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检索条件"任意字段=6th International Conference on Soft Computing in Data Science, SCDS 2021"

共 872 条记录，以下是1-10 订阅

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6th international conference on soft computing in data science, scds 2021

6th International Conference on Soft Computing in Data Scien...

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6th international conference on soft computing in data science, scds 2021

ISBN: (纸本)9789811673337

the proceedings contain 31 papers. the special focus in this conference is on soft computing in data science. the topics include: Two-Step Estimation for Modeling the Earthquake Occurrences in Sumatra by Neyman–Scott Cox Point Processes;construction of Optimal Stock Market Portfolios Using Outlier Detection Algorithm;simulating the Upcoming Trend of Malaysia’s Unemployment Rate Using Multiple Linear Regression;time Series Forecasting Using a Hybrid Prophet and Long Short-Term Memory Model;financial Analytics on Malaysia’s Equity Fund Performance and Its Timing Liquidity;An Autoregressive Distributed Lag (ARDL) Analysis of the Relationships Between Employees Provident Fund’s Wealth and Its Determinants;iris Segmentation Based on an Adaptive Initial Contour and Partly-Normalization;Identifying the Important Demographic and Financial Factors Related to the Mortality Rate of COVID-19 with data Mining Techniques;local Image Analysis of Malaysian Herbs Leaves Using Canny Edge Detection Algorithm;performance Analysis of Hybrid Architectures of Deep Learning for Indonesian Sentiment Analysis;amniotic Fluids Classification Using Combination of Rules-Based and Random Forest Algorithm;a Modified Inverse Gaussian Poisson Regression with an Exposure Variable to Model Infant Mortality;poisson and Logistic Regressions for Inhomogeneous Multivariate Point Processes: A Case Study in the Barro Colorado Island Plot;entropy-Based Fuzzy Weighted Logistic Regression for Classifying Imbalanced data;quantifying the Impact of Climatic Factors on Dengue Incidence Using Generalized Linear Mixed Model with Spatio-Temporal Bayesian Poisson Random Effects Approach;the Effect of Cancers Treatment in Quality of Life of the Patient: Meta-analysis Approach;hybrid of Time Series Regression, Multivariate Generalized Space-Time Autoregressive, and Machine Learning for Forecasting Air Pollution.

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Proceedings - 2021 IEEE/ACIS 6th international conference on Big data, Cloud computing, and data science, BCD 2021

Proceedings - 2021 IEEE/ACIS 6th International Conference on...

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6th IEEE/ACIS international conference on Big data, Cloud computing, and data science, BCD 2021

ISBN: (纸本)9781728176819

the proceedings contain 17 papers. the topics discussed include: a cryptocurrency prediction model using LSTM and GRU algorithms;intrusion prediction using long short -term memory deep learning with UNSW-NB15;unsupervised ensemble learning and its application to temporal data mining;a low-illumination image enhancement algorithm based on morphological-Retinex(MR) operator;a streaming data collection and analysis for cryptocurrency price prediction using LSTM;dolphin identification method based on improved pst;classification of breast cancer based on improved PSPNet;a machine learning approach to predict the trend of obesity prevalence at a global level;and study on breast optical spectrum classification based on machine learning.

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Accuracy enhancement of Component based selection model using Hybrid soft computing 6

Accuracy enhancement of Component based selection model usin...

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6th international conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Banga, Anjali Bhatia, Pradeep Kumar Department of Computer Science Engineering Guru Jambheshwar University of Science & Technology Haryana Hisar India

ISBN: (纸本)9798350374629

Accuracy enhancement of Component based selection model using Hybrid soft computing Abstract: Need of component based selection is growing day by day. there have been several researches in existence that analyzed existing component based selection model using soft computing techniques. Several research works focused on optimizing software component selection model in CBSE using soft computing techniques. thus there is need to improve the performance of component based selection model using hybrid soft computing techniques. Several performance parameters are considered during data analytics operation. Organization tends to utilize mapping tools in order build sustainable designs for their processes as well as capabilities. During data analytics, acquisition, security, governance and standards, Insights and analysis, storage, visualization, optimization operation are performed. Proposed research has integrated PSO optimization technique with ANN based neural network to enhance the accuracy of component based selection model. ©2024 IEEE.

