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Understanding road conditions is essential for implementing effective road safety measures and driving solutions. Road situations encompass the day-to-day conditions of roads, including the presence of vehicles and pedestrians. Surveillance cameras strategically placed along streets have been instrumental in monitoring road situations and providing valuable information on pedestrians, moving vehicles, and objects within road environments. However, these video data and information are stored in large volumes, making analysis tedious and time-consuming. Deep learning models are increasingly utilized to monitor vehicles and identify and evaluate road and driving comfort situations. However, the current neural network model requires the recognition of situations using time-series video data. In this paper, we introduced a multi-directional detection model for road situations to uphold high accuracy. Deep learning methods often integrate long short-term memory (LSTM) into long-term recurrent network architectures. This approach effectively combines recurrent neural networks to capture temporal dependencies and convolutional neural networks (CNNs) to extract features from extensive video data. In our proposed method, we form a multi-directional long-term recurrent convolutional network approach with two groups equipped with CNN and two layers of LSTM. Additionally, we compare road situation recognition using convolutional neural networks, long short-term networks, and long-term recurrent convolutional networks. The paper presents a method for detecting and recognizing multi-directional road contexts using a modified LRCN. After balancing the dataset through data augmentation, the number of video files increased, resulting in our model achieving 91% accuracy, a significant improvement from the original dataset. [ABSTRACT FROM AUTHOR]

This paper presents the latest advances in machine learning techniques and highlights deep learning (DL) methods in recent studies. This technology has recently received great attention as it can solve complex problems. This paper focuses on covering one of the deep learning algorithms (deep neural network) and learning about its types such as convolutional neural network (CNN), Recurrent Neural Networks (RNN), etc. We have discussed recent changes, such as advanced DL technologies. Next, we continue analyzing and discussing the challenges and possible solutions of machine learning, such as big data and object detection, studying more papers in deep learning, and knowing the main experiments and future directions. In addition, this review also identifies some successful deep learning applications in recent years. Moreover, in this paper, one of the deep learning methods, convolutional neural networks, is applied to detect objects in images by using the You Only Look One model and comparing it with RetinaNet using pre-trained models. The results found that CNN (using YOLOv3) is a more accurate model than RetinaNet. [ABSTRACT FROM AUTHOR]

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long ShortTerm Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John's campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John's. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R2 score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention. [ABSTRACT FROM AUTHOR]

Today according to social media, the internet, Etc. Data is rapidly produced and occupies a large space in systems that have resulted in enormous data warehouses; the progress in information technology has significantly increased the speed and ease of data flow.text mining is one of the most important methods for extracting a useful model through extracting and adapting knowledge from data sets. However, many studies have been conducted based on the usage of deep learning for text processing and text mining issues.The idea and method of text mining are one of the fields that seek to extract useful information from unstructured textual data that is used very today. Deep learning and machine learning techniques in classification and text mining and their type are discussed in this paper as well. Neural networks of various kinds, namely, ANN, RNN, CNN, and LSTM, are the subject of study to select the best technique. In this study, we conducted a Systematic Literature Review to extract and associate the algorithms and features that have been used in this area. Based on our search criteria, we retrieved 130 relevant studies from electronic databases between 1997 and 2021; we have selected 43 studies for further analysis using inclusion and exclusion criteria in Section 3.2. According to this study, hybrid LSTM is the most widely used deep learning algorithm in these studies, and SVM in machine learning method high accuracy in result shown. [ABSTRACT FROM AUTHOR]

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection and attention mechanism, each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient (MFCC), Tempogram, and short-time Fourier transform (STFT) spectral respectively. Experimental results show that, in comparison with traditional classification methods and single-stream CNN networks, TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%, which demonstrates the availability of the model proposed. [ABSTRACT FROM AUTHOR]

Logistics management plays a crucial role in supply chain operations, encompassing various tasks such as transportation, inventory management, and order fulfillment. The application of deep learning algorithms has emerged as a promising approach to optimize logistics processes and enhance efficiency. This paper explores the utilization of deep learning algorithms for optimization in logistics management, focusing on transportation routing, warehouse management, and demand forecasting. The study discusses the key deep learning algorithms employed in these areas, including deep neural networks, convolutional neural networks, recurrent neural networks, and reinforcement learning. Furthermore, it presents case studies and empirical evidence that demonstrate the effectiveness of deep learning techniques in improving logistics operations. The paper provides an overview of logistics management and the motivation for employing deep learning algorithms for optimization. [ABSTRACT FROM AUTHOR]

