Analyzing the Differential Expression of Vitiligo Genes by Bioinformatics Methods

The disease of banana Fusarium wilt currently threatens banana production areas all over the world. Rapid and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting adjustments. The objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms to identify locations that were infested or not infested with Fusarium wilt. An unmanned aerial vehicle (UAV) equipped with a five-band multi-spectral sensor (blue, green, red, red-edge and near-infrared bands) was used to capture the multi-spectral imagery. A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium wilt. The results showed that the SVM, RF, and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral imagery. The overall accuracies of the SVM, RF, and ANN were 91.4%, 90.0%, and 91.1%, respectively for the pixel-based approach. The RF algorithm required significantly less training time than the SVM and ANN algorithms. The maps generated by the SVM, RF, and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm2, accounting for 36.3%-40.1% of the total planting area of bananas in the study area. The results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%. A simulation of the resolutions of satellite-based imagery (i.e., 0.5, 1, 2, and 5 m resolutions) showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium wilt. The results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing imagery. The results provide guidance for disease treatment and crop planting adjustments. Keywords: banana fusarium wilt, UAV-based multi-spectral remote sensing, support vector machine, artificial neural network, random forest DOI: 10.25165/j.ijabe.20201303.5524 Citation: Ye H C, Huang W J, Huang S Y, Cui B, Dong Y Y, Guo A T, et al. Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery. Int J Agric & Biol Eng, 2020; 13(3): 136–142.

The Mahalanobis–Taguchi system – Neural network algorithm for data-mining in dynamic environments

Systolic architectures are established as a widely popular class of VLSI structures for repetitive and computation-intensive applications due to the simplicity of their processing elements (PEs), modularity of design, regular and nearest neighbor interconnections between the PEs, high-level of pipelinability, small chip-area and low-power consumption. In systolic arrays, the desired data is pumped rhythmically in a regular interval across the PEs to yield high throughput by fully pipelined processing. The I/O bottleneck is significantly reduced by the systolic array architectures by feeding the data at the chip-boundary, and pipelining it across the structure. The extensive reuse of data within the array allows for executing large volume of computation with only a modest increase of bandwidth. Since the FPGA devices consist of regularly placed inter-connected logic blocks, they closely resemble with the layout of systolic processors. The systolic computation within the PEs therefore could easily be mapped to the configurable logic blocks in FPGA device. Interestingly also, the artificial neural network (ANN) algorithms are quite suitable for systolic implementation due to their repetitive multiply-accumulate behaviour. Several variations of one-dimensional and two-dimensional systolic arrays are, therefore, reported in the literature for the implementation of different types of neural networks. Special purpose systolic designs for various ANN-based applications relating to pattern recognition and classification, adaptive filtering and channel equalization, vector quantization, image compression and general signal/image processing applications have been reported in the last two decades. We have devoted this chapter on the systolic architectures for the implementation of ANN algorithms in custom VLSI and FPGA platforms. The key techniques used for the design of basic systolic building blocks of ANN algorithms are discussed in detail. Moreover, the mapping of fully-connected unconstrained ANN, as well as, multilayer ANN algorithm into fully-pipelined systolic architecture is described with generalized dependence graph formulation. A brief overview of systolic architectures for advance ANN algorithms for different applications are presented at the end.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm

Wood material has a wide range of uses due to its many positive properties. In addition to its positive features, it also has negative features that limit the usage area of wooden material. One of the commonly used methods to minimize these negative properties is heat treatment application. In the study, the surface roughness values of Spruce (Picea abies) samples heat treated with ThermoWood method were investigated. Surface roughness measurements were carried out in the radial and tangential directions with the Mitutoyo SJ-201M tactile surface roughness tester. Then, the contact angle values ​​of the samples in the tangential and radial direction were determined. TS 4084 standard was used to determine the swelling and shrinkage amounts of the samples whose contact angle values ​​were determined. Surface roughness values of the samples were estimated by artificial neural network (ANN) and random forest algorithm. In the estimation of contact angle with random forest algorithm and ANN method, swelling and shrinkage amounts were entered as input. In the study, it has been determined that the predictions made in the radial direction with artificial neural networks give the most accurate results. In predictions made in the radial direction with artificial neural networks, R2 = 0.98 and RMSE = 0.03. In the radial study conducted with Random Forest Algorithm, R2 = 0.96 and RMSE = 0.11. As a result, it has been determined that the surface roughness of a wood material can be estimated by ANN and Random forest algorithm.KeywordsHeat treatmentSpruceSurface roughnessArtificial neural networkRandom forest algorithm

