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Artificial Neural Networks - Science topic

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Publications related to Artificial Neural Networks (10,000)
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In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is challenging. Thus, accurate estimation of software development efforts is always a concern for many co...
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We propose using machine learning and artificial neural networks (ANNs) to enhance residual-based stabilization methods for advection-dominated differential problems. Specifically, in the context of the finite element method, we consider the streamline upwind Petrov-Galerkin (SUPG) stabilization method and we employ ANNs to optimally choose the sta...
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ABSTRACT Identifying and assessing the disaster risk of landslide-prone regions is very critical for disaster prevention and mitigation. Owning to their special advantages, neural network algorithms have been widely used for landslide susceptibility mapping (LSM) in recent decades. In the present study, three advanced neural network models popularl...
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The current study predicts West Texas Intermediate (WTI) petroleum prices using an artificial neural network (ANN) with a whale optimization algorithm (WOA). In implementing the model, five parameters, including gold price, coal price, natural gas price, Dollar-Euro exchange rate, and Dollar-Yuan exchange rate, have been used as input to the combin...
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Water is important for the natural environment and human health. Monitoring algae concentrations yield information on the water quality. Compared with in situ measurements of water quality parameters, which are often complex and expensive, remote sensing techniques, using hyperspectral data analysis, are fast and cost-effective. The objectives of t...
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Agent-based modelling is a powerful tool when simulating human systems, yet when human behaviour cannot be described by simple rules or maximizing one’s own profit, we quickly reach the limits of this methodology. Machine learning has the potential to bridge this gap by providing a link between what people observe and how they act in order to reach...
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The capacity to substitute cement with wash sand waste powder gives a technological and long-term benefit in today’s construction industry. The current research looks at how to anticipate the properties of cement and concrete that have been fused with wash sand waste powder. The percentage of cement that has been replaced varies from 0%, 5%, 7.5%,...
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Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable mann...
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This paper presents artificial neural network modelling for the thrust force in terms of maximum and mean values and the surface roughness for drilling soda glass using ultrasonic-assisted drilling. The experimental parameters are the tool concentrations (normal and high), cutting speed, and feed rate. The feedforward architecture neural network is...
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Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for humans. This technology enhances the users' experience in various ways. Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning (ML) algorithms. The main goal...
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To prepare measures for washing synthetic fibers, which cause proliferation of microplastics in the marine ecosystem, a fundamental analysis is required. Therefore, this study established an efficient method for quantitatively analyzing microfibers using artificial neural networks, comparing the amounts of microfibers generated in the manufacturing...
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With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues, and plentiful applications have...
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Feature selection targets for selecting relevant and useful features, and is a vital challenge in turbulence modeling by machine learning methods. In this paper, a new posterior feature selection method based on validation dataset is proposed, which is an efficient and universal method for complex systems including turbulence. Different from the pr...
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This paper presents the modeling and control process of recently developed manipulator with 3 limbs having prismatic-universal-universal (3-PUU) joints. It has 3 degrees of freedom (3-DOF), consisting of 2 rotational DOFs and 1 translational DOF (2R1T). To avoid the computational complexity of solving the manipulator’s kinematics in real-time appli...
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With an increasing demand for artificial intelligence, the emulation of the human brain in neuromorphic computing has led to an extraordinary result in not only simulating synaptic dynamics but also reducing complex circuitry systems and algorithms. In this work, an artificial electronic synaptic device based on a synthesized MoS 2 memristor array...
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In this research study, the investigation of building details on the construction project cost and duration using artifcial neural networks (ANNs) which possesses the ability to generalize complex input–output relationships between given datasets was carried out. From relevant literature review, expert judgment, and extensive feld survey, system da...
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Literature on photovoltaic thermal (PV/T) system has developed significantly over the last decades. Besides a few review papers, no study has examined their citations using the most popular citation mapping technique — bibliometric. This study, for the first time, reviews total 1659 published documents from Scopus database on PV/T system from 1981...
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Photovoltaic/thermal (PV/T) are high-tech devices to transform solar radiation into electrical and thermal energies. Nano-coolants are recently considered to enhance the efficiency of PV/T systems. There is no accurate model to predict/optimize the PV/T systems’ electrical efficiency cooled by nano-coolants. Therefore, this research employs machine...
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Underwater flowlines (pipelines and cables) have become a popular option worldwide as communication lines. Correspondingly the demand for underwater trenching increased with the most desirable method internationally, jet trenching for its benefits in protecting the flowlines from external hazards. However, the trenching method is suffering from a m...
