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1st International Conference of the Indonesian-Association-for-Pattern-Recognition (INAPR)

作者： Binanto, Iwan Warnars, Harco Leslie Hendric Spits Abbas, Bahtiar Saleh Sianipar, Nesti Fronika Bina Nusantara Univ BINUS Grad Program Comp Sci Dept Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ Fac Engn Food Technol Dept Jakarta 11480 Indonesia Bina Nusantara Univ Res Interest Grp Biotechnol Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

To retrieve part of data from huge. html files and convert it to. csv files respectively is a long time work if done manually. Consequently, this work needs an automation tool. This paper discuss developing automation tools based on python language. Utilizing python's code makes the tools very small in line of code but powerful. It successfully retrieves part of data as needed. The tools have successfully been tested to other case study.

关键词： software engineering python programming halstead metric waterfall automation processing

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CCTV based system for detection of anti-virus masks 24

CCTV based system for detection of anti-virus masks

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24th IEEE Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications (IEEE SPA)

作者： Podbucki, Kacper Suder, Jakub Marciniak, Tomasz Dabrowski, Adam Poznan Univ Tech Fac Control Robot & Elect Engn Inst Automat Control & Robot Div Elect Syst & Signal Proc Jana Pawla II 24 PL-60965 Poznan Poland

ISBN: (纸本)9788362065394

This paper presents the use of neural networks to detect people with anti-virus masks. The algorithm allows to determine if a person has a correctly worn mask (covered nose and mouth). The use of neural networks has been compared with typical Haar cascade frontal face solutions available in the OpenCV library. The proposed solution has been checked for efficiency and precision, as well as for the minimum resolution requirements of the resulting facial image. The software works on still images as well as on video sequences from computer webcams and CCTV cameras.

关键词： COVID-19 neural networks convolutional neural networks anti-virus masks mask detection vision system OpenCV python programming

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Efficient Prediction In Improving Accuracy For U2R Attack In Wireless Networks Using Novel CNN Alexnet In Comparison With Support Vector Machine Algorithm 5

Efficient Prediction In Improving Accuracy For U2R Attack In...

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5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Sekar, Chandra, V Nagaraju, V Saveetha Univ Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

The major goal of this experimental effort is to use DL (Deep Learning) models to improve the accuracy value of User-to-Root (U2R) attack forecasting using the intrusion dataset. The Github database system served as the source of the research data bank used in this study. A sample size of 22543 with 30 iterations was used to forecast a User-to-Root (U2R) attack (15 from Group 1 and 15 from Group 2) which was obtained with a G-power of 0.8, alpha and beta values of 0.05 and 0.2, and a confidence range of 95%. Novel Convolutional Neural Network Alexnet (CNN Alexnet) and Support Vector Machine are used to predict the User-to-Root (U2R) attack of the infiltration with the same number of data samples (N=15), with Novel Convolutional Neural Network Alexnet (CNN Alexnet) having a higher accuracy rate. Performance evaluation of the proposed U2R attack prediction system of the Wireless Network system. The accuracy level of the Novel Convolutional Neural Network Alexnet (CNN Alexnet) classifier is confirmed at 92.4060%, and the accuracy level of the SVM is confirmed at 87.4387%. These two can be distinguished clearly from one another. The value of significance is found to be p=0.525 (p>0.05) after analyzing the results from the independent samples test and hence it is statistically not significant. The built Novel Convolutional Neural Network Alexnet (CNN Alexnet) model performs better in User-to-Root (U2R) attack prediction from intrusion data banks than the SVM classifier, according to this developed research design outcome.

关键词： Data Bank Novel CNN Alexnet Support Vector Machine Improved Accuracy Rate python programming Intrusion Prediction Efficient Prediction Security

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Contextualizing Evaluation of Performance in Volleyball: Introducing Contextual Individual Contribution Coefficients to Assess Technical Actions

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PERCEPTUAL AND MOTOR SKILLS 2023年第6期130卷 2663-2684页

作者： Lopez-Serrano, Carlos Zakynthinaki, Maria Mon-Lopez, Daniel Martin, Juan Jose Molina Tech Univ Madrid Fac Sci Phys Act & Sport INEF Dept Sports Madrid Spain Tech Univ Crete Sch Chem & Environm Engn Akrotiri Chania Greece Univ Politecn Madrid Dept Deportes Fac Ciencias Act Fıs & Deporte Madrid 28040 Spain

