Detecting the crystal system of lithium-ion batteries is crucial for optimizing their performance and safety. Understanding the arrangement of atoms or ions within the battery’s electrodes and electrolyte allows for improvements in energy densi...
The study addresses the formidable challenge of calculating atomic coordinates for carbon nanotubes (CNTs) using density functional theory (DFT), a process that can endure for days. To tackle this issue, the research leverages the Genetic Programming...
This investigation underscores the paramount imperative of discerning network intrusions as a pivotal measure to fortify digital systems and shield sensitive data from unauthorized access, manipulation, and potential compromise. The principal aim of...
Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling aut...
Breast cancer is a type of cancer with several sub-types. It occurs when cells in breast tissue grow out of control. The accurate sub-type classification of a patient diagnosed with breast cancer is mandatory for the application of proper treatment....
Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. In this paper, a no...
Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the perfo...
Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as witho...
Unsolicited emails, popularly referred to as spam, have remained one of the biggest threats to cybersecurity globally. More than half of the emails sent in 2021 were spam, resulting in huge financial losses. The tenacity and perpetual presence of the...
Background: A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep learning models. A challenging task concerning grid search is time management. Without a good...
The expeditious and precise prediction of stress variations in nonlinear boom structures is paramount for ensuring the safe, dependable, and effective operation of pump trucks. Nonetheless, balancing prediction accuracy and efficiency by constructing...
Advancing urban scholarship and addressing pressing challenges such as gentrification, housing affordability, and urban sprawl require robust predictive models. In urban sciences, the performance of these models depends heavily on hyperparameter tuni...
Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracl...
Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, mo...
Accurate intent detection-based chatbots are usually trained on larger datasets that are not available for some languages. Seeking the most accurate models, three English benchmark datasets that were human-translated into four morphologically complex...
The configuration of the hyperparameters in convolutional neural networks (CNN) is crucial for determining their performance. However, traditional methods for hyperparameter configuration, such as grid searches and random searches, are time consuming...
Machine predictive maintenance plays a critical role in reducing unplanned downtime, lowering maintenance costs, and improving operational reliability by enabling the early detection and classification of potential failures. Artificial intelligence (...
The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal d...
Leak-off pressure (LOP) is a key parameter to determine the allowable weight of drilling mud in a well and the in situ horizontal stress. The LOP test is run in situ and is frequently used by the petroleum industry. If the well pressure exceeds the L...
In recent years, with increasing social pressure and irregular schedules, many people have developed unhealthy eating habits, which has resulted in an increasing number of patients with diabetes, a disease that cannot be cured under the current medic...
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain,...
This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. We evaluated three optimization approaches—Grid Search (GS)...
Background: This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors. Methods...
Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes a...
Accurate estimation of crop evapotranspiration (ET) is essential for achieving efficient agricultural water use in the North China Plain. Although machine learning techniques have demonstrated considerable potential for ET simulation, a systematic ev...
Based on the data from two field surveys in 2015 and 2022, this paper calculates the weight of values using the entropy weight method and the variation coefficient method, and evaluates risk using the information quantity method. The information quan...
Android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic programming symbolic classifier (GPSC) t...
The accurate prediction of soil contamination in abandoned mining areas is necessary to address their environmental risks. This study employed a combined model of machine learning and geostatistics to predict the spatial distribution of soil contamin...
Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of prede...
Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization...
Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fi...
The detection of Android malware is of paramount importance for safeguarding users’ personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, the...
This study presents a method for predicting nozzle surface temperature and the timing of frost formation during hydrogen refueling using machine learning. A continuous refueling system was implemented based on a simulation model that was developed an...
A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, search direction, and estimation based on inver...
In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates)....
Determining the classification of motor competence is an essential aspect of physical activity that must be carried out during school years. The objective is to evaluate motor competence in schoolchildren using smart bands, generate percentiles of th...
To achieve an accurate, efficient, and high dynamic control performance of electric motor drives, precise phase voltage information is required. However, measuring the phase voltages of electrical motor drives online is expensive and potentially cont...
Ultra-high-performance concrete (UHPC) is a recently developed material which has attracted considerable attention in the field of civil engineering because of its outstanding characteristics. One of the key factors in concrete design is the compress...
In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the...
Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuab...
The first research attempt to dynamically optimize the CORDIC algorithm’s iteration count using artificial intelligence is presented in this paper. Conventional approaches depend on a certain number of iterations, which frequently results in ex...
Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containi...
Photovoltaic (PV) power prediction plays a significant role in supporting the stable operation and resource scheduling of integrated energy systems. However, the randomness and volatility of photovoltaic power generation will greatly affect the predi...
To solve the problems of strong professionalism and cumbersome operation required for crop soil moisture prediction, a soil moisture prediction platform has been developed for real-time irrigation decision-making based on the Django framework. This p...
Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Theref...
During the construction of deep foundation pits, closely monitoring the deformation of the foundation pit retaining structure is of vital importance for ensuring the stability and safety of the foundation pit and reducing the risk of structural damag...
Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the...
28 September 2022
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an incr...
Surrogate models are widely used in science and engineering to approximate other methods that are usually computationally expensive. Here, artificial neural networks (ANNs) are employed as surrogate regression models to approximate the finite element...
This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, a...
of 2