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The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries. [ABSTRACT FROM AUTHOR]

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation. [ABSTRACT FROM AUTHOR]

Development of a reliable and intelligent traffic monitoring system is highly desired to improve the transportation safety and establish future transportation plans due to the fast growth of vehicle population. Vehicle classification is one of the most critical subsystems where existing ones suffer from privacy concerns, requirements of complicated systems, and high maintenance cost. This paper reports a novel vehicle classification method by utilizing a triboelectric sensor to accurately identify vehicles. Novelty of this method originates from using triboelectric sensor and machine learning method with important advantages over current alternatives by providing an easy installation, simple operation, noninvasive measurement, cost-effective manufacturing, and highly accurate classification. To make a classification, vehicle toys' signals were acquired from triboelectric sensor and then applied to a deep learning algorithm. The 1932 sensor output data were grouped into a set of seven vehicle toys with different wheelbases, and number of tires passing on are used to train and optimize 1D-CNN model. The utilized 1D-CNN model achieved accuracy, f1-score, precision, and recall as 96.38%, 0.9638, 0.9658, and 0.9637, respectively. [ABSTRACT FROM AUTHOR]

The issues of classification and characterization of surface operational defects according to the results of ultrasonic, eddy current, and visual inspection methods of nondestructive testing are considered. At the same time, the visual inspection method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented that was applied to classify the images obtained from the TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics in which the obtained models are applied, and determines the accuracy of this algorithm in the RMSE metric, which was calculated within the studied test dataset and amounted to 0.011 mm. [ABSTRACT FROM AUTHOR]

Social networks are widely used platforms for sharing various information and content, including text, images, and videos.The main challenge in social networking today is the verification of data credibility and the identification of genuine social media profiles. Cybercriminals take advantage of this by utilizing fake profiles to disseminate false information. However, current research mainly concentrates on spam filtering and analyzing malicious behavior separately, disregarding the interrelated nature of these issues. We propose a more desirable global, hybrid solution that encompasses both malicious profile detection and spam detection to mitigate the spread of spam effectively. This paper offers a deep learning-based method for detecting malicious profiles and spam tweets. For the profiles to interact with them as legitimate profiles, we first provide the detection of fake profiles using an automated honeypot. Next, we detect those who make interactions as malicious profiles, and finally, we collect their shared content to find spam tweets using a convolution neural network algorithm. We suggest using collaborative filtering and content filtering algorithms from recommender systems to define accounts similar to harmful profiles and spam similar to spam material picked up by convolution neural networks. We get a highly compelling and intriguing outcome with higher accuracy (99.23%) and lesser loss than typical learning algorithms. [ABSTRACT FROM AUTHOR]

The communication between the human brain and the external devices can be established using Electroencephalograms (EEG)-based Brain–Computer Interface by converting the neural activities of the brain into electric signals. The EEG signals were isolated into an energy–frequency–time spectrum with Hilbert Huang transform that was used by the Deep Learning (DL)-based model to learn discriminative spectro-temporal patterns of the raw EEG signals of ten digits. This paper has two major contributions: first, create a novel dataset known as BrainDigiData of EEG signals of ten digits from (0–9) using a multi-channel EEG device. Second to propose a DL-based one-dimensional Convolutional neural network model BrainDigiCNN to classify the BrainDigiData of EEG signals of digits. The publicly available Mind Big Dataset (MBD) of digits was also used to evaluate the performance of the proposed model. The research done in this paper showed that the band-wise analysis of EEG signals in a complex scenario resulted in improved results as compared to the scenario used in the previously existing work for digit classification using EEG signals. The proposed BrainDigiCNN model achieved the highest average accuracy of 96.99%. The average classification accuracy of 98.27% was achieved for the MBD dataset of 14 channel device EMOTIV EPOC+ and 89.62% on the MBD dataset of 5-channel EMOTIV Insight. The statistical analysis of the proposed model on traditional Machine Learning (ML) classifiers using paired t-test resulted in a p-value less than 0.05 which shows the significant difference between the proposed model and ML classifiers. [ABSTRACT FROM AUTHOR]

