Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. …”
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OsteoNet—A Framework for Identifying Osteoporosis in Bone Radiograph Images Using Attention-Based VGG Network by Abdul Wahab Muzaffar, Farhan Riaz, Muhammad Tahir

“…A comparative analysis of the proposed method on ISBI 2014 challenge dataset against existing methods revealed that hand-crafted features mimicking gradients outperform those based on statistics, and CNN-based methods generally exhibit better performance. …”
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Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks by Weijian Liu, Haoyuan Chang, Yang Xiao, Shuisheng Yu, Chuanbo Huang, Yuntian Yao

“…A U-net model to detect the arrival time of seismic waves is constructed based on the convolutional neural network (CNN) theory. The original data for 1555 segments and synthetic data of 7764 segments were detected using Akaike’s information criterion (AIC) algorithm, the time window energy eigenvalue algorithm, and the U-net model. …”
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Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains by Lewis Chinery, Jeliazko R. Jeliazkov, Charlotte M. Deane

“…Throughout the humanization process, a sequence is guided toward a specific target gene and away from others via multiclass CNN outputs and gene-specific germline data. This guidance ensures final humanized designs do not sit ‘between’ genes, a trait that is not naturally observed. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
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FFUNet: A novel feature fusion makes strong decoder for medical image segmentation by Junsong Xie, Renju Zhu, Zezhi Wu, Jinling Ouyang

“…In addition, consistent improvements are also achieved across other four popular datasets and CNN‐based or transformer‐based segmentation networks, which illustrate that the proposed method has advantages in generalization and compactness.…”
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Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems by Shize Huang, Xiaowen Liu, Xiaolu Yang, Zhaoxin Zhang, Lingyu Yang

“…In this paper, we propose an improved projected gradient descent (PGD) algorithm and an improved Carlini and Wagner (C&W) algorithm to generate adversarial examples against Faster R-CNN object detectors. Experiments verify that both algorithms can successfully conduct nontargeted and targeted white-box digital attacks when trams are running. …”
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ECP-IEM: Enhancing seasonal crop productivity with deep integrated models. by Ghulam Mustafa, Muhammad Ali Moazzam, Asif Nawaz, Tariq Ali, Deema Mohammed Alsekait, Ahmed Saleh Alattas, Diaa Salama AbdElminaam

“…This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) and Time Series CNN to predict crop yield and then recommendation for further improvement. …”
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NeuroSight: A Deep‐Learning Integrated Efficient Approach to Brain Tumor Detection by Shafayat Bin Shabbir Mugdha, Mahtab Uddin

“…ResNet‐50 model got 93.31% test accuracy, 98.78% training accuracy, and 0.6327 validation loss. The CNN model has a 0.2960 validation loss, 92.59% test accuracy, and 98.11% training accuracy. …”
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Vision transformers for automated detection of diabetic peripheral neuropathy in corneal confocal microscopy images by Chaima Ben Rabah, Ioannis N. Petropoulos, Rayaz A. Malik, Ahmed Serag

“…The ViT model's performance was also compared to ResNet50, a convolutional neural network (CNN) previously applied for DPN detection using CCM images. …”
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Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model by Dechen Yao, Qiang Sun, Jianwei Yang, Hengchang Liu, Jiao Zhang

“…Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. …”
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A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods by Yonghui Tang, Fei Wu

“…Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.…”
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Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

“…The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. …”
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Vision-based welding quality detection of steel bridge components in complex construction environments by Tianshi Hu, Xiuping Huang, Zuolei Yang, Zhixiong Liu, Jie Zhao, Zhao Xu

“…The contour dimensions of both filler and cover welds are identified through feature point extraction, with an estimated detection error under 0.6%. (3) This paper optimizes the feature extraction of the Faster R-CNN network based on the appearance feature and detection need of welding defects, resulting in an improvement of 28.3 in mAP. …”
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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition. by Pranjul Gupta, Katharina Dobs

“…Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features-such as 'eyes'-to classify pareidolia stimuli as faces, mirroring findings in human perception. …”
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End-to-End Semantic Leaf Segmentation Framework for Plants Disease Classification by Khalil Khan, Rehan Ullah Khan, Waleed Albattah, Ali Mustafa Qamar

“…We use tomato plant leaves as a test case in our work. We test the proposed CNN-based model on the publicly available database, PlantVillage. …”
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A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model by Jingmei Zhou, Hui Chang, Xin Cheng, Xiangmo Zhao

“…The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. …”
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Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification by Mhd Saeed Sharif, Maysam Abbod, Abbes Amira, Habib Zaidi

“…The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). …”
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Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN by Peihao Tang, Zhen Li, Xuanlin Wang, Xueping Liu, Peng Mou

“…In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. …”
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A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant by Shwetha V., Arnav Bhagwat, Vijaya Laxmi, Sakshi Shrivastava

“…For accurate segmentation of the leaf disease, we utilize a UNet architecture with a custom backbone based on the MobileNetV4 CNN. The proposed segmentation model yields an average pixel accuracy of 0.91 and an mIoU (mean intersection over union) of 0.95. …”
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