Ship-type identification in a maritime context can be critical to the authorities to control the activities being carried out. Although Automatic Identification Systems has been mandatory for certain vessels, if a vessel does not have them voluntarily or not, it can lead to a whole set of problems, which is why the use of tracking alternatives such as radar is fully complementary for a vessel monitoring systems. However, radars provide positions, but not what they are detecting. Having systems capable of (...) adding categorical information to radar detections of vessels makes it possible to increase control of the activities being carried out, improve safety in maritime traffic, and optimize on-site inspection resources on the part of the authorities. This paper addresses the binary classification problem (fishing ships versus all other vessels) using unbalanced data from real vessel trajectories. It is performed from a deep learning approach comparing two of the main trends, Convolutional Neural Networks and Long Short-Term Memory. In this paper, it is proposed the weighted cross-entropy methodology and compared with classical data balancing strategies. Both networks show high performance when applying weighted cross-entropy compared with the classical machine learning approaches and classical balancing techniques. This work is shown to be a novel approach to the international problem of identifying fishing ships without context. (shrink)

Sentiment analysis aims to predict sentiment polarities (positive, negative or neutral) of a given piece of text. It lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics, and Data Mining. Sentiments can be expressed explicitly or implicitly. Arabic Sentiment Analysis presents a challenge undertaking due to its complexity, ambiguity, various dialects, the scarcity of resources, the morphological richness of the language, the absence of contextual information, and the absence of explicit sentiment words in an (...) implicit piece of text. Recently, deep learning has obviously shown a great success in the field of sentiment analysis and is considered as the state-of-the-art model in Arabic Sentiment Analysis. However, the state-of-the-art accuracy for Arabic sentiment analysis still needs improvements regarding contextual information and implicit sentiment expressed in different real cases. In this paper, an efficient Bidirectional LSTM Network (BiLSTM) is investigated to enhance Arabic Sentiment Analysis, by applying Forward-Backward encapsulate contextual information from Arabic feature sequences. The experimental results on six benchmark sentiment analysis datasets demonstrate that our model achieves significant improvements over the state-of-art deep learning models and the baseline traditional machine learning methods. (shrink)

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. (...) The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. (shrink)

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise (...) of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network and long short-term memory with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily. (shrink)

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable (...) than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value. (shrink)

Sentiment analysis is also known as opinion mining. It has an important role in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment. With the advancement of technology, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. This research aims to optimize the marketing strategy of energy products in SOE subsidiaries through sentiment analysis using the VADER and LSTM methods on (...) social media.The researcher analyzed data from twitter to identify consumer sentiment towards energy products. The results of the analysis show that the combination of VADER and LSTM is effective in identifying the nuances of sentiment, with the accuracy of sentiment classification reaching 85%. Based on the analysis, the researchers developed recommendations for marketing strategies that are more focused and responsive to public perception. This study also highlights the importance of two-way interaction between companies and consumers as part of an effective marketing strategy. The findings of this study are expected to be used as a reference by state-owned companies in the energy sector to improve the effectiveness of their marketing strategies through a sentiment analysis approach on social media. (shrink)

In natural language processing (NLP), named entity recognition (NER) and semantic classification are essential tasks. NER is a fundamental task, that identify named entities in text such as people, organizations, and locations. In Legal domain, NER is particularly important due to the variety of named entities that appear in legal documents and are important for legal analysis whereas Semantic classification is the process of giving each sentence in a text a semantic label, such as ”fact,””arguments,” or”judgement”. Both NER and Semantic (...) classi- fication are critical component of many NLP applications such as Knowledge base construction and semantic search. Semantic searching of legal documents is a powerful technique that can be used to quickly find the information that you need. By combining NER and semantic classification, one can identify the key elements of a legal document and search for documents that are related to a particular topic or contain a particular type of information. This paper proposes a novel approach to NER and Semantic classification in legal domain using Long short-term memory (LSTM) model. The proposed approach is evaluated on a corpus of legal text and the result of this study suggest that LSTM models are a promising approach for NER and Semantic classification in legal domain and can be used to improve the performance of semantic searching of legal documents. (shrink)

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact (...) on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index as a measure of FX volatility. In particular, the three major FXVIX indices from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting. (shrink)