This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have shown exceptional effectiveness in a wide range of supervised and unsupervised learning tasks in a variety of fields, including image processing, computer vision, natural language processing, and speech or voice processing. In this paper, a comprehensive analysis is conducted to assess the impact of deep learning on user authentication using both physiological and behavioural biometrics. This work encompasses the diverse deep learning approaches employed in authentication schemes tailored for smart devices. Meticulous scrutiny of commonly used datasets in these authentication studies is undertaken, accompanied by a comparative assessment of performance. The deep learning models under consideration span a spectrum of architectures, including deep neural networks, convolutional neural networks, deep auto-encoders, recurrent neural networks, and their variants. To enhance the clarity and categorization of authentication techniques for smart devices, a taxonomy is proposed based on the specific authentication metrics employed: (1) Knowledge-based Authentication (KBA), (2) Physiological Biometric-based Authentication (PBBA), (3) Behavioural Biometric-based Authentication (BBBA), (4) Physiological and Behavioural Continuous Authentication (PBBCA), and (5) Multi Modal Authentication (MMA). Furthermore, potential contributions of deep learning techniques to the realms of physiological and behavioural biometrics are discussed. Significance is placed on performance metrics, including accuracy, stability, and robustness, in evaluating these deep learning-based authentication systems. The challenges and limitations that deep learning approaches must surmount when dealing with real-world biometric data in the context of biometric identification systems are examined. This work not only underscores the transformative role of deep learning in user authentication but also offers valuable insights into the evolving landscape of biometric identification on smart devices. An examination of performance metrics provides a holistic view of the strengths and areas for improvement in deep learning based authentication solutions. [ABSTRACT FROM AUTHOR]

Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the forgetting problem by learning new incoming data in an online manner. We develop a new incremental learning framework that can learn extra data or new classes with less catastrophic forgetting. We adopt the hippocampal memory process to the deep neural networks by defining the effective maximum of neural activation and its boundary to represent a feature distribution. In addition, we incorporate incremental QR factorization into the deep neural networks to learn new data with both existing labels and new labels with less forgetting. The QR factorization can provide the accurate subspace prior, and incremental QR factorization can reasonably express the collaboration between new data with both existing classes and new class with less forgetting. In our framework, a set of appropriate features (i.e., nodes) provides improved representation for each class. We apply our method to the convolutional neural network (CNN) for learning Cifar-100 and Cifar-10 datasets. The experimental results show that the proposed method efficiently alleviates the stability and plasticity dilemma in the deep neural networks by providing the performance stability of a trained network while effectively learning unseen data and additional new classes. [ABSTRACT FROM AUTHOR]

Phishing attacks aim to steal confidential information using sophisticated methods, techniques, and tools such as phishing through content injection, social engineering, online social networks, and mobile applications. To avoid and mitigate the risks of these attacks, several phishing detection approaches were developed, among which deep learning algorithms provided promising results. However, the results and the corresponding lessons learned are fragmented over many different studies and there is a lack of a systematic overview of the use of deep learning algorithms in phishing detection. Hence, we performed a systematic literature review (SLR) to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications. We address nine research questions and provide an overview of how deep learning algorithms have been used for phishing detection from several aspects. In total, 43 journal articles were selected from electronic databases to derive the answers for the defined research questions. Our SLR study shows that except for one study, all the provided models applied supervised deep learning algorithms. The widely used data sources were URL-related data, third party information on the website, website content-related data, and email. The most used deep learning algorithms were deep neural networks (DNN), convolutional neural networks, and recurrent neural networks/long short-term memory networks. DNN and hybrid deep learning algorithms provided the best performance among other deep learning-based algorithms. 72% of the studies did not apply any feature selection algorithm to build the prediction model. PhishTank was the most used dataset among other datasets. While Keras and Tensorflow were the most preferred deep learning frameworks, 46% of the articles did not mention any framework. This study also highlights several challenges for phishing detection to pave the way for further research. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. [ABSTRACT FROM AUTHOR]

Careful monitoring of plant conditions and their diagnosis are necessary, but a human cannot control a large area of land where the crop grows. This paper proposes the solution of this problem. Early diagnosis and accurate detection of plant leaf diseases can prevent the spread of the disease. In the last decade, machine learning methods and image classification tools have been used to identify and diagnose plant diseases. This paper proposes an accurate approach to identify plant leaf diseases based on the deep convolutional neural network and gated recurrent units. The PlantVillage dataset of damaged and healthy plant leaves images is used. The proposed model is trained to identify common plant leaf diseases of 14 species. The experimental results are compared to other well-known models. This study shows that the proposed model based on deep learning provides the best solution in the diagnosis of plant diseases with high accuracy, and that the gated recurrent unit neural network considered as a classifier can improve the accuracy of the convolutional neural network model. The comparison results demonstrated that the proposed approach achieved higher performance than other models. [ABSTRACT FROM AUTHOR]

