Design and Development of Sports Training Assistance System Based on Explainable Artificial Intelligence Technology

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement; this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain.

A multi-modal Parkinson’s disease diagnosis system from EEG signals and online handwritten tasks using grey wolf optimization based deep learning model

Speaker identification is the process of automatically determining who is speaking from the known speakers by the model. It is crucial in voice-based authentication, forensic investigations, security and surveillance. In recent studies, the combination of convolutional neural network (CNN) and recurrent neural network (RNN) variants performed better than separate models of both. However, only limited studies are conducted in speaker identification using a combination of CNