A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images by Pooja Govindaraj, Sasikaladevi Natarajan, Pradeepa Sampath, Akilesh Thimma Suresh, Rengarajan Amirtharajan

“…Manual cytological examination is time-consuming, error-prone and subject to inter-observer variability. Automated and robust classification of the whole slide cytology images for cervical cancer is essential for detecting precancerous and malignant lesions. …”
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Spectra of algebras of block-symmetric analytic functions of bounded type by A. Zagorodnyuk, V. V. Kravtsiv

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Computational methods and technical means of processing signals of side electromagnetic emanation by Danil A. Shinyaev, Leonid N. Kessarinskiy, Egor A. Simakhin

“…In future studies, it is planned to train the model on an updated dataset with other neural network analogues in order to optimize the process of predicting variables in the regression model.…”
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Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features by Aiguo Lu, Zican Li, Yanwei Liu, Pandi Liu, Ke Wang

“…The Omni-Dimensional Dynamic Convolution module and Bi-Level Routing Attention mechanism are introduced to enhance the model’s adaptability to complex scenes and variable features, thereby improving its capability to capture details of small targets. …”
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Suppression strategies for pyrolysis gas parameter fluctuations in small pyrolysis-combustion coupled reactors for MSW by Shuhui Wang, Jianxiong Zhang, Jun Fu, Lin Wang, Zhifeng He, Zheng Yan

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Review of Synthetic Aperture Radar Automatic Target Recognition: A Dual Perspective on Classical and Deep Learning Techniques by Jakub Slesinski, Damian Wierzbicki

“…What makes SAR imagery particularly unique are problems, such as speckle noise, target variability, and clutter, for which there are specialized solutions described in this article. …”
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Enhancing skin lesion classification: a CNN approach with human baseline comparison by Deep Ajabani, Zaffar Ahmed Shaikh, Amr Yousef, Karar Ali, Marwan A. Albahar

“…This approach offers a scalable, resource-efficient solution to address variability in medical image analysis, effectively harnessing the complementary strengths of expert humans and CNNs.…”
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PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion by Ying Qi, Qiushi Li, Zhaoyuan Huang, Jian Li, Chenyang Wang, Teng Wan, Qiang Zhang

“…Existing methods typically adopt an align-then-fuse strategy, often overlooking the spatial variability of alignment quality, which makes it difficult to balance ghosting suppression and detail preservation when handling complex motion. …”
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Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images by Yuanjun Guan, Yang Liu, Jiayi Wang, Tao Wang, Qianchuan Yi, Wenxin Jiang, Xiaopu Gu, Yichen Zhang, Li Zhang, Tianyan Han, Binbing Huang, Lilei Hu

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Artificial intelligence in acoustic ecology: Soundscape classification in the Cerrado by Bruno Daleffi da Silva, Linilson Rodrigues Padovese

“…Five statistical models were developed and evaluated, utilizing both traditional Machine Learning and Deep Learning, with Mel Frequency Cepstral Coefficients (MFCCs) and spectrogram images as input variables. The performance comparison of these models revealed the superiority of the Convolutional Neural Network (CNN), which, although requiring higher computational costs and training time, provided high accuracy in classifications and valuable insights through the application of the LIME explainability technique. …”
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A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network by Kok Poh Wai, Chai Hoon Koo, Yuk Feng Huang, Woon Chan Chong, Ahmed El-Shafie, Mohsen Sherif, Ali Najah Ahmed

“…Study focus: This study presents a multi-step ahead water quality index (WQI) forecasting framework in Klang River to address persistent challenges such as missing data and seasonally variable hydrological patterns. A hybrid deep learning architecture was developed by combining a 1d-Convolutional Neural Network (CNN) with a dual-path Long Short-Term Memory (LSTM) network to capture long-term hydrological memory and site-specific temporal variability. …”
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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…Static features, including farmland distribution and soil types, are extracted using Convolutional Neural Networks, while temporal trends in variables such as weather patterns and policy changes are captured by the Long Short-Term Memory network. …”
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A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework by Mengji Yang, Haiqing Zhang, Xi Yu, Aicha Sekhari Seklouli, Abdelaziz Bouras, Yacine Ouzrout

“…This framework enhances the ability to capture long-term dependencies through the combined effects of efficient convolution parameter optimization and variable-oriented multivariate modeling. …”
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Enhancing Arabic handwritten word recognition: a CNN-BiLSTM-CTC architecture with attention mechanism and adaptive augmentation by Bounour Imane, Ammour Alae, Khaissidi Ghizlane, Mostafa Mrabti

“…Abstract Optical character recognition (OCR) for Arabic presents unique challenges due to the script's cursive nature, contextual letter forms, multiple ligatures, the presence of diacritics, and the high variability in handwritten styles. This work introduces an enhanced Arabic handwritten word recognition architecture that integrates the attention mechanism (AM) into an end-to-end framework combining convolutional neural networks (CNN), Bidirectional long short-term memory (BiLSTM), and connectionist temporal classification (CTC), while utilizing word beam search (WBS) for decoding. …”
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Wind speed prediction for trains on bridges using enhanced variational mode decomposition assisted feature extraction and physical auxiliary mechanism by Zhilan Zhu, Yuan Jiang, Haicui Wang, Shuoyu Liu

“…Finally, PAM is introduced into the above established model for realizing the desired deterministic and probabilistic predictions where the relationship among the wind speed data recorded at various time intervals and the data variability are considered. Numerical examples, utilizing two sets of measured wind speed data, underscore the efficacy and advantage of the developed method. …”
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Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images by Divine Senanu Ametefe, Dah John, Abdulmalik Adozuka Aliu, George Dzorgbenya Ametefe, Aisha Hamid, Tumani Darboe

“…These AI-based models offer a robust diagnostic pipeline that improves lesion localization, reduces interobserver variability, and supports clinical decision-making. …”
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A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics by Xinle Zhang, Guowei Zhang, Shengqi Zhang, Hongfu Ai, Yongqi Han, Chong Luo, Huanjun Liu

“…In this model, the Convolutional Neural Network (CNN) extracts spatial context features from static variables (e.g., climate and terrain variables), while the Long Short-Term Memory (LSTM) network captures temporal features from dynamic variables (e.g., Sentinel-2 time series from April to October). …”
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Enhanced Offline Writer Recognition System Employing Blended Multi-Input CNN and Bi-LSTM Model on Diverse Handwritten Texts by Naresh Purohit, Subhash Panwar

“…This synergy makes it particularly effective in handling the variability and complexity of offline writer recognition. …”
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SAR Small Ship Detection Based on Enhanced YOLO Network by Tianyue Guan, Sheng Chang, Chunle Wang, Xiaoxue Jia

“…Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship size and resolution, especially the widespread presence of numerous small-sized ships, continues to pose challenges for effective ship detection in SAR images. …”
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Application of big data technology in visitor behavioral characteristics identification and energy efficiency assessment of intelligent scenic spots by Wei Zhou, Jin Cui

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