Design and development of an efficient RLNet prediction model for deepfake video detection by Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala

“…While existing methods often focus on spatial features, they may overlook crucial temporal information distinguishing real from fake content and need to investigate several other Convolutional Neural Network architectures on video-based deep fake datasets.MethodsThis study introduces an RLNet deep learning framework that utilizes ResNet and Long Short Term Memory (LSTM) networks for high-precision deepfake video detection. …”
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Leveraging Deep Learning for Fault Detection and Localization in Distributed Systems by Debolina Ghosh, Jay Prakash Singh

“…A deep learning-based fault localization framework that combines Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), LSTM+CNN, and Autoencoder+LSTM models is proposed in this study. …”
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Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang, Jinlei Zhang

“…Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. …”
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Fusing multiplex heterogeneous networks using graph attention-aware fusion networks by Ziynet Nesibe Kesimoglu, Serdar Bozdag

“…Abstract Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. …”
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Hybrid deep learning model for identifying the cancer type by Singamaneni Krishnapriya, Hyma Birudaraju, M. Madhulatha, S. Nagajyothi, K.S. Ranadheer Kumar

“…In this work, we introduce a hybrid deep learning-based framework for accurate cancer type and subtype identification by using pre-trained convolutional neural networks, custom deep learning networks, and traditional machine learning classifiers. …”
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Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks by Mohamed Fathallah, Sherif Eletriby, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farok

“…This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. …”
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Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents by Lin Zhang, Yanan Li, Hongyu Zhang

“…Experimental results show that HybridSumm’s lexicon-guided two-stage framework significantly enhances the standardization of legal terminology and semantic coherence in long-text summaries, validating its practical value in advancing judicial intelligence development.…”
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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular Summation Fields (GASFs) and Gramian Angular Difference Fields (GADFs). …”
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Use of Xception Architecture for the Classification of Skin Lesions by Cledmir Tejada, Gustavo Espinoza, Daniel Subauste

“…The study utilizes the TensorFlow framework and the HAM10000 dataset, comprising a vast collection of benign and malignant skin lesion images, for training and evaluating the Xception model. …”
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Rail Transit Prediction Based on Multi-View Graph Attention Networks by Li Wang, Xin Wang, Jiao Wang

“…Thus, this paper proposes a framework with multi-view and multi-layer attention, which aims to solve the problem of node prediction involving multiple relationships. …”
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With eyes of a machine: A three-step guide for applying machine learning to visual content analysis in social research by Anna Helene Kvist Møller, Massimo Airoldi

“…Combining machine automation with human expertise and reflexivity, the proposed methodological framework bridges the gap between computer vision and social research. …”
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PatchDetect: Breast Cancer Detection combining Unet-ResNet-50 and Patch Embedding LSTM by Hadj Ahmed Bouarara, kadda benyahia

“… This study presents a novel framework for breast cancer detection, combining patch embedding, feature extraction using a pre-trained Convolutional Neural Network (CNN) model (ResNet50), Long Short-Term Memory (LSTM) networks for image sequence analysis, and Fully Connected Layers for final classification. …”
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Deep learning channel estimation algorithm for ultra-massive terahertz systems by YU Shujuan, ZHAO Yang, WEI Yuyao, ZHANG Yun, GAO Gui, ZHAO Shengmei

“…The simulation results show that the proposed scheme not only inherits the high efficiency and adaptivity of the FPN framework, but also possesses high estimation accuracy and good robustness for THz channels.…”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. …”
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Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario by Jayanta Datta, Ali Dehghan Firoozabadi, David Zabala-Blanco, Francisco R. Castillo-Soria

“…In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. …”
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Pedestrian Crossing Direction Prediction at Intersections for Pedestrian Safety by Younggun Kim, Mohamed Abdel-Aty, Keechoo Choi, Zubayer Islam, Dongdong Wang, Shaoyan Zhai

“…To address challenges posed by varying intersection geometries and camera perspectives, we developed a global coordinate system that standardizes spatial features. The framework leverages Transformer-based models, Graph Convolutional Networks (GCNs), and a hybrid Transformer+GCN approach to extract spatial and temporal features from the pedestrian behaviors. …”
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CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability by Shiraz Afzal, Muhammad Rauf, Shahzad Ashraf, Shahrin Bin Md Ayob, Zeeshan Ahmad Arfeen

“…<b>Background/Objectives:</b> Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. …”
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Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“…The framework integrates Random Forest (RF) techniques for crop-specific modeling, exploiting their robustness to nonlinear relationships in small datasets, while long short term memory (LSTM) networks extract long-term climate trends, and Convolutional Neural Network (TCNN) with dilated kernels capture multi-scale temporal dependencies, creating a complementary hierarchy that leverages the strengths of each approach. …”
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SmartRipen: LSTM-GRU feature selection& XGBoost-CNN for fruit ripeness detection by Archana Ganesh Said, Bharti Joshi

“…This study presents a novel hybrid framework for spotting prematurely ripened fruits using LSTM-GRU fusion and XGBoost CNN. …”
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HG-Mamba: A Hybrid Geometry-Aware Bidirectional Mamba Network for Hyperspectral Image Classification by Xiaofei Yang, Jiafeng Yang, Lin Li, Suihua Xue, Haotian Shi, Haojin Tang, Xiaohui Huang

“…The proposed framework comprises two stages. The first stage, termed spectral compression and discrimination enhancement, employs multi-scale spectral convolutions alongside a spectral bidirectional Mamba (SeBM) module to suppress redundant bands while modeling long-range spectral dependencies. …”
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