Gated Recurrent Neural Networks (RNNs) such as LSTM and GRU have been highly effective in handling sequential time series data in recent years. Although Gated RNNs have an inherent ability to learn complex temporal dynamics, there is potential for further enhancement by enabling these deep learning networks to directly use time information to recognise time-dependent patterns in data and identify important segments of time. Synonymous with time series data in real-world applications are missing values, which often reduce a model's ability to perform predictive tasks. Historically, missing values have been handled by simple or complex imputation techniques as well as machine learning models, which manage the missing values in the prediction layers. However, these methods do not attempt to identify the significance of data segments and therefore are susceptible to poor imputation values or model degradation from high missing value rates. This paper develops Cyclic Gate enhanced recurrent neural networks with learnt waveform parameters to automatically identify important data segments within a time series and neglect unimportant segments. By using the proposed networks, the negative impact of missing data on model performance is mitigated through the addition of customised cyclic opening and closing gate operations. Cyclic Gate Recurrent Neural Networks are tested on several sequential time series datasets for classification performance. For long sequence datasets with high rates of missing values, Cyclic Gate enhanced RNN models achieve higher performance metrics than standard gated recurrent neural network models, conventional non-neural network machine learning algorithms and current state of the art RNN cell variants. [ABSTRACT FROM AUTHOR]

Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors. This paper presents a new deep learning model called stranded-NN. This model uses a set of NN models of variable layer depths depending on the input. This way, the proposed model can classify different types of emergencies occurring in different time intervals; real-time, close-to-real-time, or periodic. The proposed stranded-NN model has been compared against existing fixed-depth MLPs and LSTM networks used by the industry. Experimentation has shown that the stranded-NN model can outperform fixed depth MLPs 15–21% more in terms of accuracy for real-time events and at least 10–14% more for close-to-real-time events. Regarding LSTMs of the same memory depth as the NN strand input, the stranded NN presents similar results in terms of accuracy for a specific number of strands. Nevertheless, the stranded-NN model's ability to maintain multiple trained strands makes it a superior and more flexible classification and prediction solution than its LSTM counterpart, as well as being faster at training and classification. [ABSTRACT FROM AUTHOR]

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate two types of photovoltaic (PV) systems (on-grid and off-grid) of different sizes and propose a reliable PV forecasting method. The novelty of our research consists in a weather data-driven feature engineering considering the operation of the PV systems in similar conditions and merging the results of deterministic and stochastic models, namely Machine Learning algorithms (Random Forest—RF, eXtreme Gradient Boost—XGB) and Deep Learning algorithms (Deep Neural Networks—DNN, Gated Recurrent Unit—GRU) into a Hybrid Meta-learning Forecasting method. It combines the estimations of the above-mentioned algorithms with relevant features to predict the PV output using a Long Short-Term Memory model. To design the PV forecast for off-grid systems, that are equally important for prosumers, and approximate the potential power of these systems, the level of load and charging state of the batteries are considered. In this context, feature engineering using the weather and PV output data, including PV characteristics, is relevant to obtaining a performant and robust PV forecast for various use cases taking into account the size and connectivity of the PV systems. On average, the Mean Absolute Error and Mean Absolute Percentage Error have halved compared to values obtained with deterministic methods and are 25% lower than the stochastic models. [ABSTRACT FROM AUTHOR]

Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company's ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company's quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers' previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics. [ABSTRACT FROM AUTHOR]

Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals' stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values. [ABSTRACT FROM AUTHOR]

Rain attenuation events are one of the foremost drawbacks in satellite communications, impairing satellite link availability. For this reason, it is necessary to foresee rain events to avoid an outage of the satellite link. In this paper, we propose and develop a method based on Machine Learning to predict events of rain attenuation without appealing to complex mathematical models. To be specific, we implement a Long–short term memory architecture that is a Deep Learning algorithm based on an artificial recurrent neural network. Furthermore, supervised learning is the learning task for our algorithms. For this purpose, rain attenuation time-series feed the Long–short term memory network at the input to train it. However, the lack of a rainfall database hinders the development of a reliable prediction method. Therefore, we generate a synthetic rain attenuation database by using the recommendations of the International Telecommunication Union. Each model is trained and validated by computational experiments, employing statistical metrics to find the most accurate and reliable models. Thus, the accuracy metric compares the outcomes of the proposal with other related methods and models. As a result, our best model reaches an accuracy of 91.88 % versus 87.99 % from the external best model, demonstrating superiority over other models/methods. On average, our proposal accuracy reaches a value of 88.08 % . Finally, we find out that this proposal can contribute efficiently to improving the performance of satellite system networks by re-routing data traffic or increasing link availabilities, taking advantage of the prediction of rain attenuation events. [ABSTRACT FROM AUTHOR]

