Interpretable Deep Learning Model for Grape Leaf Disease Classification Based on EfficientNet with Grad-CAM Visualization by Castaka Agus Sugianto, Dini Rohmayani, Jhoanne Fredricka, Mohamed Doheir

“…This study proposes a computer vision-based solution using Convolutional Neural Networks (CNN) improved by EfficientNetB0 to classify grape leaf diseases. …”
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A Novel MPE-LPP-ELM Recognition Method for the Fault Diagnosis of Spiral Bevel Gears by Jiang Lingli, Tan Hongchuang, Li Xuejun, Yang Dalian

“…In order to improve the accuracy and efficiency of fault recognition in spiral bevel gear evaluations, locality preserving projection (LPP) methods can be applied to reduce the high dimensionality feature vectors constructed by MPEs. …”
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Luminaire Detection through Brightness Enhanced Mask R-CNN in Tunnel Environment by B. Xu, L. Liu, L. Liu, X. Qin, X. Qin, L. Xie, L. Xie

“…Leveraging tunnel gray-scale images and the Mask R-CNN object detection framework, a feature fusion network based on ResNet-FPN, trained via transfer learning, which enhances performance in detecting object luminaires. …”
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Enhanced mask R-CNN for luminaire detection through brightness balancing and distribution-guided optimization in tunnels by Xiaoqiong Qin, Bintao Xu, Chenchen Wang, Lirui Liu, Linfu Xie, Rong Liu, Mingwei Hu, Xiangsheng Chen

“…Leveraging tunnel gray-scale images and the Mask R-CNN object detection framework, a feature fusion network based on ResNet-FPN, trained via transfer learning, which enhances performance in detecting object luminaires. …”
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Deep learning for real-time P-wave detection: A case study in Indonesia's earthquake early warning system by Adi Wibowo, Leni Sophia Heliani, Cecep Pratama, David Prambudi Sahara, Sri Widiyantoro, Dadan Ramdani, Mizan Bustanul Fuady Bisri, Ajat Sudrajat, Sidik Tri Wibowo, Satriawan Rasyid Purnama

“…This paper proposes a novel deep learning-based model with three convolutional layers, enriched with dual attention mechanisms—Squeeze, Excitation, and Transformer Encoder (CNN-SE-T) —to refine feature extraction and improve detection sensitivity. …”
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AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation by Abderrachid Hamrani, Daniela Leizaola, Nikhil Kumar Reddy Vedere, Robert S. Kirsner, Kacie Kaile, Alexander Lee Trinidad, Anuradha Godavarty

“…AIDA applies clustering techniques to classify skin tones without relying on labeled data, evaluating over twelve models, including K-means, density-based, hierarchical, and fuzzy logic algorithms. …”
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Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Abstract Background The aim of this study was to evaluate the potential models to determine the most important anthropometric factors associated with type 2 diabetes mellitus (T2DM). …”
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High-precision lung cancer subtype diagnosis on imbalanced exosomal data via Exo-LCClassifier by Siyu Zhan, Siyu Zhan, Hao Yu, Shuang Liu, Ke Qin, Lu Guo

“…This study aims to address these challenges by proposing an innovative deep learning-based method for predicting lung cancer subtypes.MethodsWe propose a method called Exo-LCClassifier, which integrates feature selection, one-dimensional convolutional neural networks (1D CNN), and an improved Wasserstein Generative Adversarial Network (WGAN). …”
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MA-VoxelMorph: Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images by Qing Huang, Lei Ren, Tingwei Quan, Minglei Yang, Hongmei Yuan, Kai Cao

“…To deal with this problem, we propose a 3D multi-scale attention VoxelMorph (MA-VoxelMorph) registration network. To alleviate the large deformation problem, a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction, and position-aware axial attention for long-distance dependencies between pixels capture. …”
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Metric-Based Few-Shot Learning With Triplet Selection for Adaptive GNSS Interference Classification by Felix Ott, Lucas Heublein, Tobias Feigl, Alexander Rugamer, Christopher Mutschler

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OMRoadNet: A Self-Training-Based UDA Framework for Open-Pit Mine Haul Road Extraction from VHR Imagery by Suchuan Tian, Zili Ren, Xingliang Xu, Zhengxiang He, Wanan Lai, Zihan Li, Yuhang Shi

“…The framework employs a cyclic GAN (generative adversarial network) architecture with bidirectional translation pathways, integrating pseudo-label refinement through confidence thresholds and geometric rules (eight-neighborhood connectivity and adaptive kernel resizing) to resolve domain shifts. …”
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Hyperspectral signature-band extraction and adaptation for sugar content prediction on Ziziphus mauritiana and Syzygium samarangense by Yung-Jhe Yan, Jen-Tzung Chien, Zi-Yin Hong, Wen-Li Lee, Kuo-Dung Chiou, Chi-Cho Huang, Mang Ou-Yang

“…The proposed method was evaluated on two fruit species, Ziziphus mauritiana and Syzygium samarangense. …”
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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. …”
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An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing for Fraud Detection in Payment Systems by Hadi M. R. Al Lawati, Anazida Zainal, Bander Ali Saleh Al-Rimy, Mohammad Al-Azawi, Mohamad Nizam Kassim, Sultan Ahmed Almalki, Tami Abdulrahman Alghamdi

“…The method uses Mutual Information and SelectKBest for selecting important features, applies ADASYN to handle class imbalance, and adopts Convolutional Neural Networks (CNN) to capture complex transaction patterns. …”
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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

“…., feed-forward neural networks (FNN), extreme gradient boosting (XGBoost), and random forest (RF). …”
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Unmanned Aerial Vehicle-Based RGB Imaging and Lightweight Deep Learning for Downy Mildew Detection in Kimchi Cabbage by Yang Lyu, Xiongzhe Han, Pingan Wang, Jae-Yeong Shin, Min-Woong Ju

“…Among the evaluated models, Vision Transformer (ViT)-based architectures outperformed Convolutional Neural Network (CNN)-based models in terms of classification accuracy and generalization capability. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…The suggested model integrates the feature extraction expertise of CNNs with the sequence modeling proficiency of BiLSTM networks to capture spatial linkages and temporal interdependence adeptly. …”
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Deep Learning-Based Fault Diagnosis via Multisensor-Aware Data for Incipient Inter-Turn Short Circuits (ITSC) in Wind Turbine Generators by Qinglong Wang, Shihao Cui, Entuo Li, Jianhua Du, Na Li, Jie Sun

“…Our approach is rigorously evaluated using metrics such as confusion matrices, accuracy, recall, average precision (AP), mean average precision (mAP), hypothesis testing, and feature visualization. …”
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A Dual-Labeled Multiplex Absolute Telomere Length Method to Measure Average Telomere Length by Sue Rutherford Siegel, E. Alex Calcagni, Kelsey M. Draughon, Sheree F. Logue

“…The oligomer feature facilitates a more direct comparison of results across experiments and laboratories. …”
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A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning by Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber

“…Observed phenologies were taken from German national phenology network (German Meteorological Service; DWD) between 2017 and 2021. …”
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