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The issue of image captioning, which comprises automatic text generation to understand an image's visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real- world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of the previous stage. Improvements include the use of a new activation function, regular parameter tuning, and an improved learning rate in the later stages of training. The experimental results on the flickr8k dataset showed a noticeable and satisfactory improvement in the second stage, where a clear increment was achieved in the evaluation metrics Bleu1-4, Meteor, and Rouge-L. This increment confirmed the effectiveness of the alterations and highlighted the importance of hyper-parameter tuning in improving the performance of CNN-LSTM models in image caption tasks. [ABSTRACT FROM AUTHOR]

Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall data. Six ML algorithms (i.e. multiple linear regression (MLR), M5 model tree (M5), random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), genetic programming (GP)) and four SI methods (i.e. arithmetic average (AA), inverse distance weighting (IDW), correlation coefficient weighted (CCW), normal ratio (NR)) were investigated and compared in their performance. The twelve rainfall stations, located in the Thale Sap Songkhla river basin and nearby basins, were considered as a study case. Tuning hyper-parameters for each ML method was conducted to get the most suitable model for the data sets considered. Three performance criteria matrices (i.e. NSE, OI, and r) were chosen, and the sum of those three performance criteria matrices was introduced for methods' performance comparison. The experimental results pointed out that selecting neighbouring stations were essential when applying SI methods, but not for the ML method. The overall performance showed ML better imputed missing monthly rainfall than SI due to overcoming spatial constraints. GP provided the highest performance by giving NSE = 0.825, OI = 0.877, and r = 0.909 for the training stage. Those values for the testing stage were 0.796, 0.852, and 0.902, respectively. It was followed by SVR-rbf, SVR-poly, and RF. NR provided the best performance among four SI methods, followed by CCW, AA, and IDW. When applying SI methods, it should contemplate a correlation between the target and neighbouring stations greater than 0.80. [ABSTRACT FROM AUTHOR]

Medical image segmentation significantly influences medicine for diagnostic purposes where higher precision and accuracy are demanded. One of the most cutting-edge architectures in Deep Learning (DL) designed to perform on a few training biomedical images is the U-Shape Network (UNet). Unfortunately, thousands of training biological images are out of reach in most situations, limiting the number of attainable training sets and, as a result, the ability to achieve better accuracy. Therefore, this research proposes a scalable model where the U-Net architecture has been tuned to improve its ability to perform on fewer training samples, mitigating the limited trainable data problem. Several model layers, nodes, hyper-parameters, and normalization methods have been experimented with to tune the original U-Net architecture well, considering possible underfitting and/or overfitting scenarios while maintaining reasonable accuracy. The 2018 Data Science Bowl dataset has experimented with several smaller subsets, 10%-100% of the original dataset, which has been trained separately. The model's accuracy ranged between 96.98% and 97.93% for the experimented subsets. More importantly, the reported variance in the accuracy given different training data sizes is minimal (around 1%). [ABSTRACT FROM AUTHOR]

