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

作者： Lestandy, Merinda Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach. © 2023 The Authors. Published by Elsevier B.V.

关键词： BiLSTM Depression Social Media Word Embedding

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XSpace: Self-learning platform for implementation of digital control algorithms 14

XSpace: Self-learning platform for implementation of digital...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Caceres, Pablo Cardenas Cieza Huane, Leslie Y. Horna Vilcahuaman, Ingrid M. Pauyac Estrada, Claudia R. Casano, Celso De-La-Cruz Tafur Sotelo, Julio C. Pontificia Universidad Catolica Del Peru Department of Engineering Electronics Section Lima Peru Degree Program Pontificia Universidad Catolica Del Peru Department of Engineering Control and Automation Master Engineering Lima Peru

ISBN: (纸本)9798350399431

In the electronic engineering undergraduate program, Peruvian universities offer different theoretical courses of Automatic Control that do not present enough practical approach to the algorithms that are studied during the progress of these courses;however, the approach to implementation is very important to achieve good performance in the industry and researching. In this work is presented XSpace, an innovative and friendly platform which consists of a robust hardware that allows the student to take it home;this way, the student is given the opportunity to implement digital control algorithms learned in the theoretical classes. The effectiveness of this platform is shown using a proposed methodology which evidences greater interest, efficiency and efficacy in students at the Automatic Control area. This project was implemented in the 'Control Systems' course at Pontificia Universidad Catolica del Peru (PUCP) from 2020 to 2022. Results are presented through a student survey and a history of average grades during the academic periods the platform was employed. © 2023 IEEE.

关键词： Students

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A Vision-Based Neural Networks Model for Turbine Trench-Filler Diagnosis 1

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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Isaza, Cesar Guerrero-Garcia, Fernando Anaya, Karina Jenab, Kouroush Ortega-Moody, Jorge Networking and Telecommunication Academic Program Polytechnic University of Queretaro Queretaro Mexico Productive System Master Academic Program Polytechnic University of Queretaro Queretaro Mexico School of Engineering and Computer Science Morehead State University MoreheadKY40351 United States

ISBN: (数字)9783031396199

ISBN: (纸本)9783031396182

Vision-based neural networks as artificial intelligence models have been critical in many manufacturing industries, including automotive, food, and aerospace. Machine vision and deep learning have provided practical, promising, and accessible innovations to address many problems during manufacturing and diagnostics. Recent studies offer beneficial results in implementing industrial artificial intelligence systems that require determining, comparing, and evaluating optimal technological solutions. The aerospace industry has to deal with the problem of diagnosing critical components that continually need the expertise of a trained human being. On the other hand, automatic diagnostics are becoming a critical technology to deal with this problem. However, these systems require a particular configuration of computer vision algorithms which, in the case of turbine trench-filling components, have yet to be added to the literature. Considering the above, we report in this paper a new methodology that uses a pre-trained deep neural network framework available online to automatically diagnose geometric nonconformities in aeronautical components that protect the turbines from vibrations and reduce noise from the engines to the aircraft deck. The method is based on the following stages: a computer vision system with a monochrome camera to acquire images, training of deep neural networks with transfer learning, and a stage to analyze nonconformities automatically. In addition, a benchmark of several pre-trained deep neural networks is presented to address the problem. According to the experimental results, the YOLO deep neural network topology significantly contributes to automatic diagnosis with high precision and exact rate. Finally, we show that integrating deep neural networks with control graphs is a promising strategy to optimize online diagnostics in the production lines of the aeronautical industry. © The Author(s), under exclusive license to Springer Nature Switzerland

关键词： Deep neural networks

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Quantitative Assessment of Gait in Spinocerebellar Ataxia Using Deep Learning 4

Quantitative Assessment of Gait in Spinocerebellar Ataxia Us...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

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

ISBN: (纸本)9798350360721

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. © 2024 IEEE.

