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Feature Selection and Classification Algorithm Approaches for Parkinson's Disease Detection 5

Feature Selection and Classification Algorithm Approaches fo...

5th IEEE International Conference on information Technology, information systems and Electrical Engineering, ICITISEE 2021

作者： Marcos, Hendra Warsito, Budi Wibowo, Adi Diponegoro University Doctoral Program Information System Department Informatics Universitas Amikom Purwokerto Purwokerto Indonesia Diponegoro University Faculty Of Science And Mathematics Department Of Statistics Semarang Indonesia Department Of Computer Science Informatics Diponegoro University Semarang Indonesia

ISBN: (纸本)9781665401968

Millions of people worldwide have developed Parkinson's disease (PD), which is more common by 50. Advances in information technology, especially in the field of machine learning, are being challenged for the development of early detection algorithms. This helps doctors to detect disease early and more accurately based on data. The available data has large dimensions and features so that the feature selection method can be used. This research paper aims to provide insight by comparing several algorithms for feature selection and PD classification. PD classification can help improve treatment efficiency and save time. The problem that arises is how the classification algorithm can show better and more efficient accuracy results. A large number of datasets is one of the challenges in the development of classification algorithms. The voice input dataset was obtained from the UCI machine learning repository. Bagging's algorithm out performed on average 100%, and MLP 96.58%, and RF 92.52%. © 2021 IEEE.

关键词： Classification (of information)

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Student Assessment Factors in Study Success Using the Decision Tree Algorithm

Student Assessment Factors in Study Success Using the Decisi...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dina Fitria Murad Bambang Dwi Wijanarko Hendra Mayatopani Rosilah Hassan Arba’Iah Inn Sunardi Information Systems Department Binus Online Learning Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Information System Pradita University Tangerang Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Pusat Pengajian Diploma SPACE Universiti Teknologi Malaysia Kuala Lumpur Malaysia

The evaluation of students is essential to comprehending and enhancing successful learning in an educational setting. In the era of information technology, the use of decision tree algorithms has become increasingly pertinent in this sphere. This study seeks to identify the student assessment factors that have the greatest impact on learning success when using the Decision Tree algorithm. The collection and analysis of student assessment data, including test scores, class participation, and other variables Modeling the relationship between these factors and student learning outcomes using decision trees. The results of the analysis permit the identification of the most influential factors in predicting the success of student learning. The findings of this study can aid educators, schools, and educational institutions in designing learning strategies that are more effective and tailored to the specific needs of each student, as well as in identifying students who are at high risk and require special attention. The implementation of the Decision Tree algorithm in student evaluation is a crucial step toward a more individualized and effective educational approach.

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators

Studying of Machine Learning Models for Forecasting Macroeco...

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International Conference on Advanced Communication Technology (ICACT)

作者： Lan Dong Thi Ngoc Ngo-Thi-Thu-Trang Kyoung-Mok Kim Vung Pham Ha-Nam Nguyen Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi CO Vietnam Department of Medical Information Sahmyook Health University Seoul Republic of Korea Computer Science Department Sam Houston State University Huntsville Texas TX USA Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R 2 metrics to evaluate the accuracy of the *** experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful *** study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes.

关键词： Support vector machines Economic indicators Biological system modeling Predictive models Macroeconomics Forecasting Unemployment Random forests Optimization Long short term memory

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A Secure Multiparty Quantum Homomorphic Encryption Scheme

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computers, Materials & Continua 2022年第11期73卷 2835-2848页

作者： Jing-Wen Zhang Xiu-Bo Chen Gang Xu Heng-Ji Li Ya-Lan Wang Li-Hua Miao Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan610025China Quantum Technology Lab&Applied Mechanics Group University of MilanMilan20133Italy Department of Computer Science Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyGU27XHUnited Kingdom Huawei Technologies Co.Ltd Shenzhen518129China

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private *** this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest ***,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and ***,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted ***,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic *** operation and the permission to access the data performed by the client and the server are clearly pointed ***,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

关键词： Quantum homomorphic encryption secure multiparty computation almost dishonest server security

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Online collaborative kit-build concept map 28

Online collaborative kit-build concept map

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28th International Conference on computers in Education, ICCE 2020

作者： Pinandito, Aryo Hayashi, Yusuke Hirashima, Tsukasa Department of Information Engineering Hiroshima University Japan Information System Department Computer Science Faculty Universitas Brawijaya Indonesia

ISBN: (纸本)9789869721455

Many studies show promising benefits of using concept maps as an effective approach to support learning, thus enhance students' understanding. Concept mapping with the Kit-Build concept map framework is known as one practical approach to improving students' understanding of learning material. Supporting meaningful learning in an online collaborative learning environment is challenging, where the students need an effective and meaningful interaction during the collaboration. Both the use of concept maps and collaborative learning are promising to promote an engaging learning environment, thus yielding a better learning outcome. This research introduces a newly developed concept mapping tool that fuses collaborative learning into the Kit-Build concept map framework. It allows students to learn by collaboratively working with concept maps in a distant real-time learning environment. This research investigates the tool's effectiveness in a learning environment where the students use different concept mapping approaches to learn with concept maps online collaboratively. © ICCE 2020 - 28th International Conference on computers in Education, Proceedings. All rights reserved.

