Speech Emotion Recognition on Small Sample Learning by Hybrid WGAN-LSTM Networks

Emotional expressions are a fundamental aspect of human communication, with speech being one of the most natural modes of interaction. Speech Emotion Recognition (SER) is a significant research topic in Natural Language Processing (NLP), aimed at identifying emotions such as satisfaction, frustration, and anger from speech audio using multiple classifiers. This article presents a method to emotion recognition from spontaneous Tunisian Dialect (TD) speech, marking the first work in the SER field to utilize spontaneous speech for emotion recognition in this dialect. The dataset was created from freely available YouTube videos across multiple domains and labeled with four perceived emotions: anger, satisfaction, frustration, and neutral. To address the data scarcity issue, we implemented data augmentation techniques, specifically Vocal Tract Length Perturbation (VTLP). The preprocessing of the speech signals involved cleaning the data from ambient and unwanted noises. We extracted and selected various spectral features, including Mel-Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCC). Subsequently, we applied several classification methods: Support Vector Machine (SVM), Bidirectional Long Short-Term Memory (BiLSTM), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Random Forest. Our experiments demonstrated that the Random Forest classifier achieved the highest F-score of 58.75%. The results were thoroughly discussed, analyzed, and compared across the five models using different feature extractions. This study provides valuable insights and advancements in the SER field, particularly for the TD, for future research directions for improving emotion recognition systems.

Schizophrenia (SZ) is a severe, chronic mental disorder without specific treatment. Due to the increasing prevalence of SZ in societies and the similarity of the characteristics of this disease with other mental illnesses such as bipolar disorder, most people are not aware of having it in their daily lives. Therefore, early detection of this disease will allow the sufferer to seek treatment or at least control it. Previous SZ detection studies through machine learning methods, require the extraction and selection of features before the classification process. This study attempts to develop a novel, end-to-end approach based on a 15-layers convolutional neural network (CNN) and a 16-layers CNN- long short-term memory (LSTM) to help psychiatrists automatically diagnose SZ from electroencephalogram (EEG) signals. The deep model uses CNN layers to learn the temporal properties of the signals, while LSTM layers provide the sequence learning mechanism. Also, data augmentation method based on generative adversarial networks is employed over the training set to increase the diversity of the data. Results on a large EEG dataset show the high diagnostic potential of both proposed methods, achieving remarkable accuracy of 98% and 99%. This study shows that the proposed framework is able to accurately discriminate SZ from healthy subject and is potentially useful for developing diagnostic tools for SZ disorder.

Last few decades, the availability and accessibility of the Bangla document and its content have rapidly increased due to the rapid technological advancement. Intense research needs to be performed on various Bangla documents due to the diversity of the language and associated sentiment. Document classification is one of the fundamental problems of Natural Language Processing. To handle miss-classification and convenient indexing and searching of Bangla documents on the web, researchers nowadays exploring different fields of computer science to classify Bangla documents. In this paper, Deep Learning based approaches are implemented to classify Bangla text documents. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is used here for the classification task. Here we have implemented an advanced technique that encoded the documents at their character level. Documents from three different data sources are used to validate and test of the working models. The highest classification accuracy is 95.42% that is achieved on the Prothom Alo data set using LSTM. Furthermore, we presented a comparison between two models and explained how well the classification task can be carried out using our character level approach with higher accuracy.

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 secs and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

-- Speech signals are being considered as most effective means of communication between human beings. Many researchers have found different methods or systems to identify emotions from speech signals. Here, the various features of speech are used to classify emotions. Features like pitch, tone, intensity are essential for classification. Large number of the datasets are available for speech emotion recognition. Firstly, the extraction of features from speech emotion is carried out and then another important part is classification of emotions based upon speech. Hence, different classifiers are used to classify emotions such as Happy, Sad, Anger, Surprise, Neutral, etc. Although, there are other approaches based on machine learning algorithms for identifying emotions. Speech Emotion Recognition is a current research topic because of its wide range of applications and it became a challenge in the field of speech processing too. We have carried out a brief study on Speech Emotion Analysis along with Emotion Recognition. Speech Emotion Recognition (SER) can be defined as extraction of the emotional state of the speaker from his or her speech signal. There are few universal emotions including Neutral, Anger, we have worked on different tools to be used in SER. SER is tough because emotions are subjective and annotating audio is challenging task. Emotion recognition is the part of speech recognition which is gaining more popularity and need for it increases enormously. We have classified based on different type of emotions to detect from speech. Key Words: Speech Emotion Recognition, Affective Computing, Machine Learning, Deep Learning, Audio Signal Processing, Emotion Classification, Feature Extraction, Prosodic Features, Spectral Features, Mel-Frequency Cepstral Coefficients (MFCCs), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Attention Mechanisms, Multimodal Emotion Recognition, Speaker-Independent SER, Real-Time Emotion Detection, Noise-Robust Emotion Recognition, Data Augmentation, Emotion-Aware Applications

Online leakage current classification using convolutional neural network long short-term memory for high voltage insulators on web-based service

COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situationculminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. Thepandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern,forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, havingknowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view toaccordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemicto both public health and economic sectors. Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory(LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm ConvolutionalNeural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, SouthAfrica and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developedfor 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAEand RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesserMSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africaand Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infectioncases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but alsoin terms of lesser MSE, MAE and RMSE.