In this study, we proposed novel metrics for evaluating volleyball technical performance in relation to the action context. To assess each player's relative participation, we also introduced relative contextual coefficients. We analyzed 20 games played by the world's top eight teams during the 2019 FIVB Women's Club World Championship, using Data Volley software and python programming. We evaluated inter- and intra-observer reliability and used binomial logistic regression models to estimate each variable's probability of having contributed to the team's set win. We calculated estimated confidence intervals, standard errors, and Wald values;and we employed Akaike's and Bayesian criteria to evaluate the model's goodness of fit. We identified optimal cut-off points using receiver operating characteristic curves, and we found that the proposed contextual evaluation coefficients prevented overestimation of a player's technical performance in uneven situations. We addressed the issue in which the winning team may be the one that scored the fewest points, and we were able to predict a team's victory with confidence. The proposed coefficients made multiple technical and contextual aspects of the game easily accessible and comprehensible, offering significant beneficial implications for coaches and players.

关键词： volleyball performance assessment technical performance contextual variables relative participation data volley software python programming

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Maximizing Supercapacitor Performance: Boosting Energy Storage with Convex Optimization 23

Maximizing Supercapacitor Performance: Boosting Energy Stora...

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Conference on Physical Chemistry of Semiconductor Materials and Interfaces XXIII

作者： Singh, Iqbal Kaur, Amardeep Southern Illinois Univ Edwardsville Edwardsville IL 62026 USA

ISBN: (纸本)9781510679146;9781510679153

Supercapacitors are gaining significance owing to their remarkable ability to develop advanced energy storage devices. This paper uses a new approach of using convex optimization to optimize the energy density of a supercapacitor using molybdenum disulfide as a dielectric. The convex optimization is performed using python programming. This study provides valuable insights into the potential of using this method as a tool to enhance the performance of supercapacitors, paving the path for the manufacturing of energy storage devices with smaller sizes and higher performance. The findings with the proposed model highlight the effectiveness of convex optimization in enhancing the performance of supercapacitors offering a robust approach to achieve higher energy density.

关键词： Supercapacitor Energy density Specific Capacitance Convex Optimization python programming

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Teaching Data Science

Teaching Data Science

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16th Annual International Conference on Computational Science (ICCS)

作者： Brunner, Robert J. Kim, Edward J. Univ Illinois Urbana IL 61801 USA

We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the python programming language with an emphasis on data preparation, processing, and presentation. The course had no prerequisites, and students were not expected to have any programming experience. This introductory course was designed to cover a wide range of topics, from the nature of data, to storage, to visualization, to probability and statistical analysis, to cloud and high performance computing, without becoming overly focused on any one subject. We conclude this article with a discussion of lessons learned and our plans to develop new data science courses.

关键词： Data Science Informatics Visualization Probability Statistics High Performance Computing Cloud Computing Databases python programming

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Adapting video-based programming instruction: An empirical study using a decision tree learning model

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第11期29卷 14205-14243页

作者： Kumar, Sanal T. S. Thandeeswaran, R. Vellore Inst Technol VIT Sch Comp Sci Engn & Informat Syst Vellore Tamil Nadu India

The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These platforms never considered the essential attributes of student characteristics and learning preferences while designing such a problematic subject having high dropout and failure rates. The traditional e-learning environments deliver instructional videos to the learners by assuming all learners have a single learning preference. Moreover, existing learning style models need to address the recent requirements of e-learning paradigms. To address this issue, this paper presents a novel learning style model tailored for instructional video-based programming e-learning environments that map individual learning preferences with various video design patterns. An adaptive e-learning environment was employed to assess the effectiveness of the proposed model that leveraged a decision tree classifier to divide learners into four preferences. In a paired experimental design, 195 first-year undergraduate students were randomly assigned to one of three groups where learner scores and feedback were taken as evaluation metrics. The control group partook without instructional videos for the entire semester of six months. During the same period, experimental group-1 learned with a traditional video-based e-learning environment, and experimental group-2, with the proposed learning style model, enabled an adaptive e-learning environment. Based on the proposed decision tree learning model, it is understood that the intervention group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver instructional videos based on learners' learning preferences.

