Journal articles on the topic 'ANNs'
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1
Dao, Khoi Nguyen, and Phuong Ai Huynh. "Using artificial neural network in simulating of the streamflow in the Srepok watershed." Science and Technology Development Journal 19, no. 2 (June 30, 2016): 114–20. http://dx.doi.org/10.32508/stdj.v19i2.796.
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In this study, artificial neural network (ANN) model was used to simulate the streamflow in the Srepok watershed, Vietnam. Correlation analysis of time series for precipitation and streamflow was employed to determine input data for the ANN model. This result indicated a significant correlation up to 2 day time lag and 1 day time lag for the precipitation and streamflow series data, respectively. According to the correlation analysis, three ANN models including ANN1, ANN2, and ANN3 were investigated. A 3-year data record for the precipitation and streamflow was used for ANN training and testing. The result of ANN training and testing showed that the ANN2 with 3 input data (P(t), P(t-1), and Q(t- 1)) gave the best simulation (NSE = 0.95 for training period and NSE = 0.96 for testing period) comparing to those of ANN1 and ANN3. In addition, the comparison of ANNs showed that the increase of the input data did not offer the better result.
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Li, Li, and Kyung Soo Jun. "A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks." Water 14, no. 9 (April 26, 2022): 1393. http://dx.doi.org/10.3390/w14091393.
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Climate change is driving worsening flood events worldwide. In this study, a hybrid approach based on a combination of the optimization of a hydrodynamic model and an error correction modeling that exploit different aspects of the physical system is proposed to improve the forecasting accuracy of flood water levels. In the parameter optimization procedure for the hydrodynamic model, Manning’s roughness coefficients were estimated by considering their spatial distribution and temporal variation in unsteady flow conditions. In the following error correction procedure, the systematic errors of the optimized hydrodynamic model were captured by combining the input variable selection method using partial mutual information (PMI) and artificial neural networks (ANNs), and therefore, complementary information provided by the data was achieved. The developed ANNs were used to analyze the potential non-linear relationships between the considered state variables and simulation errors to predict systematic errors. To assess the hybrid forecasting approach (hydrodynamic model with an ANN-based error correction model), performances of the hydrodynamic model, two ANN-based water-level forecasting models (ANN1 and ANN2), and the hybrid model were compared. Regarding input candidates, ANN1 considers the historical observations only, and ANN2 considers not only the historical observations that used in ANN1 but also the prescribed boundary conditions required for the hydrodynamic forecast model. As a result, the hybrid model significantly improved the forecasting accuracy of flood water levels compared to individual models, which indicates that the hybrid model is able to take advantage of complementary strengths of both the hydrodynamic model and the ANN model. The optimization of the hydrodynamic model allowing spatially and temporally variable parameters estimated water levels with acceptable accuracy. Furthermore, the use of PMI-based input variable selection and optimized ANNs as error correction models for different sites were proven to successfully predict simulation errors in the hydrodynamic model. Hence, the parameter optimization of the hydrodynamic model coupled with error correction modeling for water level forecasting can be used to provide accurate information for flood management.
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Turco, Chiara, Marco Francesco Funari, Elisabete Teixeira, and Ricardo Mateus. "Artificial Neural Networks to Predict the Mechanical Properties of Natural Fibre-Reinforced Compressed Earth Blocks (CEBs)." Fibers 9, no. 12 (December 1, 2021): 78. http://dx.doi.org/10.3390/fib9120078.
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The purpose of this study is to explore Artificial Neural Networks (ANNs) to predict the compressive and tensile strengths of natural fibre-reinforced Compressed Earth Blocks (CEBs). To this end, a database was created by collecting data from the available literature. Data relating to 332 specimens (Database 1) were used for the prediction of the compressive strength (ANN1), and, due to the lack of some information, those relating to 130 specimens (Database 2) were used for the prediction of the tensile strength (ANN2). The developed tools showed high accuracy, i.e., correlation coefficients (R-value) equal to 0.97 for ANN1 and 0.91 for ANN2. Such promising results prompt their applicability for the design and orientation of experimental campaigns and support numerical investigations.
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Shafrai, Anton V., Larisa V. Permyakova, Dmitriy M. Borodulin, and Irina Y. Sergeeva. "Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks." Information 13, no. 11 (November 7, 2022): 529. http://dx.doi.org/10.3390/info13110529.
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Various methods are used to prevent the deterioration of the biotechnological properties of brewer’s yeast during storage. This paper studied the use of artificial neural networks for the mathematical modeling of correcting the biosynthetic activity of brewer’s seed yeast of the C34 race during storage with natural minerals. The input parameters for the artificial neural networks were the suspending medium (water, beer wort, or young beer); the type of the zeolite-containing tuff from Siberian deposits; the tuff content (0.5–4% of the total volume of the suspension); and the duration of storage (3 days). The output parameters were the number of yeast cells with glycogen, budding cells, and dead cells. In the yeast stored with tuffs, the number of budding cells increased by 1.2–2.5 times, and the number of cells with glycogen increased by 9–190% compared to the control sample (without tuff). The presence of kholinskiy zeolite and shivyrtuin tuffs resulted in a significant effect. The artificial neural networks were required for solving the regression tasks and predicting the output parameters based on the input parameters. Four networks were created: ANN1 (mean relative error = 4.869%) modeled the values of all the output parameters; ANN2 (MRE = 1.8381%) modeled the number of cells with glycogen; ANN3 (MRE = 6.2905%) modeled the number of budding cells; and ANN4 (MRE = 4.2191%) modeled the number of dead cells. The optimal parameters for yeast storage were then determined. As a result, the possibility of using ANNs for mathematical modeling of undesired deviations in the physiological parameters of brewer’s seed yeast during storage with natural minerals was proven.
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Mimeche, Omar, Amir Aieb, Antonio Liotta, and Khodir Madani. "A Novel Interannual Rainfall Runoff Equation Derived from Ol’Dekop’s Model Using Artificial Neural Networks." Sensors 22, no. 12 (June 8, 2022): 4349. http://dx.doi.org/10.3390/s22124349.
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In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol’Dekop’s equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANNs), namely by ANN1 and ANN2 sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN1 output results and De Martonne index (I) were classified, and were then processed by ANN2 to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R2Adj obtained from each test gave values between 0.9103 and 0.9923.
