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Convolutional networks are commonly used in various machine learning tasks, and they are more and more popularly used in the embedded domain with devices such as smart cameras and mobile phones. The operation of convolution can be substituted by point-wise multiplication in the Fourier domain, which can save operation, but usually, it is applied with a Fourier transform before and an inverse Fourier transform after the multiplication, since other operations in neural networks cannot be implemented efficiently in the Fourier domain. In this paper, we will present a method for implementing neural network completely in the Fourier domain, and by this, saving multiplications and the operations of inverse Fourier transformations. Our method can decrease the number of operations by four times the number of pixels in the convolutional kernel with only a minor decrease in accuracy, for example, 4% on the MNIST and 2% on the HADB datasets. [ABSTRACT FROM AUTHOR]

Road segmentation is one of the essential tasks in remote sensing. Large-scale high-resolution remote sensing images originally have larger pixel sizes than natural images, while the existing models based on Transformer have the high computational cost of square complexity, leading to more extended model training and inference time. Inspired by the long text Transformer model, this paper proposes a novel hybrid attention mechanism to improve the inference speed of the model. By calculating several diagonals and random blocks of the attention matrix, hybrid attention achieves linear time complexity in the token sequence. Using the superposition of adjacent and random attention, hybrid attention introduces the inductive bias similar to convolutional neural networks (CNNs) and retains the ability to acquire long-distance dependence. In addition, the dense road segmentation result of remote sensing image still has the problem of insufficient continuity. However, multiscale feature representation is an effective means in the network based on CNNs. Inspired by this, we propose a multi-scale patch embedding module, which divides images by patches with different scales to obtain coarse-to-fine feature representations. Experiments on the Massachusetts dataset show that the proposed HA-RoadFormer could effectively preserve the integrity of the road segmentation results, achieving a higher Intersection over Union (IoU) 67.36% of road segmentation compared to other state-of-the-art (SOTA) methods. At the same time, the inference speed has also been greatly improved compared with other Transformer based models. [ABSTRACT FROM AUTHOR]

Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of the artificial intelligence model to the values of the features in the dataset to increase the studied model's adequacy. This paper focuses on the problem of effectively preprocessing data to improve the accuracy of intellectual analysis in the case of performing medical diagnostic tasks. We developed a new two-step method for data normalization of numerical medical datasets. It is based on the possibility of considering both the interdependencies between the features of each observation from the dataset and their absolute values to improve the accuracy when performing medical data mining tasks. We describe and substantiate each step of the algorithmic implementation of the method. We also visualize the results of the proposed method. The proposed method was modeled using six different machine learning methods based on decision trees when performing binary and multiclass classification tasks. We used six real-world, freely available medical datasets with different numbers of vectors, attributes, and classes to conduct experiments. A comparison between the effectiveness of the developed method and that of five existing data normalization methods was carried out. It was experimentally established that the developed method increases the accuracy of the Decision Tree and Extra Trees Classifier by 1–5% in the case of performing the binary classification task and the accuracy of the Bagging, Decision Tree, and Extra Trees Classifier by 1–6% in the case of performing the multiclass classification task. Increasing the accuracy of these classifiers only by using the new data normalization method satisfies all the prerequisites for its application in practice when performing various medical data mining tasks. [ABSTRACT FROM AUTHOR]

The goal of the recommender system is to learn the user's preferences from the entity (user–item) historical interaction data, so as to predict the user's ratings on new items or recommend new item sequences to users. There are two major challenges: (1) Datasets are usually sparse. The item side is often accompanied by some auxiliary information, such as attributes or context; it can help to slightly improve its representation. However, the user side is usually presented in the form of ID due to personal privacy. (2) Due to the influences of confounding factors, such as the popularity of items, users' ratings on items often have bias that cannot be recognized by the traditional recommendation methods. In order to solve these two problems, in this paper, (1) we explore the use of a graph model to fuse the interactions between users and common rating items, that is, incorporating the "neighbor" information into the target user to enrich user representations; (2) the d o () operator is used to deduce the causality after removing the influences of confounding factors, rather than the correlation of the data surface fitted by traditional machine learning. We propose the EGCI model, i.e., enhanced graph learning for recommendation via causal inference. The model embeds user relationships and item attributes into the latent semantic space to obtain high-quality user and item representations. In addition, the mixed bias implied in the rating process is calibrated by considering the popularity of items. Experimental results on three real-world datasets show that EGCI is significantly better than the baselines. [ABSTRACT FROM AUTHOR]

Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness. [ABSTRACT FROM AUTHOR]

