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Items from 1 to 20 out of 10,973 results

article

Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control

Ni-Bin Chang, Golam Mohiuddin, A. James Crawford, Kaixu Bai, more

Ecological Informatics > 2015 > 28 > Complete > 42-60

Monitoring the velocity field and stage variations in heterogeneous aquatic environments, such as constructed wetlands, is critical for understanding hydrodynamic patterns, nutrient removal capacity, and hydrographic impact on the wetland ecosystem. Obtaining low velocity measurements representative of the entire wetland system may be challenging, expensive, and even infeasible in some cases. Data-driven...

article

Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks

M.A. Ariana, B. Vaferi, G. Karimi, M.A. Ariana, more

Powder Technology > 2015 > 278 > Complete > 1-10

The aims of the present study are to develop and validate an artificial neural network (ANN) approach to estimate the thermal conductivity ratio (TCR) of alumina water-based nanofluids as a function of temperature, volume fraction and diameter of the nanoparticle. The ANN parameters are adjusted by back propagation learning algorithm using 285 collected experimental data sets from various literatures...

article

Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

P.M. Ajith, Birendra Kumar Barik, P. Sathiya, S. Aravindan, more

Defence Technology > 2015 > 11 > 2 > 157-165

The present study is to optimize the process parameters for friction welding of duplex stainless steel (DSS UNS S32205). Experiments were conducted according to central composite design. Process variables, as inputs of the neural network, included friction pressure, upsetting pressure, speed and burn-off length. Tensile strength and microhardness were selected as the outputs of the neural networks...

article

Optimized artificial intelligence models for predicting project award price

Jui-Sheng Chou, Chih-Wei Lin, Anh-Duc Pham, Ji-Yao Shao, more

Automation in Construction > 2015 > 54 > Complete > 106-115

Bridges are essential components of transportation systems. The bidding process is the main determinant of whether a contractor is commissioned to complete a construction project. Therefore, contractors must rapidly and precisely estimate construction costs and the bid award amount. This study involved optimizing artificial intelligence models to forecast bid award amounts for bridge construction...

article

Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS

Khader M. Hamdia, Tom Lahmer, Trung Nguyen-Thoi, Timon Rabczuk, more

Computational Materials Science > 2015 > 102 > Complete > 304-313

In this paper, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been employed to predict the fracture energy of polymer nanocomposites. The ANN and ANFIS models were constructed, trained, and tested based on a collection of 115 experimental datasets gathered from the literature. Five input parameters were considered: the volume fraction of the nano filler, the...

article

Artificial neural network based inverse design: Airfoils and wings

Gang Sun, Yanjie Sun, Shuyue Wang, G. Sun, more

Aerospace Science and Technology > 2015 > 42 > Complete > 415-428

The numerical search for the optimum shape of airfoil/wing geometry is of great interest for aircraft and turbomachinery designers. However the conventional method of design and optimization, which is to repeat the process of modifying airfoil/wing geometry data based on the flow field calculation of initial geometry, is computationally intensive and time-costly. In lieu of this, this article introduces...

article

Comparison of classical statistical methods and artificial neural network in traffic noise prediction

Vladimir Nedic, Danijela Despotovic, Slobodan Cvetanovic, Milan Despotovic, more

Environmental Impact Assessment Review > 2014 > 49 > Complete > 24-30

Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application...

article

Control and prediction of degradation of biopolymer based hydrogels with poly(ɛ-caprolactone) subunits

Rodica Diaconescu, Bogdan C. Simionescu, Geta David, R. Diaconescu, more

International Journal of Biological Macromolecules > 2014 > 71 > Complete > 147-154

Complex hydrogels consisting of natural and synthetic polymers, stabilized by combining different physical and chemical cross-linking methods, were assessed by in vitro degradation tests performed at 37°C in phosphate buffer solution. Four biopolymer-based hydrogel series were comparatively evaluated as sponge or dense films as regards mass loss, morphology changes and thermal behavior over the fixed...

