Effective classification of android malware families through dynamic features and neural networks by Gianni D'Angelo, Francesco Palmieri, Antonio Robustelli, Arcangelo Castiglione

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DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

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Deep Learning-Based Performance Prediction of Electric Submersible Pumps Under Viscous and Gas–Liquid Flow Conditions by Haiwen Zhu, Hong Yu, Qiang Sun, Qiuchen Wang, Haorong Jing, Rakhymzhan Abdikadyrov

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A Novel Subclass of Harmonic Functions: Coefficient Bounds, Distortion Bounds, and Closure Properties by Serkan Çakmak

“…Moreover, we rigorously show that this subclass is closed under both convex combinations and convolutions, meaning that any convex combination or convolution of functions in this class will also belong to the class. …”
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Integrated perception-communication-logistics multi-objective oriented path planning for emergency UAVs by XU Yunpeng, XIE Yaqi, YU Ran, HOU Luyang, WANG Kailiang, XU Lianming

“…In the first stage, a temporal graph convolution networks-based model was introduced to predict the number of personnel at the relief sites to quantify its supply and communication needs. …”
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Detection method of potato leaf disease based on YOLOv5s by Jingtao Li, Hao Chen, Guisong Li, Yueqi Liu, Yanli Yang, Xia Liu, Chang Yi Wang

“…Firstly, a deformable convolution replaces the standard convolution in YOLOv5s to ensure that the convolution region always covers the target region. …”
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CAs-Net: A Channel-Aware Speech Network for Uyghur Speech Recognition by Jiang Zhang, Miaomiao Xu, Lianghui Xu, Yajing Ma

“…The proposed model consists of two key components: (1) the Channel Rotation Module (CIM), which reconstructs each frame’s channel vector into a spatial structure and applies a rotation operation to explicitly model the local structural relationships within the channel dimension, thereby enhancing the encoder’s contextual modeling capability; and (2) the Multi-Scale Depthwise Convolution Module (MSDCM), integrated within the Transformer framework, which leverages multi-branch depthwise separable convolutions and a lightweight self-attention mechanism to jointly capture multi-scale temporal patterns, thus improving the model’s perception of compact articulation and complex rhythmic structures. …”
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On the Lebesgue measure of one generalised set of subsums of geometric series by O. P. Makarchuk, D. M. Karvatskyi

“…Despite the limited progress, the problem remained understudied in a general framework. The study of topological, metric, and fractal properties of the set of subsums for a numerical series is a separate research direction in mathematics. …”
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Extending the forecasting horizon of daily new COVID-19 cases using non-pharmaceutical measures and the effective reproduction number (Rt): A deep learning-based framework by Tuga Mauritsius

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Polymer electrolyte membrane fuel cell performance Revolutionized: Artificial intelligence-validated asymmetric flow channels enhance mass transport via hybrid analytical-numerical... by Nima Ahmadi, Ghader Rezazadeh

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YOLO-Ssboat: Super-Small Ship Detection Network for Large-Scale Aerial and Remote Sensing Scenes by Yiliang Zeng, Xiuhong Wang, Jinlin Zou, Hongtao Wu

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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…To address this, we propose a Bayesian Q-Learning framework with a convolutional neural network policy. …”
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A Practical Framework for Estimating Façade Opening Rates of Rural Buildings Using Real-Scene 3D Models Derived from Unmanned Aerial Vehicle Photogrammetry by Zhuangqun Niu, Ke Xi, Yifan Liao, Pengjie Tao, Tao Ke

“…The framework begins by extracting individual buildings from 3D models using annotated roof outlines. …”
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Pose estimation for health data analysis: advancing AI in neuroscience and psychology by Juan Yu, Daoyu Zhu

“…DMGF leverages graph-based representations to capture the temporal and structural relationships within health datasets, enabling robust modeling of disease progression and patient interactions. The framework integrates multi-modal data sources and applies temporal graph convolutional networks, ensuring both scalability and adaptability to diverse tasks. …”
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Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images by R. Raja Subramanian, R. Raja Sudharsan, Bavithra Vairamuthu, Deshinta Arrova Dewi

“…The proposed model (UlcerNet) was implemented on FogBus, the cloud framework to empirically evaluate the model performance in cloud-fog interoperable scenarios.…”
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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

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UNestFormer: Enhancing Decoders and Skip Connections With Nested Transformers for Medical Image Segmentation by Adnan Md Tayeb, Tae-Hyong Kim

“…Precise identification of organs and lesions in medical images is essential for accurate disease diagnosis and analysis of organ structures. Deep convolutional neural network (CNN)-based U-shaped networks are among the most popular and promising approaches for this task. …”
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Maize yield estimation in Northeast China’s black soil region using a deep learning model with attention mechanism and remote sensing by Xingke Li, Yunfeng Lyu, Bingxue Zhu, Lushi Liu, Kaishan Song

“…This framework integrates a one-dimensional convolutional neural network (1D-CNN), bidirectional gated recurrent units (BiGRU), and an attention mechanism to effectively characterize and weight key segments of input data. …”
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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

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CrackVision: Effective Concrete Crack Detection With Deep Learning and Transfer Learning by Abdulrahman A. Alkannad, Ahmad Al Smadi, Shuyuan Yang, Mutasem K. Al-Smadi, Moeen Al-Makhlafi, Zhixi Feng, Zhenlong Yin

“…The framework utilizes pre-trained Convolutional Neural Networks (CNNs), such as ResNet50, Xception, and InceptionV3, which are fine-tuned to address the unique challenges of concrete crack detection. …”
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