An ensemble 1D-CNN-LSTM-GRU model with data augmentation for speech emotion recognition

This study proposes a modified and adaptive Balanced Scorecard (BSC) framework tailored to the strategic context of art museums as its primary contribution. The adapted BSC categorizes museum strategies into four dimensions—Social Values and Cultural Impact (SVCI), Visitor Experience and Tourism Development (VED), Exhibition and Operational Efficiency (EOE), and Art/Cultural Education and Sustainable Growth (ACES)—providing a systematic lens for interpreting and quantifying qualitative managerial priorities. These strategic perspectives were extracted from unstructured textual data sourced from museum websites and visitor-generated online content through natural language processing and text mining techniques. Building on this strategic classification, the study develops deep learning–based predictive models that integrate both structured numerical data and text-derived qualitative variables to estimate visitor numbers. Eight deep learning architectures, including Transformer, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Generative Adversarial Network (GAN), were evaluated. The results indicate that VED exerts the most substantial influence on visitor attendance, particularly through interactive exhibitions, personalized experiences, and tourism collaborations, while other strategic dimensions provide marginal improvements in predictive accuracy. The Transformer model achieved the best predictive performance, reflecting its superior ability to capture complex relationships between structured and unstructured data. By combining an adapted BSC framework with advanced predictive modeling, this study offers both a novel theoretical approach for categorizing museum strategies and a practical tool for enhancing decision-making in visitor engagement, exhibition planning, and resource allocation.

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.

Sequence Classification is one of the on-demand research projects in the field of Natural Language Processing (NLP). Classifying a set of images or text into an appropriate category or class is a complex task that a lot of Machine Learning (ML) models fail to accomplish accurately and end up under-fitting the given dataset. Some of the ML algorithms used in text classification are KNN, Naïve Bayes, Support Vector Machines, Convolutional Neural Networks (CNNs), Recursive CNNs, Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), etc. For this experimental study, LSTM and a few other algorithms were chosen for a more comparative study. The dataset used is the SMS Spam Collection Dataset from Kaggle and 150 more entries were additionally added from different sources. Two possible class labels for the data points are spam and ham. Each entry consists of the class label, a few sentences of text followed by a few useless features that are eliminated. After converting the text to the required format, the models are run and then evaluated using various metrics. In experimental studies, the LSTM gives much better classification accuracy than the other machine learning models. F1-Scores in the high nineties were achieved using LSTM for classifying the text. The other models showed very low F1-Scores and Cosine Similarities indicating that they had underperformed on the dataset. Another interesting observation is that the LSTM had reduced the number of false positives and false negatives than any other model.

Speech emotion recognition (SER) is an important field of study because we need better ways for computers and people to communicate. People’s feelings play a significant role in their conversations. It could be useful for machines to recognize and respond to human emotions in various areas, such as healthcare, education, customer service, and virtual assistants. Deep learning technologies have made significant strides in recent years, improving the performance of systems that handle speech-related tasks. SER has also seen major advancements due to the development of complex neural network designs. This paper offers an in-depth look at current research in SER, focusing on studies that use deep learning techniques. It compares the most effective models, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and hybrid models. This study also talks about the most important things that affect SER performance, such as choosing speech emotion datasets and features. This review additionally discusses about the problems and limitations that current SER research faces. For example, datasets are not very diverse, emotions are hard to represent, speakers and languages are dependent on each other, and it is hard to use the technology in real time. This paper seeks to provide substantial insights for researchers on SER by synthesizing findings from multiple recent studies.

Text classification is one of the important applications of Natural Language Processing (NLP) in which text is classified into pre-defined categories or classes. Several state-of-the-arts have already been conducted on text classification in different Indian languages, however, limited researches on text classification performed in Odia. Moreover, the news headlines classification is a text classification technique which is performed in our study mainly focusing on Odia language. The Convolutional Neural Network (CNN) based Deep Learning technique is used to classify the Odia news headlines dataset available at Kaggle repository. The dataset consisting of approx. 15,200 Odia news headlines is split into training and testing sets of 80:20 ratio and is categorized into different classes, such as sports, business, and entertainment by using CNN. The efficacy and efficiency of the proposed CNN-based Odia news headlines classification is evaluated in contrary to other existing machine learning and deep learning models, such as Neural Networks, LSTM (Long Short-Term Memory), and Bi-LSTM (Bidirectional Long Short-Term Memory) with respect to different performance metrics like precision, accuracy, recall, F1-score, training time, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). The simulation results revealed that the proposed CNN model outperformed with respect to other models.

Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information. On a broad variety of applications such as voice recognition and prediction, the long short-term memory (LSTM) fully connected layer (FCL) and the two convolutional neural networks (CNNs) have shown superior performance over deep learning networks (DLNs). DNNs are suitable for making high points for a more divisible region and CNNs are suitable for reducing recurrence types, LSTMs are appropriate for temporary displays, in the same way as CNNs are appropriate for reducing recurrence types. The CNN, LSTM, and DNN algorithms are acceptable for viewing. The complementarity of DNNs, CNNs, and LSTMs was investigated in this research by bringing them all together under the single architectural company. The researchers got the ECG data from the MIT-BIH arrhythmia database as a result of the investigation. Our results demonstrate that the approach proposed may expressively describe ECG series and identify abnormalities via scores that outperform existing supervised and unsupervised methods in both the short term and long term. The LSTM network and FCL additionally demonstrated that the unbalanced datasets associated with the ECG beat detection problem could be consistently resolved and that they were not susceptible to the accuracy of ECG signals. It is recommended that cardiologists employ the unique technique to aid them in performing reliable and impartial interpretation of ECG data in telemedicine settings.

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.