The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images by Pei Zhang, Qijian Xu, Feng Jiang

“…In addition, the clinical feature model was constructed by using the clinical information of patients and ultrasound image features, and the predictive performance of four thyroid cancer models was evaluated and compared. …”
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Region search based on hybrid convolutional neural network in optical remote sensing images by Shoulin Yin, Ye Zhang, Shahid Karim

“…This process avoids exhaustive search for input images. Then, the features of all candidate regions are extracted by a fast region-based convolutional neural network structure. …”
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Constructing representative group networks from tractography: lessons from a dynamical approach by Eleanna Kritikaki, Matteo Mancini, Matteo Mancini, Diana Kyriazis, Natasha Sigala, Simon F. Farmer, Simon F. Farmer, Luc Berthouze

“…In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. …”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…To address these limitations, this study proposes a novel parallel boosting neural network (PBNN) framework that integrates boosting algorithms with a feedforward neural network (FFNN). …”
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FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction by Arnold Kazadi, James Doss-Gollin, Antonia Sebastian, Arlei Silva

“…Compared to existing approaches, FloodGNN-GRU (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. We evaluate FloodGNN-GRU using a LISFLOOD-FP simulation of Hurricane Harvey (2017) in Houston, Texas. …”
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Channel Estimation Using CNN-LSTM in RIS-NOMA Assisted 6G Network by Chi Nguyen, Tiep M. Hoang, Adnan A. Cheema

“…CNN-LSTM leverages both the benefits of convolutional neural network (CNN) as well as long-short term memory (LSTM), in which CNN can capture special features while LSTM can capture temporal features of time-series data. …”
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GTN-GCN: Real-Time Traffic Forecasting Using Graph Convolutional Network and Transformer by Sadia Naj Jinia, Sumaiya Binte Azad, Rima Akter, Taivan Reza Dipto, Md. Khaliluzzaman

“…A traffic network exhibits inherent characteristics of networks while also possessing unique features that hold significant research value. …”
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STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network by Nafiul Hasan, Md. Masud Rana, Md Mahmudul Hasan, AKM Azad, Dil Afroz, Md Mostafizur Rahman Komol, Mousumi Aktar, Mohammad Ali Moni

“…By leveraging scattering (S), admittance (Y), and impedance (Z) parameters as input features, an Artificial Neural Network (ANN) achieved a 99.95% classification accuracy for tumors with radii of 3 mm and 5 mm. …”
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Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks by Yinjun Zhang, Lingzhi Wang

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Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy by Nagasoujanya Annasamudram, Jian Zhao, Olaitan Oluwadare, Aashish Prashanth, Sokratis Makrogiannis

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Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks by Prathana Phukon, Petros Potikas, Katerina Potika

“…The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. …”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…It uses meta-features and correlation functions to evaluate 300 features. …”
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Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation by Lei Wang, Qun Ai

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DPSTCN: Dynamic Pattern-Aware Spatio-Temporal Convolutional Networks for Traffic Flow Forecasting by Zeping Dou, Danhuai Guo

“…However, few of the existing models are designed to fully and effectively integrate the above-mentioned features. To address these complexities head-on, this paper introduces a novel solution in the form of Dynamic Pattern-aware Spatio-Temporal Convolutional Networks (DPSTCN). …”
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Deep neural networks and fractional grey lag Goose optimization for music genre identification by Yuanye Tian

“…A dual-path recurrent network is employed for real-time music generation and evaluate the model on two benchmark datasets, ISMIR2004 and extended Ballroom, compared to the state-of-the-art models included CNN, PRCNN, BiLSTM and BiRNN. …”
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Analysis and comprehensive assessment of the development and application of the neural network dialogue system ChatGPT by D. A. Machueva, D. R. Baraev, T. M.-A. Bechurkaev

“…Today, significant and in many ways sensational results are being achieved in the field of artificial intelligence systems, and the ChatGPT bot, which is based on the GPT-3 neural network, is called a real revolution in the world of technology.The aim of the study is to analyze and evaluate the application features, advantages and limitations, as well as development factors and reasons for the extraordinary popularity of the neural network dialogue system ChatGPT.Method. …”
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Gait-Based Parkinson’s Disease Detection Using Recurrent Neural Networks for Wearable Systems by Carlos Rangel-Cascajosa, Francisco Luna-Perejón, Saturnino Vicente-Diaz, Manuel Domínguez-Morales

“…In this study, we present an investigation of different architectures based on Gated Recurrent Neural Networks to assess their effectiveness in identifying subjects with Parkinson’s disease from gait records. …”
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Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks by Jiazhu Dai, Yubing Lu

“…Graph neural networks (GNNs) are widely used for graph-structured data. …”
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Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction by Zhifei Yang, Zeyang Li, Jia Zhang

“…The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. …”
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Attention-Driven Bidirectional LSTM Neural Network for Afaan Oromo Next Word Generation by Bekan Mekonen

“…This study evaluates various deep learning models, including Long Short Term Memory (LSTM), Attention-based LSTM, Bidirectional LSTM (Bi-LSTM), Attention-based Bi-LSTM, and Recurrent Neural Network (RNN), to determine the most accurate model for Afaan Oromo next word generation. …”
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