The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC). In this retrospective study, 45 patients with clear cell RCC (29 without the PBRM1 mutation and 16 with the PBRM1 mutation) were identified in The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. To create stable ML models and balanced classes, the data were augmented to a total of 161 labeled segmentations (87 without the PBRM1 mutation and 74 with the PBRM1 mutation) by obtaining three to five different samples per patient. Texture features were extracted from corticomedullary phase contrast-enhanced CT images with the use of an open-source software package for the extraction of radiomic data from medical images. Reproducibility analysis (intraclass correlation) was performed by two radiologists. Attribute selection and model optimization were done using a wrapper-based classifier-specific algorithm with nested cross-validation. ML classifiers were an artificial neural network (ANN) algorithm and a random forest (RF) algorithm. The models were validated using 10-fold cross-validation. The reference standard was the PBRM1 mutation status. The main performance metric was the AUC value. Of 828 extracted texture features, 759 had excellent reproducibility. Using 10 selected features, the ANN algorithm correctly classified 88.2% (142 of 161) of the clear cell RCCs in terms of PBRM1 mutation status (AUC value, 0.925). Using five selected features, the RF algorithm correctly classified 95.0% (153 of 161) of the clear cell RCCs (AUC value, 0.987). Overall, the RF algorithm performed better than the ANN algorithm (z score = -2.677; p = 0.007). ML-based high-dimensional quantitative CT texture analysis might be a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC.

With the advent of the era of big data, artificial neural network (ANN) algorithms have been widely used in the field of building energy data analysis. In order to effectively use ANN algorithms to predict building energy consumption, the data-driven building energy consumption prediction with three typical ANNs: Backpropagation neural network (BPNN), generalized regression neural network (GRNN), and fuzzy neural network (FNN) were studied. The simulated data of an office building model setup by EnergyPlus is presented for a case study. The BPNN algorithm with different hidden layer numbers, GRNN algorithm with different scatter constants, and FNN algorithm with different evolution times were investigated, and the optimal parameters of each neural network algorithm for building energy consumption prediction were finally obtained. The results show that the MSEs of all ANN-based models are almost the same with very small values. But the operation time is very different, which of GRNN has the smallest value. So, the GRNN is highly recommended for building energy consumption prediction due to its both good prediction accuracy and short operation time. This study helps to guide the selections of ANNs and the determinations of related parameters of their algorithms in engineering application.

The search for better climate change adaptation techniques for addressing environmental and economic issues due to changing climate is of paramount interest in the current era. One of the many ways Pacific Island regions and its people get affected is by dry spells and drought events from extreme climates. A drought is simply a prolonged shortage of water supply in an area. The impact of drought varies both temporally and spatially that can be catastrophic for such regions with lack of resources and facilities to mitigate the drought impacts. Therefore, forecasting drought events using predictive models that have practical implications for understanding drought hydrology and water resources management can allow enough time to take appropriate adaption measures. This study investigates the feasibility of the Artificial Neural Network (ANN) algorithms for prediction of a drought index: Standardized Precipitation-Evapotranspiration Index (SPEI). The purpose of the study was to develop an ANN model to predict the index in two selected regions in Queensland, Australia. The first region, is named as the grassland and the second as the temperate region. The monthly gridded meteorological variables (precipitation, maximum and minimum temperature) that acted as input parameters in ANN model were obtained from Australian Water Availability Project (AWAP) for 1915–2013 period. The potential evapotranspiration (PET), calculated using thornthwaite method, was also an input variable, while SPEI was the predictand for the ANN model. The input data were divided into training (80%), validation (10%) and testing (10%) sets. To determine the optimum ANN model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton backpropagation algorithms were used for training the ANN network and the tangent sigmoid, logarithmic sigmoid and linear activation algorithms were used for hidden transfer and output functions. The best architecture of input-hidden neuron-output neurons was 4-28-1 and 4-27-1 for grassland and temperate region, respectively. For evaluation and selection of the optimum ANN model, the statistical metrics: Coefficient of Determination (R 2 ), Willmott’s Index of Agreement (d), Nash-Sutcliffe Coefficient of Efficiency (E), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed. The R 2 , d, E, RMSE and MAE for optimum ANN models were 0.9839, 0.9909, 0.9838, 0.1338, 0.0882 and 0.9886, 0.9935, 0.9874, 0.1198, 0.0814 for grassland and temperate region, respectively. When prediction errors were analysed, a value of 0.0025 to 0.8224 was obtained for the grassland region, and a value of 0.0113 to 0.6667 was obtained for the temperate region, indicating that the ANN model exhibit a good skill in predicting the monthly SPEI. Based on the evaluation and statistical analysis of the predicted SPEI and its errors in the test period, we conclude that the ANN model can be used as a useful data-driven tool for forecasting drought events. Broadly, the ANN model can be applied for prediction of other climate related variables, and therefore can play a vital role in the development of climate change adaptation and mitigation plans in developed and developing nations, and most importantly, in the Pacific Island Nations where drought events have a detrimental impact on economic development.

