Cybersecurity is one of the global issues because of the extensive dependence
on cyber systems of individuals, industries, and organizations. Among the cyber
attacks, phishing is increasing tremendously and affecting the global economy.
Therefore, this phenomenon highlights the vital need for enhancing user
awareness and robust support at both individual and organizational levels.
Phishing URL identification is the best way to address the problem. Various
machine learning and deep learning methods have been proposed to automate the
detection of phishing URLs. However, these approaches often need more
convincing accuracy and rely on datasets consisting of limited samples.
Furthermore, these black box intelligent models decision to detect suspicious
URLs needs proper explanation to understand the features affecting the output.
To address the issues, we propose a 1D Convolutional Neural Network (CNN) and
trained the model with extensive features and a substantial amount of data. The
proposed model outperforms existing works by attaining an accuracy of 99.85%.
Additionally, our explainability analysis highlights certain features that
significantly contribute to identifying the phishing URL.

网络安全是全球性问题之一，钓鱼网址鉴别是解决此问题的最佳方式。本论文提出了一种一维卷积神经网络模型，通过全面的特征和大量数据训练，实现了 99.85% 的准确度，并强调了对于识别钓鱼网址有重要贡献的某些特征。

PhishGuard: 基于卷积神经网络的用于检测钓鱼 URL 的模型，并使用可解释性分析

PhishGuard: A Convolutional Neural Network Based Model for Detecting  Phishing URLs with Explainability Analysis

In this study, we present a deep learning-based approach for time-series
respiration data classification. The dataset contains regular breathing
patterns as well as various forms of abnormal breathing, obtained through
non-contact incoherent light-wave sensing (LWS) technology. Given the
one-dimensional (1D) nature of the data, we employed a 1D convolutional neural
network (1D-CNN) for classification purposes. Genetic algorithm was employed to
optimize the 1D-CNN architecture to maximize classification accuracy.
Addressing the computational complexity associated with training the 1D-CNN
across multiple generations, we implemented transfer learning from a
pre-trained model. This approach significantly reduced the computational time
required for training, thereby enhancing the efficiency of the optimization
process. This study contributes valuable insights into the potential
applications of deep learning methodologies for enhancing respiratory anomaly
detection through precise and efficient respiration classification.

本研究提出了一种基于深度学习的时间序列呼吸数据分类方法，采用了一维卷积神经网络 (1D-CNN) 对呼吸数据进行分类，通过遗传算法优化了 1D-CNN 的架构，同时对预训练模型进行了迁移学习以提高算法的训练效率。这项研究为运用深度学习方法提升呼吸异常检测的准确性和效率提供了有价值的见解。

非接触式呼吸模式分类的 1D-CNN 优化

1D-CNN Optimization for Non-contact Respiration Pattern Classification

The transit method is one of the most relevant exoplanet detection
techniques, which consists of detecting periodic eclipses in the light curves
of stars. This is not always easy due to the presence of noise in the light
curves, which is induced, for example, by the response of a telescope to
stellar flux. For this reason, we aimed to develop an artificial neural network
model that is able to detect these transits in light curves obtained from
different telescopes and surveys. We created artificial light curves with and
without transits to try to mimic those expected for the extended mission of the
Kepler telescope (K2) in order to train and validate a 1D convolutional neural
network model, which was later tested, obtaining an accuracy of 99.02 % and an
estimated error (loss function) of 0.03. These results, among others, helped to
confirm that the 1D CNN is a good choice for working with non-phased-folded
Mandel and Agol light curves with transits. It also reduces the number of light
curves that have to be visually inspected to decide if they present
transit-like signals and decreases the time needed for analyzing each (with
respect to traditional analysis).

开发了一个能够检测来自不同望远镜和调查中的光曲线中的短时（transits）的人工神经网络模型，结果表明 1D CNN 是处理非相位折叠的光曲线具有短时的良好选择，并减少了传统分析所需要的时间。