Artificial Neural Network Method Research Articles

Advanced prediction of soil shear strength parameters using index properties and artificial neural network approach

This study embarks on developing predictive models for soil shear strength parameters, cohesion (c) and angle of internal friction (ϕ), in Bishoftu town, employing Artificial Neural Networks (ANN). It aims at offering a cost-effective and time-saving alternative to traditional, often expensive, and labor-intensive laboratory methods. The research utilizes soil index properties such as Sand %, Fines %, Liquid Limit, Plastic Limit, and Plasticity Index to construct separate ANN models for c and ϕ. These models use a multi-layer perceptron network with feed-forward back propagation, varying the number of hidden layers to optimize performance. The study's dataset comprises 316 soil test results, encompassing both primary and secondary data, conforming to ASTM Standards. Soil cohesion and internal friction angle were determined using the direct shear box method. The models demonstrated remarkable success in predicting shear strength parameters, evidenced by correlation values of approximately 0.99 for cohesion and 0.98 for internal friction angle, surpassing the capabilities of existing empirical methods. Further examination of the models included comparison with existing correlation techniques and cross-validation using primary soil test data. This validation process confirmed the ANN method's superior accuracy and fit for predicting shear strength parameters over selected empirical methods. This research substantiates the efficiency of ANN in geotechnical engineering, particularly for areas with limited resources for extensive soil testing. It establishes ANN as a powerful, efficient tool for estimating soil shear strength parameters, with significant implications for future planning, design, and construction projects in similar environments.

P1192 Deciphering the colonic immune landscape of UC from cellular senescence by integrated bioinformatics analysis and machine learning

Abstract Background Nowadays, there has been extensive interest in cellular senescence in gastrointestinal diseases. However, the pathological mechanism of cellular senescence in UC and its involvement in colonic immune characteristics remain enigmatic. Therefore, we aimed to investigate the contribution of cellular senescence-related signatures to the colonic immune landscape in UC based on bioinformatics combined machine learning strategy. Methods Three gene sets of UC were selected from the Gene Expression Omnibus database, including two training sets and one testing set. We identified cellular senescence-associated differentially expressed genes (DEGs) in UC and performed functional enrichment analysis. Afterwards, signature genes were determined by machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and artificial neural network (ANN). Furthermore, distinct immune microenvironment abnormalities in UC were comprehensively explored, and the correlation between the signature genes and the colonic immune landscape of UC was also evaluated. Based on the signature genes, we clustered UC samples into distinct subtypes by consensus clustering analysis. Finally, we performed validation of the signature genes in dextran sulfate sodium salt (DSS)-induced colitis. Results 41 cellular senescence-associated DEGs in UC were identified by intersecting DEGs with the cellular senescence-associated genes from the CellAge database, which were associated with cellular senescence and immune pathways. Following that, seven signature genes were determined by the machine learning method of LASSO regression, SVM-RFE, and ANN, comprising CXCL1, MMP9, CYR61, BAG3, PEX19, RPS6KA6, and ETS2, which were also validated in the testing set. Furthermore, we obtained the most correlated pairs of UC signature genes-immunocytes and found these signature genes were highly corelated with neutrophils and activated CD4+ memory T cells. Additionally, the expression profiling of human leukocyte antigen genes and immune checkpoints were also analyzed. Subsequently, two molecular subtypes related to senescence that exhibit significantly different immune characteristics were identified. Finally, the mRNA levels of the signature genes were confirmed in DSS-induced colitis, which were congruent with results gained from the datasets. Conclusion In summary, the result has emphasized the essential function of cellular senescence-associated genes in controlling the immune microenvironment for UC. The exploration of the etiopathogenesis of UC through cellular senescence is anticipated to lead to novel advancements in disease diagnosis and potential treatments.

Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite

In this research, the optical properties of the PVP: ZnTiO3 nanocomposite are studied using the spectroscopic ellipsometry technique. The preparation procedure of the ZnTiO3 nanocomposite is explained in detail. The absorbance/transmittance, surface morphology, structural information, chemical identification, and surface topography of the ZnTiO3 nanocomposite is studied using UV–Vis spectroscopy, field-emission scanning electron microscopy, Raman spectroscopy, Fourier transform infra-red, and atomic force microscopy, respectively. The ellipsometry method is used to obtain the spectra of the real and imaginary parts of the dielectric function and refractive index in the photon energy range of 0.59–4.59 eV. Moreover, using two machine learning algorithms, namely artificial neural network and support vector regression methods, the ellipsometric parameters ψ and Δ are analyzed and compared with non-linear regression. The error and accuracy of each three methods, as well as the time required for their execution, are calculated to compare their suitability in the ellipsometric data analysis. Also, the absorption coefficient was used to determine the band gap energy of the ZnTiO3 nanocomposite, which is found to be 3.83 eV. The second-energy derivative of the dielectric function is utilized to identify six critical point energies of the prepared sample. Finally, the spectral-dependent optical loss function and optical conductivity of the ZnTiO3 nanocomposite are investigated.