关键词： Students

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Deep Networks for Medical Images Classification, a Comparative Study

Deep Networks for Medical Images Classification, a Comparati...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Ibrahim, Hussain K. Rokbani, Nizar Wali, Ali Alimi, Adel M. of Sfax University Sfax Tunisia High Institute of Applied Sciences Technology Sousse University of Sousse Tunisia University of Sfax Sfax Tunisia Information Technology Wasit University Wasit Iraq Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park P.O. Box 524 Johannesburg2006 South Africa

ISBN: (纸本)9798350389838

Machine learning (ML) and deep learning (DL) have had a significant impact on healthcare, particularly in the field of medical picture categorization, in recent years. These technologies provide a multitude of advantages that enhance the quality of patient care and increase the effectiveness of healthcare services. The primary rationale for employing (ML) and (DL) in medical picture categorization lies in their remarkable capacity to identify illnesses at an early stage. Automated classification systems has the ability to rapidly analyses and interpret pictures, surpassing the speed of human capabilities. This is particularly vital for effectively handling extensive datasets and becomes critical in emergency scenarios where prompt diagnosis is of utmost importance. The objective of this investigation is to categorize medical images into four distinct categories: normal, COVID-19, lung opacity, and viral pneumonia. In this paper three deep learning models were implemented: the classical CNN, AlexNet and GoogleNet. The comparative analysis showed that AlexNet achieved an accuracy of 96.74%, a processing time of 225 seconds, and a loss of 0.1192. The GoogleNet architecture recorded an accuracy of 93.98%, a processing time of 233 seconds, and a loss of 0.1945 while the classical CNN achieved an accuracy of 94.56%, a processing time of 315 seconds, and a loss of 0.1298. Results showed that the AlexNet has a little advantage in regrads to GoogleNet and classical CNN which report very close performances'. © 2024 IEEE.

关键词： COVID-19

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Direction of Machine Learning Research in Obstetric Care in the Last 10 Years

Direction of Machine Learning Research in Obstetric Care in ...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Fairuz Iqbal Maulana Dian Lestari Puput Dani Prasetyo Adi Miftahul Hamim Anita Rahayu Vandha Pradwiyasma Widartha Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga University Surabaya 60132 Indonesia Surabaya Indonesia National Research and Innovation Agency Research Center for Telecommunication Bandung Indonesia Entrepreneurship Business Creation BINUS Business School Bina Nusantara University Jakarta Indonesia Department of Information System Pukyong National University Busan South Korea

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

This study aims to use bibliometric analysis to examine how machine learning research trends in obstetric care have evolved over time. First, 829 publications from 2014 to 2023 were collected from the Web of science database. The fundamental issues and characteristics of machine learning in obstetric care research were analyzed, such as annual publication contribution and focus, using theme analysis, concurrency analysis, and topic trend analysis. Second, productive objects, such as journals, authors, institutions, countries, regions, and Collaborative relationship mapping, are used to present who is leading the considerable attention in machine learning research in obstetric care. Third, the citation structure of authors and journals was investigated, and references were cited. This Bibliometric analysis revealed an increasing annual publication trend, a shift in focus to machine learning in obstetric care, the dominance of authors from China, and an increasing number of international collaborations, indicating machine learning in obstetric care as a vibrant and growing field and need for further scientific enrichment. Therefore, a comprehensive study on machine learning in obstetric care reviews the characteristics and trajectory of current research and helps scholars find suitable research entry points and conduct in-depth research.

关键词： Concurrent computing Reviews Databases Bibliometrics Collaboration Machine learning Market research

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Preliminary Research Design on Sensor Data Gathering for Air Quality Text Generation 4

Preliminary Research Design on Sensor Data Gathering for Air...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Aulia, Indra Hizriadi, Ainul Seniman Muhibuddin Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Data Science and Intelligent System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Computer System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia

Air pollution is one of the environmental problems faced by many countries (i.n. the developing countries). It often harms the health of people, plants, and animals as well as our environment. To decrease the risks, e ective and e cient technology for air quality monitoring is indispensable. In Indonesia, it is still limited in the case of the number of devices and the dissemination of air quality information. This paper describes the preliminary research design of air quality monitoring technology based on the Internet of Things (IoT) and Natural Language Generation (NLG) for solving these limitations. It de nes air quality knowledge in the form of a set of rules and presents the architecture design for data gathering and text generation. Data gathering uses the related sensors which are able to collect the values of parameters as air quality indicators. Then, the collected values are identi ed to be used for generating the textual information in Indonesian language to social media, such as twitter. So, in this research, the proposed solutions are able to solve the problems properly. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency 3

Implementation of K-Means Algorithm for Clustering Corn Plan...

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3rd International Conference on Applied sciences Mathematics and Informatics, ICASMI 2020

作者： Aldino, A.A. Darwis, D. Prastowo, A.T. Sujana, C. Department of Informatics Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Accounting Information System Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Computer Engineering Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia

South Lampung is a regency with the capital of Kalianda which has an area of 2,007.01 km2 that dominates the agricultural area. Based on the data of corn crops in the South Lampung Regency Agriculture Office through BPS (Central Bureau of Statistics), showing several areas with corn crops that vary in number. Therefore, a grouping of potential corn-producing regions is required to know which areas produce large or small amounts of corn. The distribution of crops is usually done based on the name of the corn-producing sub-district. The K-Means clustering method is one of the data mining methods that is non-hierarchical clustering that groups data in the form of one or more clusters. Data that have the same characteristics are grouped in one cluster and the remaining is grouped into another cluster so that the data that is in one cluster has a small degree of variation. So the authors tried to apply the K-Means clustering method from the corn crop data of the last 2 years to produce feasibility information from each sub-district. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction 2

Alert and Response of Earthquake and Tsunami for Community B...

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2nd International Conference on sciences and Technology Applications, ICOSTA 2020

作者： Retno Susilorini, Rr M. I. Pamudji, Andre Kurniawan Sanjaya, Ridwan Ismail, Amrizarois Amasto, Ardhito Hayyu Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Indonesia Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Indonesia Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang Indonesia

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that "Smart Resilient City"model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that "Smart Resilient City"model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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