Detecting cardiac abnormalities promptly is critical for preventing unexpected and premature fatalities. In this research, four types of cardiac arrhythmias such as Ventricular Tachycardia, Premature Ventricular Contraction, Normal Sinus Rhythm and Supraventricular Tachycardia are detected from the amassed Physiobank MIT-BIH cardiac arrhythmia database. Dimensionality reduction techniques like Stochastic Neighbour Embedding (SNE), Neighbourhood Preserving Embedding, Linear Local Tangent Space Alignment and Gaussian Process Latent Variable Model are used to reduce the dimension of the ECG signals. The appropriate features of dimensionally reduced ECG signals are selected by the Elephant Search Optimization (ESO) technique. Finally, classification is performed using the relevant classifiers, such as Support Vector Machine, Adaboost, Modest Adaboost based on Ridge Regression (Modest Adaboost.RR), Extreme Gradient Boost (XGboost) and Naïve Bayes (NBC) classifiers. Multiple classifiers without and with ESO feature selection for different cardiac cases have an average classification accuracy of 62.23% and 73.61%, respectively. These multiple classifiers are defined by a set of control parameters known as hyper-parameters, which must be tuned in order to achieve optimal results. Experts have developed many approaches for detecting cardiac arrhythmias, but these multiple classifiers do not always perform well when the usual parameters for machine learning classification models are employed. In this paper, various classifiers are used in conjunction with the Stochastic Gradient Descent (SGD), Particle Swarm Optimization (PSO) and Bayesian Tree-structured Parzen Estimator (BTPE) to enhance the cardiac arrhythmia classification accuracy via hyper-parameter tuning. Multiple classifiers with SGD, PSO and BTPE hyper-parameters tuning techniques for various cardiac cases have an average classification accuracy of 80.13%, 90.67% and 94.96%, respectively. The Classifier's performance is analysed based on metrics like Classification Accuracy, F1 score, Error Rate, Matthew's correlation coefficient, Jaccard Index and Cohen's Kappa Coefficient with and without ESO features selection method and hyper-parameters tuning techniques. The analysis utilizes the MATLAB R2014a software for result evaluation. The results show that the SNE-ESO approach, along with the XGboost-BTPE, achieved the highest classification accuracy of 99.89% for detecting {Ventricular Tachycardia}-{Normal Sinus Rhythm} cases. In terms of classification benchmarks, the results exhibit that the BTPE hyper-parameter tuning technique surpasses the SGD and PSO techniques. [ABSTRACT FROM AUTHOR]

One of the primary concerns in the field of seismic risk assessment for underground structures is establishing an accurate connection between seismic intensity and structural responses. The objective of this work is to conduct a rational support vector machine (SVM) model for generating mass data and improved fragility curves of cross-fault hydraulic tunnels (CFHTs). The results highlight that the 900 sets, multiple earthquake intensity measures, and cubic polynomial kernel function of the SVM model can improve reliability in evaluating structural performance. Additionally, the improved Cloud analysis method is more suitable for seismic performance than the typical Cloud analysis. [ABSTRACT FROM AUTHOR]

Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers have explored the optimisation task during the last decades to reach a state-of-the-art method. However, most of them focus on batch or offline learning, where data distributions do not change arbitrarily over time. On the other hand, dealing with data streams and online learning is a challenging problem. In fact, the higher the technology goes, the greater the importance of sophisticated techniques to process these data streams. Thus, improving hyper-parameter self-tuning during online learning of these machine learning models is crucial. To this end, in this paper, we present MESSPT, an evolutionary algorithm for self-hyper-parameter tuning for data streams. We apply Differential Evolution to dynamically-sized samples, requiring a single pass-over of data to train and evaluate models and choose the best configurations. We take care of the number of configurations to be evaluated, which necessarily has to be reduced, thus making this evolutionary approach a micro-evolutionary one. Furthermore, we control how our evolutionary algorithm deals with concept drift. Experiments on different learning tasks and over well-known datasets show that our proposed MESSPT outperforms the state-of-the-art on hyper-parameter tuning for data streams. [ABSTRACT FROM AUTHOR]

While plant diseases continue to have a severe impact on food production, farmers face a formidable challenge in trying to meet the escalating demands of a population that is expanding quickly for agricultural items like potatoes. Despite spending billions on disease management, farmers frequently struggle to effectively control disease without the aid of cutting-edge technology. The paper examines a disease diagnosis method based on deep learning. To be more precise, it uses a Convolutional Neural Network (CNN) method for the disease's detection and classification. This study examines the impact of data augmentation while conducting an extensive performance evaluation of the hyper-parameter in the setting of detecting plant diseases with a focus on potatoes. The experimental findings demonstrate the effectiveness of the suggested model's 98% accuracy. Considering growing global issues, this research aims to open new pathways for more efficient plant disease management and, eventually, increase agricultural output. [ABSTRACT FROM AUTHOR]

