This work aims to design and develop an artificial neural network (ANN) architecture for the classification of cancerous tissue in the lung. A sequential model is used for the machine learning process. ReLU and Sigmoi...
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Lung cancer is the leading cause of cancer-related deaths globally. computer-assisted detection (CAD) systems have previously been used for various disease diagnosis and hence can serve as an efficient tool for lung c...
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The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered ...
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Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Gr...
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The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and ***...
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The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.
This paper discusses a deep neural network architecture of Long Short-Term Memory (LSTM) with an autoencoder-based encoder-decoder scheme. Primarily, the proposed structure determines the time-domain features of elect...
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Typical machine learning models typically demand a substantial volume of data for effective training, ensuring optimal performance during testing. However, these models often fail to specify the extent of data require...
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The sudden advent of COVID-19 pandemic left educational institutions in a difficult situation for the semester evaluation of students;especially where the online participation was difficult for the students. Such a si...
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With the development of the machine learning age, deep learning techniques for computervision tasks use real-world data to analyze problems across various fields. In particular, it has achieved significant success in...
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The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effe...
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The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i
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