Multi-Modal Emotion Detection and Sentiment Analysis by Shoaib Sikunder Malik, Muhammad Ilyas, Yasin Ul Haq, Rabia Sana, Muhamamd Saad Razzaq, Fahad Maqbool, Muhammad Salman Pathan

“…For frames, we employ Random Forest and Convolutional Neural Networks (CNN). Afterwards, we implement model ensembling across the three modalities. …”
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FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…In response to this need, a novel lightweight neural network model, called Feature fUsion and Spatial-Channel Attention Network (FUSCANet) model, is proposed in this paper, based on the MobileViT framework, aiming at classifying multi-class skin disease images on mobile or embedded devices. …”
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SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…To the best of our knowledge, this work is the first to transform satellite cloud data into multivariate time series and introduce a unified framework for multi-scale and multimodal feature fusion. …”
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Machine learning-based coalbed methane well production prediction and fracturing parameter optimization by HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan

“…The combined multi-task learning and PSO framework successfully resolves productivity prediction and fracturing optimization challenges under small-data constraints. …”
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Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images by Shaofeng Zhang, Mengmeng Li, Wufan Zhao, Xiaoqin Wang, Qunyong Wu

“…This study introduces a novel framework, i.e., CNN-Transformer cross-attention feature fusion network (CTCFNet), for building type classification from very high resolution remote sensing images. …”
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Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity by Shailendra Bhandari, Pedro Lencastre, Rujeena Mathema, Alexander Szorkovszky, Anis Yazidi, Pedro G. Lind

“…This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. …”
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Leveraging federated learning for DoS attack detection in IoT networks based on ensemble feature selection and deep learning models by Tasneem Qasem Al-Ghadi, Selvakumar Manickam, I. Dewa Made Widia, Eka Ratri Noor Wulandari, Shankar Karuppayah

“…While deploying an Intrusion Detection System (IDS) in a centralized framework can lead to data leakage, Federated Learning (FL) offers a privacy-preserving alternative by training models locally and transmitting only the updated model weights to a central server for aggregation. …”
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Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Methods: We developed the Tongue-LiteSAM model by improving the SAM (Segment Anything Model) framework to suit tongue segmentation. Based on the basic SAM model, the improvement involved modifying the image encoder by integrating two lightweight ViT-Tiny image encoders, effectively reducing the model’s parameter count. …”
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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments by Yangtian Lin, Yujun Xia, Pengcheng Xia, Zhengyang Liu, Haodi Wang, Chengjin Qin, Liang Gong, Chengliang Liu

“…Third, we applied knowledge distillation to transfer the enhanced model to a compact YOLO11n framework, maintaining high detection efficiency while reducing computational cost, and optimizing it for deployment on devices with limited computational resources. …”
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Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approach... by Tathagat Banerjee, Prachi Chhabra, Manoj Kumar, Abhay Kumar, Kumar Abhishek, Mohd. Asif Shah

“…To capture more prominent spatial-temporal patterns, we investigated hybrid networks, including NASNet with ANN, CNN, LSTM, and CNN-LSTM variants. The framework implements a strict nine-fold cross-validation procedure. …”
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Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory by Xiao Wang, Zhongyuan Liang, Peisi Zhong, Dongmin Li, Hongqi Li, Mei Liu

“…A multi-agent deep deterministic policy gradient (MA-DDPG) framework is designed to train the proposed MA-DRL model. …”
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Prediction of Sea Surface Current Around the Korean Peninsula Using Artificial Neural Networks by Jeong‐Yeob Chae, Hyunkeun Jin, Inseong Chang, Young Ho Kim, Young‐Gyu Park, Young Taeg Kim, Boonsoon Kang, Min‐su Kim, Ho‐Jeong Ju, Jae‐Hun Park

“…Here, we present a prediction framework applicable to surface current prediction in the seas around the Korean Peninsula using three‐dimensional (3‐D) convolutional neural networks. …”
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UnionCAM: enhancing CNN interpretability through denoising, weighted fusion, and selective high-quality class activation mapping by Hao Hu, Hao Hu, Rui Wang, Hao Lin, Huai Yu

“…Deep convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks. …”
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High-precision lung cancer subtype diagnosis on imbalanced exosomal data via Exo-LCClassifier by Siyu Zhan, Siyu Zhan, Hao Yu, Shuang Liu, Ke Qin, Lu Guo

“…This study aims to address these challenges by proposing an innovative deep learning-based method for predicting lung cancer subtypes.MethodsWe propose a method called Exo-LCClassifier, which integrates feature selection, one-dimensional convolutional neural networks (1D CNN), and an improved Wasserstein Generative Adversarial Network (WGAN). …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…To address this gap, we propose a framework that co-optimizes the discrete hyperparameter spaces of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN) models under strict memory constraints. …”
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TCL: Time-Dependent Clustering Loss for Optimizing Post-Training Feature Map Quantization for Partitioned DNNs by Oscar Artur Bernd Berg, Eiraj Saqib, Axel Jantsch, Irida Shallari, Silvia Krug, Isaac Sanchez Leal, Mattias O'Nils

“…The proposed framework offers a scalable solution for deploying high-performance AI models on IoT devices, extending the feasibility of real-time inference in resource-constrained environments.…”
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A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery by Ilyas Nurmemet, Yang Xiang, Aihepa Aihaiti, Yu Qin, Yilizhati Aili, Hengrui Tang, Ling Li

“…DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. …”
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Nystromformer based cross-modality transformer for visible-infrared person re-identification by Ranjit Kumar Mishra, Arijit Mondal, Jimson Mathew

“…To address this, we propose NiCTRAM: a Nyströmformer-based Cross-Modality Transformer designed for robust VIS-IR person re-identification. Our framework begins by extracting hierarchical features from both RGB and IR images through a shared convolutional neural network (CNN) backbone, ensuring the preservation of modality-specific characteristics. …”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…Furthermore, a lightweight detail-enhancement convolution layer and a shared-convolution detection head are designed to improve the model’s capability in capturing fine-grained details. …”
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Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…Specifically, we propose a deep learning-based framework for multi-class abnormal gait recognition, comprising three key modules: data collection, data preprocessing, and gait classification. …”
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