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a prominent role in the decision-making process of autonomous driving (AD), which enables autonomous vehicles (AVs) to learn an optimal driving strategy through continuous interaction with the environment. This paper proposes a deep reinforcement learning (DRL)-based motion planning strategy for AD tasks in the highway scenarios where an AV merges into two-lane road traffic flow and realizes the lane changing (LC) maneuvers. We integrate the DRL model into the AD system relying on the end-to-end learning method. An improved DRL algorithm based on deep deterministic policy gradient (DDPG) is developed with well-defined reward functions. In particular, safety rules (SR), safety prediction (SP) module and trauma memory (TM) as well as the dynamic potential-based reward shaping (DPBRS) function are adopted to further enhance safety and accelerate learning of the LC behavior. For validation, the proposed DSSTD algorithm is trained and tested on the dual-computer co-simulation platform. The comparative experimental results show that our proposal outperforms other benchmark algorithms in both driving safety and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new line search technique, then employ it to construct a novel accelerated forward–backward algorithm for solving convex minimization problems of the form of the summation of two convex functions in which one of these functions is smooth in a real Hilbert space. We establish a weak convergence to a solution of the proposed algorithm without the Lipschitz assumption on the gradient of the objective function. Furthermore, we analyze its performance by applying the proposed algorithm to solving classification problems on various data sets and compare with other line search algorithms. Based on the experiments, the proposed algorithm performs better than other line search algorithms. [ABSTRACT FROM AUTHOR]

The modern technology universities have the necessary resource and material base for developing and transferring R&D products. However, the cost estimation process is not formalized. There are many methods of estimating the cost of R&D products' commercialization processes. However, in some cases, we cannot consider any single technique to be the best one as each of them has advantages and disadvantages. In such conditions, all efforts should be made to use a combination of the estimation techniques to arrive at a better cost and quality estimate. The effectiveness of the valuation of R&D products is of particular importance in today's economy and due to the need to analyze large data sets prepared for transfer from universities to the business environment. This paper presents the model, two methods, and general information technology for R&D products' readiness level assessment and R&D products' cost estimation. The article presents the complex method for determining the cost of R&D products, which will allow: increasing the efficiency of the transfer, commercialization, and market launch of R&D products, and promoting the interaction of all components of the national innovation infrastructure, innovations, etc. The need to consider many different indicators when evaluating R&D products has determined the need to use machine learning algorithms. We have designed a new machine learning-based model for the readiness assessment of R&D products, which is based on the principle of "crowd wisdom" and uses a stacking strategy to integrate machine learning methods. It is experimentally established that the new stacking model based on machine learning algorithms that use random forest as a meta-algorithm provides a minimum of a 1.03 times higher RMSE compared to other ensemble strategies. [ABSTRACT FROM AUTHOR]

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support. [ABSTRACT FROM AUTHOR]

In the paper by Jovanovic et al. [[11]], the authors propose a hybrid ML and swarm intelligence approach to address credit card fraud detection. 10.3390/math10081232 11 Jovanovic D., Antonijevic M., Stankovic M., Zivkovic M., Tanaskovic M., Bacanin N. Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection. Swarm and evolutionary computation (SEC) [[1]] is a broad and growing area of modern computer sciences, dealing with nature-inspired systems that are capable of displaying intelligent behaviour, thus optimising a vast range of challenging real-world scenarios that cannot be addressed via the direct application of purely theoretical exact approaches (e.g., [[2]]). [Extracted from the article]

This paper presents a machine learning model to predict the effect of Al2O3 nanoparticles content on the wear rates in Cu-Al2O3 nanocomposite prepared using in situ chemical technique. The model developed is a modification of the random vector functional link (RVFL) algorithm using artificial hummingbird algorithm (AHA). The objective of using AHA is used to find the optimal configuration of RVFL to enhance the prediction of Al2O3 nanoparticles. The preparation of the composite was done using aluminum nitrate that was added to a solution containing scattered copper nitrate. After that, the powders of CuO and Al2O3 were obtained, and the leftover liquid was removed using a thermal treatment at 850 °C for 1 h. The powders were consolidated using compaction and sintering processes. The microhardness of the nanocomposite with 12.5% Al2O3 content is 2.03-fold times larger than the pure copper, while the wear rate of the same composite is reduced, reaching 55% lower than pure copper. These improved properties are attributed to the presence of Al2O3 nanoparticles and their homogenized distributions inside the matrix. The developed RVFl-AHA model was able to predict the wear rates of all the prepared composites at different wear load and speed, with very good accuracy, reaching nearly 100% and 99.5% using training and testing, respectively, in terms of coefficient of determination R2. [ABSTRACT FROM AUTHOR]

