Remote sensing image scene classification (RSISC) has garnered significant attention in recent years. Numerous methods have been put forward in an attempt to tackle this issue, particularly leveraging deep learning methods that have shown promising p...
In spite of great progress in recent years, deep learning (DNN) and transformers have strong limitations for supporting human–machine teams due to a lack of explainability, information on what exactly was generalized, and machinery to be integr...
It is well known that deep learning (DNN) has strong limitations due to a lack of explainability and weak defense against possible adversarial attacks. These attacks would be a concern for autonomous teams producing a state of high entropy for the te...
Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multi-plant disease recognition method that combines deep featu...
Accurate indoor positioning is essential for many applications. However, current methods often fall short in complex environments due to signal fluctuations. We propose a new indoor positioning approach, that is, improved sequential deep learning (IS...
Sustainable and renewable energy sources are of great importance in today’s world. In this respect, renewable energy sources are used in many fields of technology. In order to minimize dust on PV panels and ensure their sustainability, power lo...
Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invas...
Machine learning and artificial intelligence technologies are known to be a convenient tool for analyzing multi-domain data in precision psychiatry. In the case of schizophrenia, the most commonly used data sources for such purposes are neuroimaging,...
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Ident...
Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation....
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets be...
In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A...
Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoi...
The use of conventional classification techniques to recognize diseases and pests can lead to an incorrect judgment on whether crops are diseased or not. Additionally, hot pepper diseases, such as “anthracnose” and “bacterial spot&r...
Fatigue has always been one of the major causes of structural failure, where repeated loading and unloading cycles reduce the fracture energy of the material, causing it to fail at stresses lower than its monotonic strength. However, predicting fatig...
Due to the limited number of air quality monitoring stations, the data collected are limited. Using supervised learning for air quality fine-grained analysis, that is used to predict the air quality index (AQI) of the locations without air quality mo...
Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potentia...
In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd&nd...
Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports fla...
Remote sensing image scene classification has become more and more popular in recent years. As we all know, it is very difficult and time-consuming to obtain a large number of manually labeled remote sensing images. Therefore, few-shot scene classifi...
Deep hashing has received a great deal of attraction in large-scale data analysis, due to its high efficiency and effectiveness. The performance of deep hashing models heavily relies on label information, which is very expensive to obtain. In this wo...
k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distanc...
Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Ac...
In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolution...
The maritime silk road policy of China brings opportunities to companies relating to overseas investment. Despite the investment potentials, the risks cannot be ignored and have still not been well assessed. Considering the fact that ICRG comprehensi...
Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and ride...
A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the sa...
The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the e...
Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potent...
In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or lo...
Electric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of...
Text classification is a fundamental task in natural language processing (NLP). Deep-learning-based text classification methods usually have two stages: training and inference. However, the training dataset is only used in the training stage. To make...
Cryptojacking or illegal mining is a form of malware that hides in the victim’s computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing th...
As the world is shifting toward cleaner energy sources, accurate forecasting of solar radiation is critical for optimizing the performance and integration of solar energy systems. In this study, we explore eight machine learning models, namely, Rando...
This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature...
Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specif...
The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automat...
In shallow water, passive sonar usually has great difficulty in discriminating a surface acoustic source from an underwater one. To solve this problem, a supervised machine learning method using only one hydrophone is implemented in this paper. First...
Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope...
Regional rainfall forecasting is an important issue in hydrology and meteorology. Machine learning algorithms especially deep learning methods have emerged as a part of prediction tools for regional rainfall forecasting. This paper aims to design and...
Recently, remote sensing image scene classification (RSISC) has gained considerable interest from the research community. Numerous approaches have been developed to tackling this issue, with deep learning techniques standing out due to their great pe...
This study addresses the performance of deep learning models for predicting human DNA sequence classification through an exploration of ideal feature representation, model architecture, and hyperparameter tuning. It contrasts traditional machine lear...
Deep learning has become the most popular research subject in the fields of artificial intelligence (AI) and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made p...
Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, the use of ensemble learning methods is...
We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors...
Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest ne...
Background: This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and u...
Heart disease is the leading cause of death across the world. However, such an early prediction of heart attacks can save lives if clinical data are used to predict it accurately. For this, we use four machine learning models: Naive Bayes, Decision T...
Research on self-imagined emotional imagery supports the development of practical affective brain–computer interface (BCI) systems. This study proposes a hybrid emotion induction approach that combines facial expression image cues with subseque...
Boosting the sales of e-commerce services is guaranteed once users find more items matching their interests in a short amount of time. Consequently, recommendation systems have become a crucial part of any successful e-commerce service. Although vari...
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