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IEEE International Symposium on Biomedical Imaging

作者： Abdelrahman Gamal Ahmed Sharafeldeen Eman Alnaghy Reham Alghandour Khadiga M. Ali Sameh Shamaa Amal Aboueleneen Ahmed Elsaid Tolba Samir Elmougy Mohammed Ghazal Sohail Contractor Ayman El-Baz Department of Computer Science Faculty of Computers and Information Mansoura University Egypt Department of Bioengineering University of Louisville USA Department of Radiology Faculty of Medicine Mansoura University Mansoura Egypt Department of oncology Faculty of Medicine Mansoura University Mansoura Egypt Pathology Department Faculty of Medicine Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Department of Radiology University of Louisville USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

A precise computer-aided diagnosis (CAD) system is introduced to predict two different tumor responses to neoadjuvant chemotherapy (NAC) by studying the correlation between radiological and clinical markers, and treatment response: complete response and stable disease. Predicting the NAC response assists physicians in managing the treatment plan effectively and reduces unnecessary use of NAC. First, the breast tumor is delineated from MRI modalities. Then, the proposed system extracts functional features, including the apparent diffusion coefficient (ADC), from diffusion-weighted imaging (DWI), and 2 nd -order texture features, such as gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM), from various MRI modalities. Afterward, statistical features are extracted from these matrices. Moreover, ADC features are represented statistically using percentiles with 2% intervals. Subsequently, a feature selection approach, namely the genetic algorithm (GA), is employed for each fused features individually (i.e., ADC, GLCM, and GLRLM) to extract the most relevant features. The resulting feature set undergoes a multi-stage feature generation system, introducing new features (i.e., probabilities), which are fused with its input and clinical markers and are then fed into a lightGBM classifier for final prediction. Our system was evaluated on 54 patients using leave-one-subject-out (LOSO) cross-validation, achieving a sensitivity of 89.89%, a specificity of 89.29%, and an accuracy of 89.09%, emphasizing the promise of our system in predicting breast tumor response to NAC.

关键词： Solid modeling Chemotherapy Breast tumors Magnetic resonance imaging Machine learning Feature extraction Breast cancer Genetic algorithms Radiomics Diseases

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Curriculum-Based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios

Curriculum-Based Reinforcement Learning for an Effective Mul...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Iure Rosa L. De Oliveira Kevin B. De Carvalho Alexandre S. Brandão Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Vicosa Viçosa Brazil

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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Physics Constrained Multi-Objective Bayesian Optimization to Accelerate 3d Printing of Thermoplastics

SSRN

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SSRN 2024年

作者： Lin, Jian Sattari, Kianoosh Wu, Yuchao Chen, Zhenru Mahjoubnia, Alireza Su, Changhua Department of Mechanical and Aerospace Engineering University of Missouri ColumbiaMO65211 United States Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States Food Science Program Division of Food Nutrition & Exercise Sciences University of Missouri ColumbiaMO65211 United States

Vat photopolymerization (VPP) of thermoplastics faces an issue of unpredictable printability due to the involved competing photopolymerization and dissolution reactions. The printing outcome largely depends on physicochemical properties of monomers and their compositions in resins, which also greatly determine the material properties, e.g., strength/toughness and phase transition temperature (Tg). Thus, a methodology for optimizing the resin formulation is of paramount importance in realizing highly printable thermoplastics with balanced strength/toughness and target Tg while remaining largely underexplored. Herein, we introduce a multi-objective Bayesian optimization (MOBO) algorithm with two physics informed constraints (printability and Tg) to optimize two conflicting properties: tensile strength (σT) and toughness (UT). The two constraints are formulated as two machine learning (ML) models, which are trained with weight ratios of the six input monomers and physics informed (PI) descriptors derived from their physiochemical parameters. Dimensional reduction analysis reveals that the algorithm avoids recommendation of the monomer ratios that do not pass the two constraints. The printing failure rate is reduced from 16% in the background experiments to 3% in the recommended experiments. Within only 36 iterations (72 samples), the MOBO algorithm successfully identifies five sets of ratios leading to Pareto optimal of σT and UT. Due to the constraint in Tg they show appropriate Tg for shape memory application. The partial dependence analysis indicates that σT and UT depend on both the ratios and physiochemical features of the monomers. These results underscore capability of such a smart decision-making algorithm—with constraints that are not fully understood but can be informed by prior knowledge—in planning the experiments from the vast design space, thus holding a great promise for broader applications in materials and manufacturing. © 2024, The Authors. All rights

关键词： Resins

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Quantitative particle agglutination assay using mobile holographic imaging and neural networks

Quantitative particle agglutination assay using mobile holog...