This survey starts with a general overview of the strategies for stock price change predictions based on market data and in particular Limit Order Book (LOB) data. The main discussion is devoted to the systematic analysis, comparison, and critical evaluation of the state-of-the-art studies in the research area of stock price movement predictions based on LOB data. LOB and Order Flow data are two of the most valuable information sources available to traders on the stock markets. Academic researchers are actively exploring the application of different quantitative methods and algorithms for this type of data to predict stock price movements. With the advancements in machine learning and subsequently in deep learning, the complexity and computational intensity of these models was growing, as well as the claimed predictive power. Some researchers claim accuracy of stock price movement prediction well in excess of 80%. These models are now commonly employed by automated market-making programs to set bids and ask quotes. If these results were also applicable to arbitrage trading strategies, then those algorithms could make a fortune for their developers. Thus, the open question is whether these results could be used to generate buy and sell signals that could be exploited with active trading. Therefore, this survey paper is intended to answer this question by reviewing these results and scrutinising their reliability. The ultimate conclusion from this analysis is that although considerable progress was achieved in this direction, even the state-of-art models can not guarantee a consistent profit in active trading. Taking this into account several suggestions for future research in this area were formulated along the three dimensions: input data, model's architecture, and experimental setup. In particular, from the input data perspective, it is critical that the dataset is properly processed, up-to-date, and its size is sufficient for the particular model training. From the model architecture perspective, even though deep learning models are demonstrating a stronger performance than classical models, they are also more prone to over-fitting. To avoid over-fitting it is suggested to optimize the feature space, as well as a number of layers and neurons, and apply dropout functionality. The over-fitting problem can be also addressed by optimising the experimental setup in several ways: Introducing the early stopping mechanism; Saving the best weights of the model achieved during the training; Testing the model on the out-of-sample data, which should be separated from the validation and training samples. Finally, it is suggested to always conduct the trading simulation under realistic market conditions considering transactions costs, bid–ask spreads, and market impact. [ABSTRACT FROM AUTHOR]

Algorithmic trading is a common topic researched in the neural network due to the abundance of data available. It is a phenomenon where an approximately linear relationship exists between two or more independent variables. It is especially prevalent in financial data due to the interrelated nature of the data. The existing feature selection methods are not efficient enough in solving such a problem due to the potential loss of essential and relevant information. These methods are also not able to consider the interaction between features. Therefore, we proposed two improvements to apply to the Long Short-Term Memory neural network (LSTM) in this study. It is the Multicollinearity Reduction Module (MRM) based on correlation-embedded attention to mitigate multicollinearity without removing features. The motivation of the improvements is to allow the model to predict using the relevance and redundancy within the data. The first contribution of the paper is allowing a neural network to mitigate the effects of multicollinearity without removing any variables. The second contribution is improving trading returns when our proposed mechanisms are applied to an LSTM. This study compared the classification performance between LSTM models with and without the correlation-embedded attention module. The experimental result reveals that a neural network that can learn the relevance and redundancy of the financial data to improve the desired classification performance. Furthermore, the trading returns of our proposed module are 46.82% higher without sacrificing training time. Moreover, the MRM is designed to be a standalone module and is interoperable with existing models. [ABSTRACT FROM AUTHOR]

Obtaining a dynamic model of the robotic manipulator is a complex task. With the growing application of machine learning (ML) approaches in modern robotics, a question arises of using ML for dynamic modeling. Still, due to the large amounts of data necessary for this approach, data collection may be time and resource-intensive. For this reason, this paper aims to research the possibility of synthetic dataset creation by using pre-existing dynamic models to test the possibilities of both applications of such synthetic datasets, as well as modeling the dynamics of an industrial manipulator using ML. Authors generate the dataset consisting of 20,000 data points and train seven separate multilayer perceptron (MLP) artificial neural networks (ANN)—one for each joint of the manipulator and one for the total torque—using randomized search (RS) for hyperparameter tuning. Additional MLP is trained for the total torsion of the entire manipulator using the same approach. Each model is evaluated using the coefficient of determination ( R 2 ) and mean absolute percentage error (MAPE), with 10-fold cross-validation applied. With these settings, all individual joint torque models achieved R 2 scores higher than 0.9, with the models for first four joints achieving scores above 0.95. Furthermore, all models for all individual joints achieve MAPE lower than 2%. The model for the total torque of all joints of the robotic manipulator achieves weaker regression scores, with the R 2 score of 0.89 and MAPE slightly higher than 2%. The results show that the torsion models of each individual joint, and of the entire manipulator, can be regressed using the described method, with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Offshore wind energy is increasingly being realized at deeper ocean depths where jacket foundations are now the greatest choice for dealing with the hostile environment. The structural stability of these undersea constructions is critical. This paper states a methodology to detect and classify damage in a jacket-type support structure for offshore wind turbines. Because of the existence of unknown external disturbances (wind and waves), standard structural health monitoring technologies, such as guided waves, cannot be used directly in this application. Therefore, using vibration-response-only accelerometer measurements, a methodology based on two in-cascade Siamese convolutional neural networks is proposed. The first Siamese network detects the damage (discerns whether the structure is healthy or damaged). Then, in case damage is detected, a second Siamese network determines the damage diagnosis (classifies the type of damage). The main results and claims of the proposed methodology are the following ones: (i) It is solely dependent on accelerometer sensor output vibration data, (ii) it detects damage and classifies the type of damage, (iii) it operates in all wind turbine regions of operation, (iv) it requires less data to train since it is built on Siamese convolutional neural networks, which can learn from very little data compared to standard machine/deep learning algorithms, (v) it is validated in a scaled-down experimental laboratory setup, and (vi) its feasibility is demonstrated as all computed metrics (accuracy, precision, recall, and F1 score) for the obtained results remain above 96%. [ABSTRACT FROM AUTHOR]

