Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation by Mohammed Ghazwani, Umme Hani

“…SHapley Additive exPlanations (SHAP) analysis provided valuable insights into feature contributions, highlighting wetting time and sodium saccharin as key factors influencing disintegration time.…”
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3D mesh segmentation of historic buildings for architectural surveys by Borja Javier Herráez, Eduardo Vendrell

“…The developed method has proved to be effective for feature detection and suitable for inclusion in architectural surveying applications.…”
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Machine Learning-Enabled Attacks on Anti-Phishing Blacklists by Wenhao Li, Shams Ul Arfeen Laghari, Selvakumar Manickam, Yung-Wey Chong, Binyong Li

“…This study presents a comprehensive security analysis of anti-phishing blacklists and introduces two novel cloaking attacks—Feature-Driven Cloaking and Transport Layer Security (TLS)-Based Cloaking—that exploit vulnerabilities in the automated detection systems of anti-phishing entities (APEs). …”
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Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network by Zhejun Kuang, Simin Liu, Jian Zhao, Liu Wang, Yunkai Li

“…While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. …”
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Digits Recognition for Arabic Handwritten through Convolutional Neural Networks, Local Binary Patterns, and Histogram of Oriented Gradients by Bushra Mahdi Hasan, Zahraa Jasim Jaber, Ahmad Adel Habeeb

“…In addition, a Histogram of Oriented Gradients (HOG) is a feature extraction technique that is used in computer vision and image processing for the purpose of object detection. …”
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Research on Early Diagnosis Methods for Broiler Chicken Diseases Based on Swarm Intelligence Optimization Algorithms and Random Forest by X Peng, C Chen, L Yu, X Kong, B Sun

“…The RF_WOA_DBO hybrid model achieved an accuracy of 98.29%, representing a 4.28% improvement over the baseline RF model. Comparative analysis revealed that traditional PCA methods risk losing essential pathological features by disregarding nonlinear data relationships, whereas deep learning requires substantial computational resources and high-quality datasets. …”
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An adaptive filter for anemia screening using deep convolutional neural network by Jose B. Lazaro, Jr., Jennifer C. Dela Cruz, Jocelyn F. Villaverde

“…This study introduces an automated anemia detection system powered by deep convolutional neural networks (DCNNs), CMOS image sensing, Adam optimizer, and multi-scale feature extraction (MSFE) to improve diagnostic precision and accessibility. …”
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BRAIN TUMOR DIAGNOSIS BASED ON MEDICAL IMAGES USING VISION TRANSFORMER by Masuma Mammadova, Fargana Abdullayeva

“… Brain tumor is one of the most common causes of death in modern times. Early and accurate detection of this disease can save the lives of a large part of the world’s population. …”
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A Comparative Study of Supervised and Self-Supervised Denoising Techniques for Defect Segmentation in Industrial CT Imaging by Virginia Florian, Jiayang Shi, Willem Jan Palestijn, Daniël M. Pelt, K. Joost Batenburg, Thomas Lang, Christoph Heinzl, Christian Kretzer, Stefan Kasperl, Dominik Wolfschläger, Robert H. Schmitt

“… X-ray computed tomography (CT) is a powerful imaging tool for defect detection, segmentation and feature extraction in industrial applications as it enables non-destructive evaluation. …”
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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques. by Hilman Nordin, Bushroa Abdul Razak, Norrima Mokhtar, Mohd Fadzil Jamaludin, Adeel Mehmood

“…Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). The efficacy of these methods was evaluated using the Mold Features Dataset (MFD) and a separate set of test images. …”
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AI-Powered Precision in Diagnosing Tomato Leaf Diseases by MD Jiabul Hoque, Md. Saiful Islam, Md. Khaliluzzaman

“…Principal component analysis (PCA) is a technique for dimensionality reduction, feature selection, and redundancy elimination. …”
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Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning by Zhijie Jia, Songhai Fan, Zhichuan Wang, Shuai Shao, Dameng He

“…Abstract To address the limitations of traditional partial discharge (PD) detection methods for switchgear, which fail to meet the requirements for real-time monitoring, rapid assessment, sample fusion, and joint analysis in practical applications, a joint PD recognition method of switchgear based on edge computing and deep learning is proposed. …”
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An OGFA+CNN Approach for Multi-Level Disease Identification in Fundus Images by Preethi Kulkarni, K. Srinivasa Reddy

“…By incorporating graph-based methods for global context alongside CNNs for detailed local feature extraction, the OGFA model becomes more adept at detecting subtle abnormalities and intricate changes in fundus images. …”
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Improved FraSegNet-Based Rock Nodule Identification Method and Application by Yanbo Zhang, Guanghan Zhang, Qun Li, Xulong Yao, Hao Zhou

“…The experimental results demonstrate its superior performance, achieving 97.1% accuracy in nodal feature detection with an average error of only 1.5% compared to the rock mass parameter. …”
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Local pattern aware 3D video swin transformer with masked autoencoding for realtime augmented reality gesture interaction by Suli Wang

“…Using weighted Euclidean distance and structural similarity optimization, the paper proposes an image denoising model based on maximum a posteriori probability that effectively reduces noise interference in gesture image analysis. The gesture detection and segmentation module combines EfficientNet and Transformer models. …”
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Complete Protection Strategy for Loss of Excitation in Large-Scale Synchronous Condensers Applied to UHVDC Transmission by Zhilin Guo, Liangliang Hao, Jinghan He, Zhengguang Chen, Xingguo Wang

“…Then, excitation current difference between the measured value and equivalent actual value is identified as the new fault feature. Compared with existing reverse reactive power features which also appear in healthy LSC's leading phase conditions, this current difference feature only appears in the LO E process and thus is more typical. …”
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Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis by Yehu Shen, Zhenyun Peng, Yaohui Zhang

“…Hair is a salient feature in human face region and are one of the important cues for face analysis. …”
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Enhancing Smart City Functions through the Mitigation of Electricity Theft in Smart Grids: A Stacked Ensemble Method by Muhammad Hashim, Laiq Khan, Nadeem Javaid, Zahid Ullah, Ifra Shaheen

“…Furthermore, we incorporate kernel principal component analysis (KPCA) and localized random affine shadow sampling (LoRAS) for feature engineering and data augmentation. …”
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Application of Split-frequency AVO Technology in the Evaluation of Middle-depth Thin Reservoirs in the Xihu Depression by Yangsen LI, Wei WANG, Bingying LI, Yunxin MAO, Xiaohui LIU

“…Through the actual pre-stack gather verification analysis, the advantageous frequency band information of the pre-stack gather can effectively improve the reliability of hydrocarbon detection and provide powerful technical support for the detection of hydrocarbons in the middle and deep layers of the Xihu Depression.…”
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Design of an Improved Model for Gear Fault Diagnosis Using Acoustic Data and EfficientNet-Based Deep Learning Process by Bundele Shubham, Kane P.V.

“…Traditional methods of fault detection, such as vibration-based analysis, are restricted in terms of sensor placement, high sensitivity to environmental noise, and sheer incapacity to identify subtle gear anomalies. …”
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