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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Ekosputri, Alicia Josephine Ardianti, Amanda Anggreainy, Maria Susan Artanto, Brychan Fitriawati, Nora Valentina, Fanny Angelia Santoso, Vincent Kartamulya School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia Bina Nusantara University Binus Online Learning Information Systems Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research. © 2024 IEEE.

关键词： Prediction models

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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2023 International Conference on computer science, information technology and Engineering, ICCoSITE 2023

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan Nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media ***,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and *** light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral *** achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country *** predicts the support and opposing strength of each political party/*** determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/*** sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case *** applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state ***,at the country level,the LEPM model outperforms traditional polling *** a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other *** the extensive social media data,the LEPM model provides nuanc

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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Multi-dimensional ability diagnosis for machine learning algorithms

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science China(information sciences) 2024年第12期67卷 321-322页

作者： Qi LIU Zheng GONG Zhenya HUANG Chuanren LIU Hengshu ZHU Zhi LI Enhong CHEN Hui XIONG State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Business Analytics and Statistics The University of Tennessee Computer Network Information Center Chinese Academy of Sciences Shenzhen International Graduate School Tsinghua University Hong Kong University of Science and Technology (Guangzhou)

A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired by highly organized human education systems, i.e., training the algorithms with easy samples first and gradually transforming to the hard examples can contribute to faster convergence and lower generalization error.

关键词： Adversarial machine learning

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest 15

Drug Demand Prediction Based on Epidemiology Factors Using R...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Emmanuel, Gunadi Arifin, Yulyani Sonata, Ilvico Zarlis, Muhammad Bina Nusantara University Doctor of Computer Science Program Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta Indonesia

ISBN: (纸本)9798350383829

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017-2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases. © 2023 IEEE.

关键词： Forecasting

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Intelligent Decision Support System for Higher Education Institutions 9

Intelligent Decision Support System for Higher Education Ins...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Afriliana, Nunik Meyliana Gaol, Ford Lumban Soeparno, Haryono Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information System Information System Department Jakarta Indonesia

ISBN: (纸本)9798350369922

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. Intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited. © 2023 IEEE.

关键词： Data mining

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Toward Addressing the Software Architecture Blind Spot of information System Success Factors in the Public Health Domain 44

Toward Addressing the Software Architecture Blind Spot of In...

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44th International Conference on information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Dang, Anthony Beydoun, Ghassan School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9781713893622

Context: In the public health domain, there is no shortage of failed information Systems projects. In addition to overblown budgets and elapsed deadlines (ad nauseam), technical issues exist. These include poor usability, instability, system performance, and data inconsistency issues. The issues relate to Software Engineering, and specifically Software Architecture. The role of Software Architecture underpins these issues. However, the enquiries and analyses of these failed information Systems projects have focused on the perspective of stakeholders (e.g., government ministers and health practitioners) and project managers. While stakeholders provide input into project requirements and goals, the project failure or success does not emanate from these roles exclusively. For example, system instability is not specifically a stakeholder issue but rather a Software Architecture concern. Aim: To bring to bear and address public sector health information System failure from the Software Architecture perspective. Method: A literature survey was conducted to ascertain the perceived failure and success factors within the public health domain. Results: We observed that the available literature on health information Systems appears to lack success factors which have been noted in the Software Engineering and Software Architecture literature. Software Architecture appears to be an understudied area within the public health domain. Contribution: To bring awareness to the public health domain that the information System's success requires multi-faceted perspectives and actions. Specifically, perspectives and actions involving Software Architects and Software Engineers are required to successfully address the quality attributes of the proposed systems during development. Copyright © 2023 Anthony Dang and Ghassan Beydoun.

关键词： Software architecture

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