The widespread availability of digitized fine art collections in museums and galleries has generated a demand for efficient software tools. These tools enable rapid retrieval and semantic classification of art images. Traditional image classification methods often rooted in shallow structure learning algorithms offer the capacity to extract various image features. However, there is need for deep understanding of fundamental painting knowledge during this process due to the reason that certain features may be lost. In this paper, we present an innovating approach for classifying digital paintings based on artist attribution. Our approach centers on the creations of multi-scale pyramid representation derived from a given painting image which facilitate the incorporation of both global and local information within a single image. The training of Convolutional Neural Network (CNN) algorithm is performed for the assignment of class labels on each level of the pyramid. To establish connections among localized image patches, we use Markov Random Fields through the optimization of the Gibbs energy function. We validate the proposed approach which referred to as the multi-scale CNN framework using fusion-based Markov random field using two challenging painting image datasets: WikiArt and Web Gallery of Art (WGA). The proposed model archives better results using WikiArt dataset for painting image classification, i.e., precision (74.12%), recall (70.23%), F1-score (72.65%), and accuracy (80.00%). Using Web Gallery of Art (WGA) dataset for painting image classification, the model achieves the following results: precision (75.88%), recall (77.25%), F1-score (76.44%), and accuracy (78.00%). Our proposed method outperforms state-of-the-art methods excelling not only in image classification but also in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. [ABSTRACT FROM AUTHOR]

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been proved to be powerful tools for ML-based mapping for mineral exploration. In the future, GCN deserves more attention for ML-based mapping for mineral exploration because of its ability to capture the spatial anisotropy of mineralization and its applicability within irregular study areas. Finally, we summarize the original contributions of the six papers comprising this special issue. [ABSTRACT FROM AUTHOR]

Prediction of conformational B-cell epitopes (CBCE) is an essential phase for vaccine design, drug invention, and accurate disease diagnosis. Many laboratorial and computational approaches have been developed to predict CBCE. However, laboratorial experiments are costly and time consuming, leading to the popularity of Machine Learning (ML)-based computational methods. Although ML methods have succeeded in many domains, achieving higher accuracy in CBCE prediction remains a challenge. To overcome this drawback and consider the limitations of ML methods, this paper proposes a novel DL-based framework for CBCE prediction, leveraging the capabilities of deep learning in the medical domain. The proposed model is named Deep Learning-based Temporal Convolutional Neural Network (DL-TCNN), which hybridizes empirical hyper-tuned 1D-CNN and TCN. TCN is an architecture that employs causal convolutions and dilations, adapting well to sequential input with extensive receptive fields. To train the proposed model, physicochemical features are firstly extracted from antigen sequences. Next, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. Finally, the proposed DL-TCNN is employed for the prediction of CBCE. The model's performance is evaluated and validated on a benchmark antigen–antibody dataset. The DL-TCNN achieves 94.44% accuracy, and 0.989 AUC score for the training dataset, 78.53% accuracy, and 0.661 AUC score for the validation dataset; and 85.10% accuracy, 0.855 AUC score for the testing dataset. The proposed model outperforms all the existing CBCE methods. [ABSTRACT FROM AUTHOR]

In an iron ore pelletization plant, pellets are produced inside a rotating disc pelletizer. Online pellet size distribution is an important performance indicator of the pelletization process. Image processing-based system is an effective solution for online size analysis of iron ore pellets. This paper proposes a machine learning algorithm for estimating the size class of the pellets during their production by imaging from an area inside the disc pelletizer. Instead of computing the size of each individual pellets in the acquired image, this method proposes a qualitative approach to get the overall size estimate of the pellets in production. The key idea of this paper is to find out whether the disc is producing VERY SMALL, SMALL, MEDIUM, or BIG-sized pellets. A weighted average ensemble of different convolutional neural networks such as VGG16, Mobilenet, and Resnet50 is used to achieve this objective. Furthermore, batch normalization is applied to improve the estimation performance of the proposed model. A novel data augmentation method is applied to the in situ captured images to create the data set used to train and evaluate the proposed ensemble of CNN models. Results of experiments indicate that it is possible to detect the operating state of the pelletization disc by acquiring images from the inside area of the disc with sufficient accuracy. [ABSTRACT FROM AUTHOR]

