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This correction notice provides updates and corrections to a research paper on deep machine learning model-based cyber-attacks detection in smart power systems. The paper proposes a deep learning-based attack detection model that addresses system disturbances caused by natural events and cyber-attacks. It discusses the use of principal component analysis (PCA) for feature selection and the potential of deep learning-based and decision tree classifiers for detecting cyber-attacks in intelligent energy grids. The notice also includes updates to references, clarifications on the experimental data set, and feature selection based on PCA. The study evaluates the effectiveness of the proposed model using various assessment indexes and compares it to conventional methods, concluding that the proposed model is effective in detecting cyber-attacks and demonstrates good performance in detecting destructive attacks. [Extracted from the article]

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]

The COVID-19 pandemic highlighted the importance of health and education and also revealed the need for innovative solutions relative to the challenges confronting these disciplines. This includes the development of interactive learning tools and intelligent tutoring systems that can provide feedback to both teachers and students, as well as the use of data analytics to assess student progress and adjust teaching strategies accordingly. In healthcare, machine learning algorithms have been used to predict the likelihood of disease progression, identify at-risk patients, and personalize treatment plans. Thus, in [[19]], both machine learning models such as support vector machines, decision trees, and neural networks, together with logistic regression, were used to predict likely university dropout rates either at the beginning of the course of study or at the end of the first semester. [Extracted from the article]

With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity. [ABSTRACT FROM AUTHOR]

Social media platforms such as Twitter are a vital source of information during major events, such as natural disasters. Studies attempting to automatically detect textual communications have mostly focused on machine learning and deep learning algorithms. Recent evidence shows improvement in disaster detection models with the use of contextual word embedding techniques (i.e., transformers) that take the context of a word into consideration, unlike the traditional context-free techniques; however, studies regarding this model are scant. To this end, this paper investigates a selection of ensemble learning models by merging transformers with deep neural network algorithms to assess their performance in detecting informative and non-informative disaster-related Twitter communications. A total of 7613 tweets were used to train and test the models. Results indicate that the ensemble models consistently yield good performance results, with F-score values ranging between 76% and 80%. Simpler transformer variants, such as ELECTRA and Talking-Heads Attention, yielded comparable and superior results compared to the computationally expensive BERT, with F-scores ranging from 80% to 84%, especially when merged with Bi-LSTM. Our findings show that the newer and simpler transformers can be used effectively, with less computational costs, in detecting disaster-related Twitter communications. [ABSTRACT FROM AUTHOR]

Even if the SARS-CoV-2 pandemic recedes, research regarding the effectiveness of government policies to contain the spread of the pandemic remains important. In this study, we analyze the impact of a set of epidemiological factors on the spread of SARS-CoV-2 in 30 European countries, which were applied from early 2020 up to mid-2022. We combine four data sets encompassing each country's non-pharmaceutical interventions (NPIs, including 66 government intervention types), distributions of 31 virus types, and accumulated percentage of vaccinated population (by the first five doses) as well as the reported infections, each on a daily basis. First, a Bayesian deep learning model is trained to predict the reproduction rate of the virus one month ahead of each day. Based on the trained deep learning model, the importance of relevant influencing factors and the magnitude of their effects on the outcome of the neural network model are computed by applying explainable machine learning algorithms. Second, in order to re-examine the results of the deep learning model, a Bayesian statistical analysis is implemented. In the statistical analysis, for each influencing input factor in each country, the distributions of pandemic growth rates are compared for days where the factor was active with days where the same factor was not active. The results of the deep learning model and the results of the statistical inference model coincide to a significant extent. We conclude with reflections with regard to the most influential factors on SARS-CoV-2 spread within European countries. [ABSTRACT FROM AUTHOR]