关键词： soft computing

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Dynamic Micro-cluster-Based Streaming data Clustering Method for Anomaly Detection 7th

Dynamic Micro-cluster-Based Streaming Data Clustering Method...

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7th international conference on soft computing in data science (scds)

作者： Wang, Xiaolan Ahmed, Md Manjur Husen, Mohd Nizam Tao, Hai Zhao, Qian Baoji Univ Arts & Sci Sch Comp Baoji Shaanxi Peoples R China Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Univ Kuala Lumpur Malaysian Inst Informat Technol MIIT Kuala Lumpur Malaysia Univ Barishal Dept Comp Sci & Engn Barishal 8254 Bangladesh

ISBN: (纸本)9789819904044;9789819904051

the identification of anomalies in a data stream is a difficulty for decision-making in real time. A memory-constrained online detection system that is able to quickly detect the concept drift of streaming data is required because the constant arrival of massive amounts of streaming data with changing characteristics makes real-time and efficient anomaly detection a difficult task. this is because of the nature of the data itself, which is constantly changing. In this study, a novel model for detecting anomalies using dynamic micro-clusters scheme is developed. the macro-clusters are generated from a network of connected microclusters. When new data items are added, the normal patterns that are formed in macro-clusters will update in tandem with the dynamic micro-clusters in an incremental fashion. An outlier may be understood from both a global and a local perspective by examining the global and local densities respectively. the effectiveness of the suggested approach was evaluated with the use of three different datasets. the findings of the experiment demonstrate that the suggested method is superior to earlier algorithms in terms of both the accuracy of detection and the level of computing complexity it requires.

关键词： Micro-cluster data stream Outlier detection Macro-cluster

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Tumor Detection in MRI Scans Using Low Order Krawtchouk Moments 6

Tumor Detection in MRI Scans Using Low Order Krawtchouk Mome...

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6th international conference on Intelligent computing in data sciences, ICDS 2024

作者： Naouadir, Ismail El Mekkaoui, Jaouad Hjouji, Amal El Ogri, Omar Benslimane, Mohamed Fez Morocco Sidi Mohamed Ben Abdellah University CED-ST STIC Laboratory of Inforamtion Signals Automation and Cognitivism LISAC Dhar El Mahrez Faculty of Science Fez Morocco

ISBN: (纸本)9798350351200

Magnetic resonance imaging is a radiology technique that generates detailed images of the human body's anatomy. these images, typically in grayscale, are particularly useful for visualizing soft tissues such as organs and muscles. MRI is especially effective in detecting tumors;however, identifying small tumors or anomalies can be challenging. In this paper, we introduce a new technique for detecting object coordinates using a type of orthogonal moments known as Krawtchouk moments. By employing these moments and their parameters, we can determine the exact coordinates of tumors in MRI scans through a straightforward optimization problem and a translation procedure that converts polynomial parameters to pixel coordinates. © 2024 IEEE.

关键词： Polynomials

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A Comparative Study of Machine Learning Classification Models on Customer Behavior data 7th

A Comparative Study of Machine Learning Classification Model...

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7th international conference on soft computing in data science (scds)

作者： Rusli, Nur Ida Aniza Zulkifle, Farizuwana Akma Ramli, Intan Syaherra Univ Teknol MARA Cawangan Negeri Sembilan Comp Sci Studies Coll Comp Informat & Media Kampus Kuala Pilah Kuala Pilah 72000 Negeri Sembilan Malaysia

