Neural network-based estimation of lighting condition in indoor environment with improved brain storm algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sneha Patil ◽  
Mahesh Goudar ◽  
Ravindra Kharadkar
Keyword(s):  
Neural Network ◽  
Energy Harvesting ◽  
Light Intensity ◽  
Prediction Model ◽  
Data Set ◽  
Inference System ◽  
Content Type ◽  
Prediction Analysis ◽  
Adopted Model ◽  
Better Than

Purpose For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels. Design/methodology/approach This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model. Findings The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively. Originality/value This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

CNN-based minor fabric defects detection

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhijie Wen ◽  
Qikun Zhao ◽  
Lining Tong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Estimation Algorithm ◽  
Data Set ◽  
Content Type ◽  
Novel Method ◽  
Self Similar ◽  
Better Than

PurposeThe purpose of this paper is to present a novel method for minor fabric defects detection.Design/methodology/approachThis paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.FindingsThis algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.Originality/valueTraditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system

2016 ◽  
Vol 43 (9) ◽  
pp. 822-829 ◽  
Author(s):  
Rokibul Islam ◽  
Sarder Rafee Musabbir ◽  
Irfan Uddin Ahmed ◽  
Md. Hadiuzzaman ◽  
Mehedi Hasnat ◽  
...  
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Prediction Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Probabilistic Neural Network ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Bus Service

This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

Research on thermal property and temperature rating prediction of Mongolian robe ensembles

2018 ◽  
Vol 30 (6) ◽  
pp. 747-756 ◽  
Author(s):  
Xiaofang Guo ◽  
Hui Shi ◽  
Chenglong Wei ◽  
Xiao Dong Chen
Keyword(s):  
Prediction Model ◽  
Thermal Property ◽  
Thermal Insulation ◽  
Design Methodology ◽  
Environmental Adaptation ◽  
Photographic Method ◽  
Mongolian Plateau ◽  
Content Type ◽  
Research Findings ◽  
Better Than

Purpose The purpose of this paper is to reveal the unique thermal property of Mongolian clothing from the current western clothing and explain their environmental adaptation to the climate of Mongolian plateau in China. Design/methodology/approach Thermal insulation and the temperature rating (TR) of eight Mongolian robe ensembles and two western clothing ensembles were investigated by manikin testing and wearing trials, respectively. The clothing area factor (fcl) of these Mongolian clothing was measured by photographic method and estimated equation from ISO 15831. Finally, the TR prediction model for Mongolian clothing was built and compared with current models for western clothing in ISO 7730 and for Tibetan clothing in previous article. Findings The results demonstrated that the total thermal insulation of Mongolian robe ensembles was much bigger than that of western clothing ensembles and ranged from 1.81clo to 3.11clo during the whole year. The fcl of the Mongolian clothing should be determined by photographic method because the differences between these two methods were much bigger from 0.6 to 13.9 percent; the TR prediction model for Mongolian robe ensembles is TR=25.57−7.13Icl, which revealed that the environmental adaptation of Mongolian clothing was much better than that of western clothing and similar to that of Tibetan clothing. Originality/value The research findings give a detailed information about the thermal property of China Mongolian clothing, and explain the environmental adaptation of Mongolian clothing to the cold and changing climate.

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valli Bhasha A. ◽  
Venkatramana Reddy B.D.
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Discrete Wavelet ◽  
Content Type ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Better Than

Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

Tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oluwafemi Ajayi ◽  
Reolyn Heymann
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Data Set ◽  
Content Type ◽  
Demand Pattern ◽  
The Neural Network ◽  
Input Parameters ◽  
Demand Profile

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern. Findings The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern. Research limitations/implications The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance. Practical implications Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost. Originality/value The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

Evolution of conformal antenna design with the aid of probability improved crow search algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nama Ajay Nagendra ◽  
Lakshman Pappula
Keyword(s):  
Antenna Arrays ◽  
Search Algorithm ◽  
Fundamental Problem ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Antenna Element ◽  
Content Type ◽  
Conformal Antenna ◽  
Adopted Model ◽  
Better Than

