Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learn...
Evolutionary multitasking optimization (EMTO) is currently one of the hottest research topics that aims to utilize the correlation between tasks to optimize them simultaneously. Although many evolutionary multitask algorithms (EMTAs) based on traditi...
These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face m...
The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local s...
Precise water level forecasting plays a decisive role in improving the efficiency of flood prevention and disaster reduction, optimizing water resource management, enhancing the safety of waterway transportation, reducing flood risks, and promoting e...
Background: Cervical cancer remains a major public health concern worldwide, particularly in low- and middle-income countries. Limited knowledge regarding cervical cancer prevention and elevated anxiety levels may hinder women’s participation i...
Accurate groundwater level (GWL) prediction is crucial in groundwater resource management. Currently, it relies mainly on physics-based models for prediction and quantitative analysis. However, physics-based models used for prediction often have erro...
Powered by advances in information and internet technologies, network-based applications have developed rapidly, and cybersecurity has grown more critical. Inspired by Reinforcement Learning (RL) success in many domains, this paper proposes an Intrus...
The strategies employed by teachers and students in the open inquiry-based learning approach are crucial, especially when presenting researchable questions formulated by students under the guidance of the teacher. This effectively promotes the teachi...
Finding the most interesting areas of an image is the aim of saliency detection. Conventional methods based on low-level features rely on biological cues like texture and color. These methods, however, have trouble with processing complicated or low-...
This study developed a deep learning–based water level recognition model using Closed-Circuit Television (CCTV) footage. The model focuses on real-time water level recognition in agricultural reservoirs that lack automated water level gauges, w...
Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the ac...
Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more e...
Predicting water levels of Lake Erie is important in water resource management as well as navigation since water level significantly impacts cargo transport options as well as personal choices of recreational activities. In this paper, machine learni...
Testing as an assessment technique has been widely used at all levels of education—from primary to higher school. The main purpose of the paper is to evaluate the effect of context-based testing in teaching and learning of analytical chemistry in a R...
River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrolog...
There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a sys...
Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify ma...
Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning a...
In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among th...
Using advanced communication and information technologies, teleconsultation can provide high-quality healthcare services to remote areas. To enhance service efficiency, this study develops a two-level dynamic scheduling model for teleconsultation, wh...
The present paper provides a human activity-level classification model based on the patient’s lifelog collected from wearable devices. During about two months, the heart rate, step count, and calorie consumption for a total of 182 patients were...
The importance of efficient water resource supply has been acknowledged, and it is essential to predict short-term water consumption in the future. Recently, it has become possible to obtain data on water consumption at the household level through sm...
The challenges of the modern labor market determine increased job insecurity and the growing importance of sustainable employability. Today, in an era of the growth of the world’s knowledge resources, permanent learning is indispensable in order to m...
Practising the calculation of determinants is important in linear algebra. A pioneering study involving 580 first-year university students evaluated the impact of game-based learning in higher mathematics education. The participants formed two groups...
Named Entity Recognition (NER) is a crucial component of Natural Language Processing (NLP). When dealing with the high diversity and complexity of the Chinese language, existing Chinese NER models face challenges in addressing word sense ambiguity, c...
This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of syntheti...
Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data....
High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring...
This study aimed to develop machine learning models based on conventional MRI features to classify HER2 expression levels in invasive breast cancer and explore their association with disease-free survival (DFS). A total of 678 patients from two cente...
Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point...
Traffic accidents occur frequently, causing significant losses to people’s lives and property safety. Accurately predicting the severity level of traffic accidents is of great significance. Based on traffic accident data, this study comprehensi...
This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are co...
The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help i...
Current knowledge graph reasoning methods actually use the same state representations and a fixed decision method to complete the total reasoning process. They are not able to truly use the different levels of attribute information. To overcome this...
This study proposes a methodology based on Discrete Cosine Transform (DCT) coefficients and deep learning to identify steganographic algorithms (UERD, J-UNIWARD, and JMiPOD) and to classify distortion levels in images from the ALASKA2 dataset. For ea...
Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine...
Groundwater is an essential natural resource for human societies and ecosystems. Traditional groundwater monitoring relies on in situ wells, which are susceptible to discontinuity, influencing water resource management. To overcome this deficiency, t...
This article presents the design, simulation, and real-time implementation of an intelligent water level control system using Deep Reinforcement Learning (DRL) with the Deep Deterministic Policy Gradient (DDPG) algorithm. The control policy was initi...
Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficie...
Depression screening from social media has increasingly benefited from transformer-based architectures; however, integrating symptom-level analysis with clinically grounded diagnostic screening remains challenging. This study proposes a unified two-p...
Polarimetric synthetic aperture radar (PolSAR) image classification has achieved great progress, but there still exist some obstacles. On the one hand, a large amount of PolSAR data is captured. Nevertheless, most of them are not labeled with land co...
A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this...
The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing dow...
Machine learning (ML) models utilizing high-frequency Internet of Things (IoT) monitoring data offer significant potential for stream-level nowcasting. The assessment of model performance is often a challenging task because conventional approaches ar...
Objective: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the s...
The Basrah Refinery, Iraq, similarly to other refineries, is subject to several industrial constraints. Therefore, the main challenge is to optimize the parameters of the level controller of the process unit tanks. In this paper, a PI controller is d...
Industry 4.0 depends on the ability to connect heterogeneous systems, yet students and practitioners rarely have a low-risk environment in which to practice this kind of systems integration. This article presents a virtual-prototyping architecture de...
Traditional recommendation systems, which rely on static user profiles and historical interaction data, frequently face difficulties in adapting to the rapid changes in user preferences that are typical of dynamic environments. In contrast, recommend...
The supervision of security risk level of carbofuran pesticide residues can guarantee the food quality and security of residents effectively. In order to predict the potential key risk vegetables and regions, this paper constructs a security risk ass...
of 286