Identifying promoters is challenging due to their short sequences, low conservation, and complex regulation. His-torically, this was done through slow and expensive experimental methods. Efficient patternrecognition ...
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Neural networks that are inspired by the way a human brain is structured, are known as artificial neural networks represent a unique class of machinelearning algorithms. these algorithms allow artificial neural netwo...
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ISBN:
(纸本)9798331504403
Neural networks that are inspired by the way a human brain is structured, are known as artificial neural networks represent a unique class of machinelearning algorithms. these algorithms allow artificial neural networks to acquire from data, and make resolutions based on the outcome, just like humans do. Non-linear data models, which have a whole load of variables and inputs, can predict new patterns. Artificial neural networks are already leveraged in everything from medical diagnoses to speech recognition as well as the ever-growing field of machine translation. there are multiple processes involved in the deep learning methodology that the method suggests for classifying bipolar disease. First, clinical and demographic data such as the sample's age, gender, symptom severity, and medication history would be included in the dataset of bipolar disorder patient samples and control samples. the dataset would next undergo preprocessing to remove any outliers or missing values. In the meantime, standardization and normalization would be applied to the data to ensure that each variable is on a consistent scale. data scientists may also decide to use feature selection to determine which variables are most useful for overall optimization for classification goals. In this proposed system accuracy is better as compared to the remaining conventional models. this makes it easier for us to select the elements that are most crucial to making our part a real feature. the two models ANN are the final features. Findings demonstrate that deep learning models, like as artificial neural networks (ANNs), are effective in treating neuroinformatics illnesses since datasets are readily available. When it comes to public relations, moods, and the dataset, the deep learning model predicts the correct classes with more precision. A popular application of ANN involves approximating a random function, thereby providing an inexpensive way to get to various statistics characterizing its distrib
Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant ...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant influence on the whole graph, providing accurate explanations for predictions of GNN. We noted that explanation components could help improve classification performance as unique representations of each class. therefore, we suggest the GNN performance can be further improved by using explanation components. In this paper, we propose an Explanation-Based Graph Neural Networks (EBGNN) that utilizes contrastive learning at the instance level, by applying explanation components. In EBGNN, the explanation components ensure similarity for instances within the same class, and promote separability for instances in different classes. Finally, we conducted an evaluation on five benchmark datasets (MUTAG, IMDB-BINARY, PROTEINS, NCI1, and DD). Our experiment showed a significant increase in graph classification performance compared to state-of-the-art methods.
Multidimensional hierarchical (mTree) data is very common in daily life and scientific research. However, the mTree data exploration is a laborious and time-consuming process due to its structural complexity and large...
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In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed in...
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Human action patterns are typically temporal. Since the data collected is periodic, processing time series data into a feature matrix for further learning and patternrecognition is a challenging task. Periodic data m...
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Withthe rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency a...
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Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learningdatamining algorithms the best management policy using s...
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Various computer vision techniques based on deep neural networks have been proposed to detect objects accurately and fast. However, due to the privacy, security and communication bandwidth restrictions of diverse part...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Various computer vision techniques based on deep neural networks have been proposed to detect objects accurately and fast. However, due to the privacy, security and communication bandwidth restrictions of diverse participating parties, it is sometimes prohibitive to train such models on a centralized machine. Federated learning (FL) provides a promising solution to learn a model from decentralized data. Despite the advances in FL, the diversity of client regions in which they operate and the Non-IID nature of the crowdsourced datasets reduces the accuracy of object detection models significantly. In this paper, we introduce a novel FL object detection system to efficiently train models with heterogeneous client datasets. We propose lightweight client selection methods to learn object detection models faster. Our client selection methods based on the object data distribution at clients achieves up to 74% reduction in required federated rounds compared to conventional approaches. We further extend this method by leveraging the metadata of the training images (e.g., location, direction, depth), to select clients which maximize the coverage of diverse geographical regions. We report on extensive experiments with real datasets.
Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset....
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ISBN:
(纸本)9783031821523;9783031821530
Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. this process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby underminingthe performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, the simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy.
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