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal data increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains due to the powerful ability in automatic feature representation learning, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM), one of the most dominant technology for fifth-generation (5G) wireless communication systems offers a high data rate with better spectrum efficiency. However, OFDM based systems suffer from various channel imperfections such as channel distortion, carrier frequency offset (CFO) due to transceiver local oscillator frequency mismatch, sampling frequency offset (SFO) between DAC/ADCs, nonlinear distortion (NLD) due to the nonlinear power amplifier, and fading caused by frequency selective channels. Recently OFDM based non-orthogonal multiple access (NOMA) scheme is attracting researchers' attraction owing to its high spectral efficiency, massive connectivity, and resilience to frequency selectivity for the upcoming 5G wireless communication and beyond. However, NOMA-OFDM systems suffer the same demerits as OFDM-based systems to deploy such a multicarrier system for 5G and beyond applications. This paper studies the problem of various channel imperfections on OFDM and OFDM-NOMA systems and proposes machine learning (ML) based estimation and signal detection for OFDM and NOMA-OFDM based systems in presence of the aforementioned channel imperfections. Various ML models like neural network (NN), Recurrent-NN, long-short-term-memory are adopted in this research for channel estimation-equalization, CFO-SFO estimation and compensation and mitigation of NLD in OFDM and NOMA-OFDM receiver. Extensive simulation studies show that the ML-based techniques outperform traditional LS, MMSE-based methods for OFDM receiver, and LS-SIC, MMSE-SIC based methods for NOMA-OFDM receiver. [ABSTRACT FROM AUTHOR]

In the oil and gas industries, predicting and classifying oil and gas production for hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software to predict future oil and gas production and devise optimum field development plans. However, this process costs an immense number of resources and is time consuming. Each reservoir prediction experiment needs tens or hundreds of simulation runs, taking several hours or days to finish. In this paper, we attempt to overcome these issues by creating machine learning and deep learning models to expedite the process of forecasting oil and gas production. The dataset was provided by the leading oil producer, Saudi Aramco. Our approach reduced the time costs to a worst-case of a few minutes. Our study covered eight different ML and DL experiments and achieved its most outstanding R2 scores of 0.96 for XGBoost, 0.97 for ANN, and 0.98 for RNN over the other experiments. [ABSTRACT FROM AUTHOR]

There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation. [ABSTRACT FROM AUTHOR]

Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people's opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content's sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model's performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models. [ABSTRACT FROM AUTHOR]

The occurrence or change in the diseases in a specific area can be predicted in advance with the help of plant disease forecasting model. This helps to undertake suitable management measures to avoid the losses well in advance. Disease forecasting predicts probable outbreaks or increased disease intensity over a period in a particular area. This technique helps in timely application of chemicals to plants, which also involve all activities of crop protection and intimate the farmers in the community via text messages or e-mail etc. means of communication. Environment controls the evolution and survival period of various pathogens. Environmental conditions like minimum leaf wetness duration, soil moisture, micro-level relative humidity etc. contribute in evolution of disease causing pathogens. Disease forecasting system thus helps in predicting and avoiding evolution and spread of diseases. This paper uses Machine Learning (ML) and Deep Learning (DL) algorithms to detect, classify and predict the possible pathogens/diseases in the particular type of crop/plant considering based on weather conditions. Temperature, moisture and humidity are the parameters taken into consideration. Convolution Neural Networks (CNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Support Vector Machines (SVM) and K-Nearest Neighbours (KNN) are the five algorithms implemented and compared based on the obtained output accuracy. ANN outperforms all the other algorithms compared in this paper with accuracy of 90.79%. [ABSTRACT FROM AUTHOR]