The telecommunications industry is greatly concerned about customer churn due to dissatisfaction with service. This industry has started investing in the development of machine learning (ML) models for churn prediction to extract, examine and visualize their customers' historical information from a vast amount of big data which will assist to further understand customer needs and take appropriate actions to control customer churn. However, the high-dimensionality of the data has a large influence on the performance of the ML model, so feature selection (FS) has been applied since it is a primary preprocessing step. It improves the ML model's performance by selecting salient features while reducing the computational time, which can assist this sector in building effective prediction models. This paper proposes a new FS approach ACO-RSA, that combines two metaheuristic algorithms (MAs), namely, ant colony optimization (ACO) and reptile search algorithm (RSA). In the developed ACO-RSA approach, an ACO and RSA are integrated to choose an important subset of features for churn prediction. The ACO-RSA approach is evaluated on seven open-source customer churn prediction datasets, ten CEC 2019 test functions, and its performance is compared to particle swarm optimization (PSO), multi verse optimizer (MVO) and grey wolf optimizer (GWO), standard ACO and standard RSA. According to the results along with statistical analysis, ACO-RSA is an effective and superior approach compared to other competitor algorithms on most datasets. [ABSTRACT FROM AUTHOR]

The importance of statistical methods in finding patterns and trends in otherwise unstructured and complex large sets of data has grown over the past decade, as the amount of data produced keeps growing exponentially and knowledge obtained from understanding data allows to make quick and informed decisions that save time and provide a competitive advantage. For this reason, we have seen considerable advances over the past few years in statistical methods in data mining. This paper is a comprehensive and systematic review of these recent developments in the area of data mining. [ABSTRACT FROM AUTHOR]

Text datasets come in an abundance of shapes, sizes and styles. However, determining what factors limit classification accuracy remains a difficult task which is still the subject of intensive research. Using a challenging UK National Health Service (NHS) dataset, which contains many characteristics known to increase the complexity of classification, we propose an innovative classification pipeline. This pipeline switches between different text pre-processing, scoring and classification techniques during execution. Using this flexible pipeline, a high level of accuracy has been achieved in the classification of a range of datasets, attaining a micro-averaged F1 score of 93.30% on the Reuters-21578 "ApteMod" corpus. An evaluation of this flexible pipeline was carried out using a variety of complex datasets compared against an unsupervised clustering approach. The paper describes how classification accuracy is impacted by an unbalanced category distribution, the rare use of generic terms and the subjective nature of manual human classification. [ABSTRACT FROM AUTHOR]

Non-stationary signals are often analyzed using raw waveform data or spectrograms of those data; however, the possibility of alternative time–frequency representations being more informative than the original data or spectrograms is yet to be investigated. This paper tested whether alternative time–frequency representations could be more informative for machine learning classification of seismological data. The mentioned hypothesis was evaluated by training three well-established convolutional neural networks using nine time–frequency representations. The results were compared to the base model, which was trained on the raw waveform data. The signals that were used in the experiment are three-component seismogram instances from the Local Earthquakes and Noise DataBase (LEN-DB). The results demonstrate that Pseudo Wigner–Ville and Wigner–Ville time–frequency representations yield significantly better results than the base model, while spectrogram and Margenau–Hill perform significantly worse (p < 0.01). Interestingly, the spectrogram, which is often used in signal analysis, had inferior performance when compared to the base model. The findings presented in this research could have notable impacts in the fields of geophysics and seismology as the phenomena that were previously hidden in the seismic noise are now more easily identified. Furthermore, the results indicate that applying Pseudo Wigner–Ville or Wigner–Ville time–frequency representations could result in a large increase in earthquakes in the catalogs and lessen the need to add new stations with an overall reduction in the costs. Finally, the proposed approach of extracting valuable information through time–frequency representations could be applied in other domains as well, such as electroencephalogram and electrocardiogram signal analysis, speech recognition, gravitational waves investigation, and so on. [ABSTRACT FROM AUTHOR]

Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver. [ABSTRACT FROM AUTHOR]

