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The relationship between TSH levels, maternal characteristics and racial group of the aneuploidy screening

The relationship between TSH levels, maternal characteristic...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Soylemez, Ummu Gulsum Celebiler, Hande Kaymakcalan Harkonen, Juho Bakir-Gungor, Burcu Mus Alparslan University Faculty of Engineering and Architecture Department of Software Engineering Mus Turkey School of Medicine Istanbul Bilim University Department of Pediatrics Istanbul Turkey Sociology European University Institute Florence Italy School of Engineering Abdullah Gul University Department of Computer Engineering Kayseri Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

First-trimester maternal screening is a widely used test for detecting fetal aneuploidies and neural tube defects for over two decades. Human chorionic gonadotropin hormone (beta-hCG) and pregnancy-associated plasma protein A (PAPP-A) are two serum biomarkers that are analyzed in this screening. The thyroid hormone is a critical hormone for normal pregnancy and fetal development. During the first half of pregnancy, placental and fetal development depends on the thyroid hormone levels in the mother. Therefore, thyroid abnormalities in the mother can result in unfavorable pregnancy outcomes such as intrauterine growth restriction, miscarriage, hypertensive disorders, premature birth, and an increase in the risk of low IQ in the newborn. In this study, we analyzed the first-trimester screening data collected from 410 pregnant women who were seen at Yale University Hospital Prenatal Unit;and checked for possible correlations of TSH levels with maternal characteristics, racial group PAPP-A MoM levels. © 2022 IEEE.

关键词： Obstetrics

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Impact of Extreme Class-Imbalance on Landslide-Risk Prediction and Mitigation Using Two-Stage Deep Neural Network 14

Impact of Extreme Class-Imbalance on Landslide-Risk Predicti...

引用

14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Tengtrairat, N. Woo, W.L. Parathai, P. Sundaranaga, C. Na Ayutthaya, T. Kridakorn Rinchumphu, D. School of Software Engineering Payap University Chiang Mai Thailand Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom Chiang Mai University City Research and Development Center Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Civil Engineering Chiang Mai Thailand

ISBN: (纸本)9798350336313

The classification of landslides is the one of the most challenging topics because of the complexity of the relationships of various dynamic and uncertain factors and the physical gaining data processes. Landslide incidents frequently occur in the upper northern region of Thailand due to its topography. The landslide classification method is proposed to capture the significant features from an extreme case of class-imbalance dataset. The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. A study area of this research covers an area of 25 square kilometers at Chiang Rai, in Thailand which contains 30,408 non-landslide data and 1,077 landslide data. The percentage of landslide data is 3.54% of the total data. This paper proposed the solution of landslide classification given by extreme class-imbalance dataset. The proposed method has two main steps i.e., firstly, mitigating class-imbalance dataset. and secondly two-stage learning. Performance of the proposed method benchmarks the baseline, the logistic regression (LR), the random forest classifier (RFC) methods given by enhanced dataset. In the case of imbalance dataset, the one-class method is assessed against the proposed method along with the LR and the RFC methods. Experimental results demonstrate that the proposed method has improved the landslide-risk prediction performance over the baseline, the LR, the RFC and the one-class SVM methods in terms of an average area under the curve scores by 0.48, 0.48, 0.03, and 0.06, respectively, in both enhanced dataset and imbalance dataset. © 2022 IEEE.

关键词： Landslides

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Estimating the severity of knee osteoarthritis using Deep Convolutional Neural Network based on Contrast Limited Adaptive Histogram Equalization technique

Estimating the severity of knee osteoarthritis using Deep Co...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Abdo, Amina A. El-Tarhouni, Wafa Abdulsalam, Asma Fathi Altajori, Abdelgader Bubaker College of Computer Technology-Benghazi Department of Artificial Intelligence Benghazi Libya University of Benghazi Faculty of Information Technology Benghazi Libya College of Computer Technology-Benghazi Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Osteoarthritis is a degenerative joint disease that affects larger joints, including the knee, foot, hip, and spine by infecting the cartilage, which causes bones to rub against each other in extreme pain. Knee osteoarthritis (KOA) manual inspections demand a skilled physician to examine the x-ray image. In this paper, a method for detecting the severity of knee osteoarthritis using a Deep Convolutional Neural Network (3D CNN) is proposed in order to benefit physicians by examining the X-ray images. Here, the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is used to enhance contrast in images, and this strengthens the current approach. This study presents two models: one for identifying and categorizing normal (KL 0,1) and osteoarthritic results (KL 2, 3, 4). The second model is to assist in distinguishing between severe grades (KL 3, 4) and non-severe grades (KL 2) or normal findings (KL 0,1). For examination, X-ray images from the Knee Osteoarthritis severity grading dataset and the Osteoarthritis Initiative (OAI) dataset are used. Experimental results have demonstrated that the proposed technique achieves accuracy rate of 85.50%, outperforming a number of existing approaches. © 2022 IEEE.

