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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Sukmandhani, Arief Agus Arifin, Yulyani Zarlis, Muhammad Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required;therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization. © 2023 IEEE.

关键词： Text processing

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Predicting Cardiac Complications of Myocardial Infarction Patients Using Machine Learning

Predicting Cardiac Complications of Myocardial Infarction Pa...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Baidya, Shriyansh Gupta, Vibhuti Lovejoy High School LucasTX United States Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

In the United States, heart disease is the leading cause of death, killing about 695,000 people each year. Myocardial infarction (MI) is a cardiac complication which occurs when blood flow to a portion of the heart decreases or halts, leading to damage in the heart muscle. Heart failure and Atrial fibrillation (AF) are closely associated with MI. Heart failure is a common complication of MI and a risk factor for AF. Machine learning (ML) and deep learning techniques have shown potential in predicting cardiovascular conditions. However, developing a simplified predictive model, along with a thorough feature analysis, is challenging due to various factors, including lifestyle, age, family history, medical conditions, and clinical variables for cardiac complications prediction. This paper aims to develop simplified models with comprehensive feature analysis and data preprocessing for predicting cardiac complications, such as heart failure and atrial fibrillation linked with MI, using a publicly available dataset of myocardial infarction patients. This will help the students and health care professionals understand various factors responsible for cardiac complications through a simplified workflow. By prioritizing interpretability, this paper illustrates how simpler models, like decision trees and logistic regression, can provide transparent decision-making processes while still maintaining a balance with accuracy. Additionally, this paper examines how age-specific factors affect heart failure and atrial fibrillation conditions. Overall this research focuses on making machine learning accessible and interpretable. Its goal is to equip students and non-experts with practical tools to understand how ML can be applied in healthcare, particularly for the cardiac complications prediction for patients having MI. © 2024 IEEE.

关键词： Deep learning

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News-Driven Price Movement Forecasting with Label-Prior Graph Attention 24

News-Driven Price Movement Forecasting with Label-Prior Grap...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Liou, Yi-Ting Chen, Chung-Chi Huang, Hen-Hsen Chen, Hsin-Hsi Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan AIST Japan Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798400701726

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model’s efficacy is further validated through experiments on two multilabel datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphic methods

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Multivariate stock market forecasting using lstm on Indonesian banking stock market

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Procedia computer science 2024年 245卷 1047-1056页

作者： Kenrick Steffano Claus Ivancio Elisabeth Isabellini Canggih Gelar Setyo Adhi Anang Prasetyo Computer Science Computer Science Department School of Computer Science Universitas Bina Nusantara Semarang Campus Indonesia Computer Science Computer Science Department School of Computer Science Universitas Bina Nusantara Jakarta Indonesia

Forecasting stock market prices and trends can be a major challenge for investors and traders because of its volatility and various factors that affects it. Because of that, it is crucial for investors to be able to forecast the stock market alongside its trends as accurately as possible to minimize risk and increase profit, given the complexities and uncertainties inherent in the market. This research aims in digging the potential of LSTM models to forecast the open and close price of the 6 Indonesia bank stock market which consist of 3 national bank (BNI, BRI, and Mandiri) and 3 private bank (CIMB, BCA, and OCBC) in which it also helps in enhancing investment strategies in the banking sector, making it a very helpful tools for investors to gain more profit and minimize loss while also test and evaluate its error rate and accuracy while also evaluate the LSTM performance based on its accuracy and error rates. The historical stock data from April 30, 2019, to April 30, 2024, that's used in this study as the main dataset was acquired from Yahoo Finance. The provided model average accuracy and error rate when forecasting the open and close prices can be seen with an exceptional accuracy and very low error rates which can be proven with the predicted open's MSE: 0.000303, RMSE: 0.018430, MAE: 0.013380, and R²: 0.967834, as well as predicted close's MSE: 0.000511, RMSE: 0.022322, MAE: 0.017260, and R²: 0.942172, making it considered as a robust, reliable, and accurate model for investors and traders to forecast the close and open price of the stock market in the future. The LSTM model performance highlights its capabilities to capture important multivariate patterns of the tested bank's stock market data, which offer more advantages over traditional methods.

关键词： Forecasting volatility stock market trends RNN multivariate LSTM bank accuracy error rate open price close price

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An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis 25

An Integrated Framework for Team Formation and Winner Predic...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Galbiati, Federico Gran, Ranier X. Jacques, Brendan D. Mulhern, Sullivan J. Ngan, Chun-Kit Worcester Polytechnic Institute Department of Computer Science Worcester United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350330014

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy. © 2023 IEEE.