Automated planning for problems without an explicit model is an elusive research challenge. However, if tackled, it could provide a general approach to problems in real-world unstructured environments. There are currently two strong research directions in the area of artificial intelligence (AI), namely machine learning and symbolic AI. The former provides techniques to learn models of unstructured data but does not provide further problem solving capabilities on such models. The latter provides efficient algorithms for general problem solving, but requires a model to work with. Creating the model can itself be a bottleneck of many problem domains. Complicated problems require an explicit description that can be very costly or even impossible to create. In this paper, we propose a combination of the two areas, namely deep learning and classical planning, to form a planning system that works without a human-encoded model for variably scaled problems. The deep learning part extracts the model in the form of a transition system and a goal-distance heuristic estimator; the classical planning part uses such a model to efficiently solve the planning problem. Both networks in the planning system, we introduced, work with a problem in its graphic form and there is no need for any additional information to create the state transition system or to estimate a heuristic value. We proposed three different architectures for the heuristic estimator to compare different characteristics of well- known deep learning techniques. Besides the design of such planning systems, we provide experimental evaluation comparing the implemented techniques to classical model-based methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in Greece, in normal/abrupt change value areas. Our method verifies the chaotic behavior of load time series through chaos time series analysis and with the application of deep learning recurrent neural networks produces predictions for 10 and 20 days ahead. Specifically, four different neural network models constructed (a) feed forward neural network, (b) gated recurrent unit (GRU) neural network, (c) long short-term memory (LSTM) recurrent and (d) bidirectional LSTM neural network to implement the prediction in a prediction horizon, produced through the extraction of maximum Lyapunov exponent. We constructed sequences of algorithms to feed the neural networks, creating three scenarios (a) 1-step, (b) 10-step and (c) 20-step sequences. For each neural network model, we used its predictions as inputs to predict steps forward, iteratively, to examine the accuracy of the proposed models, for horizons that are both inside and outside to that defined by Lyapunov time. The results show that the deep learning GRU neural network produces iterative predictions of high accuracy and stability, following the trend evolution of actual values, even outside the safe horizon for 1-step and 10-step cases. [ABSTRACT FROM AUTHOR]

Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work. [ABSTRACT FROM AUTHOR]

This document is a foreword from the journal "Environmental Modeling & Assessment" that discusses the role of machine learning in environmental modeling. It explains that traditional modeling methods rely on physical and statistical approaches, but machine learning algorithms, such as deep neural networks, have the potential to capture complex environmental phenomena without extensive input from domain experts. The document highlights a special issue of the journal that showcases 12 papers applying machine learning to various environmental science tasks, including monitoring and prediction, agriculture and land use, and environmental contamination. The goal is to inspire further interdisciplinary research that combines machine learning with environmental science for more accurate and data-driven modeling of natural systems. [Extracted from the article]

Identifying offensive and discriminatory content, commonly referred to as hate speech, within textual data is a critical task. This study addresses the task of identifying hate speech in textual data, focusing on the challenge of selecting optimal word embedding methods and classifiers. Leveraging the Google Jigsaw dataset, the research employs explainable artificial intelligence (XAI) for hate speech detection. Following preprocessing, which includes converting text to lowercase, removing punctuation, extra whitespace, numbers, and non-ASCII characters, a thorough analysis reveals high-frequency words. The research extensively compares three-word embedding techniques—CountVectorizer, GloVe, and bidirectional encoder representations from transformers (BERT)—in combination with two machine learning models (support vector classifier and logistic regression) and four deep learning models [artificial neural network (ANN), recurrent neural network (RNN), bidirectional gated recurrent unit (Bi-GRU), bidirectional long-short term memory (Bi-LSTM)] for hate speech detection. The fusion of BERT with a bidirectional gated recurrent unit (Bi-GRU) achieved an impressive accuracy of 92%, and an ensemble of the top-performing models further improves accuracy by nearly 2%. To enhance result interpretability, the study employs XAI techniques such as local interpretable model agnostic explanations (LIME) and Shapley additive explanations (SHAP) on the top-performing ensembled model to provide insights into its predictions. The paper concludes by suggesting potential future research directions, including exploring additional embedding techniques and models, addressing dataset generalizability, improving interpretability methods, and considering computational resource constraints. [ABSTRACT FROM AUTHOR]

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials. [ABSTRACT FROM AUTHOR]

Hesitant attitudes have been a significant issue since the development of the first vaccines—the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%. [ABSTRACT FROM AUTHOR]

This study proposes an edge computing-based facial expression recognition system that is low cost, low power, and privacy preserving. It utilizes a minimally obtrusive cap-based system designed for the continuous and real-time monitoring of a user's facial expressions. The proposed method focuses on detecting facial skin deformations accompanying changes in facial expressions. A multi-zone time-of-flight (ToF) depth sensor VL53L5CX, featuring an 8 × 8 depth image, is integrated into the front brim of the cap to measure the distance between the sensor and the user's facial skin surface. The distance values corresponding to seven universal facial expressions (neutral, happy, disgust, anger, surprise, fear, and sad) are transmitted to a low-power STM32F476 microcontroller (MCU) as an edge device for data preprocessing and facial expression classification tasks utilizing an on-device pre-trained deep learning model. Performance evaluation of the system is conducted through experiments utilizing data collected from 20 subjects. Four deep learning algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are assessed. These algorithms demonstrate high accuracy, with CNN yielding the best result, achieving an accuracy of 89.20% at a frame rate of 15 frames per second (fps) and a maximum latency of 2 ms. [ABSTRACT FROM AUTHOR]

