Multidimensional relational knowledge embedding for coreference resolution
Currently, the co-reference resolution model using a knowledge base mainly faces two problems: first, the knowledge is complex and diverse, and it is difficult to add appropriate knowledge to complement the conceptual relationships between ...
Hyperparameter optimization of pre-trained convolutional neural networks using adolescent identity search algorithm
Convolutional neural networks (CNNs) are widely used deep learning (DL) models for image classification. The selected hyperparameters for training convolutional neural network (CNN) models have a significant effect on the performance. Therefore, ...
Prediction and classification of minerals using deep residual neural network
Minerals are in great demand because of their pervasive application in atomic energy and their use as raw materials for other industries. Despite this, it is challenging to identify and classify the minerals due to the deposit's broadness, mineral ...
Accelerated fuzzy min–max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection
The fuzzy min–max (FMM) neural network effectively solves classification problems. Despite its success, it has been observed recently that FMM has overlapping between hyper-boxes in some datasets which certainly the overall classification ...
Bladder cancer gene expression prediction with explainable algorithms
In this study, we aimed to classify bladder cancer patients using tumoral and non-tumoral gene expression data. In this way, we aimed to determine which genes are effective on tumoral and normal tissues. In addition, for this purpose, we planned ...
Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model
White blood cells (WBC), which are human peripheral blood cells, are the most significant part of the immune system that defends the body against microorganisms. Modifications in the morphological structure and number of subtypes of WBC play an ...
A classification study for Turkish folk music makam recognition using machine learning with data augmentation techniques
Makam is defined as melodies that are described with typical the agâz(beginning), seyir (the orientation style), and karar (ending) features in a certain perde düzen (tone/fret tunings). For this reason, determining the makam is the basic step in ...
Proposed methodology for gait recognition using generative adversarial network with different feature selectors
Today, investigating gait recognition as a biometric technology has become necessary, especially after the COVID-19 pandemic broke out in the world. This paper proposes a deep structure procedure for precise human identification from the walking ...
A new framework for early diagnosis of breast cancer using mammography images
Breast cancer is one of the most common types of cancer in women. This type of cancer can be detected and treated at an early stage, and the quality of life of sick individuals can significantly improve. In addition to radiology specialists, tools ...
Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines
The rockburst hazard induced by the extreme release of the stress concentrated in rock mass in deep underground mines poses a significant threat to the safety and economy of the mining projects. Therefore, properly managing this hazard is critical ...
Mitigate the scale imbalance via multi-scale information interaction in small object detection
The scale imbalance of the backbone and the neck is the main reason for the inferior accuracy of small object detection when using the general object detector. The general object detector usually contains a complex backbone and a lightweight neck, ...
A syntactic multi-level interaction network for rumor detection
Online rumors could have a great impact on public order, stock prices and even the presidential election. Therefore, the detection of online rumors is imperative. Despite the satisfactory performance achieved by the current methods, there are ...
Robot arm damage detection using vibration data and deep learning
During robot operation, robot components like links and joints may experience collisions or excess loads that can lead to structural damages or cracks. A crack in a structural component can degrade the overall performance of the structure. This ...
Fault distance estimation for transmission lines with dynamic regressor selection
The transmission line is one of the most crucial electric power system components, demanding special attention since they are subject to failures that can cause disruptions in energy supply. In this scenario, the fault location emerges as a ...
Genetic algorithm optimized a deep learning method with attention mechanism for soil moisture prediction
Accurate and effective soil moisture prediction has gradually attracted attention due to the management of agricultural activities and the practical usage of water resources. Therefore, this research presents an integrated deep learning-based ...
Impact of datasets on the effectiveness of MobileNet for beans leaf disease detection
Bean is a widely cultivated crop worldwide; however, it is susceptible to various diseases that can adversely affect the quality of beans, including rust and angular leaf spot diseases. These diseases can cause significant damage by wiping out ...
A hybrid deep convolutional neural network model for improved diagnosis of pneumonia
Pneumonia is an infection that inflames the air sacs in lungs and is one of the prime causes of deaths under the age of five, all over the world. Moreover, sometimes it became quite difficult to detect and diagnose pneumonia by just looking at the ...
Faster and efficient tetrahedral mesh generation using generator neural networks for 2D and 3D geometries
Simulations are crucial for validating the design of engineering systems and their components. However, before simulations can be performed, the geometry of the component must undergo meshing, which involves dividing it into small elements to ...
SSR-TA: Sequence-to-Sequence-based expert recurrent recommendation for ticket automation
Ticket automation plays a crucial role in ensuring the normal operation of IT software systems. One of the key tasks of ticket automation is to assign experts to resolve incoming tickets. However, when facing a large number of tickets, ...
A new approach to multi-objective optimization of a tapered matrix distributed amplifier for UWB applications
Using of ultra-wideband (UWB) technology in radio transceiver systems has increased in recent years due to high-speed data transmission, low power dissipation, low cost, and low complexity. In particular, distributed amplifier (DA) is a critical ...
SMP-DL: a novel stock market prediction approach based on deep learning for effective trend forecasting
As the economy has grown rapidly in recent years, more and more people have begun putting their money into the stock market. Thus, predicting trends in the stock market is regarded as a crucial endeavor, and one that has proven to be more fruitful ...
A network analysis-based framework to understand the representation dynamics of graph neural networks
- Gianluca Bonifazi,
- Francesco Cauteruccio,
- Enrico Corradini,
- Michele Marchetti,
- Domenico Ursino,
- Luca Virgili
In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to ...
A deep learning approach for early detection of drought stress in maize using proximal scale digital images
Neural computing methods pose an edge over conventional methods for drought stress identification because of their ease of implementation, accuracy, non-invasive approach, cost-effectiveness, and ability to predict in real time. To ensure proper ...
Multilingual mixture attention interaction framework with adversarial training for cross-lingual SLU
Cross-lingual spoken language understanding (cross-lingual SLU), as a key component of task-oriented dialogue systems, is widely used in various industrial and real-world scenarios, such as multilingual customer support systems, cross-border ...
Lightweight transformer and multi-head prediction network for no-reference image quality assessment
No-reference (NR) image quality assessment (IQA) is an important task of computer vision. Most NR-IQA methods via deep neural networks do not reach desirable IQA performance and have bulky models which make them difficult to be used in the ...
1D-convolutional transformer for Parkinson disease diagnosis from gait
This paper presents an efficient deep neural network model for diagnosing Parkinson’s disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. ...
A WSFA-based adaptive feature extraction method for multivariate time series prediction
In recent years, artificial neural networks (ANNs) have been successfully and widely used in multivariate time series prediction, but the accuracy of the prediction is significantly affected by the ANNs’ input. In order to determine the ...
Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models
In gas condensate reservoirs, the dew point pressure (PDew) plays a significant role in gas and liquid assessment, reservoir characterisation, surface facility design, and reservoir simulation. Although field and laboratory measurements of PDew ...
Design of gender recognition system using quantum-based deep learning
Biometric authentication systems identify or verify a person from a digital image taken by security cameras or fingerprint readers. Digital images are used for authentication wherever a security system exists, such as in airports and banks. ...