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Meng, Jingfan, Huayi Wang, Jun Xu, and Mitsunori Ogihara. "ONe Index for All Kernels (ONIAK)." Proceedings of the VLDB Endowment 15, no. 13 (September 2022): 3937–49. http://dx.doi.org/10.14778/3565838.3565847.
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In this work, we formulate and solve a new type of approximate nearest neighbor search (ANNS) problems called ANNS after linear transformation (ALT). In ANNS-ALT, we search for the vector (in a dataset) that, after being linearly transformed by a user-specified query matrix, is closest to a query vector. It is a very general mother problem in the sense that a wide range of baby ANNS problems that have important applications in databases and machine learning can be reduced to and solved as ANNS-ALT, or its dual that we call ANNS-ALTD. We propose a novel and computationally efficient solution, called ONe Index for All Kernels (ONIAK), to ANNS-ALT and all its baby problems when the data dimension d is not too large (say d ≤ 200). In ONIAK, a universal index is built, once and for all, for answering all future ANNS-ALT queries that can have distinct query matrices. We show by experiments that, when d is not too large, ONIAK has better query performance than linear scan on the mother problem (of ANNS-ALT), and has query performances comparable to those of the state-of-the-art solutions on the baby problems. However, the algorithmic technique behind this universal index approach suffers from a so-called dimension blowup problem that can make the indexing time prohibitively long for a large dataset. We propose a novel algorithmic technique, called fast GOE quadratic form (FGoeQF), that completely solves the (prohibitively long indexing time) fallout of the dimension blowup problem. We also propose a Johnson-Lindenstrauss transform (JLT) based ANNS-ALT (and ANNS-ALTD) solution that significantly outperforms any competitor when d is large.
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O'Reilly, G., C. C. Bezuidenhout, and J. J. Bezuidenhout. "Artificial neural networks: applications in the drinking water sector." Water Supply 18, no. 6 (January 31, 2018): 1869–87. http://dx.doi.org/10.2166/ws.2018.016.
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Abstract Artificial neural networks (ANNs) could be used in effective drinking water quality management. This review provides an overview about the history of ANNs and their applications and shortcomings in the drinking water sector. From the papers reviewed, it was found that ANNs might be useful modelling tools due to their successful application in areas such as pipes/infrastructure, membrane filtration, coagulation dosage, disinfection residuals, water quality, etc. The most popular ANNs applied were feed-forward networks, especially Multi-layer Perceptrons (MLPs). It was also noted that over the past decade (2006–2016), ANNs have been increasingly applied in the drinking water sector. This, however, is not the case for South Africa where the application of ANNs in distribution systems is little to non-existent. Future research should be directed towards the application of ANNs in South African distribution systems and to develop these models into decision-making tools that water purification facilities could implement.
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Gülmez, Burak, and Sinem Kulluk. "Social Spider Algorithm for Training Artificial Neural Networks." International Journal of Business Analytics 6, no. 4 (October 2019): 32–49. http://dx.doi.org/10.4018/ijban.2019100103.
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Artificial neural networks (ANNs) are one of the most widely used techniques for generalization, classification, and optimization. ANNs are inspired from the human brain and perform some abilities automatically like learning new information and making new inferences. Back-propagation (BP) is the most common algorithm for training ANNs. But the processing of the BP algorithm is too slow, and it can be trapped into local optima. The meta-heuristic algorithms overcome these drawbacks and are frequently used in training ANNs. In this study, a new generation meta-heuristic, the Social Spider (SS) algorithm, is adapted for training ANNs. The performance of the algorithm is compared with conventional and meta-heuristic algorithms on classification benchmark problems in the literature. The algorithm is also applied to real-world data in order to predict the production of a factory in Kayseri and compared with some regression-based algorithms and ANNs models. The obtained results and comparisons on classification benchmark datasets have shown that the SS algorithm is a competitive algorithm for training ANNs. On the real-world production dataset, the SS algorithm has outperformed all compared algorithms. As a result of experimental studies, the SS algorithm is highly capable for training ANNs and can be used for both classification and regression.
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Kariri, Elham, Hassen Louati, Ali Louati, and Fatma Masmoudi. "Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach." Applied Sciences 13, no. 5 (March 2, 2023): 3186. http://dx.doi.org/10.3390/app13053186.
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Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure and function of the human brain. Their popularity has increased in recent years due to their ability to learn and improve through experience, making them suitable for a wide range of applications. ANNs are often used as part of deep learning, which enables them to learn, transfer knowledge, make predictions, and take action. This paper aims to provide a comprehensive understanding of ANNs and explore potential directions for future research. To achieve this, the paper analyzes 10,661 articles and 35,973 keywords from various journals using a text-mining approach. The results of the analysis show that there is a high level of interest in topics related to machine learning, deep learning, and ANNs and that research in this field is increasingly focusing on areas such as optimization techniques, feature extraction and selection, and clustering. The study presented in this paper is motivated by the need for a framework to guide the continued study and development of ANNs. By providing insights into the current state of research on ANNs, this paper aims to promote a deeper understanding of ANNs and to facilitate the development of new techniques and applications for ANNs in the future.
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Abalo, Jesus N. "Capabilities of Computer Algorithm Utilizing Artificial Neural Networks and its Implications to Economy: A Public Policy Analysis." Proceedings of The International Halal Science and Technology Conference 14, no. 1 (March 10, 2022): 83–88. http://dx.doi.org/10.31098/ihsatec.v14i1.489.
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The massive abundance of studies pertinent to Artificial Neural Networks (ANN) has produced exciting and dulcet effects on different industries and academic disciplines. Albeit the findings of the studies relating to ANNs invite potential enterprising opportunities, it is an incontestable fact that these enterprising opportunities, like fruits of the ANNs, valiantly interpose an economic threat to the working manpower. Employment retrenchment is portending as companies opt to enjoy the benefit yielded from the application and use of ANN mechanisms. ANNs will overshadow and replace the working manpower. This study is a meta-analysis that profoundly discourses on the implications of economic issues embedded in the application and use of ANNs. The findings hereof are critical and material considerations in the craft of effective public policy measures that necessarily balance the economic impact of the ANNs to working manpower. Thus this study aims to answer two primary inquiries; (1) what are the economic implications of the application and use of ANNs? and (2) what public policy measures balance the economic downsides of the application and use of ANNs?