article

An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)

Kwangbok Jeong, Choongwan Koo, Taehoon Hong, K. Jeong, more

Energy > 2014 > 71 > Complete > 71-79

Electricity consumption in educational facilities has increased by an annual average of 9.84% since 2006. However, it is not considered as a factor in determining the AECB (annual energy cost budget) in South Korea. Therefore, this study aims to develop an estimation model for determining the AECB in educational facilities using the SARIMA (seasonal autoregressive integrated moving average) model...

article

An ensemble-based model for two-class imbalanced financial problem

Jui-Jung Liao, Ching-Hui Shih, Tai-Feng Chen, Ming-Fu Hsu, more

Economic Modelling > 2014 > 37 > Complete > 175-183

This study proposes an ensemble-based model (EBM) for the two-class imbalanced classification problem by joining together the support vector machine (SVM), multiple feature selection combination, back-propagation neural network (BPNN) ensemble, and rough set theory (RST). To improve the significance of the rare and specific region belonging to the minority class in the decision region, we take the...

article

Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation and RSM (Response Surface Methodology) Model for Optimal Performance of Sliver Cutting Operation of Bamboo

C.N. Sakhale, S.N. Waghmare, S.K. Undirwade, V.M. Sonde, more

Procedia Materials Science > 2014 > 6 > Complete > 877-891

Bamboo enterprises are continuously sustaining national economy through providing employment opportunities for rural people including raw material collection, processing and marketing. Bamboo has many industrial uses, through this work tried to make the maximum operations feasible so that the machine can prove employment in rural areas at very low manual efforts. This paper reports on design and development...

article

A 3D numerical model of nearshore wave field behind submerged breakwaters

Amir Sharifahmadian, Richard R. Simons, A. Sharifahmadian, R.R. Simons

Coastal Engineering > 2014 > 83 > Complete > 190-204

The functional design of submerged breakwaters is still developing, particularly with respect to modeling of the nearshore wave field behind the structure. An effective design tool needs to calculate both 2D and 3D effects. A numerical method for predicting the spatial transmission coefficient for regular waves in the shadow region of a 3D submerged breakwater is proposed in this paper. Two distinct...

article

Artificial neural network for solving flow shop optimization problem with sequence independent setup time

Hajar Sadki, Karam Allali

Foundations of Computing and Decision Sciences > 2024 > Vol. 49, No. 4 > 355--383

In this paper, we will study the permutation flow shop scheduling problem (PFSSP) with sequence independent setup time (SIST). This constraint is the most common encountered in industrial production. In this case, the SIST constraint depends on the technology nature of the machine, as well as the means used to prepare it for the execution of a new job. The purpose of this paper is to develop an artificial...

article

Cell‐preserving scheme for mechanobiological research on dedifferentiation of chondrocytes

Hyun Lee, Nikolai Panitschewski, Heiko Topol, Saurabh Tandale, more

PAMM > 24 > 3 > n/a - n/a

In the present work, we introduce a scheme for efficient mechanobiological research on the dedifferentiation of chondrocytes (CHs) using a deep neural network (DNN) model that does not require sacrificing the cells and thus, saving resources. Cells are a part of active biological systems subjected to physical stimuli such as mechanical loading. Such loading affects cellular processes including proliferation,...

article

GeTe/MoTe₂ Van der Waals Heterostructures: Enabling Ultralow Voltage Memristors for Nonvolatile Memory and Neuromorphic Computing Applications

Atul C. Khot, Kiran A. Nirmal, Tukaram D. Dongale, Tae Geun Kim

Small > 20 > 42 > n/a - n/a

Advanced electronic semiconducting Van der Waals heterostructures (HSs) are promising candidates for exploring next‐generation nanoelectronics owing to their exceptional electronic properties, which present the possibility of extending their functionalities to diverse potential applications. In this study, GeTe/MoTe₂ HS are explored for nonvolatile memory and neuromorphic‐computing applications. Sputter‐deposited...

