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International Conference on Information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Desi Maya Kristin Dewi Sagita Pranata Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London UK

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Organizations face an urgent need to become faster, more efficient, and more successful in their commercial operations, given the current state of technological evolution and the arrival of Industry 4.0. Many businesses have adopted the idea of enterprise resource planning (ERP) systems to achieve this goal. The ERP system is expected to increase worker productivity, improve business performance to maximize profitability and profits, and provide accurate and up-to-date data and information to management and stakeholders. This research aims to find new variables related to integration problems in the use of ERP systems. Using the SPSS statistical program, the data collected will be examined using quantitative exploratory factor analysis (EFA). Data processing then includes collecting demographic information from respondents, reliability testing, conducting the Kaiser-Meyer-Olkin (KMO) test, testing using an anti-image matrix, and deriving new factors using a rotation component matrix. This research seeks to identify new elements underlying ongoing integration problems in ERP system implementation. The research results show several reasons for underlying integration problems and offer in-depth information to management that can be used to analyze and evaluate the ERP system in their company appropriately in the future so that the company's performance becomes superior to the competition.

关键词： Training Standards organizations Companies Enterprise resource planning Business process re-engineering Stakeholders Reliability Best practices System implementation Testing

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Quantitative Assessment of Gait in Spinocerebellar Ataxia Using Deep Learning

Quantitative Assessment of Gait in Spinocerebellar Ataxia Us...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Ming-Hui Lai Don-Gey Liu Ching-Hwa Cheng Chung-Yu Lai Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Electronic Engineering Feng Chia University Taichung Taiwan Department of Medical Equipment Development and Application Hungkuang University Taichung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Spinocerebellar ataxia is a neurological disorder characterized by impaired neural coordination, leading to unstable gait, coordination difficulties, and other motor abnormalities. This disease significantly impacts patients' daily lives, making it challenging for them to maintain balance, walk smoothly, and even perform fine motor skills. The causes of spinocerebellar ataxia are diverse, including genetic factors, brain injuries, or certain diseases. Many patients with spinocerebellar ataxia require specialized medical care and ongoing monitoring to manage its effects. This study aims to utilize Human Posture Estimation to detect the gait patterns of patients with spinocerebellar ataxia and integrate it with a deep learning model to distinguish between normal and affected individuals. The outcomes offer physicians an objective diagnostic tool and facilitate early detection and intervention for patients with impaired gait, thereby supporting the enhancement or preservation of their quality of life. The current prediction accuracy for the risk of ataxia gait is 80%, with an F1 score of 78.26%. It is necessary to conduct a detailed quantitative assessment by analyzing the gait of patients with spinocerebellar ataxia. Quantitative evaluation of the data allows for an objective assessment of the patient's gait, which helps healthcare professionals to more accurately understand and assess the gait status of patients with spinocerebellar ataxia.

关键词： Deep learning Neurological diseases Accuracy Medical services Motors Feature extraction Skeleton

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Contrast Enhancement and Exposure Correction Using a Structure Aware Distribution Fitting

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SSRN 2023年

作者： Barbosa, Matheus Rocha Scharcanski, Jacob Electrical Engineering Graduate Program Federal University of Rio Grande do Sul Porto Alegre Brazil Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil

This paper proposes a method for contrast enhancement and exposure correction in images. The method fits regionally to the image a parametric model of probability distribution, respecting the image structure and edges between regions. This is done using regional versions of the classical expressions for estimating the parameters of the distribution, obtained by replacing the sample mean present in the original expressions by an edge preserving smoothing filter. After the fitting, the cumulative distribution function (CDF) of the adjusted model and the inverse of the CDF of the desired distribution are applied. A structure aware heuristic that detects smooth regions is proposed and used to attenuate the transformations in flat regions. The method was named Structure Aware Distribution Stretch (SADS). SADS was compared with other methods from the literature using objective no-reference and full-reference image quality assessment (IQA) metrics in the tasks of simultaneous contrast enhancement and exposure correction and in the task of defogging/dehazing. The experiments indicated a superior overall performance of SADS with respect to the compared methods for the image sets used, according to the IQA metrics adopted. © 2023, The Authors. All rights reserved.

关键词： Distribution functions

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Comparison of Machine Learning Text Classification for Intent Sentiment Analysis

Comparison of Machine Learning Text Classification for Inten...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Abulwafa Muhammad Sarjon Defit Gunadi Widi Nur Cahyo Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia

Tourist destination reviews on Google Maps have become a valuable point of reference for visitors seeking enjoyable spots to visit. Additionally, users can gain insight into the reasons for writing reviews. Text classification is used to determine the motivation behind these reviews. The aim of this study is to compare machine learning (ML) text classification techniques for identifying the intent behind sentiments expressed as complaints (0), suggestions (1), opinions (2), statements(3), and awards (4). The ML techniques assessed are Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To achieve this, tourist destination review data sets, comprising 738 reviews, were collected using web scraping from the Google Maps website. Preprocessing stages, including case-folding, tokenisation, filtering and stemming, are executed prior to the data being prepped for feature extraction with TF-IDF. The employed classification models perform well with KNN achieving 1.00 accuracy on a 90:10 data split, and SVM obtaining 0.90 on the same data split. Although, the Multinomial Naive Bayes algorithm with 0.70 accuracy on a 90:10 data split is classified as fair. Comparison of methods was conducted by means of a Receiver Operating Characteristic (ROC) diagram. The testing accuracy for the SVM method was found to be 95%, MNB at 91%, and KNN at 84%.