Traditional IT with advent of convolution neural networks and deep learning can lead a trendsetter for healthcare sector, diseases identification and prediction. An essential upsurge during pandemic is virtualization of hospital functional policies and care models. Virtual models are providing solutions for disease detection without consulting a doctor in turn help to enhance patient care and performance. These architectural and operational policies are significant for healthcare domain to enable strategic decision-making for intricate and sensitive environment. The machine learning models will reveal information from the historical, optimize the present and even predict the future performance of the different areas analysed. This paper reveals the challenges of healthcare with automated Cataract Detection and Grading System. The proposed system uses an efficient deep learning model with CNN and LSTM for detecting and classifying healthy eye from cataract eye. The proposed system produced an accuracy of 98.5 for the custom dataset. [ABSTRACT FROM AUTHOR]

Rapid advancement in additive manufacturing capabilities and computational resources has catalyzed multiscale topology optimization (TO). Recently, TO in general and multiscale TO in particular have received increased attraction due to its systematic approach to design globally and locally optimized structures. The multiscale TO computational paradigm brings additional new challenges, including geometric frustration, non-smooth boundaries, and higher computational time that needs to be handled. In this paper, a novel deep learning-based computational pipeline is developed to overcome the challenges encountered in the multiscale TO framework. The outlined computational pipeline employs two neural networks (NNs) architecture: (1) SILONet and (2) ConnectivetyNet. SILONet predicts optimized microstructure for elements of macroscale optimized structure, and ConnectivetyNet improves connectivity for multiscale topology fitted with SILONet outputs. The main novelty of this paper is the utilization of two NNs architecture for alleviating challenges, such as geometric frustration, non-smooth edges, dangling structures at boundaries, and accelerating multiscale TO computations while capturing essential physics and ensuring optimized solutions. The effectiveness of the proposed approach is demonstrated by its application to several two-dimensional and three-dimensional test cases. [ABSTRACT FROM AUTHOR]

The explosive growth of geospatial data is increasing requirements for automatic and efficient data learning abilities. Many deep learning methods have been widely applied for geospatial data understanding tasks, such as road networks and geospatial object detection. However, the demands for more accurate learning of high-level features require the use of deeper neural networks. To further improve the learning efficiency of deep neural networks, in this paper, we propose an improved convolutional neural network named EDense. First, we use its dense connectivity to integrate a CNN with an extreme learning machine. Then, we expand the kernels in the convolutional layers to increase the width of the network model. Furthermore, we propose one-feature EDense (OF-EDense), which is a simplified version of EDense, to fit conditions in which the number of parameters is strictly limited. Finally, the experimental results fully demonstrate the strong learning ability and high learning efficiency of EDense. [ABSTRACT FROM AUTHOR]