An automatic traffic sign recognition system can help drivers operate the vehicle properly. Most existing systems include a detection phase and a classification phase. In this paper, a new classification method is presented based on an improved artificial neural network (ANN) algorithm for recognizing traffic signs. When the ANN algorithm is chosen to for traffic sign recognition, the key factor is to get the right weights in neural networks. Traditionally, the weights were solved by training the neural networks with a give samples set. But in most of the cases the convergence for the training is very slow, even it becomes divergence. In this paper, an improved BP neural networks algorithm was proposed. Compared with the old algorithm, a dynamic learning rate was used to get an optimization learning rate instead of a fixed learning rate. Combined the moment features, GSC features, experiments show that the iterative times for ANN training is reduced evidently.

Ocean salinity is a key parameter in oceanic and climate studies, and the accurate estimation of sea surface salinity (SSS) of coastal water is of great scientific interest. This paper reports on a modeling study of SSS using artificial neural network (ANN) and random forest (RF) algorithm. Hong Kong Sea, China was used as case study. Sea biochemistry and sea physical parameters were collected. Sea surface temperature (SST), pH, chlorophyll-a (Chl-a) and total inorganic nitrogen (TIN) were selected as input variables of models. The assessment models were based on a back propagation (BP) neural network and RF algorithm. The results showed that an optimum BP neural network prediction model has 4-20-4-1 network architecture with gradient descent learning algorithm and an activation function including the sigmoid tangent function in the input layer, a hidden layer and linear functions in the output layer. While the optimum RF model was obtained, when RF algorithm had a mtry value of 32 with ntree=2000 and nodesize=4. Optimum BP and RF models for estimating SSS performed well at prediction, regardless of training or testing sets with R 2 above 0.8. Compared with the BP model, RF model was usually slightly stable in models’ performance with respect to different models’ parameters. This research verified that the BP model and RF algorithm could provide an effective and faithful estimation of SSS of coastal water based on sea biochemistry and physical parameters.

The demand for electricity in Indonesia is continuously increasing due to the growing economy over time. In accordance with Presidential Instruction No. 10 of 2005 and Presidential Regulation No. 5 of 2006, regulations have been issued regarding energy conservation and national energy usage policies. Therefore, this paper discusses an electricity energy audit and predicts the electricity load at the PT. Telkom Indonesia Building in Lhokseumawe using Artificial Neural Network (ANN) algorithms. Energy audit involves the inspection, survey, and analysis of energy flows to identify energy-saving opportunities in buildings, aiming to reduce the input energy into the system without compromising system output. The research aims to identify electricity energy-saving opportunities. Additionally, the paper conducts predictions of electricity usage before and after the audit using the Artificial Neural Network (ANN) algorithm. Therefore, an audit of this load is needed. After inspecting the location, it was identified that the air conditioning units used exceed the required AC capacity. Consequently, a proposal for new devices is necessary to achieve potential electricity usage savings in the building. Based on the training and validation of the neural network using the Bilayered Neural Network (BNN) model with 3 layers, the most suitable model was obtained. The obtained values include an RMSE of 1210.7, R-Squared of 1.00, MSE of 1.4658e+06, MAE of 901.19, Prediction Speed ~1500obs/Sec, and Training Time of 4.8719 Sec. The results indicate potential savings of up to 17,023 kWh/month or 163,327 kWh/year. The required Payback Period to recover the invested capital is estimated at 14 months.

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

Thin-film thickness and refractive index measurements are important for quality control in many high-tech industrial manufacturing processes, such as the semiconductor, display, and battery. Many studies have been carried out to measure the thickness and refractive index of thin-films, and recently studies using an artificial neural network (ANN) algorithm have also been conducted. However, strict evaluations of ANNs were not reported in all previous studies. In this study, a multilayer perceptron type of ANN algorithm for simultaneously analyzing the thickness and refractive index of a thin-film is designed and verified by using four thin-film certified reference materials (CRMs) being traceable to the length standard. According to the number of hidden layers and the number of nodes for each hidden layer, 12 multilayer perceptron type ANN algorithms were designed and trained with a theoretical dataset generated through optics theory based on multiple interferences. Subsequently, the interference spectra measured by the four CRMs were put into the 12 trained ANNs as input, and it was checked whether or not the output values were in good agreement with the corresponding certified values of both the thickness and refractive index. As a result, an ANN algorithm having two hidden layers with 100 nodes was selected as the final algorithm and an uncertainty evaluation was performed. Finally, the combined uncertainties for the thickness and refractive index were estimated to be 2.0 nm and 0.025 at a wavelength of 632.8 nm, respectively, as measured using a spectral reflectometer with the well-trained ANN algorithm.