For large-scale optimization that covers a wide range of optimization problems encountered frequently in machine learning and deep neural networks, stochastic optimization has become one of the most used methods thanks to its low computational complexity. In machine learning and deep learning problems, nonconvex problems are common, while convex problems are rare. How to find the global minimum for nonconvex optimization and reduce the computational complexity are challenges. Inspired by the phenomenon that the stagewise stepsize tuning strategy can empirically improve the convergence speed in deep neural networks, we incorporate the stagewise stepsize tuning strategy into the iterative framework of Nesterov's acceleration- and variance reduction-based methods to reduce the computational complexity, i.e., the stagewise stepsize tuning strategy is incorporated into randomized stochastic accelerated gradient and stochastic variance-reduced gradient. The proposed methods are theoretically derived to reduce the complexity of the nonconvex and convex problems and improve the convergence rate of the frameworks, which have the complexity O (1 / μ ϵ) and O (1 / μ ϵ) , respectively, where μ is the PL modulus and L is the Lipschitz constant. In the end, numerical experiments on large benchmark datasets validate well the competitiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Effective control of rehabilitation robots is of paramount importance and requires increased attention to achieve a fully reliable, automated system for practical applications. As the domain of robotic rehabilitation progresses rapidly, the imperative for precise and dependable control mechanisms grows. In this study, we present an innovative control scheme integrating state-of-the-art machine learning algorithms with traditional control techniques. Our approach offers enhanced adaptability to patient-specific needs while ensuring safety and effectiveness. We introduce a model-free feedback linearization control method underpinned by deep neural networks and online observation. While our controller is model-free, and system dynamics are learned during training phases, we employ an online observer to robustly estimate uncertainties that the systems may face in real-time, beyond their training. The proposed technique was tested through different simulations with varying initial conditions and step references, demonstrating the controller's robustness and adaptability. These simulations, combined with Lyapunov's stability verification, validate the efficacy of our proposed scheme in effectively controlling the system under diverse conditions. [ABSTRACT FROM AUTHOR]

In machine learning and data mining applications, an imbalanced distribution of classes in the training dataset can drastically affect the performance of learning models. The class imbalance problem is frequently observed during classification tasks in real-world scenarios when the available instances of one class are much fewer than the amount of data available in other classes. Machine learning algorithms that do not consider the class imbalance could introduce a strong bias towards the majority class, while the minority class is usually despised. Thus, sampling techniques have been extensively used in various studies to overcome class imbalances, mainly based on random undersampling and oversampling methods. However, there is still no final solution, especially in the domain of multi-class problems. A strategy that combines density-based clustering algorithms with random undersampling and oversampling techniques is studied in this work. To analyze the performance of the studied method, an experimental validation was achieved on a collection of hyperspectral remote sensing images, and a deep learning neural network was utilized as the classifier. This data bank contains six datasets with different imbalance ratios, from slight to severe. The experimental results outperform the classification measured by the geometric mean of the precision compared with other state-of-the-art methods, mainly for highly imbalanced datasets. [ABSTRACT FROM AUTHOR]

Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a neural network. It has many applications in the field of banking, automobile industry, agriculture, and healthcare industry. Deep learning has played a significant role in solving complex tasks related to computer vision, such as image classification, natural language processing, and object detection. On the other hand, optimizers also play an intrinsic role in training the deep learning model. Recent studies have proposed many deep learning models, such as VGG, ResNet, DenseNet, and ImageNet. In addition, there are many optimizers such as stochastic gradient descent (SGD), Adam, AdaDelta, Adabelief, and AdaMax. In this study, we have selected those models that require lower hardware requirements and shorter training times, which facilitates the overall training process. We have modified the Adam based optimizers and minimized the cyclic path. We have removed an additional hyper-parameter from RMSProp and observed that the optimizer works with various models. The learning rate is set to minimum and constant. The initial weights are updated after each epoch, which helps to improve the accuracy of the model. We also changed the position of the epsilon in the default Adam optimizer. By changing the position of the epsilon, it accumulates the updating process. We used various models with SGD, Adam, RMSProp, and the proposed optimization technique. The results indicate that the proposed method is effective in achieving the accuracy and works well with the state-of-the-art architectures. [ABSTRACT FROM AUTHOR]