关键词： Predictive analytics

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Enhancing Code Insights through Semantic Change Impact Evaluation 15

Enhancing Code Insights through Semantic Change Impact Evalu...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex code bases create problems. By evaluating the effect of reflection changes, this study contributes to the improvement of the comprehension of code, a critical success factor for quality and efficiency. This addresses issues by using semantic analysis, change detection, refactoring detection, and semantic change detection and diffing. Our approach documents the semantic differences between the original and changed source code and gives pragmatic conclusions on how to enhance code quality and maintainability. Specifically, we divide the code into AST files using simple python scripts for semantic analysis and compare them for differences to find implications. It executes the method through the focus on development workflows and major projects as it includes the past information while still simplifying argument parsing and file processing. Based on our study findings, a better understanding of code can help developers to improve their decision-making capacity and protect the performance during the enhancement activity of the program. © 2024 IEEE.

关键词： Abstract Syntax Trees (ASTs) change detection refactoring detection semantic analysis semantic changes

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Systematic Literature Review on Metaheuristic Algorithms in SNP Analysis for Diseases 6

Systematic Literature Review on Metaheuristic Algorithms in ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies. © 2024 IEEE.

关键词： metaheuristic algorithms single nucleotide polymorphisms

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Fog-Assisted Blockchain-IoMT Healthcare Framework with Role-Based Access Control for Critically Ill Patients

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SN computer science 2024年第6期5卷 658页

作者： Mallick, Soubhagya Ranjan Lenka, Rakesh Kumar Tripathy, Pradyumna Kumar Rao, D. Chandrasekhar Sharma, Suraj Ray, Niranjan Kumar School of Technology Woxsen University Telangana Hyderabad 502345 India Department of Computer Science Central University Odisha Koraput 764021 India Computer Science and Engineering Silicon University Odisha Bhubaneswar 751024 India Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela 769015 India Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur 495009 India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India

Hospitals, clinics, pharmaceutical firms, diagnostics centres, pathology labs, insurance companies, and emergency vehicles must be connected in a unified system to better serve patients worldwide with cutting-edge medical care. Most previously existing systems rely on cloud-based platforms, which face several technological challenges, including data availability, data accessibility, processing delays, scalability concerns, problems gathering real-time data, and difficulties monitoring patients. Information about a person’s health is extremely private and should be protected with the utmost care. To address these issues, We propose a decentralised, secure, and efficient framework incorporating the internet of things, fog computing, and blockchain technology to collect, process, manage, and store healthcare data. A unique role-based access control is used in the proposed framework to prioritise critically ill patients while maintaining confidentiality, scalability, data availability, and system security. Finally, we evaluate the efficiency of our proposed framework by conducting experiments on device registration time, transaction latency, transaction throughput, and access control in medical environments using realistic healthcare scenarios. We then compare the results to existing fog models to determine the system’s performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Cloud computing Fog computing Healthcare IoMT Scalability

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Semi-automated meningioma segmentation with bounding boxes 9th

Semi-automated meningioma segmentation with bounding boxes

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Heryanto, Nur Adhianti Isnan, Mahmud Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores. © 2024 The Authors.

关键词： Magnetic resonance imaging

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Evaluation of Machine Learning on Smart Home data for Prediction of Electrical Energy Consumption

Evaluation of Machine Learning on Smart Home Data for Predic...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Widianto, Mochammad Haldi Gunawan, Alexander Agung Santoso Heryadi, Yaya Budiharto, Widodo Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Computer Science Department Bina Nusantara University Jl. K.H.Syahdan No. 9 Palmerah Kemanggisan Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Electrical energy consumption is always increasing, and this causes the supply of electrical energy to be increased to compensate. One solution is to predict electricity energy consumption using Artificial Intelligence (AI) technology in Smart Homes. Several studies' solutions for predicting electrical energy consumption usually focused only on performance but rarely evaluated Machine Learning (ML) by correlation for feature selection and utilized interpretability model. This study uses an ML model for predicting utilization (Linear Regression, Decision Tree, Random Forest, and XGBoost). Then, Feature Selection utilizes correlation to choose the best feature. After that, the interpretability model utilizes Local Interpretable Model-agnostic Explanations (LIME). The results show that XGBoost has the best Root Mean Squared Error (RMSE) value (0.318) with a percentage of the number of train and test data (90/10). After that, by eliminating features that correlate with 0.01, XGBoost improves with an increase of (0.018) to become (0.3). Then from LIME. This work also gets positive feature from XGBoost such as: "Furnance, Well dan Living Room". © 2023 IEEE.

关键词： Forecasting

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