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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hung, Che-Lun Lin, Chun-Yuan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Chang Gung University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9781728162157

Mammograms are always used to detect signs of breast cancer. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps, and they can be utilized to retravel specific features of symptoms from medical images produced by X-ray, magnetic resonance imaging, computed tomography, and so forth. Gray level run-length matrix is the one of the texture extraction methods which has been successfully used to facilitate medical image analysis. However, it is computation-intensive method. We implemented it on GPU to accelerating extraction process for mammograms. The proposed method achieves significant speedup over CPU-based implementation. © 2020 IEEE.

关键词： Extraction

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Next Sentence Prediction: The Impact of Preprocessing Techniques in Deep Learning

Next Sentence Prediction: The Impact of Preprocessing Techni...

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International Conference on computer, Control, informatics and Its Applications (IC3INA)

作者： Yuyun Andi Djalal Latief Tri Sampurno Hazriani Andriansyah Oktafiandi Arisha Mushaf Research Center for Data and Information Science National Research and Innovation Agency Bandung Indonesia Research Center for Data and Information Science National Research and Innovation Agency Jakarta Indonesia dept. of computer system Handayani University Makassar Indonesia dept. of informatics management Dipa Makassar University Makassar Indonesia

Text preprocessing in the field of Natural Language Processing (NLP) plays a crucial role in enhancing the model’s ability to understand given tasks. This process helps in avoiding potential errors that could interfere with data processing. This study aims to investigate the effectiveness of four preprocessing techniques (tokenization, case-folding, stopword removal, and stemming) in predicting the next word in a sentence. The case focuses on Indonesian language text documents preprocessing using word2vec embedding and the long short-term memory networks (LSTM) classification model. The evaluation was conducted by incorporating human perception approach and evaluation matrix which focused on the suitability of word sequences and pairs on the prediction results. The result reveals that the combination of tokenizing and casefolding can enrich the meaning of the sentences. Meanwhile the combination of tokenizing, casefolding, stopword removal, and stemming can cause lose and overlap meaning in the sentences. The prediction model performs best on one-word n-grams, with precision 0.1, recall 1.0, and F-score 0.1.

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Active Disturbance Rejection Control for 3D Crane Systems 11

Active Disturbance Rejection Control for 3D Crane Systems

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Roman, Raul-Cristian Precup, Radu-Emil Petriu, Emil M. Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 Timisoara300223 Romania Center for Fundamental and Advanced Tech. Res. Romanian Academy - Timisoara Br. Bd. Mihai Viteazu 24 Timisoara300223 Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard OttawaONK1N 6N5 Canada

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications. © 2024 The Authors.

关键词： Disturbance rejection

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Fine-Tuning Vision Transformer for Arabic Sign Language Video Recognition on Augmented Small-Scale Dataset

Fine-Tuning Vision Transformer for Arabic Sign Language Vide...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Munkhjargal Gochoo Ganzorig Batnasan Ahmed Abdelhadi Ahmed Munkh-Erdene Otgonbold Fady Alnajjar Timothy K. Shih Tan-Hsu Tan Lai Khin Wee Dept. of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates College of EECS National Central University Taoyuan Taiwan Dept. of Electrical Engineering National Taipei University of Technology Taipei Taiwan Dept. of Biomedical Engineering University of Malaya Kuala Lumpur Malaysia

With the rise of AI, the recognition of Sign Language (SL) through sign-to-text has gained significance in the field of computer vision and deep machine learning. However, there are only a few medium to large open datasets available for this task, as it requires a vast dataset of thousands of signs for words/phrases in different environments, which is a time-consuming and tedious process. Furthermore, there has been very little effort towards Arabic Sign Language Recognition (ArSLR). This research paper presents the results of fine-tuning the Vision Transformer (ViT) model on a small-scale in-house dataset of ArSL. The main goal is to attain satisfactory results by utilizing minimal computing power and a small dataset involving less than 10 individuals, with only one recording made for each sign in every environment. The dataset comprises 49 classes/signs, all of which were made with two hands and belong to the Level I category in terms of popularity. To enhance the dataset, three types of augmentations - translation, shear, and rotation were employed. The ViT model, pre-trained on the Kinetics dataset, was trained on the variation of augmented datasets with 2 to 40 times samples for each original video, where the training set includes original and augmented videos of 8 volunteers and the test set includes only original videos of one particular volunteer. Experimental results reveal that the combination of rotation and shear outperformed the others, achieving an accuracy of 93% on the 20 times augmented samples per class per signer dataset. We believe this study sheds light on small-scale dataset-based SLR tasks and video/action recognition in general.

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Padma Nyoman Crisnapati I Made Darma Susila I Komang Agus Ady Aryanto Jaturapith Krohkaew Ricky Aurelius Nurtanto Diaz I Made Suandana Astika Pande Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective.

关键词： Fuzzy logic PI control Microcontrollers 2-DOF Manuals Manipulators Three-dimensional printing PD control Root mean square Tuning

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Real-time Vital Signs Monitoring Based on COTS WiFi Devices

Real-time Vital Signs Monitoring Based on COTS WiFi Devices

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Gu, Yu Zhang, Xiang Yan, Huan Liu, Zhi Ji, Yusheng Hefei University of Technology School of Computer and Information Hefei China The University of Electro-Communications Dept. of Computer and Network Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9781665401265

Real-time vital signs (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Current solutions are mostly based on wearable sensors or cameras, the former affects the quality of sleep, while the latter is not conducive to privacy protection, and the cost of these methods is usually expensive. In this paper, we propose Wital, a real-time vital signs monitoring system based on the low-cost and widespread COTS WiFi device. Most of the existing WiFi-based vital signs monitoring solutions utilize the line of sight (LOS) WiFi signals to achieve powerful performance. However, in our daily environments, NLOS sensing is more common. In this article, we first model the relationship between the energy ratio of LOS/NLOS signals and the ability to monitor vital signs based on the Ricean-K theory and theoretically prove that blocking LOS signals in NLOS sensing is more beneficial. We have also established a real-time vital signs monitoring system to verify our method, and the experimental results prove the effectiveness of our method. © 2021 IEEE.