关键词： Blended learning Instructional video learning style python programming Adaptive e-learning environments Higher education

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Efficiency of Decision Tree Depth to Diagnose Mathematical Procedures in Number and Algebra for Seventh-grade Students

Efficiency of Decision Tree Depth to Diagnose Mathematical P...

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International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)

作者： Thiannoi, Thanyalak Junpeng, Putcharee Intharah, Thanapong Khon Kaen Univ Fac Educ Educ Measurement & Evaluat Program Khon Kaen Thailand Khon Kaen Univ Fac Sci Dept Stat Visual Intelligence Lab Khon Kaen Thailand

ISBN: (纸本)9798350379068;9798350379051

This study is dedicated to design and compare the efficiency of predictive models with different depths of decision trees for diagnosing mathematical procedures in number and algebra. Leveraging a dataset comprising responses from 509 seventh-grade students across 4 regions in Thailand, the investigation applies the python programming language with decision tree methodologies to construct predictive models capable of accurately determining students' proficiency in mathematical procedures. The findings of the research elucidate that model efficiency is contingent upon the depth of the decision tree. Notably, the model with a tree depth of 5 surpasses others in efficiency, evidencing an accuracy rate of 0.87 and an error rate of 0.37. Meanwhile, the model with a tree depth of 4 demonstrates slightly lower efficacy, with an accuracy rate of 0.76 and an error rate of 0.51. These results underscore the critical influence of decision tree depth on the performance of predictive models within the educational domain.

关键词： Mathematical Diagnostics Predictive Model Machine Learning Decision Tree Depth python programming Mathematical Procedures

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Learning to Program by Playing Learning Games 11

Learning to Program by Playing Learning Games

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11th European Conference on Game-Based Learning (ECGBL)

作者： Olsson, Marie Mozelius, Peter Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden Mid Sweden Univ Dept Comp & Syst Sci Sundsvall Sweden

ISBN: (纸本)9781911218579;9781911218562

Game-based learning is an emerging field that has become a part of university education and several researchers describe its strong learning potential. For several subjects there is a rich flora of learning games and commercial-of-the-shelf games available, but for programming education in general and python programming in particular, the situation is different. There exists an economical aspect as well and to develop learning games for programming education, might not interest the mainstream game industry and most universities do not have the resources needed to develop their own tailor-made learning games. Digital gaming in general has had a fast expansion during the last decade and the generation that now is entering the universities has a habit of regular gaming. At the same time there are several studies reporting about low motivated students in introductory programming courses. In a time when commercial games for programming education are rare, universities must look for affordable ways to construct appropriate learning games. This study has investigated the idea of using learning games developed by university students as additional learning tools in an introductory programming course. Five student constructed games for learning to program in python have been analysed. Eventually two learning games were selected and tested as part of a five week introductory programming course. The overall strategy has been action research with the aim to improve an existing programming course. A group of students have played the games in two workshops where data has been collected in a mix of questionnaires, observations and group discussions. Findings indicate that the general idea seems to work, but as for all kind of course content, there need to be a thorough assessment and iterative refinement. Despite some found bugs and interface flaws the games had a challenging gameplay as well as learning outcomes. The game-based workshops also had a catalytic effect and created ene

关键词： game-based learning GBL programming education learning games python programming

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python for Data Science For Dummies 1

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丛书名： For Dummies: Computers

2015年

作者： John Paul Mueller, Luca Massaron

ISBN: (数字)9781118844144

ISBN: (纸本)1118844181;9781118844182

Unleash the power of python for your data analysis projects with For Dummies! python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. python for Data Science For Dummies shows you how to take advantage of python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. Youll get familiar with the python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether youre new to data analysis or just new to python, python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

关键词： data science data scientists data mining data analysis data warehousing python python for data analysis python programming python programming language python data numpy scipy beautifulsoup pandas matploblib data visualization data science books data science book data analysis books data analysis book data science guide data science reference data analysis guide data science jobs big data big data tools big data solutions big data management learn about big data books on big data books on big data management choosing big data solutions how to manage big data what is big data networking data storage cloud computing data analytics data management database management

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