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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Dankar, Fida K. Madathil, Nisha Nyuad Computer Science Program Abu Dhabi United Arab Emirates Uaeu Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9781665456616

Federated Learning (FL) is a hot new topic in collaborative training of machine learning problems. It is a privacy-preserving distributed machine learning approach, allowing multiple clients to jointly train a global model under the coordination of a central server, while keeping their sensitive data private. The problem with FL systems is that they require intense communication between the server and clients to achieve the final machine learning model. Such complexity increases with the number of clients participating and the complexity of the model sought. In this paper, we introduce synthetic data generation into FL systems with the intention of reducing the number of iterations required for model convergence. In this novel method, clients generate synthetic datasets modeling their private data. The synthetic datasets are then sent to the central server and are used to generate a cognizant initial model. Our experiments show that such conscious method for generating the initial model lowers the number of iterations by a factor of more than 4 without affecting the model accuracy. As such it enhances the overall efficiency of FL systems. © 2022 IEEE.

关键词： Sensitive data

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The Impact of Recommendation System Provided Based on Online Learning Readiness in the MOOC Environment 24

The Impact of Recommendation System Provided Based on Online...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Murad, Dina Fitria Toha, Mohamad Mayatopani, Hendra Dewi, Meta Amalya Leandros, Riyan Murad, Silvia Ayunda Heryadi, Yaya Wijanarko, Bambang Dwi Bina Nusantara University Information Systems Department Binus Online Learning Jakarta Indonesia Universitas Terbuka Jakarta Indonesia Pradita University Department of Information System Tangerang Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Doctor of Computer Science Jakarta Indonesia Bina Nusantara University School of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350384307

MOOCs are one of the developments or impacts of e-learning or online learning. Massive Open Online Course (MOOC) is one of the choices for students to increase their knowledge through non-formal channels. Our research aims to determine the level of readiness of students regarding MOOC learning and opportunities for utilizing recommendation systems in students' decision-making regarding selecting MOOCs that suit their interests and needs. We conducted a survey regarding the constraints and needs of learner readiness through 5 aspects, namely 1) technical aspect, (2) social aspect, (3) communication aspect, (4) lecture readiness aspect, and (5) online discussion. The results of this research show that the main challenges that MOOC participants may face in a MOOC environment are Technical Aspects (28.25%), Social Aspects (21.47%), and Communication Aspects (18.93%). Meanwhile, the greatest strength of online learning in the MOOC environment is the social aspect (21%), the most influential and continued with technical and other aspects. Meanwhile, the lecture readiness aspect is quite good, and the online discussion aspect is the biggest aspect that influences the success of MOOC learning. Other results of this research indicate the importance of a recommendation system for MOOC students to make it easier for students to choose the right MOOC according to their interests and needs. © 2023 IEEE.

关键词： MOOC personalization Recommendation system student competency Technology Enhanced Learning

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Mobile Application for Elderly Care 2

Mobile Application for Elderly Care

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Jose, Fernando Livaldo Mahardhika, Mozan Hafiz Sukiman, Nathanael Adolf Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Utomo, Wiranto Herry Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara Univerisity Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia President University Faculty of Computing Department of Magister Science of Information Technology West Java17530 Indonesia

ISBN: (纸本)9781665400527

This paper discusses the design and implementation process of mobile applications used by nurses to communicate with the elderly or with people appointed to represent the elderly in using this mobile application. This mobile application is expected to help caregivers or nurses monitor the health condition of the elderly, and this mobile application has several functions that make it complete. These functions include submitting weekly reports, communicating with others via forums, and providing relevant information about the elderly and how to deal with them, including news and government regulations on elderly care. This mobile application uses case diagrams to describe business processes and class diagrams to illustrate database design. As for the implementation, android studio and MySQL database are used for database storage. This paper is limited to 9 references for literature review previous similar research by searching for publication titles with the sentences 'mobile application'+elderly+care using quotation marks and without quotation marks for the words elderly and care for all years of publication. © 2022 IEEE.

关键词： Mobile computing

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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Automatic Smart Crawling on Twitter for Weather information in Indonesia 8

Automatic Smart Crawling on Twitter for Weather Information ...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Purwandari, Kartika Perdana, Reza Bayu Sigalingging, Join W.C. Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate. © 2023 The Authors. Published by Elsevier B.V.