A fault in a power system is an anomalous state that must be recognized as soon as feasible. To minimize the repercussions of the fault, such as damages occurred to the device, loss of tangible assets and loss of human resources, it is critical to notice the problem promptly. In a power distribution system, there are several approaches for detecting different types of faults. In this paper, a neoteric approach using Bayesian optimized Convolutional Neural Network is used to detect and classify different symmetrical as well as unsymmetrical faults in power distribution systems. The effectiveness of the proposed CNN model is validated for an IEEE 13 bus radial distribution system grid modeled (and simulated) in PSCAD. Time series of the measured 3-phase fault currents (for eleven different categories of faults) are used to create training & testing data. This data has been imported in MATLAB software to develop a CNN classifier (whose hyper-parameters are optimized by using a Bayesian optimizer) for faults in power distribution system, under distinct fault situations by varying fault resistance, faulty node and fault inception angles. Findings of simulation clearly indicate that proposed model has very high categorizing accuracy and is superior and competitive to other techniques available in literature. [ABSTRACT FROM AUTHOR]

The rapid proliferation of high-quality false images on social media sites calls for research on legitimate image recognition systems. Copy-move forgery (CMF), which involves copying portions of an image, is one of the most commonly used image altering methods. Due to the problem of exploding and vanishing gradients, the present Convolutional Neural Network (CNN) model must be trained for up to 100 epochs to achieve the greatest accuracy. In this work, a deep CNN (DCNN) model using the residual network with 101 deep layers has been used. In order to solve the problem of exploding and disappearing gradients, the concept of skip connections has been included in the residual network. In addition, in order to maximize the performance of the suggested ResNet-101 model, the cyclical learning rate (CLR) hyper-parameter is utilized to further tune the model. The model was trained and evaluated using a variety of datasets, including MICC-F600, MICC-F2000, MICC-F220, and CoMoFoD v2. Accuracy, error rate, true positive rate (TPR), false positive rate (FPR), true negative rate (TNR), and false negative rate (FNR) were analyzed quantitatively. The proposed model achieves highest accuracy of 97.75% only after training the model for 5 epochs only for CoMoFoD v2 dataset. For MICC-F220, MICC-F600 and MICC-F2000 datasets the achieved accuracy was 96.09%, 97.63% and 96.87% respectively only after training the model up to 10 epochs. In order to demonstrate the efficacy of the suggested approach, a comparative study with various state-of-the-art-models available in the literature has been presented. [ABSTRACT FROM AUTHOR]

Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the application of deep learning methods to develop prediction models that efficiently assess the nonlinear dynamic responses of cross-fault hydraulic tunnels (CFHTs) is lacking. Thus, the objective of this study is to construct a rational artificial neural network (ANN) prediction model to generate the mass data and fragility curves of CFHTs. Firstly, an analysis of 1080 complete nonlinear dynamic time histories via incremental dynamic analysis (IDA) is conducted to obtain the mass data of the drift ratio of the CFHT. Then, the hyper-parameters of the ANN model are discussed to determine the optimal parameters based on four examined approaches to improve the prediction capacity and accuracy. Meanwhile, the traditional probabilistic seismic demand models of the predicted values obtained by the ANN model and the numerical results are compared with the statistical parameters. Eventually, the maximum likelihood estimation couping IDA method is applied to assess the seismic safety of CFHTs under different damage states. The results show that two hidden layers, ten neurons, and the ReLU activation function for the ANN model with Bayesian optimization can improve the reliability and decrease the uncertainty in evaluating the structural performance. Moreover, the amplitude of the seismology features can be used as the neurons to build the input layers of the ANN model. It is found through vulnerability analysis that the traditional seismic fragility analysis method may overestimate the earthquake resistance capacity of CFHTs compared with maximum likelihood estimation. In practical engineering, ANN methods can be regarded as an alternative approach for the seismic design and performance improvement of CFHTs. [ABSTRACT FROM AUTHOR]

