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exploratory data analysis To Identify The Most Important Feature Of University Admission Test Criteria Using Random Forest And Neural Network Algorithm 5

Exploratory Data Analysis To Identify The Most Important Fea...

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5th International Conference on Informatics and Computational Sciences (ICICoS)

作者： Gufroni, Acep Irham Purwanto, Purwanto Farikhin, Farikhin Wibowo, Adi Warsito, Budi Diponegoro Univ Doctoral Program Informat Syst Semarang Indonesia Siliwangi Univ Informat Dept Tasikmalaya Indonesia

ISBN: (纸本)9781665438070

The University institutions currently face challenges in conducting the admissions process to get new students. The process must be carried out in the right way to ensure that prospective students who are accepted have the right abilities to meet academic targets in their chosen scientific field. The admission process is carried out based on predetermined criteria by determining the weight of the requirements according to the policy for that period. There is often a mismatch in the abilities of the student candidates who are accepted with the skills needed in the chosen field, so that there is a potential dropout risk for these students. One way to avoid this is to know the essential criteria in the admission test. We create two models based on the chosen algorithm. The Random Forest algorithm has a better accuracy rate, which is 85.17%, compared to the 80,27% accuracy rate of the Neural Network algorithm. This study found the most important feature of the admission process is the school ranking, where this feature has the most significant influence compared to the other, which is more than 20% importance's rate. This study also found a significant difference in the gender distribution of the accepted applicants, with a ratio of 24% for male registrants and 76% for female registrants. For the failed registrants, there were 25% male registrants and 75% female registrants. This study is based on the admission test data so that the most important feature found in this study can be used as a basis for policymaking for admission tests to come.

关键词： exploratory data analysis University Admission Test Feature Random Forest Neural Network

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exploratory data analysis for cybersecurity

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WORLD JOURNAL OF ENGINEERING 2021年第5期18卷 734-749页

作者： Miranda-Calle, Julian Dario Reddy C., Vikranth Dhawan, Parag Churi, Prathamesh Pontifical Bolivarian Univ Medellin Colombia Dr MGR Educ & Res Inst Fac Engn & Technol Dept Comp Sci Chennai Tamil Nadu India Visvesvaraya Natl Inst Technol Nagpur Maharashtra India Narsee Monjee Inst Management Studies Dept Comp Engn Mumbai Maharashtra India

Purpose The impact of cyberattacks all over the world has been increasing at a constant rate every year. Performing exploratory analysis helps organizations to identify, manage and safeguard the information that could be vulnerable to cyber-attacks. It encourages to the creation of a plan for security controls that can help to protect data and keep constant tabs on threats and monitor their organization's networks for any breaches. Design/methodology/approach The purpose of this experimental study is to state the use of data science in analyzing data and to provide a more detailed view of the most common cybersecurity attacks, what are the most accessed logical ports, visible patterns, as well as the trends and occurrence of attacks. The data to be processed has been obtained by aggregating data provided by a company's technology department, which includes network flow data produced by nine different types of attacks within every day user activities. This could be insightful for many companies to measure the damage caused by these breaches but also gives a foundation for future comparisons and serves as a basis for proactive measures within industry and organizations. Findings The most common cybersecurity attacks, most accessed logical ports and their visible patterns were found in the acquired data set. The strategies, which attackers have used with respect to time, type of attacks, specific ports, IP addresses and their relationships have been determined. The statistical hypothesis was also performed to check whether attackers were confined to perform random attacks or to any specific machines with some pattern. Originality/value Policies can be suggested such that if an attack is conducted on a specific machine, which can be prevented by identifying the machine, ports and duration of the attacks on which the attacker is targeting and to formulate such policies that the organization should follow to tackle these targeted attacks in the future.

关键词： exploratory data analysis Attacks Cyberattacks Cybersecurity

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exploratory data analysis using Artificial Neural Networks

Exploratory Data Analysis using Artificial Neural Networks

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IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE)

作者： Sriram, D. Kalaivani, K. Ulagapriya, K. Saritha, A. Sajeevram, A. Vels Inst Sci Technol & Adv Studies Dept Comp Sci & Engn Chennai Tamil Nadu India Sri Krishna Coll Engn & Technol Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (数字)9781728192512

ISBN: (纸本)9781728192512

data analysis helps travel organizations to provide better recommendations for investing in their future trips based on its business and personal trips. This paper presents the basic concepts, various types and levels of data analysis, predictive modeling techniques and appropriate performance measures. There are basically three types of algorithms for predicting such as linear regression (machine learning model), analysis of Variance (statistical model) and artificial neural network (machine learning model). data analysis is being used in many fields such as health care, manufacturing, information technology and so on. A travel dataset provided by the uber in Kaggle is used to study the performance of chosen predicting algorithms. The primarymethodology behind this study is to analyze and find the accuracy of the most frequent category of trip among all trips taken by a customer in a region using data analysis. The parameters which are taken into consideration are category, purpose, total distance and speed of the travel. The results of precision, recall, fl score, Area Under Curve (AUC) and Receiver Operating Characteristic Curve (ROC) are evident that the Artificial neural network (ANN) based prediction is comparatively higher than other algorithms.