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts. [ABSTRACT FROM AUTHOR]

The energy sector heavily relies on a diverse array of machine learning algorithms for power load prediction, which plays a pivotal role in shaping policies for power generation and distribution. The precision of power load prediction depends on numerous factors that reflect nonlinear traits within the data. Notably, machine learning algorithms and artificial neural networks have emerged as indispensable components in contemporary power load forecasting. This study focuses specifically on machine learning algorithms, encompassing support vector machines (SVMs), long short-term memory (LSTM), ensemble classifiers, recurrent neural networks, and deep learning methods. The research meticulously examines short-term power load prediction by leveraging Chandigarh UT electricity utility data spanning the last 5 years. The assessment of prediction accuracy utilizes metrics such as normalized mean square error (NMSE), root mean squared error (RMSE), mean absolute error (MAE), and mutual information (MI). The prediction results demonstrate superior performance in LSTM compared to other algorithms, with the prediction error being the lowest in LSTM and 13.51% higher in SVMs. These findings provide valuable insights into the strengths and limitations of different machine learning algorithms. Validation experiments for the proposed method are conducted using MATLAB R2018 software. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification. This study encompasses a comprehensive evaluation aimed at assessing the accuracy of various predictive models, particularly deep learning methods when combined with SS-DRPL. Two deep learning architectures, namely hybrid Long Short-Term Memory and Recurrent Neural Networks (LSTM-RNN) and Deep Neural Networks (DNN), are employed and compared in terms of their ability to detect voice-based PD cases accurately. Additionally, several traditional machine learning models are also included to establish a baseline for comparison. The findings of the study reveal that the incorporation of SS-DRPL leads to improved model performance across all experimental setups. Notably, the LSTM-RNN architecture augmented with SS-DRPL achieves the highest F1-score of 0.94, indicating its superior ability to detect PD cases using voice-based data effectively. This outcome underscores the efficacy of SS-DRPL in enabling deep learning models to learn intricate patterns and correlations within the data, thereby facilitating more accurate PD classification. [ABSTRACT FROM AUTHOR]

Researchers and practitioners have attempted to predict the financial market by analyzing textual (e.g., news articles and social media) and numeric data (e.g., hourly stock prices, and moving averages). Among textual data, while many papers have been published that analyze social media, news content has gained limited attention in predicting the stock market. Acknowledging that news is critical in predicting the stock market, the focus of this systematic review is on papers investigating machine learning and text mining techniques to predict the stock market using news. Using Kitchenham's methodology, we present a systematic review of the literature on intelligent financial market prediction, examining data mining and machine learning approaches and the employed datasets. From five digital libraries, we identified 61 studies from 2015–2022 for synthesis and interpretation. We present notable gaps and barriers to predicting financial markets, then recommend future research scopes. Various input data, including numerical (stock prices and technical indicators) and textual data (news text and sentiment), have been employed for news-based stock market prediction. News data collection can be costly and time-consuming: most studies have used custom crawlers to gather news articles; however, there are financial news databases available that could significantly facilitate news collection. Furthermore, although most datasets have covered fewer than 100K records, deep learning and more sophisticated artificial neural networks can process enormous datasets faster, improving future model performance. There is a growing trend toward using artificial neural networks, particularly recurrent neural networks and deep learning models, from 2018 to 2021. Furthermore, regression and gradient-boosting models have been developed for stock market prediction during the last four years. Although word embedding approaches for feature representation have been employed recently with good accuracy, emerging language models may be a focus for future research. Advanced natural language processing methods like transformers have undeniably contributed to intelligent stock market prediction. However, stock market prediction has not yet taken full advantage of them. • Emerging articles (2015–2021) on news-based stock market prediction are reviewed. • There is a growing trend toward using news headlines for stock market prediction. • Recurrent neural networks are advantageous models for stock market prediction. • Stock market prediction not fully took advantage of Natural language processing yet. • Quarterly and annual reports of companies are authorized credible data sources. [ABSTRACT FROM AUTHOR]

The manufacture of large wind turbine blades requires well-controlled processing conditions to prevent defect formation and thus produce high-quality composite blades. While the physics-based models provide accurate computational capabilities for the resin infusion and curing process for the glass fiber composites, they suffer from high computational costs, making them infeasible for fast optimization computation and process control during manufacturing. In light of the limitations, we describe a machine learning (ML) approach that employs a deep convolutional and recurrent neural network model to predict the spatio-temporal temperature distribution during the vacuum assisted resin infusion molding (VARIM) process. The ML model is trained with the "big data" generated from the physics-based high-fidelity simulations. Once fully trained, it serves as a digital twin of the blade manufacturing process. Validation is made by comparing simulation results with experimental data on a unidirectional glass fiber composite laminate plate (44 plies, 2 m long and 0.5 m wide). The trained and validated ML model is then extended to evaluate the role of critical VARIM processing parameters on temperature distribution. With the predictive accuracy of 94%, at over 100 times faster computational speed than the physics-based simulations, the ML approach established herein provides a general framework for a digital twin for temperature distribution in the composite manufacturing process. [ABSTRACT FROM AUTHOR]