Deep learning is a sub-discipline of artificial intelligence that uses artificial neural networks, a machine learning technique, to extract patterns and make predictions from large datasets. In recent years, it has achieved rapid development and is widely used in numerous disciplines with fruitful results. Learning valuable information from complex, high-dimensional, and heterogeneous biomedical data is a key challenge in transforming healthcare. In this review, we provide an overview of emerging deep-learning techniques, COVID-19 research involving deep learning, and concrete examples of deep-learning methods in COVID-19 diagnosis, prognosis, and treatment management. Deep learning can process medical imaging data, laboratory test results, and other relevant data to diagnose diseases and judge disease progression and prognosis, and even recommend treatment plans and drug-use strategies to accelerate drug development and improve drug quality. Furthermore, it can help governments develop proper prevention and control measures. We also assess the current limitations and challenges of deep learning in therapy precision for COVID-19, including the lack of phenotypically abundant data and the need for more interpretable deep-learning models. Finally, we discuss how current barriers can be overcome to enable future clinical applications of deep learning. [ABSTRACT FROM AUTHOR]

A machine learning model for correcting errors in Ukrainian texts has been developed. It was established that the neural network has the ability to correct simple sentences written in Ukrainian; however, the development of a full-fledged system requires the use of spell-checking using dictionaries and the checking of rules, both simple and those based on the result of parsing dependencies or other features. In order to save computing resources, a pre-trained BERT (Bidirectional Encoder Representations from Transformer) type neural network was used. Such neural networks have half as many parameters as other pre-trained models and show satisfactory results in correcting grammatical and stylistic errors. Among the ready-made neural network models, the pre-trained neural network model mT5 (a multilingual variant of T5 or Text-to-Text Transfer Transformer) showed the best performance according to the BLEU (bilingual evaluation understudy) and METEOR (metric for evaluation of translation with explicit ordering) metrics. [ABSTRACT FROM AUTHOR]

Sentiment analysis of news headlines is an important factor that investors consider when making investing decisions. We claim that the sentiment analysis of financial news headlines impacts stock market values. Hence financial news headline data are collected along with the stock market investment data for a period of time. Using Valence Aware Dictionary and Sentiment Reasoning (VADER) for sentiment analysis, the correlation between the stock market values and sentiments in news headlines is established. In our experiments, the data on stock market prices are collected from Yahoo Finance and Kaggle. Financial news headlines are collected from the Wall Street Journal, Washington Post, and Business-Standard website. To cope with such a massive volume of data and extract useful information, various embedding methods, such as Bag-of-words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), are employed. These are then fed into machine learning models such as Naive Bayes and XGBoost as well as deep learning models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Various natural language processing, andmachine and deep learning algorithms are considered in our study to achieve the desired outcomes and to attain superior accuracy than the current state-of-the-art. Our experimental study has shown that CNN (80.86%) and LSTM (84%) are the best performing models in relation to machine learning models, such as Support Vector Machine (SVM) (50.3%), Random Forest (67.93%), and Naive Bayes (59.79%). Moreover, two novel methods, BERT and RoBERTa, were applied with the expectation of better performance than all the other models, and they did exceptionally well by achieving an accuracy of 90% and 88%, respectively. [ABSTRACT FROM AUTHOR]

As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts is no easy task. Currently, previous research on forecasting court outcomes using small experimental datasets yielded a number of unanticipated predictions utilizing machine learning (ML) models and conventional methodologies for categorical feature encoding. The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN, in order to effectively forecast court rulings using historic judicial datasets. By prioritizing and choosing features that scored the highest in the provided legal data set, only the most pertinent features were picked. After that, the LSTM+CNN model was utilized to forecast lawsuit verdicts. In contrast to previous related experiments, this composite model's testing results were promising, showing 92.05 percent accuracy, 93 percent precision, 94 percent recall, and a 93 percent F1-score. [ABSTRACT FROM AUTHOR]