The application of statistical methods to monitor a process is critical to ensure its stability. Statistical process control aims to detect and identify abnormal patterns that disrupt the natural behaviour of a process. Most studies in the literature are focused on recognising single abnormal patterns. However, in many industrial processes, more than one unusual control chart pattern may appear simultaneously, i.e., concurrent control chart patterns (CCP). Therefore, this paper aims to present a classification framework based on categories to systematically organise and analyse the existing literature regarding concurrent CCP recognition to provide a concise summary of the developments performed so far and a helpful guide for future research. The search only included journal articles and proceedings in the area. The literature search was conducted using Web of Science and Scopus databases. As a result, 41 studies were considered for the proposed classification scheme. It consists of categories designed to assure an in-depth analysis of the most relevant topics in this research area. Results concluded a lack of research in this research field. The main findings include the use of machine learning methods; the study of non-normally distributed processes; and the consideration of abnormal patterns different from the shift, trend, and cycle behaviours. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (convexity and free disposability). Unfortunately, by construction, DEA is a descriptive methodology that is not concerned about preventing overfitting. In this paper, we introduce a new methodology that allows for estimating polyhedral technologies following the Structural Risk Minimization (SRM) principle. This technique is called Data Envelopment Analysis-based Machines (DEAM). Given that the new method controls the generalization error of the model, the corresponding estimate of the technology does not suffer from overfitting. Moreover, the notion of ε -insensitivity is also introduced, generating a new and more robust definition of technical efficiency. Additionally, we show that DEAM can be seen as a machine learning-type extension of DEA, satisfying the same microeconomic postulates except for minimal extrapolation. Finally, the performance of DEAM is evaluated through simulations. We conclude that the frontier estimator derived from DEAM is better than that associated with DEA. The bias and mean squared error obtained for DEAM are smaller in all the scenarios analyzed, regardless of the number of variables and DMUs. [ABSTRACT FROM AUTHOR]

The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state. [ABSTRACT FROM AUTHOR]

Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods. [ABSTRACT FROM AUTHOR]

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1 / t 1 − 1 / γ scaling law, where γ > 0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values. [ABSTRACT FROM AUTHOR]

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person's behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions. [ABSTRACT FROM AUTHOR]

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach. [ABSTRACT FROM AUTHOR]

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature. [ABSTRACT FROM AUTHOR]

This paper studies the economically significant problem of the optimization of customs fraud control, which is a critical issue for many countries. The European Union (EU) alone handles 4693 tons of goods every minute (2018 figures). Even though 70% of goods are imported at zero tariff, the EU raised EUR 25.4 billions in 2018, and customs-related income transferred by member states to the EU accounts for nearly 13% of its overall budget. In this field, (a) the conflicting objectives are qualitative and cannot be reduced to a common measure (security and terrorism, health, drug market access control, taxes, etc.); (b) each submitted item has dozens of characteristics; (c) there are constraints; and (d) risk analysis systems have to make decisions in real time. Although the World Customs Organization has promoted the use of artificial intelligence to increase the precision of controls, the problem is very complex due to the data characteristics and interpretability, which is a requirement established by customs officers. In this paper, we propose a new Bayesian-based hybrid approach combining machine learning and multi-objective linear programming (MOLP), called multi-objective Bayesian with dynamic optimization (MOBADO). We demonstrate that it is possible to more than double (with a 237% increase) the precision of current inspection systems, freeing up almost 50% of human resources, and outperform past results with respect to each of the above objectives. MOBADO is an optimization technique that could be combined with any artificial intelligence approach capable of optimizing the quality of multi-objective risk analysis in real time. [ABSTRACT FROM AUTHOR]

This paper provides several error estimations for total variation (TV) type regularization, which arises in a series of areas, for instance, signal and imaging processing, machine learning, etc. In this paper, some basic properties of the minimizer for the TV regularization problem such as stability, consistency and convergence rate are fully investigated. Both a priori and a posteriori rules are considered in this paper. Furthermore, an improved convergence rate is given based on the sparsity assumption. The problem under the condition of non-sparsity, which is common in practice, is also discussed; the results of the corresponding convergence rate are also presented under certain mild conditions. [ABSTRACT FROM AUTHOR]

Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and exploitation are usually controlled offline by proper settings of parameters that affect the population-based algorithm performance. In this paper, we proposed a dynamic controller for one of the most well-known search algorithms, which is the Genetic Algorithm (GA). Population Diversity Controller-GA (PDC-GA) is proposed as a novel feature-selection algorithm to reduce the search space while building a machine-learning classifier. The PDC-GA is proposed by combining GA with k-mean clustering to control population diversity through the exploration process. An injection method is proposed to redistribute the population once 90% of the solutions are located in one cluster. A real case study of a bankruptcy problem obtained from UCI Machine Learning Repository is used in this paper as a binary classification problem. The obtained results show the ability of the proposed approach to enhance the performance of the machine learning classifiers in the range of 1 % to 4 % . [ABSTRACT FROM AUTHOR]

