With the rapid development of artificial intelligence and optimization algorithms, the Ant Colony Optimization (ACO) has become one of the effective tools for solving path planning problems. This paper first introduce...
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Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities ...
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Graph Neural Networks (GNNs) have been proven to be powerful tools for graph analysis. However, despite their success, existing GNNs are often sensitive to the quality of the input graph. Real-world graphs are typical...
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Device-to-device (D2D)-enabled mobile edge computing (MEC) is a promising communication network in which computation tasks on mobile devices (MDs) can not only be processed locally but also can be scheduled to the MEC...
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Generating high-quality labels is crucial for selfsupervised learning in low-light conditions, where traditional enhancement methods often struggle to balance detail enhancement and color fidelity. This paper presents...
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Pharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their respon...
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ISBN:
(纸本)9798350364828
Pharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their response to medications. A persons genetic composition can impact the likelihood of experiencing reactions or determining the effectiveness of a medication. By providing insights into the safety and effectiveness of drug therapies pharmacogenomics holds potential for significantly enhancing health outcomes. Through advancements in targeted therapies we can precisely target abnormalities that trigger tumor growth in patients. For instance IGF1R (Insulin like Growth Factor 1 Receptor) which belongs to the tyrosine kinase receptor family plays a crucial role in promoting cell growth, survival and proliferation across different types of cancers. The overexpression of IGF1R has been observed in cancer types indicating its involvement in fueling continuous growth and survival of cancer cells. Targeting IGF1R helps address the dysregulation of this receptor within cancer cells. Artificial Intelligence (AI) comes into play by enabling prediction of suitable drugs based on a patients genomic profile thereby reducing adverse effects and improving treatment effectiveness. Parallel, here has been growing concern regarding model explanation due, to the opaque nature of model predictions. This is particularly important when it comes to modeling drug responses. In our research paper we have employed AI to gain a clear understanding of the prediction model and the factors that affect its results. The findings show that lower valued counts of YAP-pS127-Caution protein tend to negatively impact the output. Similarly lower values of YAP-pS127-Caution protein and higher valued counts of YAP-pS127 -Caution protein, Xanthine, Tyrosine tends to positively impact the output. This helps as an aiding reference in knowing which feature of an unknown cell line should be focused to know
Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint i...
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High dimensional multi-attribute big data mining is a key technology for processing natural language and database access. A high-dimensional multi-attribute big data association mining based on semantic feature fusion...
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Cyberspace is the main battlefield of future war, how to command and control network operations in a timely and effective manner in the highly complex and rapidly changing network environment is very necessary. Intell...
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Uncertainty estimation (UE), as an effective means to quantify predictive uncertainty, is crucial for safe and reliable decision-making, especially in high-risk scenarios. Existing UE schemes usually assume that there...
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Uncertainty estimation (UE), as an effective means to quantify predictive uncertainty, is crucial for safe and reliable decision-making, especially in high-risk scenarios. Existing UE schemes usually assume that there are completely-labeled samples to support fully-supervised learning. In practice, however, many UE tasks often have no sufficiently-labeled data to use, such as the Multiple Instance Learning (MIL) with only weak instance annotations. To bridge this gap, this paper, for the first time, addresses the weakly-supervised issue of Multi-Instance UE (MIUE) and proposes a new baseline scheme, Multi-Instance Residual Evidential Learning (MIREL). Particularly, at the fine-grained instance UE with only weak supervision, we derive a multi-instance residual operator through the Fundamental Theorem of Symmetric Functions. On this operator derivation, we further propose MIREL to jointly model the high-order predictive distribution at bag and instance levels for MIUE. Extensive experiments empirically demonstrate that our MIREL not only could often make existing MIL networks perform better in MIUE, but also could surpass representative UE methods by large margins, especially in instance-level UE tasks. Our source code is available at https://***/liupei101/MIREL. Copyright 2024 by the author(s)
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