FRPNet: A Lightweight Multi-Altitude Field Rice Panicle Detection and Counting Network Based on Unmanned Aerial Vehicle Images by Yuheng Guo, Wei Zhan, Zhiliang Zhang, Yu Zhang, Hongshen Guo

“…The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling with multi-branch dilated convolutions to preserve fine-grained spatial information; and a Dynamic Bidirectional Feature Pyramid Network (DynamicBiFPN) employing input-adaptive kernels to optimize cross-scale feature fusion. …”
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A Hybrid Transformers-based Convolutional Neural Network Model for Keratoconus Detection in Scheimpflug-based Dynamic Corneal Deformation Videos by Hazem Abdelmotaal, Rossen Mihaylov Hazarbasanov, Ramin Salouti, M. Hossein Nowroozzadeh, Suphi Taneri, Ali H. Al-Timemy, Alexandru Lavric, Hidenori Takahashi, Siamak Yousefi

“…Methods: We used transfer learning for feature extraction from DCDVs. These feature maps were augmented by self-attention to model long-range dependencies before classification to identify keratoconus directly. …”
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Deep Neural Network-Based Detection of Modulated Jamming in Free-Space Optical Systems: Theory and Performance Under Atmospheric Fading by Manav R. Bhatnagar

“…We propose a deep neural network (DNN)-based binary classifier designed to discriminate between clean and jammed received frames. …”
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Deep Residual Network With Integrated StarDist Nuclei Segmentation for Papillary Thyroid Cancer Identification: A Pathologist-Inspired Approach by Nabila Husna Shabrina, Dadang Gunawan, Mia Rizkinia, Agnes Stephanie Harahap, Mohammad Ikhsan, Rifai Chai, Maria Francisca Ham

“…The proposed model enhances feature localization by integrating nucleus segmentation with Deep Residual Networks, yielding a practical and efficient solution for histopathological PTC analysis.…”
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A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks by Samrah Arif, M. Arif Khan, Sabih ur Rehman

“…The models’ performance was assessed by comparing them with the state-of-the-art and our previously developed Artificial Neural Network (ANN)-based method. The experimental results show that the RFR(F) method shows approximately 39.62% improvement in Mean Squared Error (MSE) over the Feature-based ANN(F) model and 37.86% advancement over the state-of-the-art. …”
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A novel wind speed prediction model based on neural networks, wavelet transformation, mutual information, and coot optimization algorithm by Faezeh Amirteimoury, Farshid Keynia, Elaheh Amirteimoury, Gholamreza Memarzadeh, Hanieh Shabanian

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An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks by Tianyong Jiang, Lin Liu, Chunjun Hu, Lingyun Li, Jianhua Zheng

“…Specifically, we employ the self-calibrated illumination (SCI) model to reconstruct low-light images, which are then processed by an improved YOLOv5-based network, YOLOv5-GAM-ASFF, incorporating a global attention mechanism (GAM) and adaptive spatial feature fusion (ASFF). …”
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Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model by S. Gegenleithner, S. Gegenleithner, M. Pirker, C. Dorfmann, R. Kern, J. Schneider

“…In this study, we investigate the potential of long short-term memory (LSTM) networks for enhancing the forecast accuracy of an underperforming PBHM and evaluate whether they are able to overcome some of the challenges presented by ARIMA models. …”
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Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network by Ahmed Saihood, Wijdan Rashid Abdulhussien, Laith Alzubaid, Mohamed Manoufali, Yuantong Gu

“…This study addresses these challenges by proposing a novel generative adversarial network (GAN) architecture tailored for semi-supervised lung nodule classification. …”
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Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data by Xuan Jin, Yawei Zhao, Xin Zhang, Yanlei Du, Jinsong Chong

“…In current studies, convolutional neural networks (CNNs) are widely employed to extract either deep space features from normalized radar cross section (NRCS) of SAR images or deep frequency features from SAR spectra, with some studies combining artificially designed scalar features to retrieve significant wave height (SWH). …”
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TMN-Net: A Hybrid 2.5D Multi-Branch Transformer Network for Coronary Artery Segmentation in Cardiac Diagnosis by Bo Zhao, Jianjun Peng, Kai Zhang, Yongyan Fan, Ce Chen, Yang Zhang

“…This study proposes the Transformer Multi-branch Network (TMN-Net), an innovative hybrid framework that integrates augmented 2.5D and transformer-based 3D processing pathways. …”
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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

“…<b>Methods</b>: MRI images were preprocessed through normalization and noise reduction. Feature extraction combined texture features from the Gray-Level Co-occurrence Matrix (GLCM) and spatial features extracted from a pretrained VGG-16 network. …”
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Detecting early gastrointestinal polyps in histology and endoscopy images using deep learning by Intissar Dhrari Hajsalem, Yassine Ben Ayed

“…Nevertheless, a lot of current technologies are still insufficient to detect tumors, which is why we created an approach using advanced method to identify polyps.MethodsOur three-stage deep learning-based method requires constructing an Encoder-Decoder Network (EDN) to determine the Region of Interest (ROI) in preprocessing, feature selection with pretrained models such as VGG16, VGG19, ResNet50 and InceptionV3, and Support Vector Machine (SVM) classifier to separate affected individuals from normal ones during the classification stage. …”
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An FPGA Prototype for Parkinson&#x2019;s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms &#x2014; Chi-squared (<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula>), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) &#x2014; and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). …”
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EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

“…Second, 1D Convolutional Neural Networks models are developed, and pre-processed EEG signals are fed as input. …”
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Content and Genres of City Image Development in Social Media: The City of Arkhangelsk by Larisa Yu. Shchipitsina

“…This article defines the phenomenon of city image in social media and describes its linguistic features. The research involved a new automatic data collection tool: a web application that trawled social networks, messengers, official websites, and portals for reactions to the trigger word Arkhangelsk and its derivatives. …”

Automated classification of epileptic seizures using modified one-dimensional convolution neural network based on empirical mode decomposition with high accuracy by Ibtihal Hassan Elshekhidris, Magdi B. M. Amien, Ahmed Fragoon

“…We used the stationary wavelet transform with mother function symlet2 for denoising EEG signal and used the empirical mode decomposition for signal decomposition. After that, features extraction step is necessary when used the support vector machine and artificial neural network, but when use the convolution neural network the features are extracted by layers.Results: The highest value of a classification accuracy was 100%, and a sensitivity 100%, a specificity 100%, and a precision 100%, which appeared five times when using the one-dimensional convolution neural network after empirical mode decomposition method.Conclusions: The efficiency of the three methods has been compared and evaluated by using four metrics: Accuracy, Sensitivity, specificity, and Precision, and the result showed the one-dimensional convolution neural network is the best method for classification with empirical mode decomposition.…”
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EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 by Dmitry E. Sokolovsky, Vladimir N. Nekrasov, Sergey A. Zemlyansky, Sergey V. Axyonov

“…Determined by the need to extract significant features from electronic medical records to automate the assessment of patients' condition. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. …”
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EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks by Md Hossain Shuvo, Debswapna Bhattacharya

“…Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. …”
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