关键词： smart crawling Social media SVM Twitter weather

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE 4

Air Quality Prediction Based on Air Pollution Emissions in t...

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4th IEEE International Conference of computer Science and information Technology, ICOSNIKOM 2022

作者： Nababan, Adli A Sutarman Zarlis, Muhammad Nababan, Erna B Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350399073

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction. © 2022 IEEE.

关键词： Air quality

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Database Design for Pre-Eclampsia Case Administration 8

Database Design for Pre-Eclampsia Case Administration

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8th International Conference on information Technology and Digital Applications, ICITDA 2023

作者： Asadi, Faisal Elwirehardja, Gregorius Natanael Trinugroho, Joko Pebrianto Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia. © 2023 IEEE.

关键词： Medical informatics

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Prabandari, Jasmine Iswarini Dyah Sari, Christy Atika Rachmawanto, Eko Hari Irawan, Candra Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant. © 2023 IEEE.

关键词： Convolutional neural networks

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Dental Disease Detection based on CNN for Panoramic Dental Radiographs 11

Dental Disease Detection based on CNN for Panoramic Dental R...

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11th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2023

作者： El Bagoury, Manar Al-Shetairy, Mirna Ahmed, Omar Mehanny, Samah S Hamdy, Tassneem Omar, Gihan Akram, Marwan Mohamed, Rana Amr, Hebatullah Gad, Walaa Ain Shams University Faculty of Computer and Information Sciences Bioinformatics Program Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Faculty of Dentistry Cairo and Galala Universities Oral Biology Department Cairo Egypt Cairo University Faculty of Dentistry Oral and Maxillofacial Radiology Department Cairo Egypt Future University in Egypt Faculty of Oral and Dental Medicine Oral Radiology Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Panoramic dental radiography is one of the examination tools frequently performed in dental clinics and hospitals as it captures a wide range of the dental region and its surroundings, but due to the complex structure of the dental area and lack of time, dentists focus on tiny parts of the images. With the help of Machine Learning, dentists can detect dental defects and anomalies faster and more accurately. In this study, a data set of 733 panoramic radiographs of adult patients was collected from the faculty of Oral and Dental Medicine, Future University, Egypt. Using a combination of pre-trained CNN architecture and a finetuned network, the collected panoramic radiographs were used to determine the optimal architecture. The purpose of this research was to develop a computer-aided detection system based on a finetuned CNN algorithm, and to evaluate the accuracy and usefulness of this system for the detection of proximal and occlusal caries, interdental and inter radicular alveolar bone loss, periapical lesions, and impacted teeth in panoramic radiographs. Different object detection models were tested to detect selected dental diseases that were found in the panoramic radiographs, and after a few experiments, the YOLOv5 architecture was proved to be the most optimal after achieving 0.61 mAP@0.5 and 0.28 mAP@ [0.5-0.95] for the total six classes. This is the best-achieved results to our knowledge. © 2023 IEEE.

关键词： Object detection

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Adopting Augmented Reality (AR) Technology in Retail Sector: Systematic Review of Prisma Methods 7

Adopting Augmented Reality (AR) Technology in Retail Sector:...

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7th International Conference on information Management and Technology, ICIMTech 2022

作者： Maryani Prabowo, Harjanto Gaol, Ford Lumban Hidayanto, Ahmad Nizar School of Information Systems BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Information Systems Department Computer Science Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Computer Science Universitas Indonesia Depok16424 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

One of the most popular technologies nowadays is augmented reality (AR). popular technologies in various industries. Many industries have adopted this AR technology, one of which is with the aim of marketing the products offered to their customers. With more and more adopting this technology, retailers are also using it. The goal of this research is to determine out what important variables are often encountered in the application of AR technology. A qualitative technique is used in this study, as well as a Systematic Literature Review Prism approach. The result are 56 papers, 35 candidate papers and 26 papers were selected. We examine where and how in retail, augmented reality (AR) is used, the aspects of Augmented reality technology that are frequently examined, and the psychological and behavioral effects that AR can have. © 2022 IEEE.

关键词： Augmented reality

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