The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and...
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Combining auto encoders and hybrid cellular automata provides a novel way to identify anomalies in structured data in the field of anomaly detection. Dimensionality reduction and extracting the features is one of the ...
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Hand gesture recognition using multichannel surface electromyography (sEMG) is challenging due to unstable predictions and inefficient time-varying feature enhancement. To overcome the lack of signal-based time-varyin...
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Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitati...
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In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under t...
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In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.
Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point cov...
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This work introduces PCA-FLANN, an innovative hybrid model combining principal component analysis (PCA) with functional link artificial neural network (FLANN) to achieve efficient non-linear dimensionality reduction a...
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With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilisti...
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Acute Lymphoblastic Leukemia (ALL) is a fast-growing blood cancer that requires prompt diagnosis for effective treatment. Automated image diagnostics offer potential solutions but often lack clinical robustness. Despi...
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Acute Lymphoblastic Leukemia (ALL) is a fast-growing blood cancer that requires prompt diagnosis for effective treatment. Automated image diagnostics offer potential solutions but often lack clinical robustness. Despite their widespread use in medical imaging, Convolutional Neural Networks (CNNs) struggle to differentiate morphologically similar ALL subtypes due to limited context and feature discrimination. Moreover, integrating contrastive self-supervised learning with hierarchical attention-based models remains underexplored in hematologic malignancy classification. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework, combined with Momentum Contrast (MoCo) for contrastive learning and a Bidirectional Encoder Transformer for classification. The Swin Transformer’s patch-based embedding and multi-level attention enhance feature discrimination across ALL subtypes, while MoCo generates distinct embeddings, minimizing overlap between cell types. BiET is employed to classify the refined feature vectors, leveraging self-attention mechanisms to improve classification accuracy. The model achieved an overall classification accuracy of 92.5%, with the precision of 90.3%, a recall of 91.1%, and an F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework reduced contrastive loss from 0.5 to 0.097 for benign cells, enhancing feature discrimination. An ablation study revealed that omitting the dynamic queue decreased accuracy by 5%, underscoring its importance for effective feature learning. This approach can be extended to other hematologic malignancies, with potential for further improvement using larger datasets and real-time diagnostic workflows to support p
Afek, Bremler-Barr, Kaplan, Cohen, and Merritt (PODC’01) in their seminal work on shortest path restorations demonstrated that after a single edge failure in a graph G, a replacement shortest path between any two ver...
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