Software systems are frequently affected by a few defects. In general, the developers make use of the data present in the file, while reporting a bug; thus, they can help to fix the bug by localizing the source code fragments that are required to be improved. This paper intends to implement the bug localization model using the improved deep learning algorithm, which helps to localize the buggy files to enhance the productivity and efficiency of the software quality assurance teams. The datasets such as "Aspect J and SWT" were utilized for assessing the proposed bug localization model. The proposed model involves a few fundamental steps like data pre-processing, word embedding, CNN-based feature detection, and CNN-based classification. Initially, the source files from which the bug has to be localized are given as input. Here, the pre-processing of files is performed by erasing punctuation followed by splitting. Here, the relevant information or features are gathered from the source files using Word2Vec, a bag of n-grams, and the term frequency–inverse document frequency (TF-IDF) technique in the word embedding process. Further, final feature vector extraction is performed by the convolution neural network (CNN). The extracted features are subjected to optimized CNN-based classification to localize the bugs. Here, the number of hidden neurons of CNN is optimized using hybridized cuckoo search-based sea lion optimization (CS-SLnO). The main objective of this work is to introduce a new optimized CNN-based classification of bug localization with hidden neurons optimization using a hybridized optimization algorithm termed CS-SLnO for improving the localization accurateness. Finally, the bug fixing ability of the proposed model is proved and certified through valuable performance analysis. The experimental results shows that the accuracy@1 of the suggested CS-SLnO-CNN is attaining the best performance when compared to other algorithms. It is 85.1% better than DeepLoc, 51.9% better than DeepLocator, 54.3% better than HyLoc, 65.1% better than LR + WE, and 73.5% better than BugLocator. Thus, it can be confirmed that the developed CS-SLnO-CNN is acquiring better bug localization performance when compared to the other deep learning models. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIFs) exhibit low current amplitude and highly diverse characteristics, which make them difficult to be detected by conventional overcurrent relays. Various machine learning (ML) techniques have been proposed to detect and classify HIFs; however, these approaches are not reliable in presence of diverse HIF and non-HIF conditions and, moreover, rely on resource-intensive signal processing techniques. Consequently, this paper proposes a novel HIF detection and classification approach based on a state-of-the-art deep learning model, the transformer network, stacked with the Convolutional neural network (CNN). While the transformer network learns the complex HIF pattern in the data, the CNN enhances the generalization to provide robustness against noise. A kurtosis analysis is employed to prevent false detection of non-fault disturbances (e.g., capacitor and load switching) and nonlinear loads as HIFs. The performance of the proposed HIF detection and classification approach is evaluated using the IEEE 13-node test feeder. The results demonstrate that the proposed protection method reliably detects and classifies HIFs, is robust against noise, and outperforms the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Background: The recent advances in the manufacturing industry, mainly fueled by the fourth industrial revolution, and the shortage of skilled welders are boosting research towards intelligent automatic welding. In this context, vision sensing and machine learning have been widely applied to the development of welding status recognition (WSR) systems. However, researchers have mainly focused on developing accurate models putting aside the analysis on computational performance, which is a relevant aspect of the development of real-time WSR systems. Objective: This work aims at devising improved modeling paradigms, considering optimization towards both accuracy and computational performance, for real-time WSR in gas tungsten arc welding. Methods: Shallow learning paradigms are compared with a benchmark deep learning model regarding classification as well as computational performance. For that purpose, metrics that account for the time a model takes to make a prediction and the hard disk space occupied by the model are proposed. The dataset used in this paper comprehends molten pool images acquired for defective and non-defective welding conditions. A dimensionality reduction technique was used to extract features from the data efficiently. For the model creation, a randomized hyperparameter search strategy was adopted. Results: The modeling workflow adopted resulted in an efficient and lightweight model suitable for being deployed as a diagnostic tool. Results have shown that shallow learning jointly with principal component analysis improved both the accuracy and hardware consumption. Specifically, compared to the benchmark deep learning model, the developed classifier presented size 84.82% smaller, and related accuracy improved by 17.32%. Conclusion: The proposed approach has shown to be an efficient way of achieving a compromise between classification and computational performance when developing a predictive model to serve as an online tool for vision-sensing-based WSR. [ABSTRACT FROM AUTHOR]

Weld fault identification using X-ray pictures is a useful nondestructive testing technique. Traditionally, this job has relied on skilled human experts, while the extraction and classification of heterogeneity required their personal involvement. To overcome those challenges, various approaches are used, including machine learning (ML) and image processing technologies. Although the detection and categorization of low contrast and poor-quality images have been improved, the end result is still unsatisfactory. Unlike earlier ML-based research, this paper provides a new deep learning network-based classification approach. In this work base convolutional neural network architecture with optimization techniques was used to enhance the performance of the architecture to obtain a best performance result in simpler CNN architecture. In this work we have obtained overall classification accuracy of 89% using simple CNN with optimization technique. [ABSTRACT FROM AUTHOR]

