(1) Background: Various Machine Learning (ML) methods are applied for the prediction of individual efficiency of cancer treatment regimens. As features for ML, different multi-omics data may be used. We proposed a next-generation ML approach termed F...
Small modular reactors (SMRs) could be beneficial in providing electricity power safely and also be viable for applications such as seawater desalination and heat production. Due to its inherent safety features, the modular high temperature gas-coole...
Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration b...
In this paper, we experimentally illustrate the effectiveness of neural networks (NNs) as non-linear equalisers for multilevel pulse amplitude modulation (PAM-M) transmission over an optical wireless communication (OWC) link. In our study, we compare...
Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint. Early diagnosis and treatment can prolong painless joi...
The accuracy with which a neural network interprets a point cloud depends on the quality of the features expressed by the network. Addressing this issue, we propose a multi-level feature extraction layer (MFEL) which collects local contextual feature...
Sarcasm detection in text poses significant challenges for traditional sentiment analysis, as it often requires an understanding of context, word meanings, and emotional undertones. For example, in the sentence “I totally love working on Christ...
In the research on intelligent perception, dynamic emotion recognition has been the focus in recent years. Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convoluti...
Multi-label text classification (MLTC) aims to assign the most appropriate label or labels to each input text. Previous studies have focused on mining textual information, ignoring the interdependence of labels and texts, thus leading to the loss of...
High dropout rates on in-session learning platforms pose a significant challenge to student retention and the overall success of educational programmes. This study proposes a novel framework that integrates multi-level stacked ensemble learning with...
The classification of high-frequency marine mammal vocalizations often faces challenges due to the limitations of acoustic features, which are sensitive to mid-to-low frequencies but offer low resolution in high-frequency ranges. Additionally, single...
Texture recognition is a fundamental task in computer vision, with diverse applications in material sciences, medicine, and agriculture. The ability to analyze complex patterns in images has been greatly enhanced by advancements in Deep Neural Networ...
Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and t...
The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining...
(1) Background: We investigated the age of swimming champions in all strokes and race distances in World Championships (1994–2013) and Olympic Games (1992–2012); (2) Methods: Changes in age and swimming performance across calendar years for 412 Olymp...
Introduction: Fertility is fundamental to human well-being, significantly impacting both individual lives and societal development. In particular, sperm morphology—referring to the shape, size, and structural integrity of sperm cells—is a...
The precise classification of crop types using hyperspectral remote sensing imaging is an essential application in the field of agriculture, and is of significance for crop yield estimation and growth monitoring. Among the deep learning methods, Conv...
This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. T...
This research proposes a virtual restoration system and method for 3D digital cultural relics based on a fuzzy logic algorithm, aiming to solve the problems of the low classification accuracy and poor splicing effect of Terra Cotta Warrior fragments....
Soil temperature is a fundamental parameter in water resources and irrigation engineering. A cost-effective model that can accurately forecast soil temperature is urgently needed. Recently, many studies have applied artificial intelligence (AI) at bo...
Accurate medical image segmentation plays a crucial role in clinical diagnosis by precisely delineating diseased tissues and organs from various medical imaging modalities. However, existing segmentation methods often fail to effectively capture low-...
Graph-based neural networks have proven to be useful in molecular property prediction, a critical component of computer-aided drug discovery. In this application, in response to the growing demand for improved computational efficiency and localized e...
LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-criti...
Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and poi...
This article deals with fault detection and the classification of incipient and intermittent open-transistor faults in grid-connected three-level T-type inverters. Normally, open-transistor detection algorithms are developed for permanent faults. Nev...
The evaluation of landslide susceptibility plays a crucial role in preventing the risks associated with landslides and debris flows, providing valuable insights for the effective prevention and mitigation of geological hazards. However, there is limi...
Polyethylene as a thermoplastic has received the uppermost popularity in a vast variety of applied contexts. Polyethylene is produced by several commercially obtainable technologies. Since Ziegler–Natta catalysts generate polyolefin with broad...
The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are p...
In contemporary industrial machinery, bearings are a vital component, so the ability to diagnose bearing faults is extremely important. Current methodologies face challenges in feature extraction and perform suboptimally in environments with high noi...
Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific c...
To mitigate the impact on detection performance caused by insufficient input information in 3D object detection based on single LiDAR data, this study designs three innovative modules based on the PointRCNN framework. Firstly, addressing the issue of...
To address the issues of manual inspection and low precision in the detection and recognition of defects in existing animal leather, this study first establishes a leather image dataset and applies an improved Gabor filtering algorithm for image prep...
Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and exten...
This paper proposes and develops a residential energy and resource consumption estimation model in the context of multi-family residential housing in Korea using a multi-layer perceptron (MLP) neural network. Eight indicators are introduced which aff...
Background/Objectives: Studies attempting to predict the development of severe respiratory failure in patients with a COVID-19 infection using machine learning algorithms have yielded different results due to differences in variable selection. We aim...
Postural instability is one of the main critical aspects to be monitored in the case of degenerative diseases, and is also a predictor of potential falls. This paper presents a multi-layer perceptron approach for the classification of four different...
Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a M...
Tam Dao National Park (TDNP) is a remaining primary forest that supports some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity cons...
As an essential step in the restoration of Terracotta Warriors, the results of fragments classification will directly affect the performance of fragments matching and splicing. However, most of the existing methods are based on traditional technology...
The shift towards renewable energy integration into smart grids has led to complex management processes, which require finer-grained energy and heat generation/ demand forecasting while considering data from monitoring devices and the integration of...
Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still...
A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been conside...
In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have be...
This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars...
Traditional drug development is a slow and costly process that leads to the production of new drugs. Virtual screening (VS) is a computational procedure that measures the similarity of molecules as one of its primary tasks. Many techniques for captur...
In this paper, a novel convolutional neural network (CNN)-based architecture, named fine segmentation network (FSN), is proposed for semantic segmentation of high resolution aerial images and light detection and ranging (LiDAR) data. The proposed arc...
Building information extraction utilizing remote sensing technology has vital applications in many domains, such as urban planning, cadastral mapping, geographic information censuses, and land-cover change analysis. In recent years, deep learning alg...
This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical rad...
Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benef...
Considering the large size of the agricultural sector in Romania, increasing the crop insurance adoption rate and identifying the factors that drive adoption can present a real interest in the Romanian market. The main objective of this research was...
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