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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Chakraborty, Debamitra Mills, Bradley N. Cheng, Jing Komissarov, Ivan Gerber, Scott Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Department of Surgery University of Rochester Medical Center RochesterNY14642 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

ISBN: (纸本)9781957171395

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Tissue

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Base on GAN Combined with CNN Architecture to Generate Brain Stroke CT Images 24

Base on GAN Combined with CNN Architecture to Generate Brain...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Shih, Guan-Yu Ting, Hsien-Wei Chen, Yi-Xiang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program inBiomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

In recent years, many fields have expanded their research methods through the integration of artificial intelligence. In the current medical field, it is widely used in image recognition to diagnose patient symptoms, train clinical prediction models, and improve clinical workflow processes to avoid missing treatment opportunities. These methods have all achieved good results so far. Now, generative AI is also impacting the development of the medical field, not only generating prescriptions that match patient conditions but also being applied in generating medical images for data augmentation and simulating various pathological conditions, thereby aiding in training deep learning models. Stroke is a leading cause of death or disability, ranking as the second leading cause of death worldwide, next to cancer. If cancers were categorized individually, stroke would surpass all single diseases, making it a primary focus of research in brain disease treatment. There are already studies where generative AI has been used to create medical images, which are then manually evaluated by doctors to determine if they were generated by AI. In this project, we plan to generate CT images of strokes using generative AI and use a trained model (to diagnose stroke) to improve accuracy. Through this project, we hope to solve the problem of scarce stroke CT images while also providing support for training stroke-related models. © 2024 Copyright ACM.

关键词： Deep learning

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Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

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Evaluation of Single and Duplex Plasma Treatment Applied to Aisi 409 Stainless Steel

SSRN

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SSRN 2024年

作者： do Monte e Silva, André Felipe Soares de Almeida, Larissa Solano Monteiro, Leandro Rossino, Luciana Sgarbi Nunes, Maelson Sousa Libório, Maxwell Santana de Carvalho Costa, Thércio Henrique Bandeira, Rafael Marinho de Sousa, Rômulo Ribeiro Magalhães Post-Graduate Materials Science and Engineering Program Federal University of Piauí PI Teresina Brazil Post-Graduate Program in Materials Science Federal University of São Carlos São Carlos Brazil Post-Graduate Program in Chemistry Federal University of Piauí PI Teresina Brazil School of Science and Technology Federal University of Rio Grande do Norte RN Natal Brazil Brazil Department of Mechanical Engineering Federal University of Piauí PI Teresina Brazil Brazil

The industrial landscape has witnessed a growing demand for components exhibiting superior performance and extended lifespan, especially in parts made of stainless steels like AISI 409 steel, widely used in automotive exhaust systems, heat exchangers, and lightweight structural components. This study investigates the enhancement of surface properties of AISI 409 stainless steel through cathodic cage plasma nitriding (CCPN) and cathodic cage plasma deposition (CCPD) processes, focusing on the novel implementation of duplex treatments. The research aims to improve the mechanical strength, wear resistance, and corrosion resistance of the steel by forming nitrides and vanadium nitride films on its surface. The duplex treatments, specifically N400D450 and N450D450, were found to significantly enhance the hardness of AISI 409 stainless steel, with the N450D450 sample achieving a maximum hardness of 1100 HV—a 461.22% increase compared to the untreated steel. This enhancement is attributed to the formation of a thick, uniform nitrided layer followed by a vanadium nitride film. The critical role of processing order was highlighted, with the sequence of nitriding followed by deposition proving more effective than the reverse. Corrosion resistance was markedly improved in the duplex-treated samples. The N450D450 sample exhibited an Ecorr of -198 mV and reduced jcorr values, indicating superior resistance to corrosive environments. These results underscore the synergistic effect of combining nitriding and vanadium deposition, which optimizes the formation of protective surface layers. Overall, this study demonstrates that duplex treatments significantly enhance the surface properties of AISI 409 stainless steel, offering the potential for extended component lifespan and reduced maintenance in industrial applications. The findings suggest that careful control of process parameters and treatment sequence is essential for achieving optimal results. © 2024, The Authors. All rights

