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6th International Conference on information Management and technology, ICIMTech 2021

作者： Murad, Dina Fitria Hassan, Rosilah Heryadi, Yaya Wijanarko, Bambang Dwi Titan Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science Technology UKM Bangi Selangor Malaysia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Based on the policy of independent study and independent campus, where students can participate in part of the study period and load from within the campus, as well as others outside the campus, so that they can complete all periods and study loads as needed qualification is determined by the study program conducted through assessors. and this requires a variety of processes and takes a lot of time. For this reason, an alternative technique is needed to conduct an automatic Recognition of Prior Learning (RPL) assessment through the application of a recommendation system. The purpose of this research is to find out how the recommendation system approach is used to predict RPL assessments and provide support for curriculum development in tertiary institutions that can meet the learning needs of the digital community. This study has succeeded in classifying learning outcomes based on independent assessment data of prospective students and can provide recommendations for curriculum development in colleges that organize online learning. This study proves that the use of a recommendation system using deep learning in the RPL assessment has an accuracy (97, 24%) and is relatively the same as the assessor's assessment. © 2021 IEEE.

关键词： Recommender systems

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, information technology, and Electrical Engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Exploring the Effect of Activation Function on Transformer Model Performance for Official Announcement Translator from Indonesian to Sundanese Languages

Exploring the Effect of Activation Function on Transformer M...

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computer science, information technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Dwi Wijanarko Dina Fitria Murad Yaya Heryadi Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Online Learning Jakarta Indonesia Information Systems Department BINUS Online Learning Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Campus Phuket Thailand

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia’s population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss.

关键词： Training COVID-19 computer science Protocols Pandemics Local government Sociology

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Digital technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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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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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

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

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.

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Integer programming Approaches to an Issue of Scheduling for a Truck-Drone Last-Mile Delivery System with Parcel Pickup Service

Integer Programming Approaches to an Issue of Scheduling for...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Kotaro Yamada Yoshiyuki Karuno Hitoshi Iima Mechanodesign Program Graduate School of Science and Technology Kyoto Institute of Technology Kyoto Japan Faculty of Mechanical Engineering Kyoto Institute of Technology Kyoto Japan Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In this paper, we consider a scheduling issue for parcel delivery and pickup services by a truck-drone last-mile delivery system. We are given a single carrier truck and multiple identical drones to serve a finite set of customers. The single carrier truck plays the role of a mobile depot for the drones, and it visits several stops in a given order of them. The given order of stops constitutes a fixed truck route, and the carrier truck is allowed to launch (resp., to retrieve) a drone to (resp., from) a customer at a stop. For each sortie of a drone, the launching stop and the retrieving stop must be the same. The load capacity of a drone is limited to one, and for a sortie of a drone at a stop, there are three options: (i) only deliver a parcel to a customer, (ii) only pick up a parcel from a customer, and (iii) deliver a parcel to a customer, and then pick up a parcel from a customer. The scheduling problem asks to find an assignment of customers to drones with sortie options and a choice of a truck stop for each sortie in the assignment. The objective is to minimize the total duration of the carrier truck over all the stops, which is equivalent to minimizing the makespan. In this paper, we first propose an extended integer program for the case with both delivery and pickup services from an existing one for the case with delivery service only. We also conduct numerical experiments to demonstrate the solution quality of the proposed integer program by utilizing an integer programming solver, and report the results.

关键词： Integer programming Heuristic algorithms programming Batteries Drones Logistics

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Dynamics of interval Cohen-Grossberg neural networks with time-varying delays based on LMI computation

Dynamics of interval Cohen-Grossberg neural networks with ti...

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作者： Tan, Manchun Song, Zhiqiang Liu, Yunfeng Li, Zhong College of Information Science and Technology Jinan University Guangzhou China Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany

The problem of the global robust exponential stability of delayed interval Cohen-Grossberg neural networks is considered. By constructing suitable Lyapunov functional and using the linear matrix inequality (LMI) technique, some sufficient conditions are derived to ensure the existence, uniqueness and robust exponential stability of the equilibrium point. Based on LMI computation, numerical examples are employed to show that the new results are less restrictive and less conservative than some existing results in the literature. © 2018 John Wiley & Sons, Ltd.

关键词： Neural networks

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Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

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computers, Materials & Continua 2021年第8期68卷 2413-2429页

作者： Tapan Kumar Das Pradeep Kumar Roy Mohy Uddin Kathiravan Srinivasan Chuan-Yu Chang Shabbir Syed-Abdul School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Indian Institute of Information TechnologySurat395007India Research Quality Management Section King Abdullah International Medical Research CenterKing Saud bin Abdulaziz University for Health SciencesMinistry of National Guard-Health AffairsRiyadh11426Kingdom of Saudi Arabia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Graduate Institute of Biomedical Informatics Taipei Medical UniversityTaipeiTaiwan

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s ***,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion *** this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is *** research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high *** research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the *** system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR *** results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of *** the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

关键词： Deep learning convolutional neural network brain tumor magnetic resonance imaging

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