Traditional methods for keyword extraction predominantly rely on statistical relationships between words, neglecting the cohesive structure of the extracted keyword set. This study introduces an enhanced method for keyword extraction, utilizing the Watts-Strogatz model to construct a word network graph from candidate words within the text. By leveraging the characteristics of small-world networks (SWNs), i.e., short average path lengths and high clustering coefficients, the method ascertains the relevance between words and their impact on sentence cohesion. A comprehensive weight for each word is calculated through a linear weighting of features including part of speech, position, and Term Frequency-Inverse Document Frequency (TF-IDF), subsequently improving the impact factors of the TextRank algorithm for obtaining the final weight of candidate words. This approach facilitates the extraction of keywords based on the final weight outcomes. Through uncovering the deep hidden structures of feature words, the method effectively reveals the connectivity within the word network graph. Experiments demonstrate superiority over existing methods in terms of precision, recall, and F1-measure.
In the realm of computer vision, image recognition serves as a pivotal task with extensive applications in intelligent security, autonomous driving, and robotics. Traditional methodologies for image recognition often grapple with computational inefficiencies and diminished accuracy in complex scenarios and extensive datasets. To address these challenges, an algorithm utilizing a siamese network architecture has been developed. This architecture leverages dual interconnected neural network submodules for the efficient extraction and comparison of image features. The effectiveness of this siamese network-based algorithm is demonstrated through its application to various benchmark datasets, where it consistently outperforms conventional approaches in terms of accuracy and processing speed. By employing weight-sharing techniques and optimizing neural network pathways, the proposed algorithm enhances the robustness and efficiency of image recognition tasks. The advancements presented in this study not only contribute to the theoretical understanding but also offer practical solutions, underscoring the significant potential and applicability of siamese networks in advancing image recognition technologies.
The significant impact of pneumonia on public health, particularly among vulnerable populations, underscores the critical need for early detection and treatment. This research leverages the National Institutes of Health (NIH) chest X-ray dataset, employing a comprehensive exploratory data analysis (EDA) to examine patient demographics, X-ray perspectives, and pixel-level evaluations. A pre-trained Visual Geometry Group (VGG) 16 model is integrated into the proposed architecture, emphasizing the synergy between robust machine learning techniques and EDA insights to enhance diagnostic accuracy. Rigorous data preparation methods are utilized to ensure dataset reliability, addressing missing data and sanitizing demographic information. The study not only provides valuable insights into pneumonia-related trends but also establishes a foundation for future advancements in medical diagnostics. Detailed results are presented, including disease distribution, model performance metrics, and clinical implications, highlighting the potential of machine learning models to support accurate and timely clinical decision-making. This integration of advanced technologies into traditional healthcare practices is expected to improve patient outcomes. Future directions include enhancing model sensitivity, incorporating diverse datasets, and collaborating with medical professionals to validate and implement the system in clinical settings. These efforts are anticipated to revolutionize pneumonia diagnosis and broader medical diagnostics. This work offers comprehensive code for developing and optimizing deep learning (DL) models for medical image classification, focusing on pneumonia detection in X-ray images. The code outlines the construction of the model using pre-trained architectures such as VGG16, detailing essential preparation steps including image augmentation and metadata parsing. Tools for data separation, generator creation, and callback training for monitoring are provided. Additionally, the code facilitates performance assessment through various metrics, including the receiver operating characteristic (ROC) curve and F1-score. By providing a systematic framework, this research aims to accelerate the development process for researchers in medical image processing and expedite the creation of accurate diagnostic tools.
The persistent emergence of software vulnerabilities necessitates the development of effective detection methodologies. Machine learning (ML) and deep learning (DL) offer promising avenues for automating feature extraction; however, their efficacy in vulnerability detection remains insufficiently explored. This study introduces the Multi-Deep Software Automation Detection Network (MDSADNet) to enhance binary and multi-class software classification. Unlike traditional one-dimensional Convolutional Neural Networks (CNNs), MDSADNet employs a novel two-dimensional multi-scale convolutional process to capture both intra-data and inter-data $n$-gram features. Experimental evaluations conducted on binary and multi-class datasets demonstrate MDSADNet's superior performance in software automation classification. Furthermore, the Mantis Search Algorithm (MSA), inspired by the foraging and mating behaviors of mantises, was incorporated to optimize MDSADNet’s hyperparameters. This optimization process was structured into three distinct stages: sexual cannibalism, prey pursuit, and prey assault. The model's validation involved performance metrics such as F1-score, recall, accuracy, and precision. Comparative analyses with state-of-the-art DL and ML models highlight MDSADNet's enhanced classification capabilities. The results indicate that MDSADNet significantly outperforms existing models, achieving higher accuracy and robustness in detecting software vulnerabilities.
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