The objective of this research is to develop accurate forecasting models for chlorophyll-α concentrations at various depths in El Mar Menor, Spain. Chlorophyll-α plays a crucial role in assessing eutrophication in this vulnerable ecosystem. To achieve this objective, various deep learning forecasting techniques, including long short-term memory, bidirectional long short-term memory and gated recurrent uni networks, were utilized. The models were designed to forecast the chlorophyll-α levels with a 2-week prediction horizon. To enhance the models’ accuracy, a sliding window method (...) combined with a blocked cross-validation procedure for time series was also applied to these techniques. Two input strategies were also tested in this approach: using only chlorophyll-α time series and incorporating exogenous variables. The proposed approach significantly improved the accuracy of the predictive models, no matter the forecasting technique employed. Results were remarkable, with |$\overline{\sigma}$| values reaching approximately 0.90 for the 0.5-m depth level and 0.80 for deeper levels. The proposed forecasting models and methodologies have great potential for predicting eutrophication episodes and acting as decision-making tools for environmental agencies. Accurate prediction of eutrophication episodes through these models could allow for proactive measures to be implemented, resulting in improved environmental management and the preservation of the ecosystem. (shrink)

With more and more investors exerting their voices through network forums or social media platforms, the relationships between online investor sentiment and stock movements have drawn more and more attention. In this paper, we crawl stock comments from China’s most popular online stock forum, East Money, and then develop a sentiment classifier using the LSTM method. Using the online investor sentiment of the stock forum, we explore the effect of online investor sentiment on the stock movements of CSI300. The (...) results show that online investor sentiment has a significant positive impact on both stock return and trading volume and remains significant after controlling book-to-market ratio, BETA, and market value. Moreover, investor sentiment has a significant positive impact on order imbalance of big trade, which represents the main flow of money in the market. As a result, investor sentiment has a positive impact on the major fund flows in the market. In other words, an increase in investor sentiment can boost the major money flows in the market to some extent. From a practical point of view, investor sentiment can assist investors to make investment decisions and help the government to regulate the stock market. (shrink)

The electroencephalogram is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL-LSTM model with a four-layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. (...) Second, four EEG characteristics were extracted via the wavelet transform using the LSTM-based sentiment classification network. The experimental results showed that the best average classification accuracy of these four features was 73.48%. This was 13% lower than in the ALL-LSTM model, indicating that inappropriate feature extraction methods could destroy the timing of EEG signals. LSTM can be used to thoroughly examine EEG signal timing and preprocessed EEG data. The accuracy and stability of the ALL-LSTM model are significantly superior to those of the WT-LSTM model. The result showed that the process of emotion generation based on EEG is sequential. Compared with EEG emotion extraction using WT, the raw EEG signal’s timing is more suitable for the LSTM network. (shrink)

Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system. This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of (...) data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory network and recurrent neural network, Bi-LSTM has the advantages of stronger recognition ability. Finally, precision, recall, accuracy, and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances. (shrink)

As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness (...) and complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting. (shrink)

Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional neural networks (CNNs) are the advanced version of DNNs that achieve 4–12% relative gain in the word error rate (WER) over DNNs. Existence of spectral variations and local correlations in speech signal makes CNNs more capable of speech recognition. Recently, it has been demonstrated that bidirectional long short-term memory (BLSTM) produces higher recognition rate in acoustic modeling because they are adequate to reinforce higher-level representations of acoustic (...) data. Spatial and temporal properties of the speech signal are essential for high recognition rate, so the concept of combining two different networks came into mind. In this paper, a hybrid architecture of CNN-BLSTM is proposed to appropriately use these properties and to improve the continuous speech recognition task. Further, we explore different methods like weight sharing, the appropriate number of hidden units, and ideal pooling strategy for CNN to achieve a high recognition rate. Specifically, the focus is also on how many BLSTM layers are effective. This paper also attempts to overcome another shortcoming of CNN, i.e. speaker-adapted features, which are not possible to be directly modeled in CNN. Next, various non-linearities with or without dropout are analyzed for speech tasks. Experiments indicate that proposed hybrid architecture with speaker-adapted features and maxout non-linearity with dropout idea shows 5.8% and 10% relative decrease in WER over the CNN and DNN systems, respectively. (shrink)

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure model and long short-term memory network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of (...) 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement. (shrink)

With the improvement of citizens’ risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk media, and (...) big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed， and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China. (shrink)

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, (...) this article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness. (shrink)

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network and long short-term memory model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their (...) models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function. (shrink)

