The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, ...
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Nowadays, Congestion Control (CC) algorithms areexpected to satisfy the diverse demands of applications running over diverse networks. To achieve this goal, the combinations, which are expected to inherit both the adv...
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Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band featu...
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Reliable three-dimensional localization of human locations is critical in a variety of computer vision applications, including robots, self-driving automobiles, security, and human-computer interaction. This research ...
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ISBN:
(数字)9798331510886
ISBN:
(纸本)9798331510893
Reliable three-dimensional localization of human locations is critical in a variety of computer vision applications, including robots, self-driving automobiles, security, and human-computer interaction. This research focuses on improving the vision system of Arslan humanoid robot and allows it to maintain a constant connection with people in its field of view. The system uses human binocular vision principles to extract depth information from photos taken with two pan cameras calibrated with servo motors for accurate angle alignment, as well as two identical USB cameras. The methodology consists of taking photos, recognizing faces, comparing face counts amongst cameras, and conducting 3D localization. The basic goals are to keep eye contact with individuals, estimate their approximate locations, and correctly establish their precise places. Methodological breakthroughs have greatly increased localization accuracy, lowering mistakes and increasing dependability. These advancements open the door for more responsive and intuitive human-computer interactions, benefiting the larger area of computer vision technology
Due to the influence of the imaging characteristics of the solar orbiting satellite and atmospheric conditions, the multi-spectral observation data often have the missing of phase image, which brings difficulties to t...
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One of the most delicate concerns is cyberbullying due to today’s worldwide web advancement. Bullying may contribute to severe consequences, including mental health issues, academic performance challenges, and job dr...
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Smart wearable systems can monitor users’ physiological data in real time, detect anomalies promptly through risk recognition technologies, provide early warnings, and assist users in taking preventive measures. Howe...
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Plant disease detection is one of the most crucial tasks in modern agriculture, and early detection is essential to minimize crop loss. In this work, we evaluate several deep learning models, including Xception, VGG-1...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Plant disease detection is one of the most crucial tasks in modern agriculture, and early detection is essential to minimize crop loss. In this work, we evaluate several deep learning models, including Xception, VGG-16, VGG-19, ResNet-101V2, ResNet-152V2, and ConvNeXtBase, for plant disease detection. We also propose an ensemble learning approach by combining the outputs of Xception, VGG-19, and ResNet-152V2 to improve prediction results. For each model, performance is evaluated using metrics such as accuracy, F1 score, precision, recall, and ROC-AUC. Among the individual models, the ensemble model achieved a test accuracy of 95.45%, a validation accuracy of 94.51%, an F1 score of 95.27%, and an ROC-AUC of 99.93%. These results demonstrate that ensemble methods are highly effective in leveraging the strengths of diverse architectures, improving both classification accuracy and robustness. This approach proves to be particularly effective for real-world applications in plant disease management.
Graph Neural Networks (GNNs) have expressed remarkable capability in processing graph-structured data. Recent studies have found that most GNNs rely on the homophily assumption of graphs, leading to unsatisfactory per...
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A model for the current-voltage characteristic of the junction between an ion-sensitive membrane and an electrolyte solution is derived and compared with numerical simulations of the Poisson-Nernst–Planck model for i...
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