A Survey of Heart Disease Prediction using Deep Learning Methods

This study investigates the application of advanced deep learning models to forecast fossil energy prices, a critical factor influencing global economic stability. Unlike previous research, this study conducts a comparative analysis of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) models. The evaluation metrics employed include Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results reveal that recurrent architectures, particularly GRU, LSTM, and Bi-LSTM, consistently outperform feedforward and convolutional models, demonstrating superior ability to capture temporal dependencies and nonlinear dynamics in energy markets. In contrast, the RNN and DNN show relatively weaker generalization capabilities. Additionally, visualizations of actual versus predicted prices for each model further emphasize superior forecasting accuracy of recurrent models. The results highlight the potential of deep learning in enhancing investment and policy decisions. Additionally, the results provide significant implications for policymakers and investors by emphasizing the value of accurate energy price forecasting in mitigating market volatility, improving portfolio management, and supporting evidence-based energy policies.

Maxout neurons for deep convolutional and LSTM neural networks in speech recognition

In this study, we investigate the effectiveness of various deep learning algorithms for customer segmentation in the banking sector, aiming to enhance targeted service delivery and customer experience. We employ a comprehensive pipeline encompassing data collection, preprocessing, feature selection, feature extraction, model development, and rigorous evaluation. Our dataset, derived from real-world banking customer profiles, was processed using normalization, encoding, and dimensionality reduction techniques. We implemented and compared eight models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN-LSTM Hybrid, Autoencoder-Based Network, and Deep Neural Network (DNN). Among them, the Autoencoder-Based model achieved the highest accuracy of 91.56%, outperforming others in terms of segmentation clarity and computational efficiency. These findings suggest that deep learning methods, particularly Autoencoder-Based architectures, offer robust solutions for real-time banking customer segmentation, enabling institutions to tailor products and services more effectively.

Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data

A trademark may be a word, phrase, symbol, sound, color, scent or design, or combination of these, that identifies and distinguishes the goods or services of a particular source from those of others. Obtaining a trademark is a complex, time intensive and costly process that involves varied steps before the trademark can be registered including searching prior trademarks, filing of the trademark application, review of the trademark application and final publication for opposition by the public. One of the crucial aspect in the review of the trademark application is determining the international classes of the filed marks based on their goods and services description. Currently, the process of identifying the international classes of a filed mark is performed manually in the United States Patent and Trademark Office (USPTO) and takes significant amount of time. Recently, word embeddings and deep neural networks (DNNs) have revolutionized the field of computer vision and demonstrated excellent performance in various natural language processing (NLP) tasks such as machine translation, speech recognition, sentence and document classification etc. to name a few. In this study, we explored fastText and different neural networks such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), bidirectional versions of both LSTM and Gated Recurrent Unit (GRU) and Recurrent Convolutional Neural Network (RCNN) to automate the international class identification for the filed marks based on their goods and services description. Overall, it was found that the trademark word embeddings with RCNN model outperformed the other models. Our study thereby seeks to provide a solution towards the manual, time intensive and laborious process of identifying international classes for the trademarks.

Over the past few decades, research has focused heavily on automatic speech recognition (ASR). Although ASR for a few languages is close to reality; ASR for the low resource languages like Malayalam is still in its infancy. Here, in this work, the authors discuss the experiment conducted on accented data of Malayalam speech using two approaches. One approach uses the spectral features for modeling using deep convolutional neural network and the other uses the influential features of speech signals for modeling using LSTM-RNN approach. The proposed methodology comprises three distinct stages; dataset preparation, feature extraction, classification, and hence the construction of deep learning models that recognize the accent-based spoken sentences in the Malayalam language. Mel-frequency cepstral coefficient (MFCC) algorithm, short-term Fourier transform (STFT), and mel spectrogram methodologies are used for feature engineering and hence the features that represent the speech signals are used for constructing the accented ASR system for the Malayalam language using long short-term memory (LSTM) a recurrent neural network (RNN). The spectrogram dataset has been constructed for the speech dataset and used for constructing the ASR model with deep convolutional neural network (DCNN). The result of the experiment shows that LSTM-based RNN outperforms DCNN for the proposed dataset that has been constructed for the experiment in the natural recording environment.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

Stacked autoencoder (SAE) is hard to achieve satisfactory performance, when input data are complex and non-stationary. Besides, the identification performance of recurrent neural network (RNN) may decrease rapidly under noisy environment. In order to deal with these problems, a novel hybrid deep neural network (DNN) based on stacked denoising autoencoder (SDAE) and gated recurrent unit neural network (GRUNN) is presented. First, the structure of the presented hybrid DNN is given. The hybrid DNN contains a SDAE, a GRUNN, and a softmax classifier. Then, the training algorithm based on action discovery (AD) is proposed to train the presented hybrid DNN. The experimental studies indicate the presented hybrid DNN processes strong anti-noise ability and adaptability to time-varying signals.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Text classification is a task to assign text documents according to its content to one or more classes automatically. Recently character-level models using deep neural networks have been developed to do classification text. Moreover, in some cases, character-level models have outperformed word-level models and traditional models, especially on user-generated dataset. The topologies that have been used for the character-level models are convolutional neural networks (CNN) and bidirectional recurrent neural networks (Bi-RNN), with its variants; long short-term memory (LSTM) and gated recurrent units (GRU). In this paper, CNN, Bi-RNN, and the combination of both are tested with character-level features and word-level features for text classification on English and Indonesian social media datasets. On small size datasets, word-level model outperformed character-level models. However, on dataset with millions of data, character-level model outperformed word-level model. Further analysis on the evaluation of word-level and character-level models is also discussed in this paper.