MOEA/D-DE algorithm is easy to implement and widely used to deal with multi-objective optimization problems, while its hyper-parameters have a great impact on the performance of the algorithm. In this paper, based on the MOEA/D-DE algorithm framework, the Sobol global sensitivity analysis method is used to improve the cross control parameter in the differential evolution operator, and the Levy flight strategy is used to control the scale factor. Then the hyper-parameters in the MOEA/D-DE algorithm obtain adaptive ability. And MOEA/DDE algorithm with adaptive hyper-parameters (MOEA/D-DEAH) is proposed. The MOEA/D-DEAH MOEA/D-DE with different hyper-parameters settings and NSGAⅡ are tested and compared with function tests and aero-dynamic stealth optimization of airfoil. The function test results show that the new algorithm has good perfor-mance, and the algorithm can obtain strong robustness by adapting the super parameters. The optimization results of airfoil aerodynamic stealth show that MOEA/D-DEAH algorithm has better optimization results than other algo-rithms.

Handwritten digit recognition is a complex problem that has stumped even the brilliant minds of this century. Getting precise results from different handwritten samples has been a challenge that needs to be addressed due to the occurrence of this issue in several sectors like document verification, post mail, deciphering, etc. Hence, we introduce our paper as a response to the requirement of an accurate model that can acutely recognize and then predict the handwriting of a variety of individuals with ease. Our model aims to do number recognition through the implementation of neural networks. We tested out different models with each optimizer to verify which model provided the best performance and with which optimizer. Optimizers are an inherent part of Deep learning, and they are used to upgrade the weights, so the model can learn accordingly and get a more accurate system. Instead of just comparing the performance of various optimizers with only one model, we compared different model performances, while trying to select the optimizer that would best suit that learning model. Rigorous training and experimentalizing have resulted in an accuracy of 98.55% for an ANN model employed with an Adagrad optimizer. [ABSTRACT FROM AUTHOR]

Hyper-parameter optimization is a crucial task for designing kernel-based machine learning models. Their values can be set by using various optimization algorithms. But a data-dependent objective function makes hyper-parameter's configuration changes over time in a dynamic environment. A dynamic environment is an environment where training data keep getting added continuously over time. To find the optimum values for the hyper-parameters in such an environment, one needs to run the optimization algorithm repeatedly over time. But due to the dependency of the objective function on data, the average time complexity of the optimization process increases. This paper work proposes a novel knowledge-based approach that uses particle swarm optimization (PSO) as the base optimization algorithm to optimize the hyper-parameters of support vector machine. We have introduced two major modules for designing this framework—a knowledge transfer module and a drift detection module. The knowledge transfer module in our proposed framework generates knowledge by running PSO and transfers this knowledge to the consequent time instances. On the other hand, the drift detection module is responsible for detecting changes in the objective function when new data get added to the existing data. This drift detection module helps to utilize the transferred knowledge at a particular time instance to reduce the execution time of the overall optimization process. The proposed framework has been evaluated using various standard datasets such as Adult, DNA, Nist-Digits, Segment, Splice, Mushroom and Usps for five consecutive time instances and found the average execution time as 30.72 s, which is better than the execution time 36.89 s recorded using general PSO. We have also found that our proposed framework performs the optimization of hyper-parameters much faster than the other existing approaches such as grid search, chained-PSO, dynamic model selection and quantized dynamic multi-PSO. [ABSTRACT FROM AUTHOR]

This paper presents the framework of the Generalized Autoregressive Score (GAS) model with a variety of symmetric conditional densities of different time-varying hyperparameters. The distinctive trait and goal of the observation-driven GAS model is to use its score and information functions as the compeller of time-variation via hyper-parameters of conditional densities. 10 robust hyper-parametric conditional densities were used as random error drivers for the GAS model with an application to the price of the United States cooking gas in the period between 2005 and 2020. Out of the 10 robust hyper-parametric conditional noises for the GAS model, the Asymmetric Student-t with one tail decay parameter (AST1) outperformed other categories of its variants and other conditional densities subjected to the GAS model, achieving a minimum reduced-error performance of AIC=11943.277 and BIC=12014.525. The hyper-parametric model obtained a location score and scale score of - 1.2634 and 0.6636, respectively, while its location information and scale information was 0.2691 and 0.0362, respectively. Furthermore, the GAS model via AST1 proved more efficient than the core volatile conditional heteroscedasticity model of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) at GARCH (1,1) via a Gaussian distributed noise. [ABSTRACT FROM AUTHOR]