关键词： Tribology

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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Correlations between water quality and precipitation in areas with different levels of human occupation

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International Journal of Energy and Water Resources 2021年第1期5卷 25-31页

作者： Rodrigues, Lucas Augusto Pereira Radomski, Fernando Aparecido Dias Godoy, Rodrigo Felipe Bedim Trevisan, Elias Crisigiovanni, Enzo Luigi Department of Environment State University of Maringá Maringá-PR Brazil Graduate Program in Materials Science and Engineering Federal University of Paraná Curitiba-PR Brazil Graduate Program in Forest Sciences Unicentro Guarapuava-PR Brazil

Riverine water is largely used for human consumption and it is well known that its quality is correlated to many external factors. Because of that, this study evaluated linear correlations between precipitation and several water quality parameters using data collected from Antas’ river (Paraná, Brazil) at locations with different levels of human occupation aiming to identify water quality irregularities related to these important external factors, especially at a point surrounded by preserved vegetation. The statistical analyses were made using Pearson’s linear correlation coefficient (r) and the open source programming software, R. The results showed that precipitation has moderate to strong correlation with turbidity, true color, total coliforms and E. coli (0.99 ≥ r ≥ 0.44), and also has weak to moderate correlations with pH (− 0.39 ≥ r ≥ − 0.58) and biochemical oxygen demand (0.48 ≥ r ≥ 0.36), regardless of the level of human occupation nearby. The high correlations observed for E. coli and total coliforms in an area protected by native vegetation, probably due to adjacent rural activities, represents a novel and major finding. Dissolved oxygen and temperature showed no significant correlations with rainfall volume (0.26 ≥ r ≥ − 0.19). Since Brazilian law ensures that water quality parameters must be measured and considered in projects of water resources management, the proposed methodology seems to be promising for government policies applications, since it comprises only simple analyzes and a free software for simulations. © Islamic Azad University (IAU) 2021.

关键词： Hydrology R software Rainfall Spatial data Statistical analysis

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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image Processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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The lower moments of nucleon structure functions in lattice QCD with physical quark masses 38

The lower moments of nucleon structure functions in lattice ...

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38th International Symposium on Lattice Field Theory, LATTICE 2021

作者： Tsuji, Ryutaro Aoki, Yasumichi Ishikawa, Ken-Ichi Kuramashi, Yoshinobu Sasaki, Shoichi Shintani, Eigo Yamazaki, Takeshi Department of Physics Tohoku University Sendai980-8578 Japan RIKEN Center for Computational Science Kobe650-0047 Japan Core of Research for the Energetic Universe Graduate School of Advanced Science and Engineering Higashi-Hiroshima739-8526 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan Faculty of pure and Applied Sciences University of Tsukuba Tsukuba305-8571 Japan Present adress is RIKEN Center for Computational Science Kobe650-0047 Japan

We present results for the nucleon structure functions and form factors obtained from 2+1 flavor lattice QCD with physical light quark masses (mπ = 135 MeV) in a large spatial extent of about 10 fm. Our calculations are performed with the PACS10 gauge configurations generated by the PACS Collaboration with the six stout-smeared O(a) improved Wilson-clover quark action and Iwasaki gauge action at β = 1.82 and 2.00 corresponding to lattice spacings of 0.085 fm and 0.064 fm respectively. The lower moments of structure functions, hxiu−d and hxi∆u−∆d given by the twist-2 operators being properly renormalized, are evaluated in the MS scheme at the renormalization scale of 2 GeV only at β = 1.82, since the renormalization factors at β = 2.00 have not yet determined nonperturbatively in the RI/MOM scheme. Instead, at two lattice spacings, we evaluate appropriate ratios of gA/gV and hxiu−d/hxi∆u−∆d, which are not renormalized in the continuum limit. These quantities thus can be directly compared with the experimental data without the renormalization. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gages

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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

作者： Raymond Sunardi Oetama Yanfi Yanfi Masagus M. Ikhsan Assiddiq U.P Sani Muhamad Isa Information System DepartmentFaculty of Engineering and Informatics Universitas Multimedia Nusantara Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.

关键词： Support vector machines Training Sentiment analysis Vocabulary Dictionaries Social networking (online) Blogs

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