean absolute error of proposed HRS system is nearly 0.6 which states that the performance of the system is significantly effective.

关键词： Big data Big data analytics Machine learning Feature extraction clustering and classification Accuracy

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Mining of DSLs and Generator Templates from Reference Applications 20

Mining of DSLs and Generator Templates from Reference Applic...

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23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)

作者： Rost, Wolf Philipps Univ Marburg Germany Tech Hsch Mittelhessen Inst Informat Sci Giessen Germany

ISBN: (纸本)9781450381352

Domain-Specific Languages (DSLs) found application in different domains. The development of Model-Driven Development (MDD) components is facilitated by a wealth of frameworks like EMF, Xtext, and Xtend. However, the development of the necessary IDE components still can take up to several weeks or even months until it can be used in a production environment. The first step during the development of such an MDD infrastructure is to analyse a set of reference applications to deduce the DSL used by the domain experts and the templates used in the generator. The analysis requires technical expertise and is usually performed by MDD infrastructure developers, who have to adhere to a close communication with domain experts and are exposed to high cognitive load and time-consuming tasks. The objective of this PhD project is to reduce the initial effort during the creation of new MDD infrastructure facilities for either a new domain or newly discovered platforms within a known domain. This should be made possible by the (semi-)automatic analysis of multiple codebases using Code Clone Detection (CCD) tools in a defined process flow. Code clones represent schematically redundant and generic code fragments which were found in the provided codebase. In the process, the key steps include (i) choosing appropriate reference applications (ii) distinguishing the codebase by clustering the files, (iii) reviewing the quality of the clusters, (iv) analysing the cluster by tailored CCD, and (v) transforming of the code clones, depending on the code clone type, to extract a DSL and the corresponding generator templates.

关键词： Model-driven software engineering information extraction MDD component creation code clone detection clustering and classification

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