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Che Mamat, Rufaizal, Azuin Ramli, Muhamad Razuhanafi Mat Yazid, Anuar Kasa, Siti Fatin Mohd Razali, and Mukhlis Nahriri Bastam. "Slope Stability Prediction of Road Embankment using Artificial Neural Network Combined with Genetic Algorithm." Jurnal Kejuruteraan 34, no. 1 (January 30, 2022): 165–73. http://dx.doi.org/10.17576/jkukm-2022-34(1)-16.
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The prediction of slope stability was performed using artificial neural networks (ANNs) in this work. The factor of safety determined by numerical analysis was used to develop ANN’s data sets. The inputs to the network are slope height, applied surcharge and slope angle. Correlation coefficients between numerical data and ANNs outputs showed the feasibility of ANNs for successfully modelling and predicting safety issues. The ANNs training phase is improved using a genetic algorithm (GA), and the results are compared to those obtained without GA trained ANNs. A sensitivity analysis is conducted to ascertain the relative contribution of different factors on slope stability. The slope angle and applied surcharge have a significant effect on slope stability.
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M. Handhal, Amna. "Synthesis of missing openhole well log data through artificial neural networks." Journal of Kufa-Physics 9, no. 2 (December 10, 2017): 56–63. http://dx.doi.org/10.31257/2018/jkp/2017/v9.i2.9420.
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A methodology is presented for deducing missing intervals of well logs data through applying artificial neural networks (ANNs) models. Three ANNs were performed for synthesizing sonic, neutron, and density logs. An example from Mishrif Formation of Nasyria oil field in southern Iraq was used to reveal the capability of ANNs model to synthesis missing intervals for these logs. Basically, ANNs models developed in this study were based on commonly multilayer perceptron and trained with backpropagation algorithm. Two statistical errors, namely, root mean squared error and correlation of determination were employed to assess the accuracy of the ANN models. Results indicated the capability of ANNs model to recreation of missing well interval with high accuracy.
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Wang, Huayi, Jingfan Meng, Long Gong, Jun Xu, and Mitsunori Ogihara. "MP-RW-LSH." Proceedings of the VLDB Endowment 14, no. 13 (September 2021): 3267–80. http://dx.doi.org/10.14778/3484224.3484226.
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Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-Sensitive Hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS- L 2 , a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L 1 distance, and show that it achieves a better tradeoff between scalability and query efficiency than all existing LSH-based solutions. We also explain why a state-of-the-art ANNS -L 1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. Finally, as a use case, we construct, using MP-RW-LSH as the underlying "ANNS- L 1 engine", a new ANNS-E (E for edit distance) solution that beats the state of the art.
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Singh, Abha, Sumit Kushwaha, Maryam Alarfaj, and Manoj Singh. "Comprehensive Overview of Backpropagation Algorithm for Digital Image Denoising." Electronics 11, no. 10 (May 17, 2022): 1590. http://dx.doi.org/10.3390/electronics11101590.
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Artificial ANNs (ANNs) are relatively new computational tools used in the development of intelligent systems, some of which are inspired by biological ANNs, and have found widespread application in the solving of a variety of complex real-world problems. It boasts enticing features as well as remarkable data processing capabilities. In this paper, a comprehensive overview of the backpropagation algorithm for digital image denoising was discussed. Then, we presented a probabilistic analysis of how different algorithms address this challenge, arguing that small ANNs can denoise small-scale texture patterns almost as effectively as their larger equivalents. The results also show that self-similarity and ANNs are complementary paradigms for patch denoising, as demonstrated by an algorithm that effectively complements BM3D with small ANNs, surpassing BM3D at a low cost. Here, one of the most significant advantages of this learning technique is that, once taught, digital images may be recovered without prior knowledge of the degradation model (noise/blurring) that caused the digital image to become distorted.
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Loke, E., E. A. Warnaars, P. Jacobsen, F. Nelen, and M. do Céu Almeida. "Artificial neural networks as a tool in urban storm drainage." Water Science and Technology 36, no. 8-9 (October 1, 1997): 101–9. http://dx.doi.org/10.2166/wst.1997.0651.
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The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented; The prediction of runoff coefficients and the restoration of rainfall data. From the results, it can be concluded that ANNs can deal with problems that are traditionally difficult for conventional modelling techniques to solve. Their advantages include good generalisation abilities, high fault tolerance, high execution speed, and the ability to adapt and learn. However, ANNs rely strongly on the quantity of data examples, their training is occasionally slow, and they are not transparent and obstruct any closer analysis and interpretation of their performance. Finally, it is expected that the future of ANNs will lie in its integration with other conventional and more advanced modelling techniques, creating so-called hybrid models.
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Umar, Muhammad, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Shumaila Javeed, Hijaz Ahmad, Sayed K. Elagen, and Ahmed Khames. "Numerical Investigations through ANNs for Solving COVID-19 Model." International Journal of Environmental Research and Public Health 18, no. 22 (November 20, 2021): 12192. http://dx.doi.org/10.3390/ijerph182212192.
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The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.
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Yi, Tao, Kun Peng, and Jian Min Xu. "Study and Prediction of Flow and Heat Transfer Characteristics in Tube with Wire Coil Inserts of Heat Exchangers." Applied Mechanics and Materials 331 (July 2013): 195–99. http://dx.doi.org/10.4028/www.scientific.net/amm.331.195.
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Based on experimental data, this study presents an application of artificial neural networks (ANNs) to predict the heat transfer rate of the wire-in-tube type heat exchanger. A back propagation algorithm, the most common learning method for ANNs, is used in the training and testing of the network. The ANNs can get rid of the complex simulation and numerical simulation, this method is simple. To solve this algorithm, a computer program was developed by using VB programming language. The consistence between experimental and ANNs approach results was achieved by a mean absolute relative error <8%. It is suggested that the ANNs model is an easy modeling tool for engineering performance prediction of heat transfer equipment, high precision, reliable prediction results.
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Torres-Pruñonosa, Jose, Pablo García-Estévez, and Camilo Prado-Román. "Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing." Mathematics 9, no. 7 (April 6, 2021): 783. http://dx.doi.org/10.3390/math9070783.