关键词： Grading

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study

UVM Based Verification Framework for Deep Learning Hardware ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Randa Aboudeif Tasneem A. Awaad Mohamed AbdElsalam Yehea Ismail Electronics and Communications Engineering Department The American University in Cairo Siemens Digital Industries Software Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Siemens Digital Industries Software Ain Shams University Cairo Egypt Siemens Digital Industries Software Cairo Egypt Electronics and Communications Engineering Department The American University in Cairo Cairo Egypt

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous Systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study.

关键词： Deep learning Runtime Design methodology Emulation Stability analysis Convolutional neural networks Reliability Hardware acceleration Field programmable gate arrays Testing

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RSA-1024 Cryptography on Artix-7 FPGA: Medical Imaging Application 16

RSA-1024 Cryptography on Artix-7 FPGA: Medical Imaging Appli...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Ghayoula, Ridha Amara, Wided Smida, Amor Fattahi, Jaouhar Waly, Mohamed I. Bahroun, Sahbi Gmati, Issam El University of Moncton Faculty of Engineering New Brunswick Canada Enit University of Tunis El Manar SysCom Laboratory Tunis Tunisia Majmaah University College of Applied Medical Sciences Majmaah Saudi Arabia Laval University Department of Computer Science && Software Engineering Quebec Canada Institut Superieur d'Informatique University of Tunis El Manar Tunis Tunisia Umm Al Qura University College of Engineering Umm Al Qura Saudi Arabia

ISBN: (纸本)9798350371154

In the domain of medical imaging, ensuring the security of confidential patient data stands as a paramount concern. The imperative for robust encryption to safeguard medical images against unauthorized access has driven the widespread adoption of the RSA algorithm, celebrated for its efficacy in data security assurance. This study specifically centers on the deployment of RSA-1024 cryptography on an Artix-7 FPGA, targeting applications in medical imaging. Acknowledging the demand for heightened security and accelerated processing, we have engineered RSA crypto-accelerators as hardware implementations of the RSA algorithm. We present a comprehensive methodology for implementing the 1024-bit RSA encryption/decryption algorithm, leveraging the square and multiply technique for modular exponentiation. Our algorithms are meticulously instantiated through VHDL code, tailored for execution on the Artix-7 xc7a35tcsg324-1 FPGA manufactured by Xilinx. Rigorous testing using the Vivado 2022.2 tool validates the functionality and dependability of our design. This research underscores the pivotal role of advanced cryptographic methodologies in safeguarding medical images and underscores the strides made in FPGA-based security solutions. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada Rhazes Lab Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9781665430654

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Artificial Neural Network (ANN) Coordinate Search Expensive Optimization Gradient-free Large-Scale Optimization Stochastic Gradient Descent (SGD)

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Gymnasium Library Interface for Multi Access Edge Computing

Gymnasium Library Interface for Multi Access Edge Computing

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ivan Filianin Alexander Kapitonov Artem Timoshchuk-Bondar Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper addresses the problem of developing reinforcement learning algorithms for capsule placement decision making tasks in a geo-distributed Multi Access Edge Computing environment. A Python library called gym-Iwmecps has been developed to speed up and simplify the process of developing machine learning algorithms with reinforcement learning for decision-making system tasks in MEC networks. At the same time, the Gymnasium Capability API has not been used previously in the tasks of organizing and testing MEC platforms. We also tested basic algorithms Q-network and DQN in conjunction with gym-lwmecps and proved the performance of the developed Python library.