关键词： Gait analysis

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The Impact of Effectiveness of Luggage Arrangement on the Airplane Passengers' Boarding Process

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Periodica Polytechnica Transportation engineering 2022年第4期50卷 369-386页

作者： Nugroho, Arianto Ajie Asrol, Muhammad BINUS Graduate Program - Master of Industrial Engineering Industrial Engineering Department Bina Nusantara University Jl. K. H. Syahdan 9. Kemanggisan Palmerah Jakarta11480 Indonesia

The boarding process is the role activity to maintain the airline's efficiency in the turnaround process on the ground. One of the scenarios to optimize the boarding process is the arrangement of passengers who enter the plane based on the amount of carry-on luggage, adjusted to the selected boarding strategy. This research aims to develop an agent-based simulation model to increase the effectiveness of passengers' boarding process by applying the luggage arrangement method for an airplane with a 180-seat configuration. The simulation results showed that applying the Ascending luggage arrangement method reduced the overall boarding process performance by 6.12%, while the Descending method increased boarding performance by 2.50%, compared to the standard Random method. © 2022 Budapest University of Technology and Economics. All rights reserved.

关键词： Sensitivity analysis

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Current advances in bioethanol synthesis from lignocellulosic biomass: sustainable methods, technological developments, and challenges

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Journal of Umm Al-Qura University for Applied Sciences 2025年 1-18页

作者： Novia, Novia Melwita, Elda Jannah, Asyeni Miftahul Selpiana, Selpiana Yandriani, Yandriani Afrah, Bazlina Dawami Rendana, Muhammad Department of Chemical Engineering Faculty of Engineering Universitas Sriwijaya Jl. Raya Palembang-Prabumulih KM 32 Inderalaya Sumatera Selatan Ogan Ilir 30662 Indonesia Master Program of Environmental Management Graduate School Universitas Sriwijaya Jl. Padang Selasa 524 Bukit Besar Sumatera Selatan Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya South Sumatra Palembang 30139 Indonesia

Bioethanol production from lignocellulosic biomass has become an economical and environmentally friendly substitute to current petroleum-based fuels. This is due to its ability to decrease carbon dioxide emissions and make use of plentiful natural resources. Current advances in this area concentrate on improving the effectiveness and expandability of the conversion procedures by using inventive pretreatment approaches, enhanced enzyme compositions, and refined fermentation technologies. Consolidated bioprocessing (CBP) and utilizing genetically modified microbes have expedited the decomposition of intricate biomass complexes and enhanced overall productivity. Furthermore, process intensification developments, such as the adoption of hybrid reactors and continuous production systems, have considerably decreased production expenses and energy usage. Nevertheless, there are still obstacles to overcome, including the raw materials inconsistency, the demand for efficient recovery and purifying techniques, and the financial viability of extensive processes. This study examines the latest developments in bioethanol production from lignocellulosic biomass. It focuses on ecological approaches, technical advancements, and the main obstacles that need to be overcome to exploit its promise as a biofuel fully. This review primarily focused on outcomes documented in the last five years from 2019 to 2024. The first segment of this work focuses on the second-generation bioethanol production process, which includes: the properties and composition of lignocellulosic biomass, pretreatment, enzymatic saccharification, and fermentation. The subsequent portion of this paper examines a techno-economic analysis, the obstacles and the prospective technology outlook. It finishes by discussing findings from research fields that haven't been investigated yet, besides the obstacles faced in bioethanol manufacturing methods. © The Author(s) 2025.

关键词： Bioethanol Enzymatic hydrolysis Fermentation Lignocellulosic biomass Pretreatment

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Mixed-Reality Simulator for Analysis of Interactions between Humans and Crawler Cranes 13

Mixed-Reality Simulator for Analysis of Interactions between...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Yamanobe, Kenta Yokota, Kazuya Uenoyama, Daigo Fujiura, Daisuke Ogura, Masataka Takahashi, Kengo Abe, Masajiro Nagaoka University of Technology Master's Program in Mechanical Engineering Nagaoka Japan Nagaoka University of Technology Department of Mechanical Engineering Nagaoka Japan Nagaoka University of Technology Bachelor's Program in Mechanical Engineering Nagaoka Japan Nagaoka University of Technology Center for Integrated Technology Support Nagaoka Japan Nagaoka University of Technology Department of System Safety Engineering Nagaoka Japan

ISBN: (纸本)9798350373332

Crawler cranes are used in various applications, including plant construction and civil engineering. However, accidents caused by hazardous events, such as overturning the entire body or overshadowing of a suspended load, can be problematic. Because crawler cranes are large and powerful machines, simulators are required to analyze the relationship between such hazardous events and human operations. This study proposed a framework for a mixed-reality simulator to analyze the interactions between humans and crawler cranes. This framework combined the high-performance graphics capabilities of Unreal Engine 5 with the high-precision multibody dynamics analysis of MATLAB Simscape to create a visually and physically realistic simulation. Moreover, the quantification of the decision-making process was possible based on the eye-tracking information of the operator. This study described the implementation of these features and discussed the prospects for human-crawler crane interaction analysis using this simulator. © 2024 IEEE.

关键词： MATLAB

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The Influence of Applying Digital Toolkits to Assist 3D Software Development on Individual Work Performance and Perceived Stress 10

The Influence of Applying Digital Toolkits to Assist 3D Soft...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Wu, Wen-Chuan Lee, Ming-Han National Ilan University Dep. of Computer Science and Information Engineering Yilan City Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan City Taiwan

ISBN: (纸本)9798350394924

Digital content industry, encompassing areas such as digital games, computer animation, digital audio-visual applications, and digital art, plays a pivotal role in today's and future developmental trends. Maya 3D software stands out as a crucial development tool in these domains. However, the multitude of built-in tools in Maya 3D can lead to overly complex interfaces, impacting development efficiency for software developers. To address this issue, this paper designs a digital toolkit for Maya 3D software, presented in a Menu Bar format. This toolkit offers features like naming, sorting, categorization, and automatic saving to JSON files for future use. Through a crossover experimental design, this paper investigates the impact of this proposed toolkit on 3D software developers' individual work performance, work engagement, and stress perception. Results shows a significant positive influence of that toolkit on software developers, particularly in enhancing work performance. It means that the proposed digital toolkit effectively improves the performance of software developers' work and reduces their stress during the software development process. © 2024 IEEE.

关键词： Digital devices

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A Player Position Tracking Method Based on a Wide-Area Pan-Tilt-Zoom Video 12

A Player Position Tracking Method Based on a Wide-Area Pan-T...

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Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support, icSPORTS 2024

作者： Yamagishi, Shunzo Xie, Chun Shishido, Hidehiko Kitahara, Itaru Master's and Doctoral Program in Intelligent Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Center for Computational Sciences University of Tsukuba Ibaraki Japan Faculty of Science and Engineering Department of Information Systems Engineering Soka University Tokyo Japan

ISBN: (纸本)9789897587191

This paper proposes a method to estimate the posture of an athlete moving on a vast field in a sporting event using a pan-tilt-zoom camera. In order to estimate the posture of an athlete on a sports field from a dynamic video sequence, our method extracts image features to search correspondence points among successive frames and computes the homography transformation matrix that compensates for changes the camera parameters (e.g., angle of view and posture). The effectiveness of this method is qualitatively verified using MLB TV broadcast video. The accuracy and error factors are also quantitatively verified by CG simulation. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Pan-Tilt-Zoom Camera Player Tracking Sports Analytics Video Processing Wide-Area

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Analyzing Significance of Facial Features for Detection of Parkinson's Disease via Static Images 4

Analyzing Significance of Facial Features for Detection of P...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chiu, Chuang-Chien Yeh, Shoou-Jeng Sun, Yen-Chi Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Cheng-Ching General Hospital Section of Neurology and Neurophysiology Taichung Taiwan Feng Chia University Master's Program of Biomedical Informatics and Biomedical Engineering Taichung Taiwan

ISBN: (纸本)9798350360721

Parkinson's disease (PD) is a neurodegenerative disorder often characterized by motor symptoms such as hypomimia, and a reduction in facial expression. We explored the significance of facial features in identifying PD through static images, focusing on detecting hypomimia. A dataset comprised of PD patients and healthy controls' static facial images were collected and analyzed. Facial feature detection was conducted using OpenCV and Dlib libraries for the precise localization of key landmarks. In the subsequent feature extraction, parameters such as symmetry, eye aspect ratio, mouth corner distance, muscle tension, and mouth aperture ratio were used. Through the standardization of these features, meaningful comparisons across diverse images were made. By analyzing hypomimia-related features alongside traditional facial parameters, the results provide a comprehensive understanding of the role of facial expressions in PD diagnosis. The findings suggested that examining facial features in static images enhances the accuracy of PD detection and understanding of the nuanced motor symptoms associated with the disease. © 2024 IEEE.

关键词： Feature extraction

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