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We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.
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Chakrabarti, Swapan, Stan R. Svojanovsky, Romana Slavik, Gunda I. Georg, George S. Wilson, and Peter G. Smith. "Artificial Neural Network—Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds." Journal of Biomolecular Screening 14, no. 10 (November 25, 2009): 1236–44. http://dx.doi.org/10.1177/1087057109351312.
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Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, R A/N, was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in R A/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.
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Choudhury, Madhurima, Abhirup Datta, and Suman Majumdar. "Extracting the 21-cm power spectrum and the reionization parameters from mock data sets using artificial neural networks." Monthly Notices of the Royal Astronomical Society 512, no. 4 (March 17, 2022): 5010–22. http://dx.doi.org/10.1093/mnras/stac736.
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ABSTRACT Detection of the H i 21-cm power spectrum is one of the key science drivers of several ongoing and upcoming low-frequency radio interferometers. However, the major challenge in such observations come from bright foregrounds, whose accurate removal or avoidance is key to the success of these experiments. In this work, we demonstrate the use of artificial neural networks (ANNs) to extract the H i 21-cm power spectrum from synthetic data sets and extract the reionization parameters from the H i 21-cm power spectrum. For the first time, using a suite of simulations, we present an ANN-based framework capable of extracting the H i signal power spectrum directly from the total observed sky power spectrum (which contains the 21-cm signal, along with the foregrounds and effects of the instrument). We have used a combination of two ANNs sequentially. In the first step, ANN1 predicts the 21-cm power spectrum directly from foreground corrupted synthetic data sets. In the second step, ANN2 predicts the reionization parameters from the predicted H i power spectra from ANN1. The two-step ANN framework can be used as an alternative method to extract the 21-cm power spectrum and the reionization parameters directly from foreground dominated data sets. Our ANN-based framework is trained at a redshift of 9.01, and for $\boldsymbol {k}$ modes in the range, $\rm {0.17\lt {\boldsymbol {k}}\lt 0.37~Mpc^{-1}}$. We have tested the network’s performance with mock data sets corrupted with thermal noise corresponding to 1080 h of observations of the SKA-1 LOW and HERA. We have recovered the H i power spectra from foreground dominated synthetic data sets, with an accuracy of $\approx 95{\!-\!}99{{\ \rm per\ cent}}$. We have achieved an accuracy of $\approx ~81{\!-\!}90{{\ \rm per\ cent}}$ and $\approx ~50{\!-\!}60{{\ \rm per\ cent}}$ for the predicted reionization parameters, for test sets corrupted with thermal noise corresponding to the SKA-1 LOW and HERA, respectively.
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Wang, Shan, Jinwei Di, Dan Wang, Xudong Dai, Yabing Hua, Xiang Gao, Aiping Zheng, and Jing Gao. "State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation." Pharmaceutics 14, no. 1 (January 13, 2022): 183. http://dx.doi.org/10.3390/pharmaceutics14010183.
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During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.
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Gómez-Peralta, Juan I., Nidia G. García-Peña, and Xim Bokhimi. "Crystal-Site-Based Artificial Neural Networks for Material Classification." Crystals 11, no. 9 (August 29, 2021): 1039. http://dx.doi.org/10.3390/cryst11091039.
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In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.
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Abujayyab, S. K. M., M. A. S. Ahamad, A. S. Yahya, and A. M. H. Y. Saad. "A NEW FRAMEWORK FOR GEOSPATIAL SITE SELECTION USING ARTIFICIAL NEURAL NETWORKS AS DECISION RULES: A CASE STUDY ON LANDFILL SITES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 131–38. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-131-2015.
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This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.
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Silva, G. A. M., and D. Mendes. "Comparison results for the CFSv2 hindcasts and statistical downscaling over the northeast of Brazil." Advances in Geosciences 35 (July 25, 2013): 79–88. http://dx.doi.org/10.5194/adgeo-35-79-2013.
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Abstract. An Artificial Neural Networks (ANNs) approach was used to reproduce the precipitation anomalies for the rainy seasons over the south and north parts of the Northeast of Brazil (NEB) during 1982–2009 period. The seasonal hindcasts of precipitation anomalies from Climate Forecast System v2 (CFSv2) model and the observed dominant modes of anomalous Sea Surface Temperature over the South and North Atlantic Ocean were used as explanatory variables separately. The reduction of dispersion between the explanatory and dependent variables after the fit of the networks suggest the ANN as an important complementary technique for the climate studies over the NEB. However, a large dataset are required to the models capture the non-linear process in more details. The practical implication of the results is that ANNs constructed here could be applied in further analyses, for example, to explore the ANN's ability in improving the seasonal climate forecasts considering that the numerical and statistical methods must be complementary tools.
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Alqahtani, Ayedh, and Andrew Whyte. "Estimation of life-cycle costs of buildings: regression vs artificial neural network." Built Environment Project and Asset Management 6, no. 1 (February 1, 2016): 30–43. http://dx.doi.org/10.1108/bepam-08-2014-0035.
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Purpose – The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy. Design/methodology/approach – A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs. Findings – The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects. Research limitations/implications – A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample. Originality/value – This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.
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Carmo, Daiane das Graças, Elizeu de Sá Farias, Thiago Leandro Costa, Elenir Aparecida Queiroz, Moysés Nascimento, and Marcelo Coutinho Picanço. "Instar Determination of Blaptostethus pallescens (Hemiptera: Anthocoridae) Using Artificial Neural Networks." Annals of the Entomological Society of America 113, no. 1 (November 23, 2019): 50–54. http://dx.doi.org/10.1093/aesa/saz059.
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Abstract Blaptostethus pallescens Poppius is an important predator of vegetable pests in tropical regions. The correct identification of the stages of the life cycle of predatory species is crucial, since different stages may present different rates of pest consumption. Artificial neural networks (ANNs) are computational tools with a structure based on the human brain. With applications in several fields, ANNs have been applied in pest management for identification of pest species, spatial distribution modeling, and insect forecasting. The objective of this study was to apply ANNs as a method for the instar determination of B. pallescens using three morphometric measures (head width, body width, and body length). Cluster analysis was performed to categorize the insects in instars according to the morphometric variables. Subsequently, the ANNs were trained for instar determination using the morphometric measures as input variables. The ANNs tested (with 2, 4, 6, 8, 10, and 12 hidden neurons) provided proper data fitting (R2 > 98%). However, due to the parsimony principle, the network with hidden layer size 6 was selected. This study shows the successful application of ANNs in the instar determination of B. pallescens, which would not be possible using classical methods.