关键词： Training Machine learning algorithms Multi-access edge computing Computational modeling Decision making Process control Reinforcement learning Libraries Mathematical models Testing

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Using quadratic complex planar splines in solving local positioning problems 16

Using quadratic complex planar splines in solving local posi...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2022

作者： Strelkovskaya, Irina Solovskaya, Irina Strelkovska, Juliya Grygoryeva, Tetyana Faculty of Cybersecurity Software Engineering and Computer Science International Humanitarian University Odesa Ukraine Institute of Criminal Justice National University Odesa Law Academy Odesa Ukraine

ISBN: (纸本)9781665468619

A solution to the task of local subscriber positioning for a Wi-Fi/Indoor network is presented. The use of the Fingerprinting method is proposed. The operation of the Fingerprinting method, that uses quadratic complex planar splines to evaluate the user's position, is explained. Presented finding a user's local position error on a Wi-Fi/Indoor network. Using the proposed method of finding a subscriber's local position in a Wi-Fi/Indoor network reduces location error. © 2022 IEEE.

关键词： Wi-Fi

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COVID 19 Navigator Taxi Application for Urban Mobility during Pandemic Period 2

COVID 19 Navigator Taxi Application for Urban Mobility durin...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Peiris, Rojith Kanchana, Bimsara Perera, Damitha Wickramarathne, Jagath Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

This research paper focuses on increasing the awareness between taxi users and can help to protect themselves in COVID-19 and take precautions. Since 2019 the global pandemic of Covid-19 is spreading at an astonishing rate and causes a negative impact for economic, social, and cultural factors. Government agencies are warning people to reduce the transportation and to maintain social distance. To stop the spread of COVID-19 required to identify persons who are susceptible to infection and need to trace the COVID-19 positive first contacts. Most people are reduced to using public transportations and taxi services due to unidentifiable health conditions in earlier users. The proposed approach can be used to track the taxi drivers and their passengers previous COVID-19 status as well as navigate the safest route by showing the COVID-19 contamination areas. By using this approach users can be aware of earlier users of the taxi service and COVID-19 status of the taxi driver or passenger before taking the trip as well as if they get touch with any COVID-19 patients, can take immediate precautions. This application helps to increase the usage of taxis by making users trust and confidence against COVID-19 infection. By testing with real users our system was able to trace down 45 passengers and 16 drivers within 3 months. © 2022 IEEE.

关键词： COVID-19

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Improved LOPCOW-SAW Method for Optimal Supplier Selection in Supply Chain Management

Improved LOPCOW-SAW Method for Optimal Supplier Selection in...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Sumanto Sumarna Hafis Nurdin Taufik Asra Irwan Agus Sobari Herman Kuswanto Indra Chaidir Ibnu Akil Felix Wuryo Handono Informatics Faculty of Engineering and Informatics Universitas Bina Sarana Informatika Jakarta Indonesia Information System Faculty of Engineering and Informatics Universitas Nusa Mandiri Jakarta Indonesia Software Engineering Faculty of Engineering and Informatics Universitas Bina Sarana Informatika Jakarta Indonesia Information System Faculty of Engineering and Informatics Universitas Nusa Mandiri Universitas Bina Sarana Informatika Computer Engineering Faculty of Engineering and Informatics Universitas Nusa Mandiri Universitas Bina Sarana Informatika

ISBN: (数字)9798331528799

ISBN: (纸本)9798331528805

Supplier selection is a critical component of Supply Chain Management (SCM), as suppliers provide raw materials or services essential for supporting a company's operations. Traditional supplier selection methods often rely on subjective assessments to determine the weighting of criteria, which can lead to biases and suboptimal decision making. To address this issue, this study combines the Logarithmic Percentage Change of Weight (LOPCOW) method with the Simple Additive Weighting (SAW) method to enhance objectivity in the supplier selection process. The LOPCOW method is used to assign criterion weights objectively based on logarithmic percentage changes, which helps minimize subjectivity, These weights are then applied using the SAW method to evaluate and rank suppliers. The results of the study show that this combined approach is more reliable for selecting suppliers objectively and accurately. The study suggests integrating other Multi-Criteria Decision-Making (MCDM) methods to further strengthen the results while recommending the inclusion of real-time data analytics to improve the model's adaptability to dynamic market conditions. The LOPCOW-SAW approach can be applied across various industries for more effective and efficient SCM supplier selection.

关键词： Industries Analytical models Adaptation models Supply chain management Data analysis Real-time systems Data models Robustness MCDM Weighted sum model

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