Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, ...
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Accurate body composition assessment is essential for evaluating health and diagnosing conditions like sar copenia and cardiovascular disease. Approaches for accurately measuring body composition, such as Dual Energy ...
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Motifs, serving as fundamental building blocks in complex networks, refer to small, frequently occurring connected subgraphs. Unlike link prediction, motif prediction focuses on whether a given set of nodes will form ...
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Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and att...
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ISBN:
(数字)9798331513320
ISBN:
(纸本)9798331513337
Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.
In this paper, we consider the Hermitian $$\{P, k + 1\}$$ -(anti-)reflexive solutions to the quaternion matrix equation $$AXB+CXD=E$$ and $$AX=E,$$ respectively. We use the complex representation method to obtain the ...
In this paper, we consider the Hermitian $$\{P, k + 1\}$$ -(anti-)reflexive solutions to the quaternion matrix equation $$AXB+CXD=E$$ and $$AX=E,$$ respectively. We use the complex representation method to obtain the necessary and sufficient conditions for the existence of the Hermitian $$\{P, k +1\}$$ -reflexive solution (resp. Hermitian $$\{P, k +1\}$$ -anti-reflexive solution), respectively, and derive their solutions when the matrix equations have the Hermitian $$\{P, k +1\}$$ -reflexive solution (resp. Hermitian $$\{P, k +1\}$$ -anti-reflexive solution). Finally, two examples are provided to verify the effectiveness of our method.
The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other m...
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Laparoscopic videos are tools used by surgeons to insert narrow tubes into the abdomen and keep the skin without large incisions. The videos captured by a camera are prone to numerous distortions such as uneven illumi...
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Laparoscopic videos are tools used by surgeons to insert narrow tubes into the abdomen and keep the skin without large incisions. The videos captured by a camera are prone to numerous distortions such as uneven illumination, motion blur, defocus blur, smoke, and noise which have impact on visual quality. Automatic detection and identification of distortions are significant to enhance the quality of laparoscopic videos to avoid errors during surgery. The video quality assessment includes two stages: classification of distortions affecting the video frames to identify their types and ranking of distortions to estimate the intensity levels. The dataset generated in ICIP2020 challenge including laparoscopic videos was utilized for training, validation, and testing the proposed solution. The difficulty of this dataset is caused by having five categories of distortions and four levels of severity. Additionally, the availability of multiple distortion categories in one video is considered the most challenging part of this dataset. The work presented in this paper contributes to solve the multi-label distortion classification and ranking problem. This paper aims to enhance the performance of distortion classification solutions. Vision transformer which is a deep learning model was used to extract informative features by transferring learning and representation from the general domain to the medical domain (laparoscopic videos). Additionally, six parallel multilayer perceptron (MLP) classifiers were added and attached to vision transformer for distortion classification and ranking. The experiment showed that the proposed solution outperforms existing distortion classification methods in terms of average accuracy (89.7%), average single distortion F1 score (94.18%), and average of both single and multiple distortions F1 score (96.86%). Moreover, it can also rank the distortions with an average accuracy of 79.22% and average F1 score of 78.44%. Hence, the high performance of t
Mobile networks where users group in communities based on their interests, and disseminate data mainly to their community using their mobile devices, are called Mobile Social Networks (MSN). However, the efficiency of...
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The application of AI and machine learning has garnered considerable attention and is evolving rapidly in academia and practice. However, considering the effort required for implementing AI and ML in business process ...
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The application of AI and machine learning has garnered considerable attention and is evolving rapidly in academia and practice. However, considering the effort required for implementing AI and ML in business process management—particularly regarding data quality and the skills of process analysts—the potential of these technologies has not yet been fully explored. Moreover, beyond data quality issues, continuous changes in business processes introduce significant uncertainty, leading to inefficiencies and the infeasibility of using existing optimization methods. We present a novel 5-step framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal execution paths in business processes using reinforcement learning. We implemented this approach on real-life event logs from our case study an energy regulator in Canada and other real-life event logs, demonstrating the feasibility of the proposed method. Additionally, to compare FORLAPS with the existing models (Permutation Feature Importance and multi-task LSTM-Based model), we experimented to evaluate its effectiveness in terms of resource savings and process time span reduction. The experimental results on real-life event log validate that FORLAPS achieves 31% savings in resource time spent and a 23% reduction in process time span. Using this innovative data augmentation technique, we propose a fine-tuned reinforcement learning approach that aims to automatically fine-tune the model by selectively increasing the average estimated Q-value in the sampled batches. The results show that we obtained a 44% performance improvement compared to the pre-trained model. This study introduces an innovative evaluation model, benchmarking its performance against earlier works using nine publicly available datasets. Robustness is ensured through experiments utilizing the Damerau-Levenshtein distance as the primary metric. In addition, we discussed the su
AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify can...
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ISBN:
(纸本)9798331523923
AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. The usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. The majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. The success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. The analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. The primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. The combination of better data governance toget
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