Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required...
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Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required,which is very costly and even prohibitive for problems that are already computationally intensive,*** problems associated with machine learning *** the past decades,many studies have been conducted to accelerate the tedious configuration process by learning from a set of training *** article refers to these studies as learn to optimize and reviews the progress achieved.
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interfer...
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In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In resp...
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Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance...
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Video colorization aims to add color to grayscale or monochrome *** existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obst...
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Video colorization aims to add color to grayscale or monochrome *** existing methods have achieved substantial and noteworthy results in the field of image colorization,video colorization presents more formidable obstacles due to the additional necessity for temporal ***,there is rarely a systematic review of video colorization *** this paper,we aim to review existing state-of-the-art video colorization *** addition,maintaining spatial-temporal consistency is pivotal to the process of video *** gain deeper insight into the evolution of existing methods in terms of spatial-temporal consistency,we further review video colorization methods from a novel *** colorization methods can be categorized into four main categories:optical-flow based methods,scribble-based methods,exemplar-based methods,and fully automatic ***,optical-flow based methods rely heavily on accurate optical-flow estimation,scribble-based methods require extensive user interaction and modifications,exemplar-based methods face challenges in obtaining suitable reference images,and fully automatic methods often struggle to meet specific colorization *** also discuss the existing challenges and highlight several future research opportunities worth exploring.
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge ...
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In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.
Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and lo...
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The characteristics that make up the general identity of engineeringtechnology (ET) degree programs and their graduates are well known;however, the explicit characteristics of ET capstone nationally is unknown. In ot...
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A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, th...
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