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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Miho Yamada Takeshi Kumaki Kyosuke Kageyama Graduate School of Science and Engineering Kindai University Osaka Japan Dept. of Electronic and Computer Engineering Ritsumeikan University Shiga Japan Dept. of Electrical Electronic and Communication Engineering Kindai University Osaka Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Hazardous sounds can signal danger and should be heard to ensure safety. Hearing-impaired people can get into accidents due to the difficulty of hearing hazardous sounds. Therefore, the hearing-impaired people live with hearing-assistance dogs. However, the number of such dogs has decreased recently, and they have become less known in society and they may even be prevented from entering public facilities such as restaurants. Therefore, the external ear model-based mobile device for hazard-sound detection that can detect hazardous sounds and alert the hearing-impaired people are proposed. In this paper, the external ear model-based mobile device for hazard-sound detection is proposed and effectiveness of the external attachment for the microphone sensor is evaluated. Four different microphone sensors are used, and the spectral envelopes of each recorded sound are compared. The vowel /a/ and the ambulance siren are used as test sounds. The results show that using an external attachment modeled after an animal ear makes it easier to detect formants, which are frequency characteristics of sound. This is due to two effects: the sound collection effect of pinna's attachment and resonance in ear canal's attachment.

关键词： Irrigation Shape Resonant frequency Ear Dogs Mobile handsets Resonance

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Smart Data Augmentation for Multiclass Brain Tumor Detection with Imbalanced Data

Smart Data Augmentation for Multiclass Brain Tumor Detection...

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International Conference on Information Technology, Information Systems and electrical engineering (ICITISEE)

作者： Rizal Dwi Prayogo Hidetaka Nambo Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Kanazawa Japan

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

Dealing with class imbalance is a significant issue in classification tasks that often leads to lower prediction performance. Many data augmentation methods have been suggested to tackle this problem, but their effectiveness can vary depending on the dataset. Utilizing augmentation methods can improve the learning and generalization abilities of the proposed convolutional neural network (CNN) model. However, it is challenging to find a single data augmentation method that can effectively address class imbalance problems across all scenarios. In this paper, we present a technique known as smart data augmentation (SDA) that is aimed at effectively improving classification performance by enhancing imbalanced data. SDA involves an equation that can be used to create an augmentation technique, combining different sampling methods for addressing multiclass imbalance and offering easy customization options. We perform the data balancing strategy including undersampling, oversampling, and SDA-based sampling. We evaluate our proposed framework using a CNN model to improve the detection of brain tumors in imbalanced data. Our experiments demonstrate that SDA-based sampling improves brain tumor detection performance by 3.546% in accuracy, 3.310% in precision, 3.432% in recall, and 3.371 % in Fl-score compared to the imbalanced data scenario. The findings suggest that utilizing SDA together with CNN can improve the classification performance of brain tumor detection with imbalanced data.

关键词： electrical engineering Data augmentation Sampling methods Brain modeling Mathematical models Data models Convolutional neural networks Information technology Biological neural networks Information systems

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Prototype design of an intelligent Internet of Things system combined green energy storage device

Prototype design of an intelligent Internet of Things system...

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

作者： Ya-Chun Chen Hsien-Huang Peter Wu Kuo-Hsien Hsia Yung-Tsung Cheng Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin Taiwan Dept. of Electrical Engineering National Yunlin University of Science and Technology Yunlin Taiwan Bachelor Program in Intelligent Robotics National Yunlin University of Science and Technology Yunlin Taiwan Dept. of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

A set of miniature energy storage device through repurposing batteries and green power generation devices combined with an intelligent Internet of Things system is designed in this study. A large quantity of recycled and repurposed lithium batteries is applied as the core of the energy storage device; photovoltaic panels and minitype wind generators matching a wind-energy and solar-energy charge controller are used for charging batteries; and finally, an intelligent home Internet of Things system is utilized for monitoring and control. The experimental results reveal that the green power generation device appears longer effective charging time during the day, but could continuously generate electricity and store energy in the evening. The charging capacity is also larger than it simply using photovoltaic panels, and the intelligent home Internet of Things system could effectively monitor the power generation in a day and calculate carbon emissions. The prototype design results satisfy the expected function of an intelligent Internet of Things system combined green energy storage device.