PurposeThe issues of radiating sources in the existence of smooth convex matters by such objects are of huge significance in the modeling of antennas on structures. Conformal antenna arrays are necessary when an antenna has to match to certain platforms. A fundamental problem in the design is that the possible surfaces for a conformal antenna are infinite in number. Furthermore, if there is no symmetry, each element will see a different environment, and this complicates the mathematics. As a consequence, the element factor cannot be factored out from the array factor.Design/methodology/approachThis paper intends to enhance the design of the conformal antenna. Here, the main objective of this task is to maximize the antenna gain and directivity from the first-side lobe and other side-lobes in the two way radiation pattern. Thus the adopted model is designed as a multiobjective concern. In order to attain this multiobjective function, both the element spacing and the radius of each antenna element should be optimized based on the probability of the Crow Search Algorithm (CSA). Thus the proposed method is named Probability Improved CSA (PI-CSA). Here, the First Null Beam Width (FNBW) and Side-Lobe Level (SLL) are minimized. Moreover, the adopted scheme is compared with conventional algorithms, and the results are attained.FindingsFrom the analysis, the gain of the presented PI-CSA scheme in terms of best performance was 52.68% superior to ABC, 25.11% superior to PSO, 13.38% superior to FF and 3.21% superior to CS algorithms. Moreover, the mean performance of the adopted model was 62.94% better than ABC, 13.06% better than PSO, 24.34% better than FF and 10.05% better than CS algorithms. By maximizing the gain and directivity, FNBW and SLL were decreased. Thus, the optimal design of the conformal antenna has been attained by the proposed PI-CSA algorithm in an effective way.Originality/valueThis paper presents a technique for enhancing the design of the conformal antenna using the PI-CSA algorithm. This is the first work that utilizes PI-CSA-based optimization for improving the design of the conformal antenna.

scholarly journals Would credit scoring work for Islamic finance? A neural network approach

2014 ◽  
Vol 7 (1) ◽  
pp. 112-125 ◽  
Author(s):  
Hussein A. Abdou ◽  
Shaair T. Alam ◽  
James Mulkeen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Credit Scoring ◽  
Islamic Finance ◽  
Decision Making Process ◽  
Data Set ◽  
Content Type ◽  
Modeling Techniques ◽  
The Uk ◽  
Training Subset

Purpose – This paper aims to distinguish whether the decision-making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit, and highlight significant variables that are crucial in terms of accepting and rejecting applicants, which can further aid the decision-making process. Design/methodology/approach – A real data set of 487 applicants is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models. Findings – The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision-making process. Originality/value – This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.

Predicting the voluntary donation to online content creators

2020 ◽  
Vol 120 (10) ◽  
pp. 1941-1957
Author(s):  
Futao Zhao ◽  
Zhong Yao
Keyword(s):  
Prediction Model ◽  
Performance Metrics ◽  
Impact Factors ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Modal Particles ◽  
Interpretive Analysis ◽  
Text Content ◽  
The Impact

PurposeThe purpose of this paper is to identify the impact factors that might influence audiences' voluntary donation to content creators on the online platforms, and to build an effective prediction model by considering both content and creator-related features.Design/methodology/approachThis study collected the real-world data of content consumption from Xueqiu.com and extracted both content and creator characteristics from the data set. The best donation prediction model based on such features was determined by evaluating four prevalent classifiers with various performance metrics. Furthermore, three feature selection methods were applied to validate the robustness of the constructed model, and then the predictability of different feature groups was examined. Finally, we conducted an interpretive analysis to identify relatively important predictors.FindingsThe experimental results show that the random classifier with all extracted features outperformed other built models and achieved excellent performance, indicating the usefulness of these factors in predicting the donations. Moreover, the predictability of content features was demonstrated to be relatively better than that of creator ones. Finally, several particularly important predictors were identified such as the number of modal particles in the article.Originality/valueThis study is among the first to investigate what factors might drive customers' voluntary donation to content contributors on social websites. Different from previous studies focusing on live video streaming, we expand the research vision by examining the donations to user-generated text content, calling for attention to other important topics in the burgeoning industry.

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