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Mendyk, Aleksander, Sinan Güres, Renata Jachowicz, Jakub Szlęk, Sebastian Polak, Barbara Wiśniowska, and Peter Kleinebudde. "From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming." Computational and Mathematical Methods in Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/863874.
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The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling ofQversus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations’ parameters. Two inputs were found important for the drug dissolution:dandt. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling ofQversusdandtresulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs’ black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.
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Jin, Ran, Reshma L. Mahtani, Neil Accortt, Tatiana Lawrence, Darcie Sandschafer, and Arturo Loaiza-Bonilla. "Clinical and treatment characteristics of patients treated with the first therapeutic oncology biosimilars bevacizumab-awwb and trastuzumab-anns in the US." Therapeutic Advances in Medical Oncology 13 (January 2021): 175883592110419. http://dx.doi.org/10.1177/17588359211041961.
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Background: In July 2019, bevacizumab-awwb and trastuzumab-anns were marketed in the USA as the first therapeutic oncology biosimilars. We aimed to investigate the initial real-world use of bevacizumab-awwb and trastuzumab-anns for cancer management in US oncology practices. Methods: A retrospective, observational analysis of data from US cancer patients (⩾18 years of age) was carried out to describe the use of bevacizumab-awwb and trastuzumab-anns during the first 12 months following their market entry, using structured data from the Flatiron Health electronic health record-derived database. Results: A total of 2952 and 2997 patients with recorded use of bevacizumab-awwb and trastuzumab-anns, respectively, were included in the analysis. The first use of bevacizumab-awwb and trastuzumab-anns was in a patient with metastatic colorectal cancer (mCRC) within 10 days of market availability and in a patient with early stage breast cancer (eBC) within 4 days, respectively. The use of these biosimilars was observed across all approved cancer indications; 68% of bevacizumab-awwb users were those diagnosed with mCRC and 72% of trastuzumab-anns users were those diagnosed with eBC. Approximately half the patients were previously exposed to reference product (RP) prior to initiation of bevacizumab-awwb or trastuzumab-anns. Among pre-exposed patients, the majority received the biosimilars [bevacizumab-awwb (63–85%) or trastuzumab-anns (75–81%)] within 28 days of the last infusion of the RP. For both biosimilars, no major differences were observed in patient characteristics between RP-naïve and pre-exposed patients. Conclusion: Initial evidence from the first 12 months following market entry suggests rapid clinical adoption of bevacizumab-awwb and trastuzumab-anns across all approved tumor types. Usage of these two biosimilars was observed in both RP-naïve patients and patients who were previously treated with RP, with no distinctive differences in patient characteristics between the two groups. A video abstract is available for this article as part of the Kanjintionline supplemental material.
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Filntisi, Arianna, Nikitas Papangelopoulos, Elena Bencurova, Ioannis Kasampalidis, George Matsopoulos, Dimitrios Vlachakis, and Sophia Kossida. "State-of-the-Art Neural Networks Applications in Biology." International Journal of Systems Biology and Biomedical Technologies 2, no. 4 (October 2013): 63–85. http://dx.doi.org/10.4018/ijsbbt.2013100105.
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Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combination of informatics, biology and medicine. This article is an effort to investigate recent advances in the area of bioinformatical applications of ANNs, with emphasis in disease diagnosis, genetics, proteomics, and chemoinformatics. The combination of neural networks and game theory in some of these application is also discussed.
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Roh, Dong Min, Minxue He, Zhaojun Bai, Prabhjot Sandhu, Francis Chung, Zhi Ding, Siyu Qi, et al. "Physics-Informed Neural Networks-Based Salinity Modeling in the Sacramento–San Joaquin Delta of California." Water 15, no. 13 (June 21, 2023): 2320. http://dx.doi.org/10.3390/w15132320.
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Salinity in estuarine environments has been traditionally simulated using process-based models. More recently, data-driven models including artificial neural networks (ANNs) have been developed for simulating salinity. Compared to process-based models, ANNs yield faster salinity simulations with comparable accuracy. However, ANNs are often purely data-driven and not constrained by physical laws, making it difficult to interpret the causality between input and output data. Physics-informed neural networks (PINNs) are emerging machine-learning models to integrate the benefits of both process-based models and data-driven ANNs. PINNs can embed the knowledge of physical laws in terms of the partial differential equations (PDE) that govern the dynamics of salinity transport into the training of the neural networks. This study explores the application of PINNs in salinity modeling by incorporating the one-dimensional advection–dispersion salinity transport equation into the neural networks. Two PINN models are explored in this study, namely PINNs and FoNets. PINNs are multilayer perceptrons (MLPs) that incorporate the advection–dispersion equation, while FoNets are an extension of PINNs with an additional encoding layer. The exploration is exemplified at four study locations in the Sacramento–San Joaquin Delta of California: Pittsburg, Chipps Island, Port Chicago, and Martinez. Both PINN models and benchmark ANNs are trained and tested using simulated daily salinity from 1991 to 2015 at study locations. Results indicate that PINNs and FoNets outperform the benchmark ANNs in simulating salinity at the study locations. Specifically, PINNs and FoNets have lower absolute biases and higher correlation coefficients and Nash–Sutcliffe efficiency values than ANNs. In addition, PINN models overcome some limitations of purely data-driven ANNs (e.g., neuron saturation) and generate more realistic salinity simulations. Overall, this study demonstrates the potential of PINNs to supplement existing process-based and ANN models in providing accurate and timely salinity estimation.
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Paulino, Rafael, and Pierre Bérubé. "A framework for the use of artificial neural networks for water treatment: development and application." Water Supply 20, no. 8 (September 2, 2020): 3301–17. http://dx.doi.org/10.2166/ws.2020.205.
