Restricted Boltzmann machine with Sobel filter dense adversarial noise secured layer framework for flower species recognition by Shyamala Devi M, Sundaravadivazhagan Balasubaramanian, Balasubramaniam S, Mohd Asif Shah

“…An appropriate technique for classification that uses deep learning technology is vital to categorize flower species effectively. …”
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Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification by Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan

“…Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. …”
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Nomogram Incorporating Inflammatory Index, Pathology, and Molecular Classification for Predicting Recurrence in Patients with Stage I-III Endometrial Cancer: A Multi-Institutional... by Xiao Y, Zheng Y, Tu Y, Tian C, Yu J, Lin H, Wen T, Jiang P, Wang Y

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Quantitative evaluation of tight gas reservoir classification based on analytic hierarchy process: A case study of Penglaizhen Formation gas reservoir in Xinchang Gas Field by ZHOU Feng,HUANG Shilin,LI Xiaoming,LIAO Kaigui,LI Yong

“…This approach simplifies complex issues and quantifies qualitative aspects, facilitating a more structured evaluation. …”
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Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Archit... by Lucas C. Ribas, Wallace Casaca, Ricardo T. Fares

“…While these methods produce realistic samples, important issues persist concerning how well they generalize across different classification architectures and their overall impact in accuracy improvement. …”
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3-D–2-D Hybrid Lightweight CNN Model: Enhancing Canopy Feature Retrieval in Hyperspectral Imaging for Accurate Plant Species Classification by Chinsu Lin, Hung-Yi Chien, Keng-Hao Liu

“…These findings highlight Hybrid-LtCNN’s scalability, interpretability, and potential for practical applications in remote sensing-based plant species classification.…”
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Recent landscape research in Hungary by Péter Csorba, Dénes Lóczy, Gábor Mezosi

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Data-Driven Control Design for Complex Systems: Theory, Challenges, and Novel Applications by Chandar Kumar, Muhammad Sabih, Shubash Kumar, Zahid Ali, Ahmed Muddassir Khan

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Study of Forest Landscape on the Territory of Pirin National Park in Relation to Soil Health by Diana Malinova, Kameliya Petrova

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The Application of Evidence Theory in Supplier Evaluation by WANG Guang-ze, YANG Gui-zhi, HU Nan-nan, ZHAO Li-hua

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A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA) by Charmayne Mary Lee Hughes, Yan Zhang, Ali Pourhossein, Terezia Jurasova

“…BackgroundPhysical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. …”
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SkinIncept: an ensemble transfer learning-based approach for multiclass skin disease classification using InceptionV3 and InceptionResNetV2 by Md. Hasan Imam Bijoy, Md. Mahbubur Rahman, Abdus Sattar, Aminul Haque, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…This study addresses this critical issue by developing a robust and accurate system for classifying Bangladesh’s ten most common skin diseases using convolutional neural networks (CNNs)-based transfer learning models. …”
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CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy by Zahra Taghados, Zohreh Azimifar, Malihezaman Monsefi, Mojgan Akbarzadeh Jahromi

“…Abstract Cervical cancer is a significant global health issue affecting women worldwide, necessitating prompt detection and effective management. …”
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“Service” versus “Education” in Anesthesiology: Domain Classification of Activities Based on Perceptions from Learners and Teachers by Li J, Lin HM, Yanez ND, He Z, Treggiari MM, Kurup V

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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

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Enhancing credit card fraud detection: highly imbalanced data case by Dalia Breskuvienė, Gintautas Dzemyda

“…This paper emphasizes the main issues in fraud detection and suggests a novel feature selection method called FID-SOM (feature selection for imbalanced data using SOM). …”
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G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud by Geng Chen, Zhiwen Zhang, Yuanxi Peng, Chunchao Li, Teng Li

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Evaluation of Water Extraction Indices Using Landsat Satellite Images (Case Study: Gamasiab River of Kermanshah) by sayyad asghari, roholah jalilyan, noshin pirozineghad, aghil madadi, milad yadeghari

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Geospatial dataset on deforestation and urban sprawl in Dhaka, Bangladesh: A resource for environmental analysisMendeley Data by Md. Fahad Khan, Md. Rakibul Islam, Shanto Kumar Basak, Ahmed Imtiaz, Abhijit Bhowmik, Dip Nandi, Mashiour Rahman, Debajyoti Karmaker

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Adversarial example defense algorithm for MNIST based on image reconstruction by Zhongyuan QIN, Zhaoxiang HE, Tao LI, Liquan CHEN

“…With the popularization of deep learning, more and more attention has been paid to its security issues.The adversarial sample is to add a small disturbance to the original image, which can cause the deep learning model to misclassify the image, which seriously affects the performance of deep learning technology.To address this challenge, the attack form and harm of the existing adversarial samples were analyzed.An adversarial examples defense method based on image reconstruction was proposed to effectively detect adversarial examples.The defense method used MNIST as the test data set.The core idea was image reconstruction, including central variance minimization and image quilting optimization.The central variance minimization was only processed for the central area of the image.The image quilting optimization incorporated the overlapping area into the patch block selection.Considered and took half the size of the patch as the overlap area.Using FGSM, BIM, DeepFool and C＆amp;W attack methods to generate adversarial samples to test the defense performance of the two methods, and compare with the existing three image reconstruction defense methods (cropping and scaling, bit depth compression and JPEG compression).The experimental results show that the central variance minimization and image quilting optimization algorithms proposed have a satisfied defense effect against the attacks of existing common adversarial samples.Image quilting optimization achieves over 75% classification accuracy for samples generated by the four attack algorithms, and the defense effect of minimizing central variance is around 70%.The three image reconstruction algorithms used for comparison have unstable defense effects on different attack algorithms, and the overall classification accuracy rate is less than 60%.The central variance minimization and image quilting optimization proposed achieve the purpose of effectively defending against adversarial samples.The experiments illustrate the defense effect of the proposed defense algorithm in different adversarial sample attack algorithms.The comparison between the reconstruction algorithm and the algorithm shows that the proposed scheme has good defense performance.…”
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