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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Alhaddad, Salman Zeki, Ahmed M. University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Information System Bahrain

ISBN: (纸本)9781665490580

Educational organizations consistently attempt to investigate students' behaviors in learning and give early predictions for intervening and improving their learning performance, especially in primary education, which is the core stone of education. Educational data mining offers different practical prediction algorithms to predict student performance and various feature selection methods to determine the dominant factors which affect student performance. This study applied three data mining algorithms, K-Nearest Neighbor, Naive Bayes, and Decision Tree, to predict the performance of students based on their socio-demographic characteristics. The feature selection methods information Gain Attribute Evaluation, Wrapper Subset Evaluation, and Classifier Subset Evaluation were used to identify the dominant factors. The research was done by adopting the CRISP methodology using a data set obtained from one of Bahrain's public primary boys' schools. The results showed that the model of the k-Nearest Neighbors algorithm with Wrapper Subset Evaluation as a feature selection method had achieved the highest accuracy rate of 67.6%. For the dominant factors, Student Age and Student Level have the most impact on student performance, followed by Parent education level. © 2022 IEEE.

关键词： Decision trees

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Parallel Implementation of KNN Algorithm for Dry Beans dataset

Parallel Implementation of KNN Algorithm for Dry Beans Datas...

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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Hammad, Salman Alhaddad, Salman Yusuf, Husain Alqaddoumi, Abdulla University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain College of Information Technology University of Bahrain Department of Information System Bahrain

ISBN: (纸本)9781665490580

In recent years, the interest in using machine learning to solve complex problems in different sectors using parallel algorithms has increased. The KNN algorithm is the most popular method to classify the dataset. The main problem in implementing this algorithm is how to quickly find K nearest neighbors. Accordingly, this research aims to study the effect of the parallel execution of the KNN algorithm by using different metrics such as speedup and efficiency. The performance of serial and parallel KNN algorithms was compared using different values of K and a different number of processers. The results show that The Message Passing Interface (MPI4py) improves performance by increasing speedup and decreasing execution time. Also, the impact of serial execution on the size of the dataset is discussed. It was found that as the size of the dataset increases, the serial time increases. Meanwhile, the use of seven processers in parallel execution with the KNN algorithm needs less time. In addition, as the number of processers increases, the speedup increases, and the efficiency decreases © 2022 IEEE.

关键词： Efficiency

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Comparing K-Nearest Neighbors, Random Forest and Naïve Bayes Models to Classify Fetal Health Using Resampling Methods

Comparing K-Nearest Neighbors, Random Forest and Naïve Baye...

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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Alhaddad, Salman Ebrahim, Majeed Zeki, Ahmed M. University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Information System Bahrain

ISBN: (纸本)9781665490580

To preserve the lives of pregnant women and their fetuses, Cardiotocography (CTG) data is used to check the fetus's condition and for early intervention if necessary. As a result of the existence of imbalanced classes of fetus's condition, the performance of some models is affected when applied to real data. Accordingly, this research aims to observe the effect of data resampling methods on models' performance by adopting the CRISP methodology. The performance of k-Nearest Neighbors (KNN), Random Forest (RF), and Naive Bayes (NB) models are compared using resampled data with several methods with the help of the CorrelationAttributeEval feature selection method for high dimension reduction. The results stated that NB and RF models' performance has not improved with the resampling methods, while KNN has significantly improved across all metrics. RF is considered a good classifier as it was the best, with an accuracy of 93.8%. NB was the only improved model using feature selection with imbalanced data. Also, NB has the worst performance in terms of accuracy compared to KNN and RF. © 2022 IEEE.

关键词： Feature Selection

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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Review previous studies of machine learning techniques to analyze road traffic accidents in different countries

Review previous studies of machine learning techniques to an...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Salman Alhaddad Ahmed M. Zeki MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Information System College of Information Technology University of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

RTAs are considered public health issues. For that, many countries and organizations, such as the Gulf Cooperation Council and United Nations, are trying to reduce the number of accidents caused by road traffic. This study reviews several previous studies that used machine learning algorithms to find the best models that can deal with the dataset related to RTAs, it is found the results differentiated by changing the dataset, some high results achieved with the NB algorithm, and the others achieved by using MLP and DT. This study includes more than 30 studies with more than 15 machine learning algorithms. There are several factors that affect the RTAs such as gender driver, age, type of vehicle, cause of accident, and lighting condition. The high number of algorithms used in the previous studies discussed during this study are DT, followed by RF, SVM, and NB respectively. This study provides a review to help researchers to understand the techniques used to analyze the dataset in the RTA sector.

关键词： Support vector machines Radio frequency Machine learning algorithms Sensitivity Reviews Prediction algorithms Road traffic

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Mass Valuation of Unimproved Land Value Case Study: Nairobi County

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Journal of Geographic information system 2023年第1期15卷 122-139页

作者： Edwin Kochulem Dennis Mwaniki Felix Mutua Department of Geomatic Engineering and Geospatial Information System (GEGIS) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya Data and Analytics Section UN-Habitat Nairobi Kenya

The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 constitution. A total of 1440 geocoded data points containing the market selling price of vacant land in Nairobi were web scraped from major property listing websites. These data points were adopted as dependent variables given as unit price of vacant land per square meter. The Covariates used in this study were categorized into Accessibility, Environmental, Physical and Socio-Economic Factors. Due to multi-collinearity problem present in the covariates, PLS and PCA methods were adopted to transform the observed features using a set of vectors. These methods resulted in an uncorrelated set of components that were used in training machine learning algorithms. The dependent variable and uncorrelated components derived feature reduction methods were used as training data for training different machine learning regression models namely;Random forest, support vector regression and extreme gradient boosting regression (XGboost regression). PLS performed better than PCA because the former maximizes the covariance between dependent and independent variables while the latter maximizes variance between the independent variables only and ignores the relationship between predictors and response. The first 9 components were identified as significant both by PLS and PCA methods. The spatial distribution of vacant land value within Nairobi County was consistent for all the three machine learning models. It was also noted that the land value pattern was higher in the central business district and the pattern spread northwards and westwards relative to the CBD. A relative low vacant land value pattern was observed on the eastern side of the county and also at the extreme periphery of Nairobi County boundary. From the accuracy metrics of R-squared and MAPE, Random Forest Reg

