Enhancing anomaly detection in plant disease recognition with knowledge ensemble by Jiuqing Dong, Jiuqing Dong, Jiuqing Dong, Heng Zhou, Alvaro Fuentes, Alvaro Fuentes, Sook Yoon, Dong Sun Park, Dong Sun Park

“…We first benchmark the anomaly detection performance of three major visual frameworks—convolutional neural networks (CNNs), vision transformers (ViTs), and vision-language models (VLMs)—under varying fine-tuning strategies. …”
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FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images by Liang Zhou, Achin Jain, Arun Kumar Dubey, Sunil K. Singh, Neha Gupta, Arvind Panwar, Sudhakar Kumar, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Brij B. Gupta

“…Abstract Cancer is among the most dangerous diseases contributing to rising global mortality rates. …”
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Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control by Luying Feng, Lianghong Gui, Wenzhu Xu, Xiang Wang, Canjun Yang, Yaochu Jin, Wei Yang

“…Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models were specifically applied to estimate knee joint angles and hip joint moments, achieving a Mean Absolute Error (MAE) of 4.43° and 0.12 Nm/kg, respectively. …”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…Abstract Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…In this research, several machine learning predictive models based on extra trees, decision tree, support vector machine, random forest, multilayer perceptron artificial neural network, K-nearest neighbors, convolutional neural network, ensemble learning, and adaptive boosting using a dataset gathered from a coke plant are developed to predict CRI. …”
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Improving Road Semantic Segmentation Using Generative Adversarial Network by Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri

“…Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. …”
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Cytopathological quantification of NORs using artificial intelligence to oral cancer screening by Tatiana Wannmacher LEPPER, Luara Nascimento do AMARAL, Ana Laura Ferrares ESPINOSA, Igor Cavalcante GUEDES, Maikel Maciel RÖNNAU, Natália Batista DAROIT, Alex Nogueira HAAS, Fernanda VISIOLI, Manuel Menezes de OLIVEIRA NETO, Pantelis Varvaki RADOS

“…The present study aimed to define argyrophilic proteins of the nucleolar organizer region (AgNOR) cut-off risk points by oral exfoliative cytological smears comparing specialized humans with a convolutional neural network (CNN) system AgNOR Slide-Image Examiner. …”
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An Anchor-Free Method Based on Transformers and Adaptive Features for Arbitrarily Oriented Ship Detection in SAR Images by Bingji Chen, Chunrui Yu, Shuang Zhao, Hongjun Song

“…Ship detection is a crucial application of synthetic aperture radar (SAR). Most recent studies have relied on convolutional neural networks (CNNs). …”
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xLSTM Interaction Multilevel SSM-Assisted Decoding Network for Remote Sensing Image Change Detection by Chunpeng Wu, Shuli Cheng, Anyu Du, Liejun Wang, Wenbin Tang

“…With the advancements of convolutional neural networks (CNNs) and Transformers in deep learning, the accuracy of RSCD has significantly improved. …”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). …”
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Automated Models for Predicting Software Defects in Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) Parallel Programs Using Deep Learning by Amani Saad Althiban, Hajar M. Alharbi, Lama A. Al Khuzayem, Fathy Elbouraey Eassa

“…Using a balanced dataset of 1,500 C++ files, three neural architectures—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model—were evaluated. …”
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Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach by Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos, Isibor Kennedy Ihianle

“…Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. …”
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A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention by Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou, Lei Guo

“…Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). …”
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RainHCNet: Hybrid High-Low Frequency and Cross-Scale Network for Precipitation Nowcasting by Lei Wang, Zheng Wang, Wenjun Hu, Cong Bai

“…Recent advancements in deep learning have led to the development of radar echo extrapolation methods. However, most convolutional neural network-based methods focus primarily on high-frequency information, neglecting essential low-frequency cues necessary for forecasting high-intensity rainfall. …”
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Enhancing Learning-Based Cross-Modality Prediction for Lossless Medical Imaging Compression by Daniel S. Nicolau, Lucas A. Thomaz, Luis M. N. Tavora, Sergio M. M. Faria

“…Subsequently, a decider based on a Convolutional Neural Network is employed to estimate the best coding approach to be selected among the two alternatives, before the coding step. …”
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Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI by Johanna C. Walker, Conner Swineford, Krupali R. Patel, Lea R. Dougherty, Jillian Lee Wiggins

“…The recent emergence of deep learning methods, particularly convolutional neural networks (CNNs), applied to fMRI data presents a promising avenue in psychiatry research, offering advantages over traditional analyses by requiring minimal assumptions and enabling detection of higher-level patterns and intricate, nonlinear relationships within inherently complex fMRI data. …”
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Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms by Zhan Zhang, Qingping Song, Minzheng Duan, Hailei Liu, Juan Huo, Congzheng Han

“…Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. …”
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An explainable Bi-LSTM model for winter wheat yield prediction by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Subrata Chakraborty, Renuganth Varatharajoo, Abdullah Alamri, Shilpa Gite, Chang-Wook Lee

“…Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. …”
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A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images by B.E. Malyugin, S.N. Sakhnov, L.E. Axenova, K.D. Axenov, E.V. Kozina, V.V. Vronskaya, V.V. Myasnikova

“…The architecture of the neural network was a convolutional neural network UNET. To evaluate the effectiveness of the proposed model, the Dice coefficient (DSC) was used. …”
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Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings by Soh-Yeon Lee, Sully Lee, Se-Hoon Kim, HyeSun Chang, Won-Yang Cho, Min-Ok Ryu, Jihye Choi, Hwa-Young Yoon, Kyoung-Won Seo

“…Abstract Background Myxomatous mitral valve disease (MMVD) represents the most prevalent cardiac disorder in dogs, frequently resulting in mitral regurgitation (MR) and congestive heart failure. …”
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