For the detection of remote and fuzzy small targets, machinelearning detection has a higher probability of missed detection. It is more dangerous when applied to vehicle detection. Therefore, this paper proposes and ...
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Student retirement is an important issue in all higher education institutions, affecting the income of each faculty and the university. In this research, a model for predicting student retirement is created and the fa...
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Production sequencing methodology using Deeplearning Seq2Seq-LSTM is applied to a tire production case study in Quebec, Canada. Production and demand data are used to predict the most likely product sequences to opera...
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Heart disease has become the leading cause of death worldwide. In this paper a feature based on the Tunable Q Wavelet Transform is proposed to detect the abnormal heart sound. The 2016 PhysioNet / CinC heart audio dat...
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Breast cancer is a primary cause of death for women. Millions of women worldwide were suspected with breast cancer diagnosis in 2022. But early-stage breast cancer is still treatable. Patients' recoveries become b...
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In terms of music genre classification, neural networks and machinelearning models have their respective advantages. This paper aims to compare the performance and feature extraction capability between neural network...
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Across the globe, almost 2.5 quintillion bytes of data is produced every day. There is an exponential increase in the amount of data that is created. Out of this 2.5 quintillion, almost 50% constitutes image data. Thi...
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Cancer driver genes play a key role in tumor development, and the mutation or aberrant expression of these genes, which mainly occurs at the somatic cell level, has become a focus of research in recent years. Identify...
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ISBN:
(纸本)9798350364941;9798350364958
Cancer driver genes play a key role in tumor development, and the mutation or aberrant expression of these genes, which mainly occurs at the somatic cell level, has become a focus of research in recent years. Identifying lung cancer driver genes from millions of somatic cell mutations is undoubtedly a challenging task. In order to accurately predict lung cancer driver genes, researchers have proposed numerous computational methods, most of which employ machinelearning techniques such as random forests and support vector machines (SVMs). However, simply merging features extracted from different data sources and using the selected features for training machinelearning models is not an optimal strategy. New research points out that gene expression data can effectively characterize the similarity between genes. Based on this finding, an advanced deep learning-based analysis method, BiLSTM-Driver is proposed which significantly improves the prediction accuracy of lung cancer driver genes by probing deeply into the mutational features of genes and their neighbors through the Bidirectional Long Short-Term Memory networks. This method performs well in lung cancer research, especially in the prediction of lung adenocarcinoma and lung squamous carcinoma, and achieves better performance in AUC performance, precision and recall, achieving the performance of 0.994,0.991, and 0.987, and 0.987,0.993, and 0.991,respectively, which is significantly better than other algorithms. The proposed method BiLSTM-Driver provides an innovative way to identify new lung cancer driver genes. Through in-depth analysis of lung cancer-related genomic data, BiLSTM- Driver is able to accurately identify potential oncogenes and reveal their key roles in lung cancer development.
learning interpretable models has become a major focus of machinelearning research, given the increasing prominence of machinelearning in socially important decision-making. Among interpretable models, rule lists ar...
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ISBN:
(纸本)9798400704901
learning interpretable models has become a major focus of machinelearning research, given the increasing prominence of machinelearning in socially important decision-making. Among interpretable models, rule lists are among the best-known and easily interpretable ones. However, finding optimal rule lists is computationally challenging, and current approaches are impractical for large datasets. We present a novel and scalable approach to learn nearly optimal rule lists from large datasets. Our algorithm uses sampling to efficiently obtain an approximation of the optimal rule list with rigorous guarantees on the quality of the approximation. In particular, our algorithm guarantees to find a rule list with accuracy very close to the optimal rule list when a rule list with high accuracy exists. Our algorithm builds on the VC-dimension of rule lists, for which we prove novel upper and lower bounds. Our experimental evaluation on large datasets shows that our algorithm identifies nearly optimal rule lists with a speed-up up to two orders of magnitude over state-of-the-art exact approaches. Moreover, our algorithm is as fast as, and sometimes faster than, recent heuristic approaches, while reporting higher quality rule lists. In addition, the rules reported by our algorithm are more similar to the rules in the optimal rule list than the rules from heuristic approaches.
In today's era, unreliable news results in several consequences impacting the society changing the people's perspective. There are several approaches and techniques for detecting the fake news, also new method...
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