Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF–CNN–LSTM Model by Tian Hang, Jing Wen, Shubin Zheng

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Revolutionizing Lung Segmentation with Machine Learning: A Critical Review of Techniques in Medical Imaging by Momina Aisha, Moazma Ijaz, Nimra Tariq, Sehar Anjum, Sidra Siddiqui, Usman Hashmi

“…This review highlights advancements in automated lung segmentation, focusing on traditional ML methods and state-of-the-art DL approaches, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). …”
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An Ensemble Learning Approach for Glaucoma Detection in Retinal Images by Marwah M. Mahdi, Mohammed Abdulkreem Mohammed, Haider Al-Chalibi, Bashar S. Bashar, Hayder Adnan Sadeq, Talib Mohammed Jawad Abbas

“…To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. …”
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A systematic review of multimodal fake news detection on social media using deep learning models by Maged Nasser, Noreen Izza Arshad, Abdulalem Ali, Hitham Alhussian, Faisal Saeed, Aminu Da'u, Ibtehal Nafea

“…The findings showed that the Transformer models and Recurrent Neural Networks (RNNs) are the most popular deep learning techniques for detecting multimodal fake news, followed by the Convolutional Neural Networks (CNNs) techniques. …”
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Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images by Kotaro Waki, Katsuya Nagaoka, Keishi Okubo, Masato Kiyama, Ryosuke Gushima, Kento Ohno, Munenori Honda, Akira Yamasaki, Kenshi Matsuno, Yoki Furuta, Hideaki Miyamoto, Hideaki Naoe, Motoki Amagasaki, Yasuhito Tanaka

“…We used 480 cases (4295 images) for the training dataset, and the rest for validation. The Bilinear convolutional neural network (CNN) model (especially when pre-trained on fractal images) demonstrated diagnostic precision that was comparable to or better than other models for distinguishing between high-risk and non-high-risk groups. …”
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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

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Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review by Ahmad Moayad Naser, Rhea Vyas, Ahmed Ashraf Morgan, Abdul Mukhtadir Kalaiger, Amrin Kharawala, Sanjana Nagraj, Raksheeth Agarwal, Maisha Maliha, Shaunak Mangeshkar, Nikita Singh, Vikyath Satish, Sheetal Mathai, Leonidas Palaiodimos, Robert T. Faillace

“…Two primary AI-driven models that are currently being explored are deep convolutional neural networks (DCNNs) for enhanced image-based detection and natural language processing (NLP) for improved risk stratification using electronic health records. …”
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ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

“…First, we propose a Siamese neural network based on adaptive deformable convolution (ADC) modules. …”
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Combining readily available population and land cover maps to generate non-residential built-up labels to train Sentinel-2 image segmentation models by Diogo Duarte, Cidália C. Fonte

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Relation extraction based on CNN and Bi-LSTM by Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG

“…Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.…”
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YOLO-EFM: Efficient traffic flow monitoring algorithm with enhanced multi-level information fusion by Shizhou Xu, Kaidi Cui

“…The study establishes a generalized efficient layer aggregation network incorporating Sobel convolution and develops a novel feature focus module that effectively aggregates information from different feature map levels. …”
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ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images by Md Alamin Talukder, Md Abu Layek, Md Aslam Hossain, Md Aminul Islam, Mohammad Nur-e-Alam, Mohsin Kazi

“…Methods The ACU-Net model combines convolutional neural networks (CNNs) with attention mechanisms to enhance feature extraction and spatial coherence. …”
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Intelligent prediction method of network performance based on graph neural network by Yijiang LI, Huibiao YE, Renhua XIE, Jiali LOU, Danna ZHUANG, Chuanhuang LI

“…There are some problems in the traditional network performance prediction technology, such as incomplete network state acquisition and poor accuracy of network performance evaluation.Combined with the characteristics of graph neural network learning and reasoning network relational data and the captured global information of the network, on the basis of the current network performance prediction methods, an intelligent prediction method of network performance based on graph neural network was proposed.Aiming at the complex network information, through the research of network system abstraction and network performance modeling, the network information can be transformed into the graph space convolution was used to process the message passing process of graph network nodes to realize the relationship reasoning between network information.The graph neural network model for network performance prediction was studied, and a graph neural network architecture which could deal with traffic matrix, network topology, routing strategy and node configuration was proposed.Finally, the experiments show that the model can better achieve accurate prediction of the network performance including delay, jitter and packet loss rate.…”
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Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism by Jianjian Yang, Haifeng Han, Xuan Dong, Guoyong Wang, Shaocong Zhang

“…The method first converts current and vibration signals into two-dimensional grayscale images, extracts local features through multi-layer convolutional neural networks, and captures global information using the self-attention mechanism in the Vision Transformer (ViT). …”
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A novel speaker verification approach featuring multidomain acoustics based on the weighted city-block Minkowski distance by Khushboo Jha, Sumit Srivastava, Aruna Jain

“…Parameters are computed based on the confusion matrix, template matching distance functions, dynamic acoustic conditions, and additive white Gaussian noise. A deep convolutional neural network classifier is assessed on open-source LibriSpeech and Speaker in the Wild corpora, surpassing the current methodologies. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Methods: This paper introduces a novel hybrid model that integrates the strengths of convolutional neural networks (CNNs) and vision transformer (ViT) to overcome these obstacles. …”
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DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering by Dongfen Ye, Jianqiang Zhou, Gang Huang

“…In recent years, there has been significant interest in leveraging Pre-trained Language Models (PLMs) and Graph Convolutional Networks (GCNs) to address EQA tasks. PLMs usually function as context encoders, while GCNs are employed to capture latent semantic relationships between answer spans and the passage/question. …”
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Optimizing Pre-Trained Models for Medical Dataset Classification with a Fine-Tuning Approach by N. Kumar, T. Christopher

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PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions by Yash Semlani, Mihir Relan, Krithik Ramesh

“…To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). …”
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A Hybrid Deep Learning Approach for Skin Lesion Segmentation With Dual Encoders and Channel-Wise Attention by Asaad Ahmed, Guangmin Sun, Anas Bilal, Yu Li, Shouki A. Ebad

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