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Abstract Artificial neural networks (ANNs) are increasingly being used in water treatment applications because of their ability to model complex systems. The present study proposed a framework to develop and validate ANNs for drinking water treatment and distribution system water quality applications. The framework was used to develop ANNs to identify the optimal ozone dose required for effective UV disinfection and to meet regulatory requirements for disinfection by-products (DBPs) in the distribution system. Treatment at a full-scale treatment plant was successfully modelled, with treated water UV transmittance as the output variable. ANNs could be used to identify operating setpoints that minimize operating costs for effective disinfection during drinking water treatment. However, because of the limited data available to train and validate the distribution system ANNs (i.e. n = 48; 15 years of quarterly measurements), these could not be used to reliably identify operating setpoints that also ensure compliance with DBP regulations.
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Abbaspour-Gilandeh, Yousef, and Mohammadreza Abbaspour-Gilandeh. "Application of Computational Intelligence Methods for Predicting Soil Strength." Acta Technologica Agriculturae 22, no. 3 (September 1, 2019): 80–85. http://dx.doi.org/10.2478/ata-2019-0015.
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Abstract The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a regression model. Field tests were conducted on three soil textures and obtained results were analyzed by application of a factorial experiment based on a Randomized Complete Block Design with five replications. The four independent variables of percentage of soil moisture content, soil bulk density, electrical conductivity and sampling depth were used to predict soil cone index by ANNs, ANFIS and a regression model. The ANNs design was that of back propagation multilayer networks. Predictions of soil cone index with ANFIS were made using the hybrid learning model. Comparison of results acquired from ANNs, ANFIS and regression models showed that the ANFIS model could predict soil cone index values more accurately than ANNs and regression models. Considering the ANFIS model, a novel result on soil compaction modeling, relative error (ε), and regression coefficient (R2) were calculated at 2.54% and 0.979, respectively.
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Miłosz, Marek, and Janusz Gazda. "Effectiveness of artificial neural networks in recognising handwriting characters." Journal of Computer Sciences Institute 7 (September 30, 2018): 210–14. http://dx.doi.org/10.35784/jcsi.680.
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Artificial neural networks are one of the tools of modern text recognising systems from images, including handwritten ones. The article presents the results of a computational experiment aimed at analyzing the quality of recognition of handwritten digits by two artificial neural networks (ANNs) with different architecture and parameters. The correctness indicator was used as the basic criterion for the quality of character recognition. In addition, the number of neurons and their layers and the ANNs learning time were analyzed. The Python language and the TensorFlow library were used to create the ANNs, and software for their learning and testing. Both ANNs were learned and tested using the same big sets of images of handwritten characters.
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Wei, Xing, and Jun Li. "Applications of Artificial Neural Networks in Bridge Engineering." Advanced Materials Research 243-249 (May 2011): 1984–87. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.1984.
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Artificial neural networks (ANNs) have been widely applied to many bridge engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in member capacity prediction, reliability analysis, optimal design of structural systems, fatigue life prediction, construction control, material constitutive model , slope stability, bridge health monitoring. The objective of this paper is to provide a general view of some ANNs applications for solving some types of bridge engineering problems. A brief introduction to ANNs is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in bridge engineering are presented.
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Haroon, Muhammad, Seungbum Koo, DongIk Shin, and Changhyuk Kim. "Torsional Behavior Evaluation of Reinforced Concrete Beams Using Artificial Neural Network." Applied Sciences 11, no. 10 (May 14, 2021): 4465. http://dx.doi.org/10.3390/app11104465.
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Artificial neural networks (ANNs) are an emerging field of research and have proven to have significant potential for use in structural engineering. In previous literature, many studies successfully utilized ANNs to analyze the structures under different loading conditions and verified the accuracy of the approach. Several studies investigated the use of ANNs to analyze the shear behavior of reinforced concrete (RC) members. However, few studies have focused on the potential use of an ANN for analysis of the torsional behavior of an RC member. Torsion is a complex problem and modeling the torsional fracture mechanism using the traditional analytical approach is problematic. Recent studies show that the nonlinear behavior of RC members under torsion can be modeled using ANNs. This paper presents a comprehensive analytical and parametric study of the torsional response of RC beams using ANNs. The ANN model was trained and validated against an experimental database of 159 RC beams reported in the literature. The results were compared with the predictions of design codes. The results show that ANNs can effectively model the torsional behavior of RC beams. The parametric study presented in this paper provides greater insight into the torsional resistance mechanism of RC beams and its characteristic parameters.
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Kartam, Nabil, and Tanit Tongthong. "Potential of artificial neural networks for resource scheduling." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 3 (June 1997): 171–85. http://dx.doi.org/10.1017/s0890060400003103.
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AbstractIn a construction project, resource leveling techniques are necessary as a primary schedule-improvement tool to reduce overall project cost by decreasing day-to-day fluctuation in resource usage and resource idleness. There are, however, some limitations in traditional resource leveling techniques. Conventional heuristic approaches cannot guarantee a near-optimum solution for every construction project; a given heuristic may perform well on one project and poorly on another. The existing optimization approaches, such as linear programming and enumeration methods, are best applicable only to small size problems. Recently, there has been success in the use of Artificial Neural Networks (ANNs) for solving some optimization problems. The paper discusses how state-of-the-art ANNs can be a functional alternative to traditional resource leveling techniques. It then investigates the application of different ANN models (such as backpropagation networks, Hopfield networks, Boltzmann machines, and competition ANNs) to resource leveling problems. Because the development of ANNs involves not only science but also experience, the paper presents various intuitive yet effective ways of mapping resource leveling problems on different applicable ANN architectures. To demonstrate the application of ANNs to resource leveling, a simple ANN model is developed using a Hopfield network. The conclusion highlights the usefulness and the limitations of ANNs when applied to resource leveling problems.
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Liang, Yaru, Qiguang Li, Peisong Chen, Lingqing Xu, and Jiehua Li. "Comparative study of back propagation artificial neural networks and logistic regression model in predicting poor prognosis after acute ischemic stroke." Open Medicine 14, no. 1 (April 4, 2019): 324–30. http://dx.doi.org/10.1515/med-2019-0030.
