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arXiv 2023年

作者： Bidollahkhani, Michael Atasoy, Ferhat Abedini, Elnaz Davar, Ali Hamza, Omid Sefaoğlu, Fırat Jafari, Amin Yalçın, Muhammed Nadir Abdellatef, Hamdan Department of Computer Engineering Institute of Graduate Studies Karabuk University Turkey Göttingen Germany Department of Biomedical Engineering Institute of Graduate Studies Karabuk University Turkey Department of Genetics and Bioengineering Faculty of Engineering and Architecture Kastamonu University Turkey Department of Medical Science Faculty of Medicine Karabuk University Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field. © 2023, CC BY-NC-ND.

关键词： Machine learning

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CsPbBr3 perovskite quantum-dot paper exhibiting a highest 3 dB bandwidth and realizing a flexible white-light system for visible-light communication

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Photonics Research 2021年第12期9卷 2341-2350页

作者： KONTHOUJAM JAMES SINGH XIAOTONG FAN ANNADA SANKAR SADHU CHUN-HO LIN FANG-JYUN LIOU TINGZHU WU YU-JUNG LU JR-HAU HE ZHONG CHEN TOM WU HAO-CHUNG KUO Department of Photonics&Institute of Electro-Optical Engineering College of Electrical and Computer EngineeringNational Yang Ming Chiao Tung UniversityHsinchu 30010Taiwan International Ph.D.Program in Photonics(UST) College of Electrical and Computer EngineeringNational Yang Ming Chiao Tung UniversityHsinchu 30010Taiwan Department of Electronic Science Fujian Engineering Research Center for Solid-State LightingXiamen UniversityXiamen 361005China School of Materials Science and Engineering University of New South WalesSydneyNSW 2052Australia Research Center for Applied Sciences Academia SinicaTaipei 11529Taiwan Department of Materials Science and Engineering City University of Hong KongKowloon TongHong KongChina

We propose a flexible white-light system for high-speed visible-light communication(VLC)applications,which consists of a semipolar blue InGaN/GaN single-quantum-well micro-light-emitting diode(LED)on a flexible substrate pumping green CsPbBr3 perovskite quantum-dot(PQD)paper in nanostructure form and red CdSe QD *** highest bandwidth for CsPbBr3 PQD paper,229 MHz,is achieved with a blue micro-LED pumping source and a high data transmission rate of 400 Mbps;this is very promising for VLC *** 817 MHz maximum bandwidth and a 1.5 Gbps transmission speed are attained by the proposed semipolar blue *** proposed flexible white light system and the high-bandwidth PQD paper could pave the way for VLC wearable devices.

关键词： pumping communication quantum

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On the Evaluation of Access-point Handovers for UAVs in Long-distance Missions

On the Evaluation of Access-point Handovers for UAVs in Long...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Fagundes, Leonardo A. Souza, Vitor Barbosa Brandao, Alexandre S. Núcleo de Especialização em Robótica Federal University of Viçosa Department of Electrical Engineering and Graduate Program on Computer Science Viçosa Brazil Federal University of Viçosa Graduate Program on Computer Science Department of Informatics Viçosa Brazil

ISBN: (纸本)9780738131153

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications either to interconnect the UAVs with each other or to connect them with Ground Control Stations (GCS). Due to the complexity and difficulty of performing long distance experiments with these robots, simulation becomes a viable and necessary alternative. However, focusing either on the modeling of UAV operations or communication dynamics, available simulation tools are not able to capture the complex interdependencies between these two aspects, especially in applications where extensive communications are required. The main contribution of this work is the modeling and implementation of strategies for enhancing connectivity of a UAV performing long-distance navigation tasks, taking into account applications' requirements. The proposed strategies aim at assessing the impact of frequent handovers between wireless access points in terms of packet loss and available bandwidth. The first strategy prioritizes maintaining a connection until the signal-to-noise ratio (SNR) observed by the robot reaches a predefined threshold. For this, it is assumed that the data to be transmitted is small enough so that the robot may change its modulation to obtain smaller bandwidths while improving connection time. The second one prioritizes bandwidth availability, thus, it always searches for a network that is able to offer data rates higher than a minimum threshold. A hybrid model is validated by considering the characteristics of each individual strategy. The capabilities of the simulation tool designed for this work are illustrated through some case-study scenarios. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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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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Predicting Response to Neoadjuvant Chemotherapy using Multi-Modal MRI Radiomics and Machine Learning Approaches

Predicting Response to Neoadjuvant Chemotherapy using Multi-...

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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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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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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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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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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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