关键词： Green energy Photovoltaic systems Wind energy generation Wind energy Green products Prototypes Companies

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Binarization and Classification of RGB Images

Binarization and Classification of RGB Images

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International Symposium on Multiple-Valued Logic

作者： Tagir Nukenov Kamila Abdiyeva Oliver Keszocze Shinobu Nagayama Martin Lukac Graduate School of Information Sciences Hiroshima City University Hiroshima Japan Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign IL USA Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798331507442

ISBN: (纸本)9798331507459

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in various real-world information processing tasks, such as object classification. However, CNNs tend to be computationally and power intensive, making them less suitable for implementation on wearable and embedded systems. One potential solution is using binary models, which have been proven to significantly reduce memory and computational requirements. Nevertheless, this reduction in resources often leads to a decrease in accuracy and raises questions about the most effective way to handle non-binary inputs such as color images. While a direct mapping from binarization to accuracy loss is almost impossible to predict analytically, we empirically study the problem of input binarization and its impact on the classification task. In particular, we explore how multiple-valued binarization, as opposed to single value binarization, will affect the neural model classification ability. We conducted a study in which we explored three different color channel feature fusion methods and found that extracting features from each color channel separately and then fusing those features at later stages is the most effective approach for preserving classification accuracy in the binary network with multiple-valued thresholding inputs. We conducted experiments on two color image datasets in classification tasks with increasing difficulty. The results demonstrate that while the accuracy for the GTSRB datasets remain comparable to its full-precision counterpart, the binarization process noticeably affects the representational power and accuracy of CNNs when applied to the more complex in textures CIFAR-10 dataset.

关键词： Accuracy Filters Image color analysis Memory management Color Information processing Feature extraction Logic Wearable devices Mutual information

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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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Cancer Prediction Using Clinical and Genomic Data Fusion: A Systematic Review

Cancer Prediction Using Clinical and Genomic Data Fusion: A ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kelvin Julian Kuncahyo Setyo Nugroho Rudi Nirwantono Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies.

关键词： Accuracy Machine learning algorithms Genomics Data integration Medical services Predictive models Data models Bioinformatics Cancer Systematic literature review

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases Data mining Information technology

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Detection of Various Dental Conditions on Dental Panoramic Radiography Using Faster R-CNN

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IEEE Access 2023年 11卷 127388-127401页

作者： Chen, Shih-Lun Chen, Tsung-Yi Mao, Yi-Cheng Lin, Szu-Yin Huang, Ya-Yun Chen, Chiung-An Lin, Yuan-Jin Chuang, Mian-Heng Abu, Patricia Angela R. Chung Yuan Christian University Department of Electronic Engineering Taoyuan320314 Taiwan Feng Chia University Department of Electronic Engineering Taichung407102 Taiwan Chang Gung Memorial Hospital Department of General Dentistry Taoyuan61363 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing Department of Program on Semiconductor Manufacturing Technology National Cheng Kung University Tainan717005 Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei243303 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon1108 Philippines

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model's accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions. © 2013 IEEE.

关键词： Image segmentation

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Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

SSRN

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SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Deterministic inverse design of spectral line shapes using thin-film-based Fano resonators

Deterministic inverse design of spectral line shapes using t...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Young Jin Yoo Joo Hwan Ko Jin-Hwi Park Hae-Gon Jeon Young Min Song School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Republic of Korea AI Graduate School Gwangju Institute of Science and Technology Gwangju Republic of Korea

We present an inverse design approach for spectral line shapes using thin-film-based Fano resonators. Stochastic gradient descent (SGD)-based learning provides CMOS-compatible process parameters for spectral line shap... 详细信息

ISBN: (纸本)9781957171258

关键词： Coupled resonators Fano resonance Line shapes Optical resonators Refractive index Stochastic gradient descent

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