Background: Due to the acoustoelastic effect, ultrasonic guided waves have been used to estimate mechanical stress in a cheap and nondestructive fashion. Machine learning has been applied to map complex waveforms to stress estimates, though important aspects to construct and deploy real-time monitoring systems, such as accuracy and hardware consumption, to date, have not been concomitantly explored. Objective: The goal of the present paper is to propose a data modeling methodology that optimizes accuracy and computational implementation, towards devising the best practices for real-time ultrasonic-based stress estimation. Methods: We evaluate shallow and deep learning models with dimensionality reduction, which are compared to the most recent work showing overall better results for the approach presented herein both in terms of accuracy and hardware resources consumption. We generated a dataset to evaluate the models on a test rig with a plate measuring (i) ultrasonic guided waves excited by broadband signals and (ii) different stress conditions. The models are created and tested using a Monte-Carlo hold-out procedure with a repeated k-fold randomized hyperparameter search to evaluate their robustness in different stress conditions. Results: We dramatically improve the current state of the art by proposing and evaluating a more efficient model construction procedure. Results show that by using shallow models and principal component analysis, we were able to improve the model performance in terms of predictive power and inference hardware consumption. Specifically, we obtained an accuracy improvement of about 10% and hardware consumption used for inference smaller in three orders of magnitude when compared to the state-of-the-art reported with deep neural network models. Conclusions: Results herein depicted impact the way practitioners may proceed for building efficient embedded predictive models for stress estimation using guided waves, enabling more precise, decentralized, and real-time nondestructive monitoring. [ABSTRACT FROM AUTHOR]

Rapid and exponential development of textual data in recent years has yielded to the need for automatic text summarization models which aim to automatically condense a piece of text into a shorter version. Although various unsupervised and machine learning-based approaches have been introduced for text summarization during the last decades, the emergence of deep learning has made remarkable progress in this field. However, deep learning-based text summarization models are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a novel abstractive summarization model is proposed in this paper which utilized the combination of convolutional neural network and long short-term memory integrated with auxiliary attention in its encoder to increase the saliency and coherency of generated summaries. The proposed model was validated on CNN\Daily Mail and DUC-2004 datasets and empirical results indicated that not only the proposed model outperformed existing models in terms of ROUGE metric but also its generated summaries had higher saliency and readability compared to the baseline model according to human evaluation. [ABSTRACT FROM AUTHOR]

As a widely known disease diabetes mellitus makes the human body produce quite less hormone and also tend to cause increased glucose that results in abnormal metabolism of varied organs in the body like eyes, kidneys, etc. Diabetic analysis has attracted the research community to treat some missing values and class imbalance issues. The performance of diabetes mellitus classification by the usage of machine learning techniques is comparatively low. We suggest this paper on imbalanced dataset with missing values, an efficient prediction algorithm for diabetes mellitus classification using Deep 1D-Convolutional Neural Network values. The outlier detection is used for removing missing values first. Then, oversampling method (SMOTE) is used to reduce the influence of imbalance class on prediction performance. Finally, predictions are produced using a DCNN classifier and are evaluated using a selective set of evaluation indicators. Experiments on the Pima Indian diabetes dataset (PIDD) from UCI Repository (University of California at Irvine) have yielded positive results. Our proposed DCNN algorithm has been shown to be successful and superior. [ABSTRACT FROM AUTHOR]

Modeling the semivariogram to characterize spatial continuity requires expert geostatistical knowledge and domain expertise about the spatial phenomenon of interest. Moreover, although practitioners may have experience in semivariograms, their interpretations may vary due to experimental semivariogram noise and ambiguity. In general, modeling semivariograms remains highly subjective. This paper presents a data-driven, deep learning-based automated semivariogram modeling method known as automatic semivariogram modeling with convolution-based deep learning (ASMC) that improves the utilization of available spatial information to reduce the subjectivity of semivariogram modeling. Training models are generated by sequential Gaussian simulation (SGS) and labeled with their associated semivariogram parameters (i.e., maximum correlation length, aspect ratio of major and minor direction ranges, and azimuth of major direction). ASMC consists of two convolutional neural networks (CNNs). The first CNN model maps the sparse spatial samples to the exhaustive SGS-derived spatial models, and the second CNN maps the SGS spatial model to the semivariogram parameters. Both CNNs are trained with realistic spatial training data, and their validity is also checked with validation data withheld from training. Two-dimensional synthetic, but realistic, case studies demonstrate that the first CNN successfully learns the spatial characteristics among spatial data and generates realistic subsurface model estimates. The second CNN learns the spatial context of the estimated subsurface model and successfully predicts the semivariogram parameters with greater than 96% accuracy. The proposed machine, deep learning-based workflow improves the utilization and objectivity of spatial data in semivariogram-based spatial continuity modeling. With the optimal design of the experiment for training and tuning of model hyperparameters, this method may be generalized for application for a wide range of spatial modeling projects. [ABSTRACT FROM AUTHOR]