What makes a word easy to learn? Early‐learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child‐directed speech, computed using n‐gram and long‐short‐term‐memory (LSTM) language models. Predictability derived from LSTMs was generally a (...) better predictor than predictability derived from n‐gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross‐linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase (...) in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree, K nearest (...) neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

The novel coronavirus disease is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have (...) control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory and gated recurrent unit have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait. (shrink)

The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a (...) gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology. (shrink)

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other (...) known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts. (shrink)

Current methods of chaos-based action recognition in videos are limited to the artificial feature causing the low recognition accuracy. In this paper, we improve ChaosNet to the deep neural network and apply it to action recognition. First, we extend ChaosNet to deep ChaosNet for extracting action features. Then, we send the features to the low-level LSTM encoder and high-level LSTM encoder for obtaining low-level coding output and high-level coding results, respectively. The agent is a behavior recognizer for producing (...) recognition results. The manager is a hidden layer, responsible for giving behavioral segmentation targets at the high level. Our experiments are executed on two standard action datasets: UCF101 and HMDB51. The experimental results show that the proposed algorithm outperforms the state of the art. (shrink)

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search (...) records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results. (shrink)

BackgroundDepression and anxiety create a large health burden and increase the risk of premature mortality. Mental health screening is vital, but more sophisticated screening and monitoring methods are needed. The Ellipsis Health App addresses this need by using semantic information from recorded speech to screen for depression and anxiety.ObjectivesThe primary aim of this study is to determine the feasibility of collecting weekly voice samples for mental health screening. Additionally, we aim to demonstrate portability and improved performance of Ellipsis’ machine learning (...) models for patients of various ages.MethodsStudy participants were current patients at Desert Oasis Healthcare, mean age 63 years. Two non-randomized cohorts participated: one with a documented history of depression within 24 months prior to the study, and the other without depression. Participants recorded 5-min voice samples weekly for 6 weeks via the Ellipsis Health App. They also completed PHQ-8 and GAD-7 questionnaires to assess for depression and anxiety, respectively.ResultsProtocol completion rate was 61% for both groups. Use beyond protocol was 27% for Group Positive and 9% for Group Negative. The Ellipsis Health App showed an AUC of 0.82 for the combined groups when compared to the PHQ-8 and GAD-7 with a threshold score of 10. Performance was high for senior participants as well as younger age ranges. Additionally, many participants spoke longer than the required 5 min.ConclusionThe Ellipsis Health App demonstrated feasibility in using voice recordings to screen for depression and anxiety among various age groups and the machine learning models using Transformer methodology maintain performance and improve over LSTM methodology when applied to the study population. (shrink)

In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results (...) showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length. (shrink)

At present, most of the research in the field of medical-assisted diagnosis is carried out based on image or electronic medical records. Although there is some research foundation, they lack the comprehensive consideration of comprehensive image and text modes. Based on this situation, this article proposes a fusion classification auxiliary diagnosis model based on GoogleNet model and Bi-LSTM model, uses GoogleNet to process brain computed tomographic images of ischemic stroke patients and extract CT image features, uses Bi-LSTM model (...) to extract the electronic medical record text, integrates the two features using the full connection layer network and Softmax classifier, and obtains a method that can assist the diagnosis from two modes. Experiments show that the proposed scheme on average improves 3.05% in accuracy compared to individual image or text modes, and the best performing GoogleNet + Bi-LSTM model achieves 96.61% accuracy; although slightly less in recall, it performs better on F1 values, and has provided feasible new ideas and new methods for research in the field of multi-model medical-assisted diagnosis. (shrink)

With the rapid expansion of Internet technology, this research aims to explore the teaching strategies of ceramic art for contemporary students. Based on deep learning, an automatic question answering system is established, new teaching strategies are analyzed, and the Internet is combined with the automatic QA system to help students solve problems encountered in the process of learning. Firstly, the related theories of DL and personalized learning are analyzed. Among DL-related theories, Back Propagation Neural Network, Convolutional Neural Network, Long Short-Term (...) Memory, and Gated Recurrent Unit are compared to implement a single model and a mixed model. Secondly, the collected student questions are selected and processed, and experimental parameters in different models are set for comparative experiments. Experiments reveal that the average accuracy and Mean Reciprocal Rank of traditional retrieval methods can only reach about 0.5. In the basic neural network, the average accuracy of LSTM and GRU structural models is about 0.81, which can achieve better results. Finally, the accuracy of the hybrid model can reach about 0.82, and the accuracy and MRR of the Bidirectional Gated Recurrent Unit Network-Attention model are 0.87 and 0.89, respectively, achieving the best results. The established DL model meets the requirements of the online automatic QA system, improves the teaching system, and helps students better understand and solve problems in the ceramic art courses. (shrink)