关键词： exploratory data analysis linear regression analysis of variance hypothesis testing artificial neural network interquartile ratio outliers

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exploratory data analysis for Failure Detection and Isolation in Complex Systems 70

Exploratory Data Analysis for Failure Detection and Isolatio...

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70th Annual Reliability and Maintainability Symposium (RAMS)

作者： Zaman, Navid Jun, You-Jung Chan, Daniel PHM Technol Unit 9-120 Queens ParadeNorth Fitzroy Melbourne Vic 3068 Australia PHM Technol 120 Queens ParadeNorth Fitzroy Melbourne Vic Australia

ISBN: (纸本)9798350307696

Failure detection and isolation (FDI) is a crucial step in diagnostics and is quickly shifting to towards using analytical techniques such as machine learning and deep learning, rather than traditional rules-based approaches. This is partially due to the availability of sensor systems, hardware and networking allowing for a vast collection and processing of data. However, this information is prone to issues such as noise, corruption, poor formatting and recording practices. In most cases, a diagnostics project may stall midway due to late discovery of these problems. This paper proposes exploring the data beforehand, to locate issues in the data and/or optimize data quality to maximize performance or explain possible performance loss. Various techniques such as data visualization, statistical analysis and feature importance are mentioned. Most importantly, a domain knowledge set is to be integrated with such correlation-based methods to ensure that data quality decisions are made with understanding of the system. The limitations of such analysis including scalability and interpretation issues are discussed as well, leading to proposals of possible future paths to improvement such as sensor fusion and AI-based recommendations.

关键词： exploratory data analysis Failure Detection and Isolation Reliability Availability and Maintenance Safety

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exploratory data analysis with interactive evolution

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6th Annual Genetic and Evolutionary Computation Conference (GECCO 2004)

作者： Malinchik, S Bonabeau, E Icosyst Corp Cambridge MA 01238 USA

ISBN: (纸本)3540223436

We illustrate with two simple examples how Interactive Evolutionary Computation (IEC) can be applied to exploratory data analysis (EDA). IEC is particularly valuable in an EDA context because the objective function is by definition either unknown a priori or difficult to formalize. The first example involves what is probably the simplest possible transformation of data: linear projections. While the concept of linear projections is simple to grasp, in practice finding the appropriate two-dimensional projection that reveals important features of high-dimensional data is no easy task. We show how IEC can be used to quickly find the most informative linear projection(s). In another, more complex example, IEC is used to evolve the "true" metric of attribute space. Indeed, the assumed distance function in attribute space strongly conditions the information content of a two-dimensional display of the data, regardless of the dimension reduction approach. The goal here is to evolve the attribute space distance function until "interesting" features of the data are revealed when a clustering algorithm is applied.

关键词： interactive evolutionary computation data mining exploratory data analysis

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exploratory data analysis of Population Level Smartphone-Sensed data 16th

Exploratory Data Analysis of Population Level Smartphone-Sen...

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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Mansoor, Hamid Gerych, Walter Alajaji, Abdulaziz Buquicchio, Luke Chandrasekaran, Kavin Agu, Emmanuel Rundensteiner, Elke Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9783031254765;9783031254772

Mobile health involves gathering smartphone-sensor data passively from user's phones, as they live their lives 'In-the-wild", periodically annotating data with health labels. Such data is used by machine learning models to predict health. Purely Computational approaches generally do not support interpretability of the results produced from such models. In addition, the interpretability of such results may become difficult with larger study cohorts which make population-level insights desirable. We propose Population Level Exploration and analysis of smartphone DEtected Symptoms (PLEADES), an interactive visual analytics framework to present smartphone-sensed data. Our approach uses clustering and dimension reduction to discover similar days based on objective smartphone sensor data, across participants for population level analyses. PLEADES enables analysts to apply various clustering and projection algorithms to several smartphonesensed datasets. PLEADES overlays human-labelled symptom and contextual information from in-the-wild collected smartphone-sensed data, to empower the analyst to interpret findings. Such views enable the contextualization of the symptoms that can manifest in smartphone sensor data. We used PLEADES to visualize two real world in-the-wild collected datasets with objective sensor data and human-provided health labels. We validate our approach through evaluations with data visualization and human context recognition experts.