ISBN: (纸本)9789819904044;9789819904051

Competent marketers aim to accurately predict consumer desires and needs. With the advancement of machine learning, different machine learning models could be applied to solve various challenges, including precisely determining consumer behavior. Meanwhile, discount coupons are a frequent marketing approach to boost sales and encourage recurring business from existing customers. Accordingly, the current study seeks to analyze customer behavior by assessing an in-vehicle coupon recommendation system dataset as the case study. the dataset, whichwas obtained from the University of California-Irvine (UCI) machine learning repository, could predict consumer decisions as to whether accept the discount coupon influenced by demographic and environmental factors, such as driving destinations, age, current time, and weather. this study also compared six machine learning classification models, including Bayesian Network (BayesNet), Naive Bayes, Instance-Bases Learning with Parameter-K(Lazy-IBK), Tree J48, Random Forest, and RandomTree, on the dataset to identify a suitable model for predicting customer behavior through two test modes, namely cross-validation and percentage split. the model performance was evaluated by analyzing factors, such as accuracy, precision, processing time, recall, and F-measure, for the model development. the findings discovered that Naive Bayes and Lazy-IBK consumed the least amount of prediction time, although with the lowest accuracy. RandomTree was the highest processing time, whereas Random Forest provided the highest accuracy, precision, recall and F-measure values.

关键词： data mining Machine learning Classification Customer behavior

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Implementation and Optimization of a data-Driven soft Sensor in a Welding Process 6

Implementation and Optimization of a Data-Driven Soft Sensor...

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6th international conference on software Engineering and Computer science, CSECS 2023

作者： Hensler, Fabian Jesser, Alexander Strebe, Nico Group Manufacturing Support Würth Elektronik eiSos Waldenburg Germany Institute ICPS Hochschule Heilbronn Künzelsau Germany

ISBN: (纸本)9798350306378

soft sensors have been a popular tool for years when it comes to recording certain parameters that are difficult to access. therefore, in this paper, a basic data-driven soft sensor is implemented in a welding process in order to be able to replace chosen hardware sensors in the future. the focus of this work, however, is on the data pre-processing, which offers numerous opportunities to improve the performance of the soft sensor itself. In the case of process data, as in the example of the welding machine, the clustering of data plays a particularly important role. During this investigation, it was possible to achieve significant improvements in terms of accuracy with an algorithm for clustering the data that was rewritten from scratch. Consequently, the data prepared by PCA is processed with this cluster algorithm and then used for a linear regression model. Furthermore, after the clustering algorithm has been developed and tested in Octave it gets rewritten in Python which is more application-oriented. the calculation time as well as the accuracy of the result are altogether superior compared to alternative approaches. © 2023 IEEE.

关键词： Principal component analysis

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Zero-Inflated Time Series Model for Covid-19 Deaths in Kelantan Malaysia 7th

Zero-Inflated Time Series Model for Covid-19 Deaths in Kelan...

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7th international conference on soft computing in data science (scds)

作者： Ismail, Muhammad Hazim Roslee, Hasan Basri Yaacob, Wan Fairos Wan Sapri, Nik Nur Fatin Fatihah Univ Teknol MARA Coll Comp Informat & Media Math Sci Studies Kota Baharu 15050 Kelantan Malaysia Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Shah Alam 40450 Selangor Malaysia Sch Math Sci Coll Comp Informat & Media Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9789819904044;9789819904051

the development of zero-inflated time series models is well known to account for excessive number of zeros and overdispersion in discrete count time series data. By using Zero-inflated models, we analyzed the daily count of COVID-19 deaths occurrence in Kelantan with excess zeros. Considering factors such as COVID-19 deaths in neighboring state and lag of 1 to 7 days of COVID-19 death in Kelantan, the Zero-Inflated models (Zero-Inflated Poisson (ZIP) and the Zero-Inflated Negative Binomial (ZINB)) were employed to predict the COVID-19 deaths in Kelantan. the ZIP and ZINB were compared with the basic Poisson and Negative Binomial models to find the significant contributing factors from the model. the final results show that the best model was the ZINB model with lag of 1,2,5 and lag of 6 days of Kelantan COVID-19 death, lag of 1-day COVID-19 deaths in neighboring State of Terengganu and Perak significantly influenced the COVID-19 deaths occurrence in Kelantan. the model gives the smallest value of AIC and BIC compared to the basic Poisson and Negative Binomial model. this indicate that the Zero Inflated model predict the excess zeros in the COVID-19 deaths occurrence well compared to the basic count model. Hence, the fitted models for COVID-19 deaths served as a novel understanding on the disease transmission and dissemination in a particular area.