This paper introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. The main novelty of this framework is the utilization of parameters such as density and nodal deflections to predict optimized topologies for a wide range of design space. In this research, a three-stage NN framework is employed, wherein the first stage is a feedforward deep neural network. The second stage uses a convolutional neural network (CNN), and the final stage is the post-processing of results obtained from CNN. The first stage maps input design variables vector to output density distribution image. The image output of the first stage and design variables vector inputted to the first network serve as inputs to the second stage, which outputs optimized structure. Finally, post-processing of this optimized structure is done to ensure volume fraction constraint and optimal solution. These structures can be tessellated per the nodal displacement of an FEA solved larger structure. Because of this framework, the proposed approach makes TO computations faster and captures essential physics while using NNs to determine the final structure. The application of the proposed model to three-dimensional problems is also demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning model that integrates a convolutional neural network and a gated recurrent unit with groups of neighboring stations to accurately predict PM2.5 concentrations at 25 stations in Seoul, South Korea. The deep learning model uses observations obtained from one Korea Meteorological Administration (KMA) station, 25 National Institute of Environmental Research (NIER) stations, and 28 automatic weather stations (AWSs) throughout Seoul. To train the deep learning model, we use all available meteorological and air quality data observed between 2015 and 2017. With the trained model, we predict PM2.5 concentrations at all 25 NIER stations in Seoul for 2018. This study also proposes a geographical polygon group model that determines the optimal number of neighboring NIER stations required to increase the accuracy of PM2.5 concentration predictions at the target station. Comparing the model measures for each of the 25 monitoring sites in 2018, we find that the geographical polygon group model achieves an index of agreement of 0.82–0.89 and a Pearson correlation coefficient of 0.70–0.83. Compared to the method using only meteorological and air quality data from one target station (average IOA = 0.77) to predict PM2.5 concentrations at the 25 stations in Seoul, the proposed method using geographical correlation-based neighboring NIER stations as polygonal groups (average IOA = 0.85) improves the PM2.5 prediction accuracy by an average of about 10%. This approach, based on deep learning, can be updated to predict air pollution or air quality indices up to several days in advance. [ABSTRACT FROM AUTHOR]

Spatio-temporal data are ubiquitous in the agricultural, ecological, and environmental sciences, and their study is important for understanding and predicting a wide variety of processes. One of the difficulties with modeling spatial processes that change in time is the complexity of the dependence structures that must describe how such a process varies, and the presence of high-dimensional complex datasets and large prediction domains. It is particularly challenging to specify parameterizations for nonlinear dynamic spatio-temporal models (DSTMs) that are simultaneously useful scientifically and efficient computationally. Statisticians have developed multi-level (deep) hierarchical models that can accommodate process complexity as well as the uncertainties in the predictions and inference. However, these models can be expensive and are typically application specific. On the other hand, the machine learning community has developed alternative "deep learning" approaches for nonlinear spatio-temporal modeling. These models are flexible yet are typically not implemented in a probabilistic framework. The two paradigms have many things in common and suggest hybrid approaches that can benefit from elements of each framework. This overview paper presents a brief introduction to the multi-level (deep) hierarchical DSTM (H-DSTM) framework, and deep models in machine learning, culminating with the deep neural DSTM (DN-DSTM). Recent approaches that combine elements from H-DSTMs and echo state network DN-DSTMs are presented as illustrations. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral images. Multi-task Feature Leverage is incorporated by doing two-ordered feature extraction. The first order feature extraction was done by using Two Dimensional Empirical Wavelet Transforms (2D-EWT) and the second-order feature extraction was done by using Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) for the approximation image of 2D-EWT. Because of the possibility of working on prominent feature, the proposed work uses approximation image than the raw image. The classification was carried out by using Random Forest (RF), Multi- Support Vector Machine (MSVM) and Extreme learning machine (ELM). [ABSTRACT FROM AUTHOR]