关键词： Interactive visual analytics In-the-wild smartphone-sensed data exploratory data analysis

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exploratory data analysis of Long-Term Meteorological data at a DOE Site for Nuclear Safety

Exploratory Data Analysis of Long-Term Meteorological Data a...

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2024 Annual Conference on Transactions of the American Nuclear Society, ANS 2024

作者： Yu, Xiao-Ying Oak Ridge National Laboratory P.O. Box 2008 1 Bethel Valley Road Oak RidgeTN37831-6324 United States

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exploratory data analysis in Engineering Admission Prediction for Indian Universities 18

Exploratory Data Analysis in Engineering Admission Predictio...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Gupta, Meenakshi Alpana Manav Rachna University Computer Science & Technology Faridabad India

ISBN: (纸本)9789380544519

The main aim of every student and parent belonging to the current generation is to gain best education to have bright future and best career opportunities. Undoubtedly there are multiple career opportunities these days which are boosting up among students because of high-pay packages and good careers. Engineering is one of the career opportunities that is attracting too many youngsters but getting admission to the country's best colleges is very tough. To pass various admission tests, students often devise the most difficult technique such as enrolment in coaching centres. The purpose of this study is to identify the importance and contribution of multiple factors such as 10th and 12th percentage of marks, school board, coaching centers, preparation strategy, online content, family background, effect of social media, drop cases, etc. By identifying the major and important factors, students will be highly benefited at an early stage of their preparation and therefore can improve their overall chances of admission. Students will be able to design their strategy for cracking the entrance examinations easily and shall be able to improve their overall chances of getting high ranks in various entrance examinations with the aid of this research. The suggested approach uses a data analytics approach to visualize the data and displays an exploratory data analysis of the admission-affecting elements. Box charts, bar charts, scatter charts, and heat maps are different ways to represent the relationship between components. Hence, this study attempts to assist the ways to students in raising their chances of admission to the desired college as well as their academic performances. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： engineering exploratory data analysis Undergraduate admission visualization

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exploratory data analysis of software repositories via GPU processing 26

Exploratory data analysis of software repositories via GPU p...

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26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014

作者： Da Silva, Jose Ricardo Clua, Esteban Murta, Leonardo Sarma, Anita Universidade Federal Fluminense Niterói Brazil Computer Science and Engineering University of Nebraska Lincoln LincolnNE United States

Analyzing software repositories with thousands of artifacts is data intensive, which makes interactive exploration analysis of such data infeasible. We introduce a novel approach, Dominoes, that can support automated exploration of relationships amongst project elements, where users have the flexibility to explore on the fly the numerous types of project relationships. Dominoes organizes data extracted from software repositories into multiple matrices that can be treated as domino pieces (e.g., [commit|method]). It allows connecting such pieces based on a set of matrix operations to derive additional domino pieces. These derived domino pieces represent semantics on project entity relationships (e.g., number of commits in which two methods co-occurred) and can be used for further explorations. This opens a vast possibility of data analysis, since these domino pieces can be iteratively combined. Our proposed matrix representation and operations allow for fast and efficient processing of a large volume of data by using a highly parallel architecture, such as GPUs. Copyright © 2014 by Knowledge Systems Institute Graduate School.

关键词： exploratory data analysis GPU computing Software dependencies

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Two forms of immediate reward reinforcement learning for exploratory data analysis

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NEURAL NETWORKS 2008年第6期21卷 847-855页

作者： Wu, Ying Fyfe, Colin Lai, Pei Ling Univ W Scotland Appl Computat Intelligence Res Unit Dumfries Scotland So Taiwan Univ Tainan Taiwan

We review two forms of immediate reward reinforcement learning: in the first of these, the learner is a stochastic node while in the second the individual unit is deterministic but has stochastic synapses. We illustrate the first method on the problem of Independent Component analysis. Four learning rules have been developed from the second perspective and we investigate the use of these learning rules to perform linear projection techniques such as principal component analysis, exploratory projection pursuit and canonical correlation analysis. The method is very general and simply requires a reward function which is specific to the function we require the unit to perform. We also discuss how the method can be used to learn kernel mappings and conclude by illustrating its use on a topology preserving mapping. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

关键词： reinforcement learning exploratory data analysis

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