The main neural network architectures applied for product demand forecasting are examined. Particular attention is given to Recurrent Neural Networks (RNN), which demonstrate high efficiency when working with time series and detecting dependencies within data. A detailed analysis is provided for the Long Short-Term Memory (LSTM) architecture, an advanced variant of RNN that effectively addresses the vanishing gradient problem, a common issue in traditional RNN. Due to its capability to retain information over extended periods, LSTM is ideal for tasks requiring the analysis of long-term dependencies, such as seasonal fluctuations in product demand. The application of Convolutional Neural Networks (CNN) is analyzed, which show strong performance when processing structured data, such as images or matrices. With their ability to effectively detect spatial dependencies, CNN can be useful for demand forecasting when complex relationships between various data features need to be considered, particularly for demand evaluations dependent on multiple factors. The architectures Feedforward Neural Networks (FNN), Gated Recurrent Units (GRU), Attention-based models (ABM) and Autoencoders (AE) are considered, which can also be applied to solve demand forecasting problems. A comparative analysis of the architectures is made in terms of forecast accuracy and ability to work with time series. By evaluating the training speed, data processing time, and reliability of these architectures in terms of the stability of results and forecast reliability, it has been determined that LSTM architectures are the most effective for product demand forecasting. These networks provide high accuracy and reliability of results and perform well with time series, which is crucial for forecasting demand under dynamic conditions.

Liquefaction susceptibility using machine learning based on SPT data

Automatic emotion recognition is a challenging task since emotion is communicated through different modalities. Deep Convolution Neural Networks (DCNN) and transfer learning have shown success in automatic emotion recognition using different modalities. However significant improvement in accuracy is still required for practical applications. Existing methods are still not effective in modelling the temporal relationships within emotional expressions or in identifying the salient features from different modes and fusing them to improve accuracies. In this paper, we present an automatic emotion recognition system using audio and visual modalities. VGG19 models are used to capture frame level facial features followed by a Long Short Term Memory (LSTM) to capture their temporal distribution at a segment level. A separate VGG19 model captures auditory features from Mel Frequency Cepstral Coefficients (MFCC). The extracted auditory and visual features are fused together and a Deep Neural Network (DNN) with attention is used in classification using majority voting. Voice Activity Detection (VAD) on the audio stream improves performance by reducing the outliers in learning. The system is evaluated using Leave One Subject Out (LOSO) and K-fold cross-validation and our system outperforms state of the art methods on two challenging databases.

Accurate forecast of container throughput is an important basis for reasonably planning port construction, making port operation plan and adjusting port development direction. Aiming at the complex nonlinear characteristics of port container throughput, a hybrid model based on selective deep-ensemble for container throughput forecasting (HMSD) is presented in this paper. Firstly, this model decomposes the original container throughput time series into several intrinsic mode functions and a residual by empirical mode decomposition (EMD). Considering highly nonlinear characteristics of each intrinsic mode function, the proposed model constructs three deep neural networks, namely, long short term memory (LSTM), gated recurrent unit (GRU) and convolutional neural network (CNN), as base learners to predict intrinsic mode functions. Then, this model establishes selective deep-ensemble forecasting model by improved group method of data handling (GMDH) on intrinsic mode functions and obtains their ensemble forecasting results. Furthermore, this model uses an autoregressive integrated moving average model to predict the linear residual. Finally, the forecasting result of the total container throughput is obtained by integrating the forecasting results of all intrinsic mode functions and the residual. The empirical analysis of container throughput data of Xiamen port and Shanghai port in China shows that the HMSD model has better forecasting effect than other models.

Parts of Speech (pos) tagging corresponds to techniques of tagging accurate pos tags of each word of a sentence. It is a pivotal and indispensable part of sentiment analysis, synthesizing text to speech, analyzing textual data and many more Natural Language Processing (NLP) tasks. Though there are many researches done in the field of languages like English, Chinese etc., it is still comparatively unexplored for Bangla. An excellent pos tagger in Bangla language can accelerate NLP of Bangla textual data. In this study, we have developed a novel hybrid Deep Learning (DL) model with two parallel input networks with Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM), and an output network of CNN. CNN works well regarding the local dependencies of words in sentences, and BiLSTM works well with the global dependencies of words regarding the context in a sentence. No other work has been done with local features of textual data along with long term word dependencies in Bangla pos tagging studies. The model can work with any external Word Embeddings. In this study, it is implemented with both Embedding Layer and Word2Vec layer. In the evaluation process, Mathews Correlation Coefficient (MCC), Precision-Recall curve are used along with accuracy and F1-score due to the dataset's imbalanced characteristic. Our proposed model accompanied by Word2Vec layer has outperformed Vanilla Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), BiLSTM networks, and also one recent study using the same dataset with the highest accuracy of 0.974, F1-score of 0.883, and MCC value of 0.936.