Multilayer network is a potent platform which paves a way to study the interactions among entities in various networks with multiple types of relationships. In this study, the dynamics of discrete-time quantum walk on...
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A stark increase in the vision-based applications in recent years has made the interpretation of imagery data a challenging problem at scale. For accurate visual scene comprehension, the need for accurate segmentation...
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Richmond and Richmond (American Mathematical Monthly 104 (1997), 713–719) proved the following theorem: If, in a metric space with at least five points, all triangles are degenerate, then the space is isometric to a ...
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The inverse problem of electrocardiography is solved using potential recordings from numerous torso electrodes. However, there is a growing interest in minimizing the number of used electrodes to enhance clinical appl...
The inverse problem of electrocardiography is solved using potential recordings from numerous torso electrodes. However, there is a growing interest in minimizing the number of used electrodes to enhance clinical applicability. A total of 8 datasets of patients with pacemakers obtained from the EDGAR database were used. The inverse problem assuming a single dipole cardiac source was solved for 3 different electrode configurations. The configurations included all $196\ \pm\ 28$ electrodes, 9 corresponding to the 12-lead ECG, and the 9 most significant electrodes. The significance of the electrodes was derived by the greedy algorithm from the singular value decomposition of the transfer matrix. The accuracy of the inverse solution was expressed as the localization error (LE) computed as the Euclidean distance between the pacemaker electrode and the inverse solution. The average LE for all 8 datasets using all torso electrodes was $26.1\pm 6.1\ mm$ , whereas using 12-lead ECG electrodes yielded a LE of $40.5\pm 23.1\ mm$ , and the greedy algorithm's selection resulted in a LE of $29.6\pm 14.0\ mm$ . The results suggest that the use of the most significant electrodes outperformed the use of the 12-lead ECG. The study emphasizes the need for back electrodes for an accurate inverse solution.
The Quantum Alternating Operator Ansatz (QAOA) represents a branch of quantum algorithms designed for solving combinatorial optimization problems. A specific variant, the Grover-Mixer Quantum Alternating Operator Ansa...
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The classification of bone fractures from radiographs is an important yet challenging task in clinical diagnosis. Diagnosing fractures through X-rays remains difficult for orthopedic specialists due to image quality i...
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ISBN:
(数字)9798331507213
ISBN:
(纸本)9798331507220
The classification of bone fractures from radiographs is an important yet challenging task in clinical diagnosis. Diagnosing fractures through X-rays remains difficult for orthopedic specialists due to image quality issues, which can result in errors, misalignments, and potential harm to patients. However, recent advancements in artificial intelligence (AI) and deep learning have revolutionized medical imaging, with state-of-the-art methods now capable of handling 2D and 3D images. This study focuses on deep-learning approaches for the classification and detection of bone fractures in radiograph images and aims to analyze and compare various deep-learning algorithms and techniques used in fracture detection. It also highlights current cutting-edge approaches in this field, providing insights and guidance for future research and practical applications. In this paper, the application of Fine-tuned DenseNet169 for the automated classification of bone fractures in X-ray images is explored. By using deep learning approaches, our method seeks to enhance the accuracy and efficiency of fracture detection. We trained and evaluated the DenseNet169 model on the MURA Stanford dataset and achieved 83% accuracy in distinguishing fractured and non-fractured elbow bones. The model’s performance highlights the potential of DenseNet169 to assist radiologists in clinical settings, promoting better patient outcomes through prompt and reliable fracture diagnosis.
In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a hig...
In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation.
In this study, we consider endpoints communicating over a software-defined networking (SDN)-based architecture using source routing, i.e., packets are routed through a path selected by the packet sender, and we provid...
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Showing the design and layout of the building using a model, making this model requires time and accuracy because the model is built on a scale that has been adjusted to the building to be made later, a problem that o...
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The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preproce...
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ISBN:
(数字)9798350364699
ISBN:
(纸本)9798350364705
The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.
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