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AbstractObjectiveTo investigate the predictive value of clinical variables on the poor prognosis at 90-day follow-up from acute stroke onset, and compare the diagnostic performance between back propagation artificial neural networks (BP ANNs) and Logistic regression (LR) models in predicting the prognosis.MethodsWe studied the association between clinical variables and the functional recovery of 435 acute ischemic stroke patients. The patients were divided into 2 groups according to modified Rankin Scale scores evaluated on the 90th day after stroke onset. Both BP ANNs and LR models were established for predicting the poor outcome and their diagnostic performance were compared by receiver operating curve.ResultsAge, free fatty acid, homocysteine and alkaline phosphatase were closely related with the poor outcome in acute ischemic stroke patients and finally enrolled in models. The accuracy, sensitivity and specificity of BP ANNs were 80.15%, 75.64% and 82.07% respectively. For the LR model, the accuracy, sensitivity and specificity was 70.61%, 88.46% and 63.04% respectively. The area under the ROC curve of the BP ANNs and LR model was 0.881and 0.809.ConclusionsBoth BP ANNs and LR model were promising for the prediction of poor outcome by combining age, free fatty acid, homocysteine and alkaline phosphatase. However, BP ANNs model showed better performance than LR model in predicting the prognosis.
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Marohnić, Tea, Robert Basan, and Ela Marković. "Estimation of Cyclic Stress–Strain Curves of Steels Based on Monotonic Properties Using Artificial Neural Networks." Materials 16, no. 14 (July 15, 2023): 5010. http://dx.doi.org/10.3390/ma16145010.
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This paper introduces a novel method for estimating the cyclic stress–strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant literature, and divided into subgroups according to alloying elements content (unalloyed, low-alloy, and high-alloy steels). Only monotonic properties that were proven to be relevant for the estimation of points on the stress–strain curve were used. The performance of the developed ANNs was assessed using an independent set of data, and the results were compared to experimental values, values obtained by existing empirical estimation methods, and by previously developed ANNs. The results showed that the new approach which combines relevant monotonic properties and plastic strain amplitudes as inputs to ANNs for cyclic stress–strain curve estimation is better than the previously used approach where ANNs estimate the parameters of the Ramberg–Osgood material model separately. This shows that a more favorable approach to the estimation of cyclic stress–strain behavior would be to directly estimate corresponding material curves using monotonic properties. Additionally, this may also reduce inaccuracies resulting from simplified representations of the actual material behavior inherent in the material model.
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Oermann, Eric K., Marie-Adele S. Kress, Brian T. Collins, Sean P. Collins, David Morris, Stanley C. Ahalt, and Matthew G. Ewend. "Predicting Survival in Patients With Brain Metastases Treated With Radiosurgery Using Artificial Neural Networks." Neurosurgery 72, no. 6 (March 5, 2013): 944–52. http://dx.doi.org/10.1227/neu.0b013e31828ea04b.
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Abstract BACKGROUND: Artificial neural networks (ANNs) excel at analyzing challenging data sets and can be exceptional tools for decision support in clinical environments. The present study pilots the use of ANNs for determining prognosis in neuro-oncology patients. OBJECTIVE: To determine whether ANNs perform better at predicting 1-year survival in a group of patients with brain metastasis compared with traditional predictive tools. METHODS: ANNs were trained on a multi-institutional data set of radiosurgery patients to predict 1-year survival on the basis of several input factors. A single ANN, an ensemble of 5 ANNs, and logistic regression analyses were compared for efficacy. Sensitivity analysis was used to identify important variables in the ANN model. RESULTS: A total of 196 patients were divided up into training, testing, and validation data sets consisting of 98, 49, and 49 patients, respectively. Patients surviving at 1 year tended to be female (P = .001) and of good performance status (P = .01) and to have favorable primary tumor histology (P = .001). The pooled voting of 5 ANNs performed significantly better than the multivariate logistic regression model (P = .02), with areas under the curve of 84% and 75%, respectively. The ensemble also significantly outperformed 2 commonly used prognostic indexes. Primary tumor subtype and performance status were identified on sensitivity analysis to be the most important variables for the ANN. CONCLUSION: ANNs outperform traditional statistical tools and scoring indexes for predicting individual patient prognosis. Their facile implementation, robustness in the presence of missing data, and ability to continuously learn make them excellent choices for use in complicated clinical environments.
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Azeem, Muhammad, Shumaila Javaid, Ruhul Amin Khalil, Hamza Fahim, Turke Althobaiti, Nasser Alsharif, and Nasir Saeed. "Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges." Bioengineering 10, no. 7 (July 18, 2023): 850. http://dx.doi.org/10.3390/bioengineering10070850.
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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.
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Zhang, Sung-Uk. "Degradation Classification of 3D Printing Thermoplastics Using Fourier Transform Infrared Spectroscopy and Artificial Neural Networks." Applied Sciences 8, no. 8 (July 25, 2018): 1224. http://dx.doi.org/10.3390/app8081224.
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Fused deposition modeling (FDM) is the most popular technology among 3D printing technologies because of inexpensive and flexible extrusion systems with thermoplastic materials. However, thermal degradation phenomena of the 3D-printed thermoplastics is an inevitable problem for long-term reliability. In the current study, thermal degradation of 3D-printed thermoplastics of ABS and PLA was studied. A classification methodology using deep learning strategy was developed so that thermal degradation of the thermoplastics could be classified using FTIR and Artificial Neural Networks (ANNs). Under given data and predefined rules for ANNs, ANN models with nine hidden layers showed the best results in terms of accuracy. To extend this methodology, other thermoplastics, several new datasets for ANNs, and control parameters of ANNs could be further investigated.
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Park, Sung Yun, Sangjoon Lee, Jae Hoon Jeong, and Sung Min Kim. "Application of Artificial Neural Networks for Diagnosing Acute Appendicitis." Applied Mechanics and Materials 479-480 (December 2013): 445–50. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.445.