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, a, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP. [ABSTRACT FROM AUTHOR]

Stochastic configuration networks (SCNs), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCNs is affected by the assignation and selection of some network parameters significantly. Harris hawk optimizer (HHO) algorithm is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of Harris hawk. In this paper, a SCNs based on HHO algorithm is first introduced, termed as HHO-SCNs. As the performance of SCNs is related to regularization parameter r and scale factor lambda of weights and biases, then HHO is employed to give better parameters for SCNs automatically. A numerical function and six benchmark datasets are used to verify the regression performance of the proposed model. Three benchmark datasets are introduced to illustrate the effectiveness of the proposed model for classification performance. Experimental results demonstrate the feasibility and validity of HHO-SCNs compared with incremental random vector functional link, SCNs, fast SCNs, and SCNs based on whale optimization algorithm. The proposed HHO-SCNs improves the generalization performance of standard SCNs, and provides a new idea for expanding the development and application of SCNs. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based activity recognition for the Human–Robot Interaction environment. The observations of the object state are acquired from the vision sensor in the real-time scenario. The activity recognition system examined in this paper comprises activities labeled as classes (pour, rotate, drop objects, and open bottles). The image processing unit processes the images and predicts the activity performed by the robot using deep learning methods so that the robot will do the actions (sub-actions) according to the predicted activity. [ABSTRACT FROM AUTHOR]

Dam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Extreme Gradient Boosting (XG-Boost), were adopted to forecast reservoir inflows for the monthly and daily timeframes. Results showed that: (1) For the monthly timeframe, all the four models were proficient in obtaining efficient monthly reservoir inflows by scoring at least an R² of 0.5; with the XG-Boost ranked as the best model, followed by the MLPNN, SVR, and lastly ANFIS. (2) the XG-Boost still outperforms all other models for forecasting daily inflow; but however, with reduced performance. The models were still ranked in the same order, with the ANFIS showing very poor performance in scenario-2, scenario-3, and scenario-4. (3) For daily inflows, the best scenarios are scenario-5, scenario-6, scenario-7 as the models were trained based on the 1,3,5, days-lag forecasted inflow, and overall, the XG-Boost outperforms all the other models. [ABSTRACT FROM AUTHOR]

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter

Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the application of deep learning methods to develop prediction models that efficiently assess the nonlinear dynamic responses of cross-fault hydraulic tunnels (CFHTs) is lacking. Thus, the objective of this study is to construct a rational artificial neural network (ANN) prediction model to generate the mass data and fragility curves of CFHTs. Firstly, an analysis of 1080 complete nonlinear dynamic time histories via incremental dynamic analysis (IDA) is conducted to obtain the mass data of the drift ratio of the CFHT. Then, the hyper-parameters of the ANN model are discussed to determine the optimal parameters based on four examined approaches to improve the prediction capacity and accuracy. Meanwhile, the traditional probabilistic seismic demand models of the predicted values obtained by the ANN model and the numerical results are compared with the statistical parameters. Eventually, the maximum likelihood estimation couping IDA method is applied to assess the seismic safety of CFHTs under different damage states. The results show that two hidden layers, ten neurons, and the ReLU activation function for the ANN model with Bayesian optimization can improve the reliability and decrease the uncertainty in evaluating the structural performance. Moreover, the amplitude of the seismology features can be used as the neurons to build the input layers of the ANN model. It is found through vulnerability analysis that the traditional seismic fragility analysis method may overestimate the earthquake resistance capacity of CFHTs compared with maximum likelihood estimation. In practical engineering, ANN methods can be regarded as an alternative approach for the seismic design and performance improvement of CFHTs.

Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient's blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Pornographic websites become increasingly obdurate via disguising and misleading the under-aged people, which obstruct the development of a healthy and safe network environment. The conventional strategies of bare skin classification perform the detection process through a single feature of these sites, which would be unsuccessful to solve the more complicated and tedious situations. Hence, this research focuses on proposing a well-adapted port image classification model using an optimized deep convolutional neural network (Deep CNN). The significance of this research relies on the proposed Aquila Coyote (AqCO) optimization algorithm, developed with the integration of Aquila hunters and the coyote hunters, which engages in tuning the hyper-parameters of the Deep CNN classifier. Moreover, the significant features of the image are utilized by the classifier, which further boosts the classification accuracy of the proposed model. The analysis is done based on performance parameters, such as accuracy, sensitivity, and specificity in such a way to evaluate the efficiency of the proposed porn image classification model. The maximal accuracy of the proposed model is 95.91% for the B-Praneeth dataset, which is high as compared to the existing methods of porn image classification methods. [ABSTRACT FROM AUTHOR]

Using artificial neural network (ANN) to solve the problem of time series water quality prediction has become increasingly mature. In this paper, through the study of leaky-integral echo state neural network (Leaky ESN), combined with the historical water quality data of Dongzhen Reservoir in Fujian Province, a single-day water quality prediction model was constructed, and the Bayesian optimization algorithm was used to realize the automatic optimization of hyper-parameters in the network. On this basis, multi-day prediction models were constructed by further improving the network, which used the historical water quality data of the previous 7 days to predict the water quality of the next 3 days, 5 days and 7 days. Then the prediction models were applied to the water quality prediction of the study. The experimental results show that the single-day prediction model with Bayesian optimization has high accuracy. The multi-day prediction models can also achieve good prediction effect, and have more practical application value. They are more suitable for early warning of water quality. [ABSTRACT FROM AUTHOR]

The major issue that is being faced by the medical professional in the world of health care is the accurate diagnosis of conditions and diseases of the affected patient. In the current scenario, Coronavirus (COVID-19) infections are major challenging in the health care sector. The medical diagnosis for COVID-19 prediction is done with the support of X-ray images. Detection of COVID-19 with X-ray images is an effective method but it is critical for diagnosis, evaluation, and medication. Since the X-ray images are of low contrast, the accuracy will be low while using the traditional feature extraction in machine learning. The proposed method uses a novel technique by combining multiple CNN (Convolutional Neural network) for classification. The samples for multiple CNN (Convolutional Neural Network) are divided into n-subsets by using the bootstrap sampling method. The n-subset samples are finely tuned with Adaptive momentum technique (Adam) optimizer, the output of this layer is fed into CNN (Convolutional Neural Network) model as input, still hyper-tuning at each layer with Bayesian optimizer. This technique avoids de-noising, and features are also enhanced when compared to conventional methods. Experimental results of this technique provide an accuracy of 97.35% for the prediction of COVID-19 from X-ray images. [ABSTRACT FROM AUTHOR]

The paper develops Bayesian estimators and credible intervals for the reliability function and hazard rate function of Topp-Leone distribution under Type-II censored data by considering informative prior and non-informative prior using symmetric and asymmetric loss functions. Further, Monte Carlo simulation study is carried out to find the Bayes estimates for reliability function and hazard rate along with their credible intervals. Real life data set is also taken up for illustration purpose. Robustness of hyper-parameters is also discussed. [ABSTRACT FROM AUTHOR]

In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems. [ABSTRACT FROM AUTHOR]

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions. [ABSTRACT FROM AUTHOR]

Gaussian process regression (GPR) is frequently used for uncertain measurement and prediction of nonstationary time series in the Internet of Things data, nevertheless, the generalization and regression efficacy of GPR are directly impacted by its selection of hyper-parameters. In the study, a non-inertial particle swarm optimization with elite mutation-Gaussian process regression (NIPSO-GPR) is proposed to optimize the hyper-parameters of GRP. NIPSO-GPR can adaptively obtain hyper-parameters of GPR via uniform non-inertial velocity update formula and adaptive elite mutation strategy. When compared with several frequently used algorithms of hyper-parameters optimization on linear and nonlinear time series sample data, experimental results indicate that GPR after hyper-parameters optimized by NIPSO-GPR has better fitting precision and generalization ability.