The Air Quality Index (AQI) shows the state of air pollution in a unique and more understandable way. This work aims to forecast the AQI in Algeciras (Spain) 8 hours in advance. The AQI is calculated indirectly through the predicted concentrations of five pollutants (O3, NO2, CO, SO2 and PM10) to achieve this goal. Artificial neural networks (ANNs), sequence-to-sequence long short-term memory networks (LSTMs) and a newly proposed method combing a rolling window with the latter (LSTMNA) are employed as the (...) forecasting techniques. Besides, two input approaches are evaluated: using only the data from the own time series of the pollutant in the first case or adding exogenous variables in the second one. Several window sizes are employed (24, 28 and 72 hours) with ANNs and LSTMNAs. Additionally, several feature ranking methods are applied in the exogenous approach to select the most relevant lagged variables to feed the models. Results show how the proposed exogenous approach increases the performance of the prediction models. Besides, the newly proposed method LSTMNA provides the best performances in most of the cases evaluated. Hence, it constitutes an exciting alternative to standard LSTMs and ANNs to predict pollutants concentrations and, consequently, the AQI. (shrink)

Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. (...) Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network. (shrink)

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also (...) supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology. (shrink)

This research aims to analyze the influencing factors of migrant children’s education integration based on the convolutional neural network algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set, Stanford sentiment data set, and news opinion data set are used to analyze the classification accuracy, loss value, Hamming loss, precision, recall, and micro-F1 of the ALGCNN model. Then, on the big data (...) platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest, the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children. The average final score of local children was greatly higher than that of migrant children, whereas the scores of migrant children’s listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model is established based on the CNN algorithm. In short, the children’s education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children’s educational integration, and this work provides a reference for the analysis of migrant children’s educational integration. (shrink)

Speech recognition technology has played an indispensable role in realizing human-computer intelligent interaction. However, most of the current Chinese speech recognition systems are provided online or offline models with low accuracy and poor performance. To improve the performance of offline Chinese speech recognition, we propose a hybrid acoustic model of deep convolutional neural network, long short-term memory, and deep neural network. This model utilizes DCNN to reduce frequency variation and adds a batch normalization layer after its convolutional layer to ensure (...) the stability of data distribution, and then use LSTM to effectively solve the gradient vanishing problem. Finally, the fully connected structure of DNN is utilized to efficiently map the input features into a separable space, which is helpful for data classification. Therefore, leveraging the strengths of DCNN, LSTM, and DNN by combining them into a unified architecture can effectively improve speech recognition performance. Our model was tested on the open Chinese speech database THCHS-30 released by the Center for Speech and Language Technology of Tsinghua University, and it was concluded that the DLD model with 3 layers of LSTM and 3 layers of DNN had the best performance, reaching 13.49% of words error rate. (shrink)

Three decades ago, William Ramsey, Steven Stich & Joseph Garon put forward an argument in favor of the following conditional: if connectionist models that implement parallelly distributed processing represent faithfully human cognitive processing, eliminativism about propositional attitudes is true. The corollary of their argument (if it proves to be sound) is that there is no place for folk psychology in contemporary cognitive science. This understanding of connectionism as a hypothesis about cognitive architecture compatible with eliminativism is also endorsed by Paul (...) Churchland, a radical opponent of folk psychology and a prominent supporter of eliminative materialism. I aim to examine whether current connectionist models based on long-short term memory (LSTM) neural networks can back up these arguments in favor of eliminativism. Nonetheless, I will rather put my faith in the eliminativism of the limited domain. This position amounts to the claim that even though connectionist cognitive science has no need whatsoever for folk psychology qua theory, this does not entail the illegitimacy of folk psychology per se in other scientific domains, most notably in humanities, but only on condition that one regards folk psychology as mere heuristics. (shrink)

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term (...) Memory are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations. (shrink)

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer (...) in the parking stations. To achieve that, various machine learning models, namely vector autoregression, support vector regression, eXtreme gradient boosting, k-nearest neighbors, and deep learning models specifically long short-time memory, gated recurrent unit, convolutional neural network, CNN-LSTM, and multilayer perceptron, were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time. (shrink)