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Resilient modulus (Mr) of subgrade soils is considered as one of the most important factors for designing flexible pavements using empirical methods as well as mechanistic-empirical methods. The resilient modulus is commonly measured by a dynamic triaxial loading test, which is complex and expensive. In this research, back-propagation artificial ne...
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Today’s environmental, energy and economic challenges require changes in the control strategies of HVACs’ systems, since they account for more than 60% of the building energy consumption. An optimal control law should be applied to reduce this consumption. To achieve this goal, a detailed description of the building is required, its construction co...
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This paper aims to investigate the spatial price dispersion and estimate the price determinants of a homogeneous product sold by marketing channels in the industrial automation market in the south of Germany. Empirical studies reveal that different prices are charged for the same homogeneous products, which shows a clear deviation from the law of o...
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Saturated hydraulic conductivity (Ksat), one of the critical soil hydraulic properties, is used to model many soil hydrological processes. Measurement of Ksat on a routine basis is a labor-intensive, time-consuming, and expensive process. Alternatively, prediction of Ksat values from easy to obtain soil features is more economical and saves time. A...
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Photoplethysmography (PPG) based methods have gained popularity in recent times for arrhythmia detection. However, limited research has been carried out for multiple arrhythmia detection using PPG signals. Dynamic time warping (DTW) is a widely used time series technique for the comparison of speech and word recognition. However, the use of the DTW...
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Can we trust our eyes? Until recently, we rarely had to question whether what we see is indeed what exists, but this is changing. Artificial neural networks can now generate realistic images that challenge our perception of what is real. This new reality can have significant implications for cybersecurity, counterfeiting, fake news, and border secu...
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In this article we study the stochastic gradient descent (SGD) optimization method in the training of fully connected feedforward artificial neural networks with ReLU activation. The main result of this work proves that the risk of the SGD process converges to zero if the target function under consideration is constant. In the established convergen...
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Because the experimental trials in civil engineering field are difficult and time-consuming, the application of artificial intelligence (AI) techniques is attracting considerable attention, with their use enabling successful results to be more easily obtained. In this study, we investigated the effect of fiber size, fiber amount, water content, and...
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PM2.5 can bring serious harm to people's health and life because it easily causes cardiovascular disease and increases the risk of cancer. Hence, monitoring PM2.5 real‐timely becomes a key problem in environmental protection. Towards this end, this paper proposes an improved picture‐based prediction method of PM2.5 concentration using artificial ne...
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In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this arti...
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Conventional methods of spatial prediction, such as Kriging, require assumptions such as stationarity and isotropy, which are not easy to evaluate, and often do not hold for spatial data. For these methods, the spatial dependency structure between data should be accurately modeled, which requires expert knowledge in spatial statistics. On the other...
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In this study, the flow parameters of Reiner–Philippoff nanofluid flow with high‐order slip properties, activation energy, and bioconvection have been analyzed using artificial neural networks (ANNs). Local Nusselt number (LNN), local Sherwood number (LSN), and motile density number (MDN) are considered as flow parameters. Numerical values have bee...
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Properties data for chemical compounds are essential information for the design and operation of chemical processes. Experimental values are reported in the literature, but that are too scarce compared with exploding demand for data. When the data are not available, various estimation methods are employed. The group contribution method is one of th...
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In recent years, ultra-wideband perfect absorbers (UWPAs) based on metamaterial nanostructures have been widely studied due to their excellent performance. However, designing and optimizing the absorber quickly and accurately remains challenging in the broad-band range. In this work, by adopting the genetic algorithm combined with artificial neural...
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In this paper, reducing the number of necessary measuring points for estimating a reflected electromagnetic spectrum of a printed color patch is presented. In our previous work, a machine learning-based method was proven to be superior to Cubic Hermite interpolation in estimating spectrum based on six measured values provided by measuring reflectio...
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The studies show that ballast tank sediments pose several problems, including the continuing risk of invasive and pathogenic species transfer. Moreover, Regulation B.5 of The IMO's BWM convention states “ships should be designed and constructed with a view to minimize the uptake and undesirable entrapment of Sediments, facilitate removal of Sedimen...
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Self-Compacting Concrete (SCC) is a fluid concrete that can be placed without any vibration. To the best of our knowledge, no method exists today that can accurately predict the properties of this type of concrete which is highly sensitive to small changes in the mix design, because its characteristics depend on several factors. The present study a...
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The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensor...
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Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for t...