The utilization of numerical methods, such as computational fluid dynamics (CFD), has been widely established for modeling patient-specific hemodynamics based on medical imaging data. Hemodynamics assessment plays a crucial role in treatment decisions for the coarctation of the aorta (CoA), a congenital heart disease, with the pressure drop (PD) being a crucial biomarker for CoA treatment decisions. However, implementing CFD methods in the clinical environment remains challenging due to their computational cost and the requirement for expert knowledge. This study proposes a deep learning approach to mitigate the computational need and produce fast results. Building upon a previous proof-ofconcept study, we compared the effects of two different artificial neural network (ANN) architectures trained on data with different dimensionalities, both capable of predicting hemodynamic parameters in CoA patients: a one-dimensional bidirectional recurrent neural network (1D BRNN) and a three-dimensional convolutional neural network (3D CNN). The performance was evaluated by median point-wise root mean square error (RMSE) for pressures along the centerline in 18 test cases, which were not included in a training cohort. We found that the 3D CNN (median RMSE of 3.23 mmHg) outperforms the 1D BRNN (median RMSE of 4.25 mmHg). In contrast, the 1D BRNN is more precise in PD prediction, with a lower standard deviation of the error (±7.03 mmHg) compared to the 3D CNN (±8.91 mmHg). The differences between both ANNs are not statistically significant, suggesting that compressing the 3D aorta hemodynamics into a 1D centerline representation does not result in the loss of valuable information when training ANN models. Additionally, we evaluated the utility of the synthetic geometries of the aortas with CoA generated by using a statistical shape model (SSM), as well as the impact of aortic arch geometry (gothic arch shape) on the model's training. The results show that incorporating a synthetic cohort obtained through the SSM of the clinical cohort does not significantly increase the model's accuracy, indicating that the synthetic cohort generation might be oversimplified. Furthermore, our study reveals that selecting training cases based on aortic arch shape (gothic versus non-gothic) does not improve ANN performance for test cases sharing the same shape. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) have provided great advances for the task of sign language recognition (SLR). However, recurrent neural networks (RNN) in the form of long–short-term memory (LSTM) have become a means for providing solutions to problems involving sequential data. This research proposes the development of a sign language translation system that converts Panamanian Sign Language (PSL) signs into text in Spanish using an LSTM model that, among many things, makes it possible to work with non-static signs (as sequential data). The deep learning model presented focuses on action detection, in this case, the execution of the signs. This involves processing in a precise manner the frames in which a sign language gesture is made. The proposal is a holistic solution that considers, in addition to the seeking of the hands of the speaker, the face and pose determinants. These were added due to the fact that when communicating through sign languages, other visual characteristics matter beyond hand gestures. For the training of this system, a data set of 330 videos (of 30 frames each) for five possible classes (different signs considered) was created. The model was tested having an accuracy of 98.8%, making this a valuable base system for effective communication between PSL users and Spanish speakers. In conclusion, this work provides an improvement of the state of the art for PSL–Spanish translation by using the possibilities of translatable signs via deep learning. [ABSTRACT FROM AUTHOR]

Highway safety researchers focus on crash injury severity, utilizing deep learning--specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)--as the preferred method for modeling accident severity. Deep learning's strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent. The framework optimizes hidden layers and integrates data augmentation, Gaussian noise, and dropout regularization for improved generalization. Sensitivity and factor contribution analyses identify influential predictors. Evaluated on three diverse crash record databases--NCDB 2018-2019, UK 2015-2020, and US 2016-2021--the D-RNN model excels with an ACC score of 89.0281%, a Roc Area of 0.751, an F-estimate of 0.941, and a Kappa score of 0.0629 over the NCDB dataset. The proposed framework consistently outperforms traditional methods, existing machine learning, and deep learning techniques. [ABSTRACT FROM AUTHOR]

