Optimization of CNN Activation Functions using Xception for South Sulawesi Batik Classification by Aswan Aswan, Eva Yulia Puspaningrum, Billy Eden William Asrul

“…The results demonstrate that selecting an optimal activation function substantially enhances convolutional neural network classification of complex batik patterns. The findings offer practical guidance for development of resource aware batik identification systems in support of cultural digitization and education initiatives.…”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…The CNN model is adjusted for different convolutional layers and fully connected layers to identify patterns and features in brain tumor pictures using an enhanced salp swarm algorithm (SSA) with kernel extreme learning machine (KELM). …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…In the modern era of computers, numerous disciplines are witnessing the development of massive data volumes. Statistics are important in healthcare engineering because they provide insights into various diseases and match patient data. …”
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TableBorderNet: A Table Border Extraction Network Considering Topological Regularity by Jing Yang, Shengqiang Zhou, Xialing Li, Yuchun Huang, Honglin Jiang

“…Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. …”
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Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach by Mahbuba Aktar, Nur Islam, Chaoyu Yang

“…A key limitation lies in the absence of robust detection frameworks capable of accommodating the script’s complex visual patterns and nuances. To address this gap, we propose an enhanced object detection model based on the YOLOv11 architecture, incorporating a ResNet50 backbone for improved feature extraction. …”
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A Comprehensive Review of EEG-Based Seizure Detection Techniques by Goldwyn Sudhakar Jebaraj, Konguvel Elango

“…By discussing the pros and cons of different methods, current limitations, and ethical considerations, this review seeks to help new researchers develop better EEG-based seizure classification and prediction systems with real-world impact for patients.…”
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Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures by Weijie Zhang, Veronika Belcheva, Tatiana Ermakova

“…The models are assessed based on classification accuracy and interpretability, applying Grad-CAM and Attention-Rollout to analyze decision-making patterns. Results indicate that hybrid models outperform both standalone CNNs and ViTs, achieving a better balance between local feature extraction and global context awareness. …”
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Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models by Mohammad Liton Hossain, S. M. Nasif Shams, Saeed Mahmud Ullah

“…These findings underscore the transformative potential of deep learning in enhancing renewable energy forecasting accuracy in developing urban regions, providing critical insights for future energy policy and infrastructure development in Dhaka City.…”
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Histogram-based gradient boosting machine with SHAP-driven interpretability for predicting intensity of urban heat Island effect by Nhat-Duc Hoang

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Synchronized Survey Scan Approach Allows for Efficient Discrimination of Isomeric and Isobaric Compounds during LC-MS/MS Analyses by Keabetswe Masike, Ntakadzeni Madala

“…Thus, the method was shown to distinguish (by differences in fragmentation patterns) between diCQA and their isobars, caffeoylquinic acid (CQA) glycosides. …”
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A Graph Representation Learning-Based Method for Event Prediction by Xi Zeng, Guangchun Luo, Ke Qin, Pengyi Zheng

“…Predicting events allows for the exploration of the developmental trajectories and summarization of patterns associated with these events. …”
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Client aware adaptive federated learning using UCB-based reinforcement for people re-identification by Dinah Waref, Yomna Alayary, Nadeen Fathallah, Mohamed A. Abd El Ghany, Mohammed A.-M. Salem

“…Finally, we introduce a feature-level attention mechanism focusing on discriminative features for re-identification. …”
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Deep Learning‐Based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data by Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

“…Abstract Microseismic monitoring is crucial for characterizing and assessing fracture systems developed during subsurface operations like geothermal reservoir development, CO2 sequestration, and hydraulic fracturing. …”
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CircDock6 drives metabolic dysfunction–associated steatotic liver disease progression in mice and mouse hepatocytes via mmu-let-7g-5p/insulin-like growth factor 1 receptor regulati... by Hongpeng Lu, Xiaoyun Ding, Peifei Li

“…Objective Circular RNAs belong to a category of noncoding RNAs that feature a unique continuous, covalently bonded ring configuration. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. …”
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SKIN AVILABLE MASTOID AREA ON THE STAGE OF ITS STRAIN by D.S. Avetikov, D.V. Steblovskii, I.I. Starchenko

“…Thus, in the experimental group that studied a thorny layer of the epidermis cells there is a progressive increase in the state hydropic degeneration, while some of them in cytoplasmic vacuoles with fluid, occupying almost the entire space of the cytoplasm. This morphological pattern indicative of the extent of extreme severity hydropic degeneration is the development of balloon degeneration, which is known to be a morphological equivalent focal necrosis. …”
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Russian President and local self-government: the evolution of relationships by M. Yu. Dityatkovsky

“…The paper examines the dynamics of the relationship between the Russian Presidents and local self-government, determines the five stages of the development of such relations and their characteristic features, as well as the prospects for these relations after the adoption of amendments to the Constitution of the Russian Federation in 2020.Stage 1. …”
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Machine learning-guided field site selection for river classification by Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

“…Results revealed clear differences in variable distributions between the 148 high‐certainty sites and the 51 high‐uncertainty sites, a pattern that was validated by our field surveys. Furthermore, the 41 newly identified high‐uncertainty sites were found under-represented in the initial surveyed sites and thus their selection for the next round of field surveys will help fill the important feature gaps left by the initial survey. …”
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NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides by Chengzhi Xie, Yijie Wei, Xinwei Luo, Huan Yang, Hongyan Lai, Fuying Dao, Juan Feng, Hao Lv

“…NeXtMD systematically extracts four functionally relevant sequence-derived descriptors—residue composition, inter-residue correlation, physicochemical properties, and sequence patterns—and utilizes a two-stage prediction strategy. …”
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MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model by Mohammed S. Alshuhri, Haitham F. Al-Mubarak, Abdulrahman Qaisi, Ahmad A. Alhulail, Abdullah G. M. AlMansour, Yahia Madkhali, Sahal Alotaibi, Manal Aljuhani, Othman I. Alomair, A. Almudayni, F. Alablani

“…A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. …”
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