Complex systems are usually affected by various sources of uncertainty, and it is essential to account for mechanisms that ensure the proper management of such disturbances. This paper introduces a novel approach to solve symbolic regression problems, which combines the potential of Grammatical Evolution to obtain solutions by describing the search space with context-free grammars, and the ability of Modal Interval Analysis (MIA) to handle quantified uncertainty. The presented methodology uses an MIA solver to evaluate the fitness function, which represents a novel method to manage uncertainty by means of interval-based prediction models. This paper first introduces the theory that establishes the basis of the proposed methodology, and follows with a description of the system architecture and implementation details. Then, we present an illustrative application example which consists of determining the outer and inner approximations of the mean velocity of the water current of a river stretch. Finally, the interpretation of the obtained results and the limitations of the proposed methodology are discussed. [ABSTRACT FROM AUTHOR]

The paper is devoted to an emerging trend in control—a machine learning control. Despite the popularity of the idea of machine learning, there are various interpretations of this concept, and there is an urgent need for its strict mathematical formalization. An attempt to formalize the concept of machine learning is presented in this paper. The concepts of an unknown function, work area, training set are introduced, and a mathematical formulation of the machine learning problem is presented. Based on the presented formulation, the concept of machine learning control is considered. One of the problems of machine learning control is the general synthesis of control. It implies finding a control function that depends on the state of the object, which ensures the achievement of the control goal with the optimal value of the quality criterion from any initial state of some admissible region. Supervised and unsupervised approaches to solving a problem based on symbolic regression methods are considered. As a computational example, a problem of general synthesis of optimal control for a spacecraft landing on the surface of the Moon is considered as supervised machine learning control with a training set. [ABSTRACT FROM AUTHOR]

In the paper, visual navigation of a drone is considered. The drone navigation problem consists of two parts. The first part is finding the real position and orientation of the drone. The second part is finding the difference between desirable and real position and orientation of the drone and creation of the correspondent control signal for decreasing the difference. For the first part of the drone navigation problem, the paper presents a method for determining the coordinates of the drone camera with respect to known three-dimensional (3D) ground objects using deep learning. The algorithm has two stages. It causes the algorithm to be easy for interpretation by artificial neural network (ANN) and consequently increases its accuracy. At the first stage, we use the first ANN to find coordinates of the object origin projection. At the second stage, we use the second ANN to find the drone camera position and orientation. The algorithm has high accuracy (these errors were found for the validation set of images as differences between positions and orientations, obtained from a pretrained artificial neural network, and known positions and orientations), it is not sensitive to interference associated with changes in lighting, the appearance of external moving objects and the other phenomena where other methods of visual navigation are not effective. For the second part of the drone navigation problem, the paper presents a method for stabilization of drone flight controlled by autopilot with time delay. Indeed, image processing for navigation demands a lot of time and results in a time delay. However, the proposed method allows to get stable control in the presence of this time delay. [ABSTRACT FROM AUTHOR]

Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances' energy consumption prediction. [ABSTRACT FROM AUTHOR]

In recent years, the breakthrough of neural networks and the rise of deep learning have led to the advancement of machine vision, which has been commonly used in the practical application of image recognition. Automobiles, drones, portable devices, behavior recognition, indoor positioning and many other industries also rely on the integrated application, and require the support of deep learning and machine vision. As for these technologies, there is a high demand for the accuracy related to the recognition of portraits or objects. The recognition of human figures is also a research goal that has drawn great attention in various fields. However, the portrait will be affected by various factors such as height, weight, posture, angle and whether it is covered or not, which affects the accuracy of recognition. This paper applies the application of deep learning to portraits with different poses and angles, especially the actual distance of a single lens for the shadowed portrait (depth estimation), so that it can be used for automatic control of drones in the future. Traditional methods for calculating depth using images are mainly divided into three types: one—single-lens estimation, two—lens estimation, and three—optical band estimation. In view of the fact that both the second and third categories require relatively large and expensive equipment to effectively perform distance calculations, numerous methods for calculating distance using a single lens have recently been produced. However, whether it is the use of traditional "units of distance measurement calibration", "defocus distance measurement", or the "three-dimensional grid space messages distance measurement method", all of these face corresponding difficulties and problems. Additionally, they have to deal with outside disturbances and process the shadowed image. Therefore, under the new research method, OpenPose, which is proposed by Carnegie Mellon University, this paper intends to propose a depth algorithm for a single-lens occluded portrait to estimate the actual portrait distance for different poses, angles of view and obscuration. [ABSTRACT FROM AUTHOR]

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%. [ABSTRACT FROM AUTHOR]

