FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

AbstractHTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

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Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

Open Life Sciences ◽

10.2478/s11535-014-0356-4 ◽

2014 ◽

Vol 9 (12) ◽

pp. 1226-1234

Author(s):

Kadir Temizel ◽

Mehmet Odabas ◽

Nurettin Senyer ◽

Gokhan Kayhan ◽

Sreekala Bajwa ◽

...

Keyword(s):

High Performance ◽

Fuzzy Inference ◽

High Water ◽

Stress Conditions ◽

Ann Model ◽

Inference System ◽

Leaf Reflectance ◽

Neuro Fuzzy ◽

Salinity Levels ◽

Artificial Neural Network Ann

AbstractLack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

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Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

Hydrology Research ◽

10.2166/nh.2020.141 ◽

2020 ◽

Vol 51 (3) ◽

pp. 423-442

Author(s):

Naser Dehghanian ◽

S. Saeid Mousavi Nadoushani ◽

Bahram Saghafian ◽

Morteza Rayati Damavandi

Keyword(s):

Flood Control ◽

Mean Squared Error ◽

Absolute Error ◽

Ann Model ◽

Runoff Generation ◽

Source Areas ◽

Walnut Gulch ◽

Artificial Neural Network Ann ◽

Semi Arid ◽

Flood Source

Abstract An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

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Determination of number of check dams by artificial neural networks in arid regions of Iran

Water Science & Technology ◽

10.2166/wst.2015.268 ◽

2015 ◽

Vol 72 (6) ◽

pp. 952-959 ◽

Cited By ~ 1

Author(s):

Seyed Ali Asghar Hashemi ◽

Hamed Kashi

Keyword(s):

Curve Number ◽

Absolute Error ◽

Coefficient Of Determination ◽

Ann Model ◽

Effective Parameters ◽

Check Dams ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

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Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽

10.11113/matematika.v35.n4.1263 ◽

2019 ◽

Vol 35 (4) ◽

pp. 53-64

Author(s):

Siti Nabilah Syuhada Abdullah ◽

Ani Shabri ◽

Ruhaidah Samsudin

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Forecast Errors ◽

Ann Model ◽

Mode Decomposition ◽

The Neural Network ◽

Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

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Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.170-173.1013 ◽

2012 ◽

Vol 170-173 ◽

pp. 1013-1016

Author(s):

Fu Qiang Gao ◽

Xiao Qiang Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ground Vibration ◽

Absolute Error ◽

Coefficient Of Determination ◽

Ann Model ◽

Monitoring Point ◽

Blasting Operation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Prediction of peak particle velocity (PPV) is very complicated due to the number of influencing parameters affecting seism wave propagation. In this paper, artificial neural network (ANN) is implemented to develop a model to predict PPV in a blasting operation. Based on the measured parameters of maximum explosive charge used per delay and distance between blast face to monitoring point, a three-layer ANN was found to be optimum with architecture 2-5-1. Through the analysis of coefficient of determination (CoD) and mean absolute error (MAE) between monitored and predicted values of PPV, it indicates that the forecast data by the ANN model is close to the actua1 values.

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Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay

Applied Sciences ◽

10.3390/app11209760 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9760

Author(s):

Zhongkai Huang ◽

Dongmei Zhang ◽

Dongming Zhang

Keyword(s):

Pressure Coefficient ◽

Earth Pressure ◽

Numerical Data ◽

Absolute Error ◽

Ann Model ◽

Soil Stiffness ◽

Wall Deflection ◽

Proposed Model ◽

Ann Models ◽

Artificial Neural Network Ann

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

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Three-body problem - from Newton to supercomputer plus machine learning

10.21203/rs.3.rs-395522/v1 ◽

2021 ◽

Author(s):

Shijun LIAO ◽

Xiaoming Li ◽

Yu Yang

Keyword(s):

Machine Learning ◽

Periodic Orbits ◽

High Performance ◽

Body Problem ◽

Ann Model ◽

Three Body Problem ◽

Starting Point ◽

Families Of Periodic Orbits ◽

Artificial Neural Network Ann ◽

Three Body

Abstract The famous three-body problem can be traced back to Newton in 1687, but quite few families of periodic orbits were found in 300 years thereafter. In this paper, we propose an effective approach and a roadmap to numerically gain planar periodic orbits of three-body systems with arbitrary masses by means of machine learning based on an artificial neural network (ANN) model. Given any a known periodic orbit as a starting point, this approach can provide more and more periodic orbits (of the same family name) with variable masses, while the mass domain having periodic orbits becomes larger and larger, and the ANN model becomes wiser and wiser. Finally, we have an ANN model trained by means of all obtained periodic orbits of the same family, which provides a convenient way to give accurate enough predictions of periodic orbits with arbitrary masses for physicists and astronomers. It suggests that the high-performance computer and artificial intelligence (including machine learning) should be the key to gain periodic orbits of the famous three-body problem.

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Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

Vietnam Journal of Earth Sciences ◽

10.15625/0866-7187/42/3/15008 ◽

2020 ◽

Vol 42 (3) ◽

Cited By ~ 2

Author(s):

Thai Binh Pham ◽

Sushant K. Singh ◽

Hai-Bang Ly

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Absolute Error ◽

Coefficient Of Determination ◽

Ann Model ◽

Coefficient Of Consolidation ◽

Experimental Approaches ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Accuracy Performance

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

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Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN

Advances in Civil Engineering ◽

10.1155/2021/9952781 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Krishna Prakash A ◽

Jane Helena H ◽

Paul Oluwaseun Awoyera

Keyword(s):

Compressive Strength ◽

Fly Ash ◽

Mechanical Strength ◽

Experimental Investigations ◽

Second Phase ◽

Ann Model ◽

Conventional Concrete ◽

Prediction Ability ◽

Concrete Blocks ◽

Artificial Neural Network Ann

This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. The ANN model is based on the Levenberg–Marquardt principle which is executed using MATLAB. The inputs are given in the percentage ratio of cement: fly ash: crushed stone aggregate (FA): coarse aggregate (CA) for the process of learning, testing, and validation. The selected model is subjected to several trials in terms of mean square error, containing 4 input, 2 sets of 10 hidden layers, and one output components. In this study, a total of 2600 blocks of different mixes were tested as per IS 2185-1 (2005) to assess 3, 7, 14, 21, and 28 days’ strength. The experimental investigations were carried out in two phases. In the first phase, experimental investigations to identify the optimum mix proportions of cement, aggregate, fly ash, and ground granulated blast furnace slag to achieve desired compressive strength was carried out. In the second phase, the identified mix proportions were analysed using ANN to predict the compressive strength of interlocking blocks. The results indicate that the proposed ANN model developed to determine the mechanical strength and cost of interlocking blocks has excellent prediction ability.

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Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

European Transport/Trasporti Europei ◽

10.48295/et.2021.84.6 ◽

2021 ◽

pp. 1-16

Author(s):

Milad Baradaran Shahidin

Keyword(s):

Regression Model ◽

Travel Time ◽

Public Transportation ◽

Transportation Systems ◽

Linear Regression Method ◽

Ann Model ◽

Travel Time Prediction ◽

Time Prediction ◽

Artificial Neural Network Ann ◽

High Level

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

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