Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…It utilizes a federal learning framework which employs Convolutional Neural Networks as well as Long-Term Short Memory. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

“…This lightweight model, with its low parameter count, is particularly well-suited for this task. The proposed framework involves a process that extracts features from video frames in a time sequence, employing methods such as Convolutional Neural Networks (CNN), CNN-Gated Recurrent Units (CNN-GRU), Temporal CNN, and Temporal PoinWise. …”
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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…This study proposes a multimodal bearing fault classification approach that relies on vibration and motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The method fuses features from multiple signals to enhance the accuracy of fault detection. …”
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TWD-DepNet: a deep network enhanced by three-way decisions for EEG-based depression detection by Chunlei Shi, Shouzhu Yan, Wei He, Haiyan Wang

“…The framework encompasses a complete pipeline from signal preprocessing to dynamic decision optimization. …”
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Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model by Abdoul-Aziz Maiga, Edwin Ataro, Stanley Githinji

“…The framework was validated through simulation using open-access benchmark datasets (InSDN and CICIDS2017). …”
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Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification. by M Amudha, K Brindha

“…To address this, a lighter CNN framework is proposed to enhance with two key components. …”
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Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment by Neha Tarigopula, Sandrine Tornay, Ozge Mercanoglu Sincan, Richard Bowden, Mathew Magimai.-Doss

“…To address this limitation, we leverage and analyze the spatio-temporal representations from Inflated 3D Convolutional Networks (I3D) and integrate them into the KL-HMM framework to assess sign language videos on both manual and non-manual components. …”
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Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis by Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori, Birgit Betina Ertl-Wagner, Farzad Khalvati

“…Abstract Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. …”
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A novel Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) algorithm for hybrid feature extraction and multi-class brain tumor classification using ResNet-50 and... by Mahananda Malkauthekar, Avinash Gulve, Ratnadeep Deshmukh

“…The P-BTLBO method combines probabilistic modeling with a bi-level optimization framework to make feature selection better. This makes it easier to explore and exploit in environments with a lot of dimensions. …”
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Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting by Amirmasoud Amini, Mehri Dolatshahi, Reza Kerachian

“…We focus on applying two hyperparameter tuning methods (i.e., random search and tree structured Parzen estimator) to improve the performance of the proposed rainfall nowcasting models. The proposed framework was applied to the eastern drainage catchment (EDC) in Tehran city. …”
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Automated violence monitoring system for real-time fistfight detection using deep learning-based temporal action localization by Baolong Qi, Baoyuan Wu, Bailing Sun

“…This paper presents a novel multimodal spatio-temporal fistfight detection model (MSTFDet) that integrates RGB images and human skeletal data to identify violent behaviors accurately. The proposed framework leverages both Context-Aware Encoded Transformer (CAET) for modeling interactions between individuals and their environment and Spatial–Temporal Graph Convolutional Networks (ST-GCN) for capturing intra-person and inter-person dynamics from skeletal data. …”
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ERA-MADDPG: An Elastic Routing Algorithm Based on Multi-Agent Deep Deterministic Policy Gradient in SDN by Wanwei Huang, Hongchang Liu, Yingying Li, Linlin Ma

“…The architecture’s processing flow, including real-time data layer information collection and dynamic policy generation, enables the ERA-MADDPG algorithm to exhibit strong elasticity by quickly adjusting routing decisions in response to topology changes. The actor-critic framework combined with Convolutional Neural Networks (CNN) to implement the ERA-MADDPG routing algorithm effectively improves training efficiency, enhances learning stability, facilitates collaboration, and improves algorithm generalization and applicability. …”
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BlindFG: Learning Contextual Fishing Trajectories for Unreported Fishing Gear Classification by Changha Lee, Chan-Hyun Youn

“…FCSom utilizes the completed trajectories within a 1D convolutional classification framework. By aggregating candidate predictions across multiple fishing type hypotheses, the system effectively discriminates among various fishing behaviors, even in the presence of partial AIS data. …”
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Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs by Samir Abdaljalil, Hasan Kurban, Rachad Atat, Erchin Serpedin, Khalid Qaraqe

“…We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</sc>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. …”
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Deep Learning Model-Based Detection of Anemia from Conjunctiva Images by Najmus Sehar, Nirmala Krishnamoorthi, C. Vinoth Kumar

“…This score demonstrates the framework’s exceptional capability in detecting anemia through a non-invasive approach. …”
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DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms by Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…IntroductionThe early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations.MethodsIn this paper, we introduce an innovative multi-stream framework that seamlessly integrates three state-of-the-art convolutional neural networks, namely, EfficientNetV2B0, ResNet50V2, DenseNet121, and Multi-Stream models to analyze stereotypical movements, particularly hand-flapping behaviors automatically. …”
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GuardianMPC: Backdoor-Resilient Neural Network Computation by Mohammad Hashemi, Domenic Forte, Fatemeh Ganji

“…To ensure resilience against such backdoor attacks, this work presents GuardianMPC, a novel framework leveraging secure multiparty computation (MPC). …”
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Diverse behavior clustering of students on campus with macroscopic attention by Wanghu Chen, Zongjuan Wu, Siqi Zeng, Hongle Guo, Jing Li

“…To verify the effectiveness of the framework, this paper takes the university campus scene as a case study. …”
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VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security by Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Stefanos Gritzalis, Jong Hyuk Park

“…This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). …”
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Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation by A. S. M. Sharifuzzaman Sagar, Muhammad Zubair Islam, Jawad Tanveer, Hyung Seok Kim

“…This study proposes a novel segmentation framework Adaptive Multiscale U-Net (AMU-Net) combining Convolutional Neural Networks (CNNs) and transformer-based encoder–decoder architectures. …”
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