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CLEO: Applications and Technology, A and T 2022

作者： Luo, Yi Joung, Hyou-Arm Esparza, Sarah Rao, Jingyou Garner, Omai Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Computer Science Department University of California Los AngelesCA90095 United States Department of Pathology and Laboratory Medicine University of California Los AngelesCA90095 United States

A quantitative particle agglutination assay using mobile holography and neural networks is demonstrated to rapidly and accurately measure C-reactive protein (CRP) concentration in human serum samples. © Optica Pu... 详细信息

ISBN: (纸本)9781957171050

关键词： Holography

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Deep-Learning–Based Screening and Ancillary Testing for Thyroid Cytopathology

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American Journal of Pathology 2023年第9期193卷 1185-1194页

作者： Dov, David Elliott Range, Danielle Cohen, Jonathan Bell, Jonathan Rocke, Daniel J. Kahmke, Russel R. Weiss-Meilik, Ahuva Lee, Walter T. Henao, Ricardo Carin, Lawrence Kovalsky, Shahar Z. I-Medata AI Center Tel Aviv Sourasky Medical Center Tel Aviv-Yafo Israel Department of Pathology Duke University Medical Center Durham NC United States Department of Head and Neck Surgery & Communication Sciences Duke University Medical Center Durham NC United States Department of Head and Neck Surgery Kaplan Medical Center Rehovot Israel Biological Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biostatistics and Bioinformatics Duke University Durham NC United States Computer Science Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics University of North Carolina at Chapel Hill Chapel Hill NC United States

Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification. © 2023 American Society for Investigative Pathology

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Scientometric Analysis of Artificial Intelligence Research in Agriculture 4

Scientometric Analysis of Artificial Intelligence Research i...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Pramono, Andi Hamim, Miftahul Prihatin, Sukeipah Yuli Arifuddin, Rahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Politeknik Kota Malang Informatics Study Program Malang Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ($\mathrm{N}=287$), followed by the United Kingdom at second ($\mathrm{N}=139$) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index. © 2022 IEEE.

关键词： Agriculture

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Development of Flexible Sendust Composite Sheets with Enhanced Magnetic Permeability for Microwave Absorbers and Antennas

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IEEE Transactions on Magnetics 2025年

作者： Whalen, Matthew P. Plouff, David T. Shevchenko, Nicholas B. Mirotznik, Mark Xiao, John Q. University of Delaware Quantum Science and Engineering Program NewarkDE19716 United States University of Delaware Department of Physics and Astronomy NewarkDE19716 United States University of Delaware Center for Composite Materials NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

The demand for high-frequency soft magnetic composites with elevated permeability and flexibility is growing for applications in microwave absorbers and magnetodielectric antennas. However, balancing high permeability with mechanical flexibility remains challenging. This study presents flexible Sendust (FeSiAl) composite sheets, fabricated with a hot-pressing technique that aligns flakes within an acrylic rubber and epoxy resin matrix. The alignment significantly enhances relative permeability at high frequencies, achieving single-layer film properties of μr' = 14.8 and μr"= 20.4 at 1 GHz, alongside high saturation magnetization (118 eμg) and high hesitivity (44.7 kΩ m-1), outperforming commonly used materials such as NiZn ferrite. Our method supports customizable thickness without sacrificing permeability, establishing a versatile platform for advanced conformal microwave absorber and antenna applications. © 2025 IEEE.

关键词： Saturation magnetization

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Utilizing probabilistic entanglement between sensors in quantum networks

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Physical Review Applied 2024年第6期22卷 064085-064085页

作者： Emily A. Van Milligen Christos N. Gagatsos Eneet Kaur Don Towsley Saikat Guha Department of Physics The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard Tucson Arizona 85721 USA Program in Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Cisco Quantum Lab Los Angeles USA College of Computer Science University of Massachusetts Amherst Massachusetts USA Department of Electrical and Computer Engineering The University of Maryland College Park Maryland 20742 USA

One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.

关键词： Quantum entanglement

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A Novel Technique of Pulmonary Nodules Auto Segmentation Using Modified Convolutional Neural Networks

A Novel Technique of Pulmonary Nodules Auto Segmentation Usi...

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IEEE International Symposium on Biomedical Imaging

作者： B. Youssef A. Alksas A. Shalaby A. Mahmoud E. van Bogaert S. Contractor M. Ghazal A. Elmaghraby A. El-Baz Department of Bioengineering University of Louisville KY USA Department of Engineering Mathematics and Physics Alexandria University Egypt Lyda Hill Department of Bioinformatics UT Southwestern Medical Center Dallas TX USA Department of Radiology University of Louisville KY USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Computer Science and Engineering Department University of Louisville KY USA

Lung cancer represents the dominant cause of cancer associated deaths worldwide. Researchers have proposed many methods for medical image analysis of CT scans through the decades but in recent times, deep learning (DL) has gained a lot of consideration in the field of analysis of biomedical images . Image processing techniques are used on image data after extracting CT scan features, to determine if the patient’s found nodule is benign or malignant. The most important of these techniques is segmentation which helps identify the shape,size and volume of the nodule. We experiment with several methods of Convolutional Neural Networks (CNN) to target the best approach for pulmonary nodule segmentation. Our aim is to find the compromise between best accuracy, cheapest processing costs and scalability. First of all, it is important to observe that our method is proposed for the segmentation of lung nodules on a previously defined region of interest (ROI) instead of the whole image as input to our networks to cover the fast processing target and experimenting with 3D U-Net and variations of 3D V-Net. We performed analysis to measures of sensitivity and specificity and compared different segmentation approaches using our proposed modified U-Net with a novel adaptive focal loss function and our implementation of a modified V-Net with different architectures. The modified U-Net with the updated loss gave the best sensitivity and specificity of 0.9132 and 0.9807 respectively.

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