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use (...) a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient of the speech signal is used as the input of the improved long and short-term memory network. To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed. (shrink)

Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to (...) extract the spatiotemporal dynamics of each gesture. Applying state-of-the-art deep neural networks, we implemented and compared different architectures where convolutional neural networks models demonstrated recognition rates of 97.147%, 3DMultiHeaded_CNN models showed rates of 98.553%, and rates of 99.234% were demonstrated by CNN_LSTM models. The collected data contained sufficient information to distinguish the bowing techniques studied, and deep learning methods were capable of learning the movement patterns that distinguish these techniques. Each of the learning algorithms investigated produced high classification accuracies which supported the feasibility of training classifiers. The resulting classifiers may provide the foundation of a digital assistant to enhance musicians' time spent practicing alone, providing real-time feedback on the accuracy and consistency of their musical gestures in performance. (shrink)

To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer (...) sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment. (shrink)

Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements (...) in both accuracy and interpretability. This article proposed encoder–decoder-based machine learning model, a popular tool in NLP, to classify user’s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder–decoder algorithms, attention-based decoder obtained the best accuracy (77%). (shrink)

Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency (...) domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models. (shrink)

Emotion recognition plays an important role in intelligent human–computer interaction, but the related research still faces the problems of low accuracy and subject dependence. In this paper, an open-source software toolbox called MindLink-Eumpy is developed to recognize emotions by integrating electroencephalogram and facial expression information. MindLink-Eumpy first applies a series of tools to automatically obtain physiological data from subjects and then analyzes the obtained facial expression data and EEG data, respectively, and finally fuses the two different signals at a decision (...) level. In the detection of facial expressions, the algorithm used by MindLink-Eumpy is a multitask convolutional neural network based on transfer learning technique. In the detection of EEG, MindLink-Eumpy provides two algorithms, including a subject-dependent model based on support vector machine and a subject-independent model based on long short-term memory network. In the decision-level fusion, weight enumerator and AdaBoost technique are applied to combine the predictions of SVM and CNN. We conducted two offline experiments on the Database for Emotion Analysis Using Physiological Signals dataset and the Multimodal Database for Affect Recognition and Implicit Tagging dataset, respectively, and conducted an online experiment on 15 healthy subjects. The results show that multimodal methods outperform single-modal methods in both offline and online experiments. In the subject-dependent condition, the multimodal method achieved an accuracy of 71.00% in the valence dimension and an accuracy of 72.14% in the arousal dimension. In the subject-independent condition, the LSTM-based method achieved an accuracy of 78.56% in the valence dimension and an accuracy of 77.22% in the arousal dimension. The feasibility and efficiency of MindLink-Eumpy for emotion recognition is thus demonstrated. (shrink)

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS-LSTM model written in Python is developed in this paper which combines rough set theory and the long- and short-term memory network model. The model proposed calculates the prediction (...) score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R2 of 0.96. Compared with the recurrent neural network model and BP neural network model, the training time of the RS-LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction. (shrink)

Solar energy has become an integral part of the economy of developed countries, so it is important to monitor the pace of its development, prospects, as well as the largest companies that produce solar panels since the supply of solar energy in a particular country directly depends on them. The study analyzes the shares of Canadian Solar Inc. and First Solar Inc. The purpose of the study is to study the possibility of forecasting the stock price of solar energy companies (...) using neural networks for the purpose of subsequent investment. The recurrent neural network LSTM is used in the article and this approach is based on complexity theory. Machine learning technologies are now being actively implemented in various sectors of the economy and are considered effective. The program used assigns different significance to the data of the last months and the data for the first months of the 1st year. The first year of the last 5 years of the company’s activity is taken as the first year since more distant data no longer have significant significance for the forecast. In the course of the study, a forecast of the stock price of Canadian Solar Inc. and First Solar Inc. for 245 days was obtained. Based on the results obtained, the following conclusions were made: 20 neurons of the network is not enough to make an accurate forecast, but the level of confidence in such a forecast is high enough, neural network forecasts are applicable in investing and are accurate enough to determine medium- and long-term trends, but these forecasts are not applicable for traders. The direction of improving the accuracy of neural network predictions is promising for further research. (shrink)

Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and (...) Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences. (shrink)

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions. The (...) recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis network, a recurrent independent component correlation algorithm [independent component analysis ] network, a t-SNE support vector machine network, a t-SNE back propagation neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency. (shrink)