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems

Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models—artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.

Sensitivity analysis of the mechanical properties on atherosclerotic arteries rupture risk with an artificial neural network method

Considering the differences between individuals, in this paper, an uncertainty analysis model for predicting rupture risk of atherosclerotic arteries is established based on a back-propagation artificial neural network. The influence of isotropy and anisotropy on the rupture risk of atherosclerotic arteries is analyzed, and the results demonstrate the effectiveness of the artificial neural network in predicting the rupture risk. Moreover, the rupture risk of atherosclerotic arteries at different inflation sizes are simulated. This study contributes to a better understanding of the underlying mechanisms of atherosclerotic arteries rupture and promotes the advancement of artificial neural networks in atherosclerosis research.

Artificial Neural Network (ANN) Method for the Integration of Solar and Wind Hybrid Energy Sources into Telecommunication Systems

In order to sustain monetary growth, the introduction of renewable energy into the electricity grid is crucial especially for many foreign African locations. Thus, in order to reap sustainable strength, these foreign locations may also outfit their airport electrical equipment with advanced synthetic intelligence technologies. For a distributive hybrid solar strength grid, the paper attempts to propose an actual-time energy management algorithm. It continues with the combining of photovoltaic and wind energy for network simulation. As a function of the number of wind aero-mills and photovoltaic solar panels, a multi-goal approach is proposed to optimize the spectral efficiency of the location and the energy performance. For the MATLAB software simulation, radio criteria for Cell Wireless Interoperability Medium Access (WiMAX) technologies are taken into account. The obtained effects are much mitigated however theoretically encouraging for the mixing of green energy integration into the modern telecommunication structures

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network (ANN) and adaptive-network-based fuzzy inference system (ANFIS) for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA-125), and carcinoembryonic antigen (CEA), are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9% in predicting ovarian cancer. With an accuracy rate of at least 89%, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

Classification of filigree silver with Artificial Neural Networks according to production methods

The jewelry industry uses precious stones and metals in various ways while ornaments and jewelry are made. One of the methods used is the filigree method. The most critical factor in the filigree method is human and craftsmanship. However, rapid technological developments make the machine use in filigree mandatory. As a result, filigree products produced by handwork can be created using serial molds in the factory environment. This study aims to classify the molded product filigree silver using artificial neural networks. Filigree products produced by filigree masters and as mold products were compared to distinguish the filigree products. The color of the silver jewelry, the state of the jewelry, the silver setting status, the brass metal used in the silver jewelry, the form of the inner filling motif, the shape of the roof wire, the smoothness of the structure, the proper placement of the inner filling, the symmetrical status of the motifs on the jewelry are trained in the system using Deep Learning, which is an artificial neural networks method through thehe data collected from features such as the use of valuable or worthless stones. The success of classifying filigree jewelry handcrafts or mold products using Deep Learning through artificial neural network methods was evaluated. As a result of the study, the classification with deep learning was conducted successfully.

Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach

Silt is the leading cause of the erosion of the turbine's underwater components during hydropower generation. This erosion subsequently decreases the machine's efficiency. The present study aims to develop statistical correlations for predicting the efficiency of a hydropower plant based on the Kaplan turbine. Historical data from a Kaplan turbine-based hydropower plant was employed to create the model. Curve fitting, multilinear regression (MLR), and artificial neural network (ANN) techniques were used to develop models for predicting the machine's efficiency. The results show that the ANN method is better at predicting the machine's efficiency than the MLR and curve fitting methods. It got an R2-value of 0.99966, a MAPE of 0.0239%, and an RMSPE of 0.1785%. Equipment manufacturers, plant owners, and researchers can use the established correlation to evaluate the machine's condition in real-time. Additionally, it offers utility in formulating effective operations and maintenance (O&M) strategies.

OPTIMASI METODE JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK PERAMALAN CURAH HUJAN BULANAN DI KOTA DENPASAR

Rainfall is a natural phenomenon that depends on many factors that are an important part of life on earth. The high intensity of rainfall can lead to disasters. Therefore, this study aims to forecast monthly rainfall. The data used was obtained from BMKG Bali Province, namely monthly rainfall data for Denpasar City from 2009 to 2019. The method used is backpropagation artificial neural network. The artificial neural network method is an information processing method inspired by the human nervous system. Optimal backpropagation network architecture is needed so that the prediction results have a low error rate, by optimizing the use of training data and test data taken from sample data. Based on the results of the testing and prediction process with the parameters of one hidden layer with 50 neorons, epoch 11 and learning rate 0.01, the results obtained with the MSE value in network testing are 0.037. So it can be concluded that the backpropagation artificial neural network method has good accuracy results used as a reference for decision making in predicting monthly rainfall in Denpasar City in the future.