The quality of machine learning models can suffer when inappropriate data is used, which is especially prevalent in high-dimensional and imbalanced data sets. Data preparation and preprocessing can mitigate some problems and can thus result in better models. The use of meta-heuristic and nature-inspired methods for data preprocessing has become common, but these approaches are still not readily available to practitioners with a simple and extendable application programming interface (API). In this paper the EvoPreprocess open-source Python framework, that preprocesses data with the use of evolutionary and nature-inspired optimization algorithms, is presented. The main problems addressed by the framework are data sampling (simultaneous over- and under-sampling data instances), feature selection and data weighting for supervised machine learning problems. EvoPreprocess framework provides a simple object-oriented and parallelized API of the preprocessing tasks and can be used with scikit-learn and imbalanced-learn Python machine learning libraries. The framework uses self-adaptive well-known nature-inspired meta-heuristic algorithms and can easily be extended with custom optimization and evaluation strategies. The paper presents the architecture of the framework, its use, experiment results and comparison to other common preprocessing approaches. [ABSTRACT FROM AUTHOR]

This paper studies the problem of efficiently tuning the hyper-parameters in penalised least-squares reconstruction for XCT. Discovered through the lens of the Compressed Sensing paradigm, penalisation functionals such as Total Variation types of norms, form an essential tool for enforcing structure in inverse problems, a key feature in the case where the number of projections is small as compared to the size of the object to recover. In this paper, we propose a novel hyper-parameter selection approach for total variation (TV)-based reconstruction algorithms, based on a boosting type machine learning procedure initially proposed by Freund and Shapire and called Hedge. The proposed approach is able to select a set of hyper-parameters producing better reconstruction than the traditional Cross-Validation approach, with reduced computational effort. Traditional reconstruction methods based on penalisation can be made more efficient using boosting type methods from machine learning. [ABSTRACT FROM AUTHOR]

The parallel processing of the service function chain (SFC) is expected to provide better low-delay service delivery, because it breaks through the bottleneck of traditional serial processing mode in which service delay increases linearly with the SFC length. However, the provision of parallelized SFC (PSFC) is much more difficult due to the unique construction of PSFCs, inevitable parallelization overhead, and delay balancing requirement of PSFC branches; therefore, existing mechanisms for serial SFC cannot be directly applied to PSFC. After a comprehensive review of recent related work, we find that traffic scheduling mechanisms for PSFCs is still lacking. In this paper, a delay-aware traffic scheduling mechanism (DASM) for PSFCs is proposed. DASM first transforms PSFC into several serial SFCs by releasing the upstream VNF constraints so as to handle them independently while keeping their parallel relations. Secondly, DASM realizes delay-aware PSFC traffic scheduling based on the reinforcement learning (RL) method. To the best knowledge of the authors, this is the first attempt to address the PSFC traffic scheduling problem by transforming them into independent serial SFCs. Simulation results show that the proposed DASM outperforms the advanced PSFCs scheduling strategies in terms of delay balance and throughput. [ABSTRACT FROM AUTHOR]

Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors. The purpose of network representation learning is to learn the structural relationships between network vertices. Knowledge representation learning is oriented to model the entities and relationships in knowledge bases. In this paper, we first introduce the idea of knowledge representation learning into network representation learning, namely, we propose a new approach to model the vertex triplet relationships based on DeepWalk without TransE. Consequently, we propose an optimized network representation learning algorithm using multi-relational data, MRNR, which introduces the multi-relational data between vertices into the procedures of network representation learning. Importantly, we adopted a kind of higher order transformation strategy to optimize the learnt network representation vectors. The purpose of MRNR is that multi-relational data (triplets) can effectively guide and constrain the procedures of network representation learning. The experimental results demonstrate that the proposed MRNR can learn the discriminative network representations, which show better performance on network classification, visualization, and case study tasks compared to the proposed baseline algorithms in this paper. [ABSTRACT FROM AUTHOR]

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. A spiking neural network (SNN) is one of the research directions of brain-like computing, characterized by better biogenesis and stronger computing power than the traditional neural network. A liquid state machine (LSM) is a neural computing model with a recurrent network structure based on SNN. In this paper, a learning algorithm based on an evolutionary membrane algorithm is proposed to optimize the neural structure and hyperparameters of an LSM. First, the object of the proposed algorithm is designed according to the neural structure and hyperparameters of the LSM. Second, the reaction rules of the proposed algorithm are employed to discover the best neural structure and hyperparameters of the LSM. Third, the membrane structure is that the skin membrane contains several elementary membranes to speed up the search of the proposed algorithm. In the simulation experiment, effectiveness verification is carried out on the MNIST and KTH datasets. In terms of the MNIST datasets, the best test results of the proposed algorithm with 500, 1000 and 2000 spiking neurons are 86.8%, 90.6% and 90.8%, respectively. The best test results of the proposed algorithm on KTH with 500, 1000 and 2000 spiking neurons are 82.9%, 85.3% and 86.3%, respectively. The simulation results show that the proposed algorithm has a more competitive advantage than other experimental algorithms. [ABSTRACT FROM AUTHOR]

The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences. [ABSTRACT FROM AUTHOR]