Solar irradiance, the power of sunlight received on a given surface area during a specific time, is crucial in determining the efficiency and performance of solar power systems, as it directly influences the electricity units generated by photovoltaic (PV) cells. In recent years, deep learning and machine learning techniques have been leveraged to enhance the accuracy of solar adsorption and wind power forecasting. In this context, this study presents a comparative study of various deep learning models for very short term solar irradiance forecasting, aiming to find the most effective model for this specific purpose for our local city Karachi. The key findings indicate that the LSTM model outperforms the other architectures, achieving the highest R-squared value and the lowest RMSE. These results emphasize the importance of accurate forecasting models in optimizing renewable energy generation and grid management and their potential applications in various sectors. [ABSTRACT FROM AUTHOR]

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. In this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed. [ABSTRACT FROM AUTHOR]

Nowadays, stroke survivors usually can regain their limb function through rehabilitation training prescribed by physicians. With the development of technologies such as the Internet of Things, artificial intelligence and the rapid increase in research in the field of digital health, a growing number of cutting-edge research focuses on sustaining medical services with digital technologies. In addition, some new approaches have been widely taken in rehabilitation. In the process of upper limb rehabilitation training for stroke survivors, this paper collects a series of inertial sensing time series data through the data collection mechanism. This paper utilizes Dynamic Time Warping-K-NearestNeighbor (DTW-KNN), Long Short-Term Memory (LSTM) neural network and Gated Recurrent Unit (GRU) neural network to process and analyze the time series data of rehabilitation actions, and finally realize the classification and evaluation of different completion situations of various upper limb rehabilitation actions. The experimental results show that the classification accuracy of DTW-KNN under different completion situations of the three upper limb rehabilitation actions are 72.2%, 47.2%, and 69.4%, respectively, while the corresponding classification accuracy of LSTM is 97.2%, 94.4% and 91.7%. LSTM also has significant advantages over DTW-KNN in terms of classification time. In addition, GRU not only has a high classification accuracy rate similar to LSTM, but also has a slight advantage over LSTM in classification time. • The paper uses inertial sensor data to verify and model neural networks. • This study applies deep learning in upper limb functionality evaluation. • The paper compares different approaches in evaluation accuracy and time. • This study can be extended to different upper limb rehabilitation scenarios. [ABSTRACT FROM AUTHOR]

The use of big data in deep neural networks has recently surpassed traditional machine learning techniques in many application areas. The main reasons for the use of deep neural networks are the increase in computational power made possible by graphics processing units and tensor processing units, and the new algorithms created by recurrent neural networks and CNNs. In addition to traditional machine learning methods, deep neural networks have applications in anticipating electricity load. Using a real dataset for one-step forecasting, this article compares three deep learning algorithms for short-term power load forecasting: LSTM, GRUs, and CNN. The statistics come from the Turkish city of Zonguldak and include hourly electricity usage loads and temperatures over a period of three years, commencing in 2019 and ending in 2021. The mean absolute percentage error is used to compare the performances of the techniques. Forecasts are made for twelve representative months from each season. The main reason for the significant deviations in the forecasts for January, May, September, and December is the presence of religious and national holidays in these months. This was solved by adding the information obtained from religious and national holidays to the modeling. This is not to say that CNNs are not good at capturing long-term dependencies and modeling sequential data. In all experiments, LSTM, GRUs and encoder-decoder LSTM outperformed simple CNN designs. In the future, these new architectural methods can be applied to long- or short-term electric charge predictions and their results can be compared to LSTM, GRUs and their variations. [ABSTRACT FROM AUTHOR]

This study introduces the Constitutive Neural Network (ConNN) model, a machine learning algorithm that accurately predicts the temporal response of complex fluids under specific deformations. The ConNN model utilizes a recurrent neural network architecture to capture the time dependent stress responses, and the recurrent units are specifically designed to reflect the characteristics of complex fluids (fading memory, finite elastic deformation, and relaxation spectrum), without presuming any equation of motion of the fluid. We demonstrate that the ConNN model can effectively replicate the temporal data generated by the Giesekus model and the Thixotropic-Elasto-Visco-Plastic (TEVP) fluid model under varying shear rates. To test the performance of the trained model, we subject it to an oscillatory shear flow, with periodic reversals in flow direction, which has not been trained on. The ConNN model successfully replicates the shear moduli of the original models, and the trained values of the recurrent parameters match the physical prediction of the original models. However, we do observe a slight deviation in the normal stresses, indicating that further improvements are necessary to achieve more rigorous physical symmetry and improve the model prediction. [ABSTRACT FROM AUTHOR]

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future. [ABSTRACT FROM AUTHOR]

In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measurements and 24 h predictions from nine models by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM10 predictions for the following 24 h. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multilayer perceptrons (MLPs). Regardless of the type of memory cell chosen, our results consistently show that the proposed framework outperforms the CAMS models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Furthermore, we examine the impact of outliers on the overall performance of the model. [ABSTRACT FROM AUTHOR]