article

Effective Electromagnetic Properties of Composite Material Computed From Neural Network Approach

Abelin Kameni, Den Palessonga, Zahraa Semmoumy, Mohamed Bensetti

International Journal of Numerical Modelling: Electronic Networks, Devices... > 37 > 5 > n/a - n/a

Thanks to their lightweight, composite materials have become widely used in the automotive and aerospace industries. The design of components made from these materials is carried out by numerical modeling which can sometimes be tedious because of the need to take into account the internal structure of these materials. Obtaining the effective properties of an equivalent homogeneous material to replace...

article

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling

Majid Basafa, Atieh Hashemi, Aidin Behravan

Biotechnology and Applied Biochemistry > 71 > 5 > 1094 - 1104

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional “one‐factor‐at‐a‐time” approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has...

article

Machine learning analysis of oxidative stress‐related phenotypes for specific gene screening in ovarian cancer

Chenxiang Pan, Chunyu Pan, Lili Chen, Aidi Lin

Environmental Toxicology > 39 > 10 > 4763 - 4775

Background Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress‐related prognostic signature to enhance the prognosis prediction of CC patients using bioinformatics. Methods The genes differentially expressed and associated with oxidative stress were extracted with...

article

Shear wave velocity prediction for fractured limestone reservoirs based on artificial neural network

Gang Feng, Zhe Yang, Xing‐Rong Xu, Wei Yang, more

Geophysical Prospecting > 72 > 7 > 2646 - 2665

Shear wave velocity is an essential parameter in reservoir characterization and evaluation, fluid identification and prestack inversion. However, conventional data‐driven or model‐driven shear wave velocity prediction methods exhibit several limitations, such as lack of training data sets, poor model generalization and weak model robustness. In this study, a model‐ and data‐driven approach is presented...

article

Hybrid data‐driven and physics‐based simulation technique for seismic response evaluation of steel buckling‐restrained braced frames considering brace fracture

Ali Sadrara, Siamak Epackachi, Ali Imanpour, Mohammad Zaman Kabir

Earthquake Engineering & Structural Dynamics > 53 > 11 > 3358 - 3380

This paper proposes a hybrid data‐driven and physics‐based simulation technique for seismic response evaluation of steel Buckling‐Restrained Braced Frames (BRBFs) considering brace fracture. Buckling‐Restrained Brace (BRB) fracture is represented by cumulative plastic deformation capacity. A dataset, consisting of 95 past BRB laboratory tests and 120 simulated BRB responses generated using the finite...

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Search results

Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control

Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks

Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

Optimized artificial intelligence models for predicting project award price

Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS

Artificial neural network based inverse design: Airfoils and wings

Comparison of classical statistical methods and artificial neural network in traffic noise prediction

Control and prediction of degradation of biopolymer based hydrogels with poly(ɛ-caprolactone) subunits

An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)

An ensemble-based model for two-class imbalanced financial problem

Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation and RSM (Response Surface Methodology) Model for Optimal Performance of Sliver Cutting Operation of Bamboo

A 3D numerical model of nearshore wave field behind submerged breakwaters

Artificial neural network for solving flow shop optimization problem with sequence independent setup time

Cell‐preserving scheme for mechanobiological research on dedifferentiation of chondrocytes

GeTe/MoTe₂ Van der Waals Heterostructures: Enabling Ultralow Voltage Memristors for Nonvolatile Memory and Neuromorphic Computing Applications

Effective Electromagnetic Properties of Composite Material Computed From Neural Network Approach

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling

Machine learning analysis of oxidative stress‐related phenotypes for specific gene screening in ovarian cancer

Shear wave velocity prediction for fractured limestone reservoirs based on artificial neural network

Hybrid data‐driven and physics‐based simulation technique for seismic response evaluation of steel buckling‐restrained braced frames considering brace fracture

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