Deep learning-based models have shown to be one of the main active research topics in emotion recognition systems from Electroencephalogram (EEG) signals. However, a significant challenge is to effectively recognize new emotions that are incorporated sequentially, as current models must perform retraining from scratch. In this paper, we propose a Class-Incremental Learning (CIL) method, named Incremental Learning preserving the Learned Feature Space (IL2FS), in order to enable deep learning models to incorporate new emotions (classes) into the already known. IL2FS performs a weight aligning to correct the bias on new classes, while it incorporates margin ranking loss and triplet loss to preserve the inter-class separation and feature space alignment on known classes. We evaluated IL2FS over two public datasets (DREAMER and DEAP) for emotion recognition and compared it with other recent and popular CIL methods reported in computer vision. Experimental results show that IL2FS outperforms other CIL methods by obtaining an average accuracy of 59.08 ± 08.26% and 79.36 ± 04.68% on DREAMER and DEAP, recognizing data from new emotions that are incorporated sequentially. [ABSTRACT FROM AUTHOR]

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream data mining of networks, such as node classification and graph clustering. Existing algorithms commonly ignore the global topological information of the network in network representation learning, leading to information loss. The complete subgraph in the network commonly has a community structure, or it is the component module of the community structure. We believe that the structure of the community serves as the revealed structure in the topology of the network and preserves global information. In this paper, we propose SF-NRL, a network representation learning algorithm based on complete subgraph folding. The algorithm preserves the global topological information of the original network completely, by finding complete subgraphs in the original network and folding them into the super nodes. We employ the network representation learning algorithm to study the node embeddings on the folded network, and then merge the embeddings of the folded network with those of the original network to obtain the final node embeddings. Experiments performed on four real-world networks prove the effectiveness of the SF-NRL algorithm. The proposed algorithm outperforms the baselines in evaluation metrics on community detection and multi-label classification tasks. The proposed algorithm can effectively generalize the global information of the network and provides excellent classification performance. [ABSTRACT FROM AUTHOR]

Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model's confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic. [ABSTRACT FROM AUTHOR]

This paper aims to develop a new training strategy to improve efficiency in estimation of weights and biases in a feedforward neural network (FNN). We propose a local linear approximation (LLA) algorithm, which approximates ReLU with a linear function at the neuron level and estimate the weights and biases of one-hidden-layer neural network iteratively. We further propose the layer-wise optimized adaptive neural network (LOAN), in which we use the LLA to estimate the weights and biases in the LOAN layer by layer adaptively. We compare the performance of the LLA with the commonly-used procedures in machine learning based on seven benchmark data sets. The numerical comparison implies that the proposed algorithm may outperform the existing procedures in terms of both training time and prediction accuracy. [ABSTRACT FROM AUTHOR]

An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created a global emergency in many sectors. The spread of the disease can be subdued through timely vaccination. The COVID-19 vaccination process in various countries is ongoing and is slowing down due to multiple factors. Many studies on European countries and the USA have been conducted and have highlighted the public's concern that over-vaccination results in slowing the vaccination rate. Similarly, we analyzed a collection of data from the gulf countries' citizens' COVID-19 vaccine-related discourse shared on social media websites, mainly via Twitter. The people's feedback regarding different types of vaccines needs to be considered to increase the vaccination process. In this paper, the concerns of Gulf countries' people are highlighted to lessen the vaccine hesitancy. The proposed approach emphasizes the Gulf region-specific concerns related to COVID-19 vaccination accurately using machine learning (ML)-based methods. The collected data were filtered and tokenized to analyze the sentiments extracted using three different methods: Ratio, TextBlob, and VADER methods. The sentiment-scored data were classified into positive and negative tweeted data using a proposed LSTM method. Subsequently, to obtain more confidence in classification, the in-depth features from the proposed LSTM were extracted and given to four different ML classifiers. The ratio, TextBlob, and VADER sentiment scores were separately provided to LSTM and four machine learning classifiers. The VADER sentiment scores had the best classification results using fine-KNN and Ensemble boost with 94.01% classification accuracy. Given the improved accuracy, the proposed scheme is robust and confident in classifying and determining sentiments in Twitter discourse. [ABSTRACT FROM AUTHOR]

In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used to train and test the model, and existing optimization benchmarks only provide a limited amount of data. To help with this problem, artificial problem generation has been shown to be a useful tool for augmenting existing benchmark problems. In this paper, we are interested in the problem of knowledge transfer between the artificially generated and existing handmade benchmark problems in the domain of continuous numerical optimization. That is, can an algorithm selection model trained purely on artificially generated problems correctly provide algorithm recommendations for existing handmade problems. We show that such a model produces low-quality results, and we also provide explanations about how the algorithm selection model works and show the differences between the problem data sets in order to explain the model's performance. [ABSTRACT FROM AUTHOR]