关键词： Machine Learning Property Valuation GIS PLS PCA

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Parkinson's Disease Detection Using data Mining Models: A Comparative Study

Parkinson's Disease Detection Using Data Mining Models: A Co...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Reem Jamal Majed Rashid Moahmmed Al-Heddi Ahmed M. Zeki Big Data Science and Analytics College of Science University of Bahrain Sakhir Bahrain Department of Information System College of Information Technology University of Bahrain Sakhir Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

data Mining techniques have emerged in the healthcare industry over the past years in determining the occurrences and development of diseases. This paper aim is to conduct a comparative study between three data Mining models in predicting Parkinson’s Disease using biological voice measurements dataset. The three models that are evaluated are: Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). data oversampling using Synthetic Minority Oversampling Technique (SMOTE) method was used to balance the data and feature selection was applied to select the most relevant attributes. The CRISP-DM framework is used to perform the analysis using Weka machine learning tool. The results showed that the KNN model obtained the best accuracy of 94.11% on balanced dataset.

关键词： Support vector machines Industries Radio frequency Analytical models Biological system modeling Nearest neighbor methods Feature extraction

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Empowering Circular Economy Goals through Cognitive Infocommunications in Dual Training Programs 14

Empowering Circular Economy Goals through Cognitive Infocomm...

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14th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2023

作者： Horváth, Ildikó Vér, Csaba Balogh, Brigitta Corvinus University Corvinus Institute of Advanced Studies Institute of Data Analytics and Information System Hungary Eötvös Lóránd Research Network Piarista str. 4 BudapestH-1052 Hungary University of Pécs Faculty of Engineering and Information Technology Boszorkány str.2 Pécs Hungary

ISBN: (纸本)9798350325652

This paper explores the integration of cognitive infocommunications (CogInfoCom) in dual training programs as a means of empowering circular economy (CE) goals. It posits that the symbiosis of CogInfoCom, virtual reality (VR), and OpenAI solutions can cultivate an innovative educational framework promoting sustainable practices and a circular economy mindset. The research investigates the potential benefits of immersive VR environments, AI-powered simulations, and adaptive learning systems in creating a transformative, experience-based education that bolsters understanding and application of circular economy principles. © 2023 IEEE.

关键词： Learning systems

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Enhancing Breeding Control: The IoT-Driven Automated system for Budgie Farming 11

Enhancing Breeding Control: The IoT-Driven Automated System ...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nooyimsai, Saranpong Thaloy, Jarinya Jansengrat, Parinya Janratchakool, Weena Krohkaew, Jaturapith Crisnapati, Padma Nyoman Thwe, Yamin Rajamangala University of Technology Srivijaya Department of Information Technology Nakhon Si Thammarat Thailand Rajamagala University of Technology Department of Big Data Management and Analytics Thanyaburi Pathum Thani Thailand Rajamagala University of Technology Department of Computer Science Thanyaburi Pathum Thani Thailand Institute of Technology and Business Stikom Bali Department of Information System Bali Indonesia

ISBN: (纸本)9798350305968

In Thailand's Pak Phanang Nok Hong Yok Farm, where budgies are raised, it emphasizes the value of proper care and surroundings. The primary problem this research addresses is the need for efficient and automated management of budgie farms, encompassing aspects such as feeding, watering, and temperature control and overcoming language barriers for Thailand's budgie farmers. An innovative Internet of Things (IoT) solution is suggested, departing from conventional approaches to handle challenges connected to manual care and environmental management for farmers. This system provides solutions for these challenges while enhancing monitoring and facilitating automated control. Through this IoT-based solution, the research intends to increase productivity, save time, and decrease bird mortality while enabling users to manage their farms remotely. Comprehensive methodologies involving breeding, environmental considerations, and hardware and software design are outlined, resulting in significant advancement in budgie farming practices and showcasing IoT's potential in agriculture. © 2023 IEEE.

关键词： Birds

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A Comparative Study on Classifying Diabetes Disease using data Mining Models

A Comparative Study on Classifying Diabetes Disease using Da...

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Saeed, Saad Ebrahim Al Telaq, Burair Hassan Zeki, Ahmed M. Univestity of Bahain Big Data Science and Analytics Collage of Science Sakhir Bahrain Univestity of Bahain Department of Information System Collage of Information Technology Sakhir Bahrain

ISBN: (纸本)9781665416566

Diabetes is a disease that results from the inability of the pancreas to produce sufficient amounts of the insulin hormone which is responsible for regulating sugar rates in blood;or from the body's ineffective use of the insulin produced due to obesity or lack of physical activity. The field of data Mining (DM) helps in enhancing the healthcare system by identifying the main causes of diseases as well as predicting their incidence in the future. The problem at hand is DM comparison for determining the most accurate algorithm that can serve diabetes patient's data. As a methodology, Cross-Industry Standard Process for data Mining (CRISP-DM) was followed in this paper. The best performance in terms of accuracy was Decision Tree J48 with an accuracy of 96% higher than both KNN 93.3% when K value equals 3, and SVM which obtained 92.1%. In the KNN model, the study found increasing parameters of K slightly decrease the accuracy, however, this model achieved the highest measurement in terms of ROC in this study. © 2021 IEEE.

关键词： Decision trees

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