The aim of this work is to develop efficient named entity recognition from the given text that in turn improves the performance of the systems that use natural language processing (NLP). The performance of IoT-based devices such as Alexa and Cortana significantly depends upon an efficient NLP model. To increase the capability of the smart IoT devices in comprehending the natural language, named entity recognition (NER) tools play an important role in these devices. In general, the NER is a two-step process that initially the proper nouns are identified from text and then classify them into predefined categories of entities such as person, location, measure, organization and time. NER is often performed as a subtask while processing natural languages which increases the accuracy level of a NLP task. In this paper, we propose deep neural network architecture for named entity recognition for the resource-scarce language Hindi, based on convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM) neural network and conditional random field (CRF). In the proposed approach, initially, we use skip-gram word2vec model and GloVe model to represent words in semantic vectors which are further used in different deep neural network-based architectures. In the proposed approach, we use character- and word-level embedding to represent the text that includes information at fine-grained level. Due to the use of character-level embeddings, the proposed model is robust for the out-of-vocabulary words. Experimental results show that the combination of Bi-LSTM, CNN and CRF algorithms performs better as compared to the other baseline methods such as recurrent neural network, long short-term memory and Bi-LSTM individually. [ABSTRACT FROM AUTHOR]

Crop diseases have always been a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automatic recognition and severity estimation of crop diseases on leaves plays a crucial role in agricultural sector. In this paper, we propose a series of automatic image-based crop leaf diseases recognition and severity estimation networks, i.e., BR-CNNs, which can simultaneously recognize crop species, classify crop diseases and estimate crop diseases severity based on deep learning. BR-CNNs based on binary relevance (BR) multi-label learning algorithm and deep convolutional neural network (CNN) approaches succeed in identifying 7 crop species, 10 crop diseases types (including Healthy) and 3 crop diseases severity kinds (normal, general and serious). Compared with LP-CNNs and MLP-CNNs, the overall performance of BR-CNNs is superior. The BR-CNN based on ResNet50 achieves the best test accuracy of 86.70%, which demonstrates the feasibility and effectiveness of our network. The BR-CNN based on the light-weight NasNet also achieves excellent test accuracy of 85.28%, which can provide more possibilities for the development of mobile systems and devices. [ABSTRACT FROM AUTHOR]

In this paper, we analyze a machine-learning-based non-iterative phase retrieval method. Phase retrieval and its applications have been attractive research topics in optics and photonics, for example, in biomedical imaging, astronomical imaging, and so on. Most conventional phase retrieval methods have used iterative processes to recover phase information; however, the calculation speed and convergence with these methods are serious issues in real-time monitoring applications. Machine-learning-based methods are promising for addressing these issues. Here, we numerically compare conventional methods and a machine-learning-based method in which a convolutional neural network is employed. Simulations with several conditions show that the machine-learning-based method realizes fast and robust phase recovery compared with the conventional methods. We also numerically demonstrate machine-learning-based phase retrieval from noisy measurements with a noisy training data set for improving the noise robustness. The machine-learning-based approach used in this study may increase the impact of phase retrieval, which is useful in various fields, where phase retrieval has been used as a fundamental tool. [ABSTRACT FROM AUTHOR]