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Soybean Price Prediction by Using Lstm Models Based on Brazilian Climatic Data

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Effective Alzheimer’s Stage Classification Using Transfer Learning Based on MRI Images

Effective Alzheimer’s Stage Classification Using Transfer L...

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Vocational Education and Electrical Engineering, International Conference on

作者： Hamdani Hamdani Anindita Septiarini Theresia Amelia Pawitra Aldion Sumampouw Andi Tejawati Tri Basuki Kurniawan Dept. of Informatics Faculty of Engineering Mulawarman University Samarinda Indonesia Dept. of Industrial Engineering Faculty of Engineering Mulawarman University Samarinda Indonesia Magister Information of Technology Postgraduate Program Universitas Bina Darma Palembang Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most prevalent cause of dementia, significantly impacting cognitive functions and daily activities. Accurate and timely classification of AD stages is of paramount importance for the effective treatment and management of the disease. The aim of this research is to employ transfer learning in a convolutional neural network (CNN) to classify the stages of AD using magnetic resonance imaging (MRI) brain images, building on the aforementioned foundational principles. The CNN model employed in this study is MobileNetV3-Small, which has been trained on a comprehensive dataset of labeled MRI scans with the objective of accurately distinguishing the various stages of AD. The dataset, referred to as the “Augmented Alzheimer MRI Dataset,” comprises $\mathbf{3 3, 9 8 4}$ augmented and $\mathbf{6, 4 0 0}$ original MRI images, which have been categorized into four stages: Very Mild Demented, Moderate Demented, Mild Demented, and NonDemented. The methodology comprises three principal stages: data preparation, model training, and model evaluation. A systematic assessment of various hyperparameters, including split ratio, learning rate, batch size, and dropout rate, was conducted to optimize model performance. The results demonstrate that fine-tuning the model significantly enhances classification accuracy, achieving a maximum accuracy of $\mathbf{9 6 \%}$ compared to $\mathbf{7 2 \%}$ without fine-tuning. A smaller learning rate was found to improve performance metrics by preventing the model from converging to local minima. These findings highlight the potential of transfer learning and hyperparameter optimization in developing robust models for the early detection and classification of AD, contributing to better patient outcomes and advancing the field of medical image analysis.

关键词： Training Analytical models Accuracy Systematics Magnetic resonance imaging Computational modeling Transfer learning Data models Convolutional neural networks Alzheimer's disease

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ardian Budi Kusuma Atmaja Egi Muhammad Idris Hidayat Bambang Riyanto Trilaksono Simon Siregar Natsir Habibullah School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department Computer Engineering Faculty of Applied Science Doctoral Program of Electrical Engineering Institut Teknologi Bandung Telkom University

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees.

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Positive Predictive Value of the Thumb-Palm Test for General Population Screening of Ascending Aortic Aneurysm

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The American journal of cardiology 2021年 161卷 116-117页

作者： James A Diao Gloria J Wu Arjun K Manrai Computational Health Informatics Program Boston Children's Hospital Boston Massachusetts Department of Biomedical Informatics Harvard Medical School Boston Massachusetts.

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Bibliometric Analysis of Trends and Directions in Machine Learning Applied to FinTech

Bibliometric Analysis of Trends and Directions in Machine Le...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International Conference on

作者： Erman Arif Suherman Suherman Aris Puji Widodo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services.

关键词： Symbiosis Technological innovation Machine learning algorithms Bibliometrics Collaboration Machine learning Market research Dynamic scheduling Fraud Risk management

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Bluetooth Based Home Automation Using Android Phone

Bluetooth Based Home Automation Using Android Phone

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Bhardwaj, Priyanka Roy, Manidipa Singh, Sanjay Kumar Jain, Vanshika Novita, Mega Mukhtar, Agus Setyoadi, Yuris ABES Engineering College U.P Ghaziabad India Universitas Pgri Semarang Postgraduate Program of Science Education J1. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Mechanical Engineering Ji. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia

ISBN: (纸本)9798350329186

Electronic devices and appliances are increasingly becoming a quintessential part of every household with the recent development and innovations in the field of technology affecting the day-to-day lives of individuals. Automation has caught the fame as people struggle to keep up with the demands of work, making it an easy solution to operate devices and machines to meet the individual needs. The paper describes the creation and execution of an affordable, versatile, and safe home automation system that is controlled through a mobile phone. The system relies on an independent Arduino BT board, which is connected to home appliances via relays attached to its input/output ports. Wireless communication is used to connect the mobile phone and the Arduino BT board. The system is designed to be both economical and expandable, allowing for control of a range of devices with minimal changes to its basic structure. The focus of the paper is to explain how to manage and regulate electronic devices using Android smartphones. The paper also outlines a home automation system that prioritizes security and safeguards user privacy. This system is designed to be affordable and flexible, making it possible to control various devices with minimalchanges to its core structure. Additionally, the appliances in the system are protected by passwords to ensure that only authorized users can access them. © 2023 IEEE.

关键词： Domestic appliances

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