Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging by Corin F. Otesteanu, Reto Caldelari, Volker Heussler, Raphael Sznitman

“…This study focuses on the liver stage development of the model organism Plasmodium berghei, employing fluorescent microscopy imaging and convolutional neural networks (CNNs) for analysis. Convolutional neural networks have been recently proposed as a viable option for tasks such as malaria detection, prediction of host-pathogen interactions, or drug discovery. …”
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Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture by Shihao Zhou, Benren Pan, Dongbin Lu, Yiming Zhong, Guannan Wang

“…First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. …”
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Geological Knowledge‐Guided Dual‐Branch Deep Learning Model for Identification of Geochemical Anomalies Related to Mineralization by Ying Xu, Renguang Zuo, Yang Bai

“…The spatial branch was constructed using the convolutional neural network and convolutional autoencoder, which can effectively capture spatial geochemical patterns and extract spatial relationships between neighboring pixels. …”
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism by Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

“…This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). …”
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A Real Time Cardiomyopathy Detection Tool Using Ml Ensemble Models by Salvador de Haro, Esteban Becerra, Pilar González-Férez, José M. García, Gregorio Bernabé

“…These models, implemented within an ensemble framework, leverage advancements in deep learning to improve the accuracy and robustness of magnetic resonance imaging (MRI) segmentation. …”
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STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai

“…However, current approaches relying on Convolutional Neural Networks (CNNs) for Euclidean data and Graph Convolutional Networks (GCNs) for non-Euclidean structures struggle to simultaneously capture heterogeneous data properties and complex spatio-temporal dependencies. …”
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Confidence-Based Fusion of AC-LSTM and Kalman Filter for Accurate Space Target Trajectory Prediction by Caiyun Wang, Jirui Zhang, Jianing Wang, Yida Wu

“…This study proposes a confidence-based dual-model fusion framework that separately processes linear and nonlinear trajectory components to enhance prediction accuracy and robustness. …”
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Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery by Ruikang Luo, Yaofeng Song, Longfei Ye, Rong Su

“…To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. …”
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Research on Shale Oil Well Productivity Prediction Model Based on CNN-BiGRU Algorithm by Yuan Pan, Xuewei Liu, Fuchun Tian, Liyong Yang, Xiaoting Gou, Yunpeng Jia, Quan Wang, Yingxi Zhang

“…Unconventional reservoirs are characterized by intricate fluid-phase behaviors, and physics-based shale oil well productivity prediction models often exhibit substantial deviations due to oversimplified theoretical frameworks and challenges in parameter acquisition. …”
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. …”
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Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording by Shengjie Yang, Jiaqi Xue, Ziqi Li, Shiqing Zhang, Zhang Zhang, Zhifeng Huang, Ken Kin Lam Yung, King Wai Chiu Lai

“…This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole‐cell recordings. …”
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Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System by Guowen Dai, Jinjun Tang, Jie Zeng, Yuting Jiang

“…To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). …”
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DamageScope: An Integrated Pipeline for Building Damage Segmentation, Geospatial Mapping, and Interactive Web-Based Visualization by Sultan Al Shafian, Chao He, Da Hu

“…This study introduces DamageScope, an integrated deep learning framework designed to detect and classify building damage levels from post-disaster satellite imagery. …”
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Adaptive fusion of multi-cultural visual elements using deep learning in cross-cultural visual communication design by HuPei Wang

“…We address the challenge of creating culturally appropriate digital interfaces by developing a comprehensive framework that combines convolutional neural networks, attention mechanisms, and generative adversarial networks to analyze, extract, and adaptively fuse cultural features from diverse visual communication design elements. …”
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Species-independent analysis and identification of emotional animal vocalizations by Stavros Ntalampiras

“…Towards keeping all potentially relevant audio content, the constructed framework operates on log-Mel spectrograms. Similarities/dissimilarities are learned by a suitably designed Siamese Neural Network composed of convolutional layers. …”
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WISP: Workframe for Interferogram Signal Phase-Unwrapping by Timofey F. Khirianov, Aleksandra I. Khirianova, Egor V. Parkevich, Ilya Makarov

“…This paper proposes an iterative framework WISP (Workframe for Interferogram Signal Phase-unwrapping for phase reconstruction from interferograms of complex phase objects. …”
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Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis by Jia Xu, Yan Wang, Renyi Xu, Hailin Wang, Xinzhi Zhou

“…The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. …”
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An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model by Nisha Y., Jagadeesh Gopal

“…Here, it introduces a new building block identified as the Fuzzy Scoring Resnet-Convolutional Neural Network(FS-Resnet CNN) framework to optimize the network. …”
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Hybrid residual modeling of pan evaporation in hyper-arid climates: Benchmarking interpretable neural architectures against physical drivers by Abdullah A. Alsumaiei

“…Study Region: Kuwait, Arabian Peninsula, Western Asia Study Focus: This study proposes an interpretable hybrid residual learning framework that integrates stepwise linear regression (SWLR) with four neural network architectures: Feedforward Neural Networks (FNN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Echo State Networks (ESN). …”
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Enhancing Robustness Against Adversarial Attacks in Multimodal Emotion Recognition With Spiking Transformers by Guoming Chen, Zhuoxian Qian, Dong Zhang, Shuang Qiu, Ruqi Zhou

“…Inspired by the sparse, asynchronous spiking activity and inherent nonlinearity of spiking neural networks (SNNs), we propose a novel framework, the Sliding Parallel Spiking Convolutional Vision Transformer (SPSNCVT), designed for robust and efficient multimodal emotion recognition. …”
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