In this paper, the ability of Bayesian and convolutional neural networks (CNNs), as two different machine learning methods, to recognize Arabic handwritten words is analyzed. Our contribution is threefold. First, we describe the main highlights of the dynamic Bayesian network (DBN) architecture, especially when compared to standard Bayesian networks. For that, some structural features are extracted from word image and considered as input for different architectures of Bayesian networks (BNs) such as Naïve Bayes (NB), Tree Augmented Naïve Bayes (TAN), Forest Augmented Naïve Bayes (FAN) and Hidden Markov model (HMM). Features are extracted based on the word baseline which has been estimated to mainly cope with the problems of inclination and distortions. Decisions about word classification are then inferred using multiples models of BNs. Second, we model a deep learning architecture: a CNN that convolves learned features with input data and uses 2D convolutional layers that makes it well suited to 2D word image processing. Third, we compare the behavior of DBN–CNN and propose to combine them to exploit their advantages. Experiments are carried on the standard IFN–ENIT database. The obtained results show the relatively high accuracy of the DBN and CNN combination: 95.20% compared to the remaining models. [ABSTRACT FROM AUTHOR]

Automatic modulation classification plays an important role in many fields to identify the modulation type of wireless signals in order to recover signals by demodulation. In this paper, we contribute to explore the suitable architecture of deep learning method in the domain of communication signal recognition. Based on architecture analysis of the convolutional neural network, we used real signal data generated by instrument as dataset, and achieved compatible recognition accuracy of modulation classification compared with several representative structure. We state that the deeper network architecture is not suitable for the signal recognition due to its different characteristic. In addition, we also discuss the difficult of training algorithm in deep learning methods and employ the transfer learning method in order to reap the benefits, which stabilize the training process and lift the performance. Finally, we adopt the denoising autoencoder to preprocess the received data and provide the ability to resist finite perturbations of the input. It contributes to a higher recognition accuracy and it also provide a new idea to design the denoising modulation recognition model. [ABSTRACT FROM AUTHOR]

This paper presents a learning mechanism based on hybridization of static and dynamic learning. Realizing the detection performances offered by the state-of-the-art deep learning techniques and the competitive performances offered by the conventional static learning techniques, we propose the idea of exploitation of the concatenated (parallel) hybridization of the static and dynamic learning-based feature spaces. This is contrary to the cascaded (series) hybridization topology in which the initial feature space (provided by the conventional, static, and handcrafted feature extraction technique) is explored using deep, dynamic, and automated learning technique. Consequently, the characteristics already suppressed by the conventional representation cannot be explored by the dynamic learning technique. Instead, the proposed technique combines the conventional static and deep dynamic representation in concatenated (parallel) topology to generate an information-rich hybrid feature space. Thus, this hybrid feature space may aggregate the good characteristics of both conventional and deep representations, which are then explored using an appropriate classification technique. We also hypothesize that ensemble classification may better exploit this parallel hybrid perspective of the feature spaces. For this purpose, pyramid histogram of oriented gradients-based static learning has been incorporated in conjunction with convolution neural network-based deep learning to produce concatenated hybrid feature space. This hybrid space is then explored with various state-of-the-art ensemble classification techniques. We have considered the publicly available INRIA person and Caltech pedestrian standard image datasets to assess the performance of the proposed hybrid learning system. Furthermore, McNemar's test has been used to statistically validate the outperformance of the proposed technique over various contemporary techniques. The validated experimental results show that the employment of the proposed hybrid representation results in effective detection performance (an AUC of 0.9996 for INRIA person and 0.9985 for Caltech pedestrian datasets) as compared to the individual static and dynamic representations. [ABSTRACT FROM AUTHOR]

In the multi-robot system, how to achieve effective and reasonable task coordination between multi-robots is an important problem;, multi-robot task scheduling is the term used for the coordination of the key technologies. Therefore, in this paper we combined the pilot scheduling method with the following method and the behavior method of the robot based on task scheduling, and we then studied how to improve the traditional robot scheduling effect, which is a deep-learning algorithm that is applied to multi-robot scheduling to formulate an action selection strategy. We thus proved the effectiveness of this idea experimentally. Based on the above research foundation, this paper continues to build a simple simulation experiment platform, which simply sets up three obstacles and completes the task of robot scheduling on the platform. [ABSTRACT FROM AUTHOR]