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The purpose of this study is to develop an appendicitis diagnosis system, by using artificial neural networks (ANNs). Acute appendicitis is one of the most common surgical emergencies of the abdomen. Various methods have been developed to diagnose appendicitis, but these methods have not shown good performance in the Middle East and Asia, or even in the West. We used the structures of ANNs with 801 patients. These various structures are a multilayer neural network structure (MLNN), a radial basis function neural network structure (RBF), and a probabilistic neural network structure (PNN). The Alvarado clinical scoring system was used for comparison with the ANNs. The accuracy of MLNN, RBF, PNN, and Alvarado was 97.84%, 99.80%, 99.41% and 72.19%, respectively. The AUC of MLNN, RBF, PNN, and Alvarado was 0.985, 0.998, 0.993, and 0.633, respectively. The performance of ANNs was significantly better than the Alvarado clinical scoring system (P<0.001). The models developed to diagnose appendicitis using ANNs showed good performance. We consider that the developed models can help junior clinical surgeons diagnose appendicitis.
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Jasiński, Tomasz. "Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)." Energies 13, no. 5 (March 9, 2020): 1263. http://dx.doi.org/10.3390/en13051263.
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The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.
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Jubair Al-Hiealy, Mohammed Rasheed, Mohammad Shahir Bin Abdul Majed Shikh, Abdurrahman Bin Jalil, Suhaila Abdul Rahman, and Muath Jarrah. "Management switching angles real-time prediction by artificial neural network." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 110. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp110-119.
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Artificial neural networks (ANNs) is an efficient way for different types of real-world prediction problems. In the past decade, it has given a tremendous surge in a global research activities. ANNs embody much certainty and provide a great deal of promise This paper has present artificial neural network (ANN) technique analysis and prediction for management switching angles real-time. The proposes to be used ANN for prediction and selected obtine angles for implement the timing diagram for mulitlvel inverter circuit. In order to control the fundamental component, ANNs are used to solve the analysis of non-linear equation of the output timing diagram in order to determine the switching angles. Substantially, the number of switching devices are reducing as possible basically for reducing a switching loss in the system, also have been used ANNs technique to optimize a switching angles behavior to reduce total harmonic distortion (THD) at voltage and current output waveform equal THDV 8.05% THDA 5.1%. For the proposed controllers, the performance and results by the ANNs were obtained and compared by using MATLAB software.
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NEDJAH, NADIA, RODRIGO MARTINS DA SILVA, and LUIZA DE MACEDO MOURELLE. "ANALOG HARDWARE IMPLEMENTATIONS OF ARTIFICIAL NEURAL NETWORKS." Journal of Circuits, Systems and Computers 20, no. 03 (May 2011): 349–73. http://dx.doi.org/10.1142/s0218126611007347.
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There are several possible implementations of artificial neural network that are based either on software or hardware systems. Software implementations are rather inefficient due to the fact that the intrinsic parallelism of the underlying computation is usually not taken advantage of in a mono-processor kind of computing system. Existing hardware implementations of ANNs are efficient as the dedicated datapath used is optimized and the hardware is usually parallel. Hardware implementations of ANNs may be either digital, analog, or even hybrid. Digital implementations of ANNs tend to be of high complexity, thus of high cost, and somehow imprecise due to the use of lookup table for the activation function. On the other hand, analog implementation of ANNs are generally very simple and much more precise. In this paper, we focus on possible analog implementations of ANNs. The neuron is based on a simple operational amplifier. The reviewed implementations allow for the use of both negative and positive synaptic weights. An alternative implementation permits the realization of the training process.
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Perchiazzi, Gaetano, Marieann Högman, Christian Rylander, Rocco Giuliani, Tommaso Fiore, and Göran Hedenstierna. "Assessment of respiratory system mechanics by artificial neural networks: an exploratory study." Journal of Applied Physiology 90, no. 5 (May 1, 2001): 1817–24. http://dx.doi.org/10.1152/jappl.2001.90.5.1817.
Full textAbstract:
We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANNBIO) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANNMOD) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient ( R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANNBIO showed a performance expressed by R= 0.40 and ANNMOD by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.
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Sabir, Zulqurnain, Thongchai Botmart, Muhammad Asif Zahoor Raja, Wajaree Weera, and Fevzi Erdoğan. "A stochastic numerical approach for a class of singular singularly perturbed system." PLOS ONE 17, no. 11 (November 28, 2022): e0277291. http://dx.doi.org/10.1371/journal.pone.0277291.
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In the present study, a neuro-evolutionary scheme is presented for solving a class of singular singularly perturbed boundary value problems (SSP-BVPs) by manipulating the strength of feed-forward artificial neural networks (ANNs), global search particle swarm optimization (PSO) and local search interior-point algorithm (IPA), i.e., ANNs-PSO-IPA. An error-based fitness function is designed using the differential form of the SSP-BVPs and its boundary conditions. The optimization of this fitness function is performed by using the computing capabilities of ANNs-PSO-IPA. Four cases of two SSP systems are tested to confirm the performance of the suggested ANNs-PSO-IPA. The correctness of the scheme is observed by using the comparison of the proposed and the exact solutions. The performance indices through different statistical operators are also provided to solve the SSP-BVPs using the proposed ANNs-PSO-IPA. Moreover, the reliability of the scheme is observed by taking hundred independent executions and different statistical performances have been provided for solving the SSP-BVPs to check the convergence, robustness and accuracy.
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Patel, A. R., and J. D. Summers. "Student vs Machine: Comparing Artificial Neural Network Predictions with Student Estimates of Market Price Using Function Structure Models." Proceedings of the Design Society 2 (May 2022): 1669–78. http://dx.doi.org/10.1017/pds.2022.169.
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AbstractThis paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research.
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TAMBOURATZIS, TATIANA. "STRING MATCHING ARTIFICIAL NEURAL NETWORKS." International Journal of Neural Systems 11, no. 05 (October 2001): 445–53. http://dx.doi.org/10.1142/s0129065701000874.
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Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity, and activation values of the ANN output nodes that can be used as indicators of substitution, insertion (addition) and deletion spelling errors.
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Paez, Thomas L. "Neural Networks in Mechanical System Simulation, Identification, and Assessment." Shock and Vibration 1, no. 2 (1993): 177–99. http://dx.doi.org/10.1155/1993/243060.
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Artificial neural networks (ANNs) have been used in the solution of a variety of mechanical system design, analysis, and control problems. This paper describes the ANNs that have been most frequently used in mechanical system applications. It also summarizes some of the applications that have been developed for ANNs, and briefly reviews the literature where descriptions of the developments and applications can be found. Some recommendations regarding ANN applications in mechanical system simulation, identification, and assessment are provided.