关键词： COVID-19 deaths Count model Zero-inflated Poisson (ZIP) Zero-Inflated Negative Binomial (ZINB)

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AI-Powered Students’ Collaboration and Evaluator using LDA 6

AI-Powered Students’ Collaboration and Evaluator using LDA

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Vignesh, S. Shree Sharmitha Devi, K.B. Libisena, P.S. Computer Science and Business Systems K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331522667

the difficulties in teaching, evaluating, and grading soft skills like communication and teamwork make it difficult to incorporate them into programming courses. A pedagogicalframework and a customized evaluation rubric created for an after-school Java programming course serve as the foundation for organized, AI-driven method of assessing and grading teamwork abilities. In order to determine their advantages and disadvantages, the study looks at how well various AI methods evaluate teamwork in group tasks. Latent Dirichlet Allocation (LDA) was used in place of conventional rule-based methods to uncover hidden collaboration roles and examine students' contributions within different project roles. To determine the best practices in terms of precision, interpretability, and viability, machine learning (ML) models were also used. While comprehensive evaluation still requires teachers to address topics beyond the rubric, like code quality, plagiarism, and project completion, the results, which are based on data from four academicyears, show that both LDA and ML models offer trustworthy grading recommendations. the LDA method is effective since it requires little initial setup and does not require established rule structures, whereas ML models provide excellent accuracy with lessmanual labor, even if they require historical data for training. Both approaches use topic-based distributions to provide teachers with understandable, comprehensible insights which helps teachers to highlighting the students room for improvement. © 2025 IEEE.

关键词： Students

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Evaluating the Development of soft Skills through the Integration of Digital Making Activities in Undergraduate computing Courses 10th

Evaluating the Development of Soft Skills Through the Integr...

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10th international conference on Learning and Collaboration Technologies (LCT) Held as Part of the 25th international conference on Human-Computer Interaction (HCII)

作者： Konstantinou, Dora Parmaxi, Antigoni Zaphiris, Panayiotis Cyprus Univ Technol 30 Archbishop Kyprianos St CY-3036 Limassol Cyprus

ISBN: (纸本)9783031344107;9783031344114

the purpose of this exploratory study was the integration of Digital Making activities in the curriculum of a computing course aiming to examine the guidelines for integrating Digital Making activities in the curriculum of a computing course as well as to explore the development of participants' soft skills. this exploratory study was performed during the Spring 2021 semester in an Introductory Programming course at a higher education institution in the Mediterranean in which ten (10) undergraduate students from various nationalities were enrolled. Students were working in randomly generated groups in a self-directed learning environment in which web resources along with manuals of the provided equipment were accessible by each participant. Each group had to work in a self-regulated learning environment to innovate, design and implement two (2) 'Smart Artefacts' which can solve real-life problems, using Microbit programmable boards and reusable materials within an unstructured Digital Making Curriculum. data was collected from students, peers and the instructor using a mixed methodology that included self and peer assessment questionnaires along with observation and diary recordings. Existing assessment tools from the ATS2020 project were adopted for the evaluation of soft skills development. the analysis of the collected data was performed in two phases: a) the quantitative data were analyzed using paired t-test analysis, b) the qualitative data were analyzed and discussed with a group of experts. the preliminary results of the particular exploratory study, demonstrate the development of students' soft skills through Digital Making with emphasis on the skills of Collaboration, Creativity, Critical thinking and Communication. However, there were unclear outcomes for Cultural Respect and Connectivity skills. Implications for practitioners and researchers regarding the InstructionalDesign and AssessmentMethodology framing the integration of Digital Making activities i

关键词： Higher Education Human-Computer Interaction (HCI) Digital Making soft Skills Development Makerspaces 6Cs

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