This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a feat...
The Internet of Medical Things (IoMT) is a bio-network of associated medical devices, which is slowly improving the healthcare industry by focusing its abilities on enhancing personal healthcare benefits with medical data. Moreover, the IoMT tries to...
This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely
Walnut (Juglans regia L.), a critical economic species, experiences substantial declines in fruit quality and yield due to Walnut Leaf Scorch (WLS). This issue is particularly severe in the Xinjiang Uygur Autonomous Region (XUAR)—one of Asia&rs...
Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain...
Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting...
Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is...
In order to better solve the phenomenon of low oxygen in the corner of return airway caused by abnormal gas emission in goaf during shallow coal seam mining, by analyzing the source and reason of low oxygen phenomenon, a prediction model of oxygen co...
To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eigh...
Studying the spatial variation patterns and influencing factors of soil organic matter (SOM) in hilly and basin areas is of great significance for guiding agricultural production practices. This study takes Lanxi City as an example and comprehensivel...
The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in electrified vehicles’ battery managemen...
A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random...
Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wav...
Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban are...
The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa...
State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new...
The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer’s disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups...
The knowledge of water surface changes provides invaluable information for water resources management and flood monitoring. However, the accurate identification of water bodies is a long-term challenge due to human activities and climate change. Sent...
Low-noise free-space laser communication has widespread applications in military and rescue fields, but atmospheric turbulence severely affects communication quality. This paper proposes an intelligent classification and adaptive noise reduction syst...
Under the global warming trend, the diffusion of air pollutants has intensified, causing extremely serious environmental problems. In order to improve the air quality–meteorology correlation model’s prediction accuracy, this work focuses...
Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influen...
This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used...
This paper proposes a priority-aware EP-ALOHA framework with predictive radio resource allocation for heterogeneous machine-to-machine (M2M) devices in 5G massive machine-type communication (mMTC) networks. The proposed framework extends conventional...
Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. In this study, the classification of emotional states using electroencephalography (EEG) data w...
Wildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and pred...
Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we d...
The intrinsic structural stability of ABO3 perovskite materials is a pivotal factor determining their efficiency and durability in photovoltaic applications. However, accurately predicting stability, commonly measured by the energy above hull metric,...
The urban growth boundary (UGB) plays an important role in the regulation of urban sprawl and the conservation of natural ecosystems. The delineation of UGBs is a common strategy in urban planning, especially in metropolitan areas undergoing fast exp...
Relief well designs mainly focus on conventional parameters such as well diameter and well spacing by engineering experience, lacking rigorous analysis. The impact of wellhead elevation remains unclear. This paper proposes a multi-objective optimizat...
Currently, Chemoinformatic methods are used to perform the prediction for FBPase inhibitory activity. A genetic algorithm-random forest coupled method (GA-RF) was proposed to predict fructose 1,6-bisphosphatase (FBPase) inhibitors to treat type 2 dia...
Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefit...
The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resou...
Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no...
In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alo...
Accurate crop mapping in fragmented agricultural landscapes is challenged by overlapping crop calendars and redundancy among multi-source time-series variables. Using Sentinel-1/2 imagery from December 2022 to December 2023, we constructed 275 season...
S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore,...
Reclaimed water irrigation is increasingly being applied to address global water scarcity, yet its long-term effects on soil nitrogen cycling and salinity dynamics, particularly in agricultural and agroforestry systems, remain complex and insufficien...
Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions...
To address the coupled effects of multiple factors on the flowability of fabric-form concrete during construction, as well as the reliance of conventional mixture-proportion design on empirical experience and its low trial-mixing efficiency, this stu...
This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the...
Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) diagnosis and treatment rely heavily on computed tomography (CT) imaging to evaluate tumor characteristics and lymph node (LN) involvement, crucial for staging, prognosis, and thera...
Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-base...
Machine learning tools represent a key methodology for the shape optimization of complex geometries in the turbomachinery field. One of the current challenges is to redesign High-Pressure Turbine (HPT) stages to couple them with innovative combustion...
This paper describes an intelligent, CAD-based decision support and optimization framework for predicting fabric waste directly from geometry attributes of apparel pattern segments. It is applicable during the early design stage, providing a pre-scre...
The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed. This...
ClinVar is a web platform that stores ∼789,000 genetic associations with complex diseases. A partial set of these cataloged genetic associations has challenged clinicians and geneticists, often leading to conflicting interpretations or uncertain...
Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision t...
Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological difference...
Accurate daily solar radiation estimates are needed to plan solar energy systems in arid regions, where long ground records are unavailable at most candidate sites. This study evaluates metaheuristic-optimized Random Forest models for daily global ho...
Effective cancer treatment requires a clear subtype. Due to the small sample size, high dimensionality, and class imbalances of cancer gene data, classifying cancer subtypes by traditional machine learning methods remains challenging. The gcForest al...
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