Multi-objective optimization analysis of combined heating and cooling transcritical CO2 system integrated with mechanical subcooling utilizing hydrocarbon mixture based on machine learning

To meet the simultaneous needs of high temperature disinfection and freezing in the field of food processing, a new concept of combined heating and cooling transcritical CO2 system integrated with dedicated mechanical subcooling utilizing hydrocarbon mixture is proposed. The system performance in terms of thermodynamics, economy and environment is studied and compared with the baseline combined heating and cooling transcritical CO2 system and four traditional combined heating and cooling solutions, considering the influence of temperature glide and the heat transfer deterioration. The new proposed system is further optimized by using the machine learning method of artificial neural network and non-dominated sorting genetic algorithm. Multi-objective optimization is conducted considering the objective function of energy efficiency, initial capital cost and life cycle carbon emissions of the new system, to obtain the optimum components and concentration ratio of the hydrocarbon mixture. The results indicate the thermodynamic performance and environmental benefits of subcooling subsystem with hydrocarbon mixture are better than those of the pure system. In contrast to that using pure R290 and R601, the coefficient of performance is enhanced by 8.20 % and 8.13 % and the life cycle carbon emission is reduced by 8.54 % and 9.31 %, respectively, when R290/R601 (50/50) is used. However, the initial capital cost is 9.25 % and 10.23 % higher than that of pure R290 and R601, respectively. Finally, the hydrocarbon mixture corresponding to the optimal design point is R1270/R601a (53/47), the corresponding discharge pressure is 12.86 MPa, and the subcooling degree is 37.50 °C. This study can provide a theoretical reference for the application of CO2 refrigeration and heat pump technology.

Optimization of GFRP-concrete-steel composite column based on genetic algorithm - artificial neural network

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS.

A Study of Seating Suspension System Vibration Isolation Using a Hybrid Method of an Artificial Neural Network and Response Surface Modelling

A reliable prediction model can greatly contribute to the research of car seating system vibration control. The novelty of this paper lies in the development of a hybrid method of an artificial neural network (ANN) and response surface methodology (RSM) to predict the peak seat-to-head transmissibility ratio of a seating suspension system and to evaluate its ride comfort for different seat design parameters. Additionally, this method can remove the experimental design of the RSM model. In this paper, four seat design parameters are selected as input parameters and arranged using the central composite design method. The peak transmissibility ratio from seat to head at 4 Hz is chosen as the response target output value. To illustrate this hybrid method, the response target output value of the peak transmissibility ratio is calculated from the frequency response of a five-degrees-of-freedom (5-DOF) lumped-parameter biodynamic seating suspension model. The input design parameters and the response target output values are used to train an ANN to establish the relationship between the seat design parameters and the peak transmissibility ratio. At the same time, the input design parameters and the response target output values predicted by the ANN are used to develop the relationship between the seat design parameters and the peak transmissibility ratio using the response surface method and linear regression models. The hybrid of the ANN and response surface methods makes the planning or design of experiments not essential. The hybrid model of the ANN and response surface method is more accurate and convenient than a linear regression model for the study of seating system vibration isolation.

Simulasi Progres Proyek Konstruksi Time Performance Menggunakan Earned Value Management dengan Integrasi Artificial Neural Network

Digitization of construction using technologies such as AI, Big Data, Machine Learning, and Internet of Things (IoT) can help improve the productivity and efficiency of the construction industry. Machine learning methods such as Artificial Neural Network (ANN) are used to solve complex problems, including in construction projects. In addition, Earned Value Management (EVM) is used as a method to analyze and control project performance and estimate project completion time. Although EVM has the disadvantage of predicting estimated project completion times that are linear in nature, the use of non-linear methods such as Artificial Neural Network can help improve the accuracy of estimating project completion times that are complex and have a high degree of variation. This research aims to analyze the physical achievements of the work and predict estimates of work completion using Earned Value Management which is integrated with the Artificial Neural Network on the Advanced Development Project for Facilities and Infrastructure of the Sanggabuana Karawang Training Area Maintenance Detachment (Sarpras Denharrahlat) belonging to the Indonesian Ministry of Defense. By using EVM and ANN, project management can be improved in terms of cost control, scheduling, and better estimation of project completion time. The data analysis method used is EVM. The data analyzed is Week, Planned Value and Earned Value. The three data are then plotted and translated in graphical form to calculate the Schedule Variance and Schedule Performance Index to evaluate the project. After conducting an analysis using the EVM, then a predictive model is formed to calculate the estimated project completion time using the Artificial Neural Network (ANN) method. Next, optimization of the predictive model is carried out to reduce the level of prediction error. The Software used to build predictive models is RapidMiner. At week 26, work progress was 100% ahead of plan, with an Earned Schedule value of 31.00. This indicates that the project has been completed in accordance with the target time. Certain parameter modifications to the optimal model include training cycles of 100, learning rate of 0.316, and momentum of 0.316. With these parameter settings, the model produces the most accurate predictions with a low prediction error rate.