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The random distribution of glass and basalt fibers in normal concrete produces a composite that alters the behavior of hardened concrete members. Hence, it is quite complicated to predict the strengths of glass fiber reinforced concrete (GFRC) and basalt fiber reinforced concrete (BFRC). In the present study, an artificial neural network (ANN) mode...
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Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are...
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The energy recovery of the grate cooler is a significant part of reducing production costs and tackling the environmental challenges of the cement industry. ASPEN Plus and neural networks predictive model were used to model, simulate and predict the grate clinker cooler in this paper. First, the process flow model and thermodynamic efficiency asses...
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Convolutional Neural Network (CNN) is a type of artificial neural network which is trained using image data. This network architecture consists of a convolutional base and a dense head which helps in classification tasks. This trained networks is then used to solve complex problems like determining the magnitude of damage caused by corrosion. Typic...
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For two decades, the dominance of nature-inspired optimization algorithms has been irresistible in solving many complex problems. In fact, most of these algorithms are integrated with some other intelligent technique to prove the effectiveness of each method. Out of these algorithms, the last decade has witnessed a good research contribution for Te...
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Article history: Information about the reality of the traffic accident, the clearness of the roads and the status of the accident can be obtained from the traffic accident announcements. By using the words in the radio or telephone announcements, you can be informed about the status of the accident. Inferences can be made with machine learning meth...
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Artificial Neural Network (ANN) has served as an important pillar of machine learning which played a crucial role in fueling the robust artificial intelligence (AI) revival experienced in the last few years. Inspired by the biological brain architecture of living things, ANN has shown widespread success in pattern recognition, data analysis and cla...
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Water is commonly used as the dielectric medium in High Voltage Pulse (HVP) disintegration. It is hypothesized that deionized water can achieve better efficiency than tap water and is prevailingly used in different laboratory-scale HVP applications. Since tap water is more easily accessible and inexpensive than deionized water, particularly in indu...
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09-Apr-2022 Dear Mr. belhocine: Thank you for reviewing manuscript # HTJ-03-2022-OA-0210 entitled "Frictional Material Selection of Dry Clutch Disc Based on the Weighted Factor and Finite Element Methods" for Heat Transfer. On behalf of the Editors of Heat Transfer, we appreciate the voluntary contribution that each reviewer gives to the Journal...
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The process of material discovery and design can be simplified and accelerated if we can effectively learn from existing data. In this study, we explore the use of machine learning techniques to learn the relationship between the structural properties of pyrochlore compounds and their lattice constants. We proposed a support vector regression (SVR)...
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It is crucial to further improve the overall performance of heat exchanger tube and make it adapt to higher flux conditions. In this paper, a novel type of tube inserts, symmetrical wing longitudinal swirl generators (SWLSGs), was proposed and its thermal hydraulic performance was numerically investigated under laminar flow. The effect of four geom...
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Due to the interaction between local and global instability modes at elevated temperatures, predicting the capacity of thin-walled columns in the fire situation is a complex endeavor. This work investigates the application of machine learning techniques to assess the resistance of slender steel columns with I-shaped cross-sections at elevated tempe...
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Artificial neural networks (ANNs) model has made remarkable achievements in many fields. Therefore, we have greater expectation for it, expecting it to have the same intelligence as human beings. However, ANNs still can’t perform continual learning like humans at present. The serious defect of ANNs model is called the catastrophic forgetting proble...
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Optimum well controls to maximize net present value (NPV) in a waterflooding operation are often obtained from an iterative process of employing numerical reservoir simulation and optimization algorithms. It is often challenging to implement gradient-based optimization algorithms because of the large number of variables and the complexities to embe...
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Wind energy is an important green energy. The use of wind energy can alleviate the pressure caused by the shortage of traditional energy. The wind speed is affected by many factors, which makes it difficult to forecast accurately. Most wind speed forecasting methods only consider the wind speed data. The other influence factors are usually ignored....
Research Proposal
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This Special Issue focuses on the use of artificial neural networks in the modeling of non-linear systems and fault diagnosis and their use in renewable energy systems such as wind turbines and photovoltaic panels. Both theoretical and experimental work and, especially, the combination of these are welcome. an Open Access Journal by MDPI
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Cu3(HHTT)2 (HHTT: 2,3,7,8,12,13‐hexahydroxytetraazanaphthotetraphene) is a novel two‐dimensional conjugated metal‐organic framework (2D c‐MOF) with efficient in‐plane d‐π conjugations and strong interlayer π‐π interactions while the growth of Cu3(HHTT)2 thin films has never been reported until now. Here, we present the successful fabrication of hig...