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

作者： Nunome, Toshiro Wakayama, Yamato Nagoya Institute of Technology Computer Science Program Department of Engineering Graduate School of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350324174

This paper investigates the effect of bitrate control methods on QoE of multi-view video and audio streaming with MPEG-DASH. We adopt three bitrate control methods for conventional single-view video streaming to the MVV-A system with MPEG-DASH. We conduct a subjective experiment changing available network bandwidth and investigate the effect of the methods on QoE. © 2023 IEEE.

关键词： Video streaming

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Prediction of Oil Palm Conditions Using Deep Learning Based on the Visible Atmospherically Resistant Index on UAV Imagery

Prediction of Oil Palm Conditions Using Deep Learning Based ...

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2023 International Conference on Informatics Engineering, Science and Technology, INCITEST 2023

作者： Mimboro, Prasetyo Soewito, Benfano Soeparno, Haryono Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Immersive Learning in Using the Metaverse of Satellite Communication Systems Science 9

Immersive Learning in Using the Metaverse of Satellite Commu...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Akbar, Komputerio Meyliana Warnars, Harco Leslie Hendric Spits Jayadi, Riyanto Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798350383010

Education stands out as one of the most impactful applications of the metaverse, holding immense potential for the future. Within the realm of satellite communication system science education, the integration of immersive learning through the metaverse adds a layer of interest and interactivity to the teaching and learning process. Numerous design and evaluation techniques have been employed in a growing body of research, aiming to understand how immersive learning via the metaverse can effectively contribute to specific educational objectives. Hence, this research is dedicated to delving into the development of an immersive learning metaverse model for the science of satellite communication systems. The research contributes a model that advocates for a holistic learning approach by integrating metaverse technology, VR, the 6E Instructional Process Model, and the MSLQ evaluation instrument. The objective of this model is to enhance the efficiency, effectiveness, and interactivity of learning within the field of satellite communication systems science. © 2024 IEEE.

关键词： Satellite communication systems

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Transfer Learning Method for Malaria Identification 8

Transfer Learning Method for Malaria Identification

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herli, Cecilia Audrey Wijaya, Daniel Zerge Hansen Angela, Michelle Pragusma, Zakaria Berlam Wulandari, Ajeng Anggreainy, Maria Susan Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350342604

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase. © 2023 IEEE.

关键词： Convolutional neural networks

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Server Cloud Based Security Surveillance Systems: a Design Science Research 11

Server Cloud Based Security Surveillance Systems: a Design S...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nurenie, Alexander Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design Science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area. © 2023 IEEE.

关键词： Cost effectiveness

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Deep Learning Approach with EfficientNet-CBAM for Apple Disease Detection

Deep Learning Approach with EfficientNet-CBAM for Apple Dise...

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computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Aldiki Febriantono Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science School of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Disease attacks have the potential to decrease apple productivity, particularly in Poncokusumo Village, Malang Regency. The manual process of disease identification requires a significant amount of time and specialized knowledge, which results in inefficiency and frequently blocks early treatment. The objective of this research is to create a precise deep learning model for the detection of diseases in apples in order to ensure that it functions effectively in situations that are real. To accomplish this objective, directly collected disease image data on fruits from apple fields in Poncokusumo Village. The EfficientNet model implements the CBAM attention mechanism. The EfficientNet B0 version was employed as the basis for the model for this work, with CBAM being implemented to improve feature extraction and identify infection spots within the image. Based on the research findings, the EfficientNet-CBAM model has an F1 score of 92% and an accuracy of 93.67%. The attention mechanism of the model is effective in identifying infected areas, enabling both quicker and more precise detection. This method has the potential to enhance the precision and efficacy of disease identification, as well as offer producers advantages by applying preventive measures earlier.

关键词： Deep learning Productivity Measurement Technological innovation Accuracy Attention mechanisms Pesticides Manuals Mobile applications Diseases

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR) 9

E-Government Interoperability: Provincial-Level Architecture...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wijayanti, Diana Urbaya, Satriawansyah Sitompul, Taufiq Adrian, Verry BINUS Graduate Program Bina Nusantara University Department of Information Systems Jakarta11480 Indonesia Program USAID Indonesia Jakarta12930 Indonesia University of Oslo Department of Informatics Oslo0316 Norway Binus Graduate Program - Doctor Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Provincial Health Office Center of Data and Information Technology Jakarta10160 Indonesia

ISBN: (纸本)9798350390025

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users. © 2024 IEEE.

关键词： e-government

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Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix Eigenvalues 13th

Fast Cauchy Sum Algorithms for Polynomial Zeros and Matrix...

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Pan, Victor Y. Go, Soo Luan, Qi Zhao, Liang Department of Computer Science Lehman College of CUNY BronxNY United States Program in Computer Science The Graduate Center of the City University of New York New York United States Program in Mathematics The Graduate Center of the City University of New York New York United States

ISBN: (纸本)9783031304477

Given a black box oracle that evaluates a univariate polynomial p(x) of a degree d, we seek its zeros, aka the roots of the equation. At FOCS 2016, Louis and Vempala approximated within an absolutely largest zero of such a real-rooted polynomial at the cost of the evaluation of Newton’s ratio (formula presented) points x and then extended this algorithm to approximation of an absolutely largest eigenvalue of a symmetric matrix at a record Boolean cost. By applying distinct approach and techniques we obtain much more general results at the same computational cost. Our use of Cauchy integrals and randomization is non-trivial and pioneering in this field. Somewhat surprisingly, the Boolean complexity of the accelerated versions of our algorithms in [25, 26] reached below the known lower bounds on the Boolean complexity of polynomial root-finding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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AxRSU- : Higher-Order m-Bit Approximate Encoders for Radix- Squarer Units

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Circuits, Systems, and Signal Processing 2024年第6期43卷 1-30页

作者： da Rosa, Morgana M. A. Paim, Guilherme da Costa, Eduardo A. C. Soares, Rafael Bampi, Sergio Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program in Microelectronics Federal University of RGS Porto Alegre Brazil Graduate Program in Electronics and Computer Engineering Catholic University of Pelotas Pelotas Brazil

Approximate computing has emerged as a design alternative to enhance design efficiency by capitalizing on the inherent error resilience observed in numerous applications. Various error-resilient and compute-intensive applications, such as signal, image and video processing, computer vision, and supervised machine learning, necessitate dedicated hardware accelerators for mean squared error estimation during runtime. In these application domains, using efficient arithmetic operators, particularly a squarer unit, represents one of the most effective strategies for low-power design. This work introduces an approximate Radix- $$2^{m}$$ squarer unit, denoted as AxRSU- $$2^{m}$$ . The proposed squarer unit employs m-bit approximate encoders to execute operations on m-bit data concurrently. The AxRSU- $$2^{m}$$ under consideration explores encoders with m equal to 2 (AxRSU-4), 3 (AxRSU-8), and 4 (AxRSU-16). These approximate encoders exhibit low complexity and diminish the necessary partial products operating on m bits simultaneously, thereby substantially enhancing energy efficiency and reducing circuit area in the AxRSU- $$2^{m}$$ . To illustrate the trade-off between error and quality in the AxRSU- $$2^{m}$$ , we apply it to an SSD (sum squared difference) hardware accelerator designed for video processing, with a square-accumulate serving as a case study. Our findings reveal a novel Pareto front, presenting eight optimal AxRSU- $$2^{m}$$ solutions that achieve accuracy levels ranging from 3.76 to 75.53%. These solutions yield energy savings ranging from 46.20 to 95.57% and circuit area reductions ranging from 37.68 to 66.73%.

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