QSAR studies of quinoline alkaloids camptothecin derivatives for prediction anticancer activity using linear and nonlinear methods

AbstractQuinoline alkaloid camptothecin (CPT) derivatives are compounds with a wide range of inhibitory activities and they are the basis of several groups of drugs. CPT is one of the prominent lead compounds in anticancer drug development. It inhibits DNA topoisomerase I (topo I) enzyme and has shown remarkable anticancer activity against lung, colon, rectum, ovarian, breast, and stomach cancers. Quantitative structure–activity relationship (QSAR) is basically a statistical approach correlating the response activity data with descriptors encoding chemical information. In the current research, a QSAR study was performed for modeling and predicting the anticancer activity (pIC50) of 76 CPT derivatives as an inhibitor of DNA topo I using the genetic algorithm multiple linear regression (GA‐MLR) and back propagation artificial neural network (BP‐ANN) methods. The results of this study indicate that the use of constitutional and geometrical descriptors provide good estimate for pIC50. The obtained results of statistical criteria, internal and external validation show the superiority of BP‐ANN model than GA‐MLR to predict the pIC50 values of the investigated compounds. QSAR model developed in this study can be used for design and development of novel potent CPT derivatives and for predicting their anticancer activity.

A Real Estate Appraisal Model with Artificial Neural Networks and Fuzzy Logic: A Local Case Study of Samsun City

There have been great innovations in the field of real estate appraisal which have replaced the classical methods by recently established modern methods that involve computer technologies known as artificial intelligence. here are several artificial intelligent methods like fuzzy logic and artificial neural networks. In this study, two different real estate appraisal applications that are based on the artificial neural network and fuzzy logic methods are compared. An actual data set is taken from real estate agencies to develop a real estate appraisal model in Samsun city in Turkey. All of the real estate data belong to a certain time interval in May 2020. The selected parameters are scored by a valuation expert and the scores are then subjected to normalization. The obtained values are inputted into a fuzzy logic and artificial neural networks editor in MATLAB software and the valuation model is created. The values we obtain from the artificial neural networks and fuzzy logic are compared with actual sales data. It is observed that using these methods in real estate appraisal provides advantages in terms of time, cost and tangibility, as well as the means to create large database for real estate appraisal in a certain region.

FORECASTING GROSS DOMESTIC PRODUCT (GDP) OF OECD COUNTRIES BASED ON DIGITAL TRANSFORMATION INDICATORS: ANN APPROACH

The ongoing COVID-19 pandemic process we all face currently has highlighted the fact that digitalization of economic activities is very crucial. Digital economy became even more important after the pandemic. When the governments allocate a greater proportion of Gross Domestic Product on some public services, the lower the proportion spent on other goods and services. For this reason, many countries want to estimate the Gross Domestic Product spent on public services within the scope of public control and risk management. The main goal in this study is to predict Gross Domestic Product per capita based on digital indicators such as fixed-broadband subscriptions, mobile-cellular telephone penetration rate, fixed-telephone subscriptions, internet penetration rate, research and development expenditure and patent applications. Artificial Neural Networks are employed to predict Gross Domestic Product per capita via digital indicators. Artificial Neural Network method is used to estimate the digital economy for 29 OECD countries between 2002–2018. The results demonstrate that Artificial Neural Networks can be utilized effectively in applications of digital economy forecasting. According to the results, coefficient of determination (R2 = 0.92) is very high, meaning that Artificial Neural Networks can be effectively utilized in Gross Domestic Product per capita estimations through digital indicators. This article differs from previous studies as it uses digital economic indicators in predicting Gross Domestic Product per capita by applying Artificial Neural Networks model.

Cost Efficient Training Method for Artificial Neural Networks based on Engine Measurements

Cost Efficient Training Method for Artificial Neural Networks based on Engine Measurements

Hybrid Method of Artificial Neural Network and Simulated Annealing Algorithm for Optimizing Wideband Patch Antennas

Hybrid Method of Artificial Neural Network and Simulated Annealing Algorithm for Optimizing Wideband Patch Antennas

Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images.

Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer. First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model. The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively. The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.