关键词： Wi-Fi

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Brain tumor identification using fcm threshold method and morphological area selection

Brain tumor identification using fcm threshold method and mo...

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2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

作者： Setyawan, Robert Asrori, Riris Bayu Fajar Shidik, Guruh Fanani, Ahmad Zainul Anggi Premunendar, Ricardus Dept. Informatics Engineering of Computer Science Faculty Dian Nuswantoro University Semarang Indonesia

ISBN: (数字)9781728190686

ISBN: (纸本)9781728190686

Brain tumors segmentation has become a popular research topic in the last five years, proved by the emergence of many methods proposed to segment brain tumors accurately. In this study, the authors propose a brain tumor segmentation method based on the FCM method with a modification of the threshold value, which will later be used to convert an MRI image to a binary image with only the tumor area detected. The segmentation process divided into three stages, with steps is preprocessing segmentation and post-processing. In the preprocessing stage, the skull bones from MRI images are removed, then the noise is removed using Wiener filters, then proceed with the segmentation stage using FCM Thresh, and finally applying morphological area selection to select areas from segmentation results. From a total of 100 positive tumor MRI images that we acquire from the BRATS 2015 dataset, we obtained an average similarity of 0.7592. We achieved an improvement of 0.06 in term of SSIM value from the previous method. © 2020 IEEE.

关键词： Magnetic resonance imaging

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Cyber-physical-logistical systems: State-of-the-art 2

Cyber-physical-logistical systems: State-of-the-art

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2nd IEEE International Conference on Advanced Trends in Information Theory, ATIT 2020

作者： Holovatenko, Illia Pysarenko, Andrii Igor Sikorsky Kyiv Polytechnic Institute Dept. of Informatics and Computer Science Kyiv Ukraine

ISBN: (纸本)9781728197999

The purpose is to review current research in the field of logistics management of cyber-physical systems by means of artificial intelligence. The structure of the article is as follows: review of the degree of study and the formation of a sound assessment of the state of affairs;selection of the most effective approaches and methods of solving problems for each component;identification of problem areas in methodological and theoretical principles and outlining the prospects for further research and solutions to existing problems. © 2020 IEEE.

关键词： Cyber Physical System

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Performance analysis graph-based keyphrase extraction in Indonesia scientific paper

Performance analysis graph-based keyphrase extraction in Ind...

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2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

作者： Asrori, Riris Bayu Setyawan, Robert Muljono, Muljono Dept. Informatics Engineering of Computer Science Faculty Dian Nuswantoro University Semarang Indonesia

ISBN: (纸本)9781728190686

The increasing number of research papers from Indonesia and the higher the need to manage large amounts of documents, including memory capacity and the availability of time to manage research documents, keyphrase can provide a picture representing a text document. Thus, the management of documents in large quantities will become easier to prepare and find a collection of documents. For this reason, proper and correct keyphrase extraction can assist in managing paper documents. This study tried to compare three extraction methods using unsupervised graph-based techniques, including TextRank, TopicRank, and PositionRank. We compare the extraction results with the keyphrase made by the authors of the paper to see the three method's performance. From the results of our research, we found that the method TextRank Providing the highest performance compared to other methods using the ROUGE test with average Recall, Precision, and F1-Score are 0.558, 0.71, and 0.225. However, the extracted phrases are longer compared to others. The performance of TopicRank, although it does not provide higher results with TextRank, the extracted keyphrase is more compact than TextRank and PositionRank methods. © 2020 IEEE.

关键词： Extraction

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Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting

arXiv

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arXiv 2024年

作者： Ünsal, Serbulent Özdemir, Sinem Kasap, Bünyamin Kalaycı, M. Erşan Turhan, Kemal Doğan, Tunca Acar, Aybar C. Department of Biostatistics and Medical Informatics Faculty of Medicine Graduate School of Health Sciences Karadeniz Technical University Trabzon Turkey Graduate School of Informatics Middle East Technical University Ankara Turkey Health Sciences University Trabzon Kanuni Training and Research Hospital Medical Microbiology Laboratory Trabzon Turkey Biological Data Science Lab Dept. of Computer Engineering Department of Computer Engineering Hacettepe University Ankara Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and while deep learning models excel with abundant data, they also face difficulties when data is scarce. HOPER addresses this issue by integrating three distinct modalities—protein sequences, biomedical text, and protein-protein interaction (PPI) networks—to create a comprehensive protein representation. The model utilizes autoencoders to generate holistic embeddings, which are then employed for PFP tasks using transfer learning. HOPER outperforms existing methods on a benchmark dataset across all Gene Ontology categories, i.e., molecular function, biological process, and cellular component. Additionally, we demonstrate its practical utility by identifying new immune-escape proteins in lung adenocarcinoma, offering insights into potential therapeutic targets. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research, potentially enabling more accurate and scalable protein function prediction. HOPER source code and datasets are available at https://***/kansil/HOPER © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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