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Stock market index prediction using transformer neural network models and frequency decomposition
In an increasingly complex and volatile environment, government officials, researchers, and investors alike would like to possess models that...
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Hybrid Neural Network Models for Detecting Fake News Articles
The prevalence of world-wide access to the Internet has come at a cost. A lot of misleading information is posted on public news websites and social...
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Probabilistic graph model and neural network perspective of click models for web search
Click behavior is a typical user behavior in the web search. How to capture and model users’ click behavior has always been a common research topic....
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A new deep neural network for forecasting: Deep dendritic artificial neural network
Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural...
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Detection of plant leaf diseases using deep convolutional neural network models
Food demand is exponentially increasing due to the increase in population in every country; hence, increasing the yield is one of the focus areas for...
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Exploring the Impact of Delay on Hopf Bifurcation of a Type of BAM Neural Network Models Concerning Three Nonidentical Delays
In this research, a kind of BAM neural networks containing three nonidentical time delays are explored. Exploiting fixed point knowledge, we examine...
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Mutual Information-Based Neural Network Distillation for Improving Photonic Neural Network Training
AbstractPhotonic neural networks are among the most promising recently proposed neuromorphic solutions for providing fast and energy efficient Deep...
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Explainable generalized additive neural networks with independent neural network training
Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly...
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Novelty fused image and text models based on deep neural network and transformer for multimodal sentiment analysis
The rapid growth of various online platforms has made it easier than ever for people to share their feelings or opinions in the form of both textual...
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Network intrusion detection based on variational quantum convolution neural network
With the rapid development of quantum machine learning (QML), quantum convolutional neural networks (QCNN) have been proposed and shown advantages in...
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An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction
Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts...
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Performance evaluation of convolution neural network models for detection of abnormal and ventricular ectopic beat cardiac episodes
The fast and accurate detection of abnormal cardiac episodes is essential for quick diagnosis high-risk patients prone to irregular cardiac...
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Analysis of CNN models in classifying Alzheimer's stages: comparison and explainability examination of the proposed separable convolution-based neural network and transfer learning models
Dementia is a condition that affects brain functions and usually occurs with age, resulting in a decrease in cognitive functions (such as thinking,...
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An integrated fuzzy neural supervision and attention-based graph neural network for improving network clustering
In recent years, graph neural network (GNN) and auto-encoding (AE) have been widely utilized in multiple data mining problems. These architectures...
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The analysis of network video quality assessment based on different fuzzy neural network
Nowadays, people watch network video through any way, such as mobile phone, tables. However, during the transmission process of network video, the...
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Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network
Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user...
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Method for Reducing Neural-Network Models of Computer Vision
AbstractThis article proposes an approach to reducing fully connected neural networks using classical and modified pretraining of deep neural...
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Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures
Cracks are one of the forms of damage to concrete structures that debase the strength and durability of the building material and may pose a danger...
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Novel robust Elman neural network-based predictive models for bubble point oil formation volume factor and solution gas–oil ratio using experimental data
Bubble point oil formation volume factor ( B ob ) and solution gas–oil ratio ( R s ) are two crucial PVT parameters used for modeling and volumetric...
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Amperage prediction in mono-wire cutting operation using multiple regression and artificial neural network models
Operational parameters such as cutting speed and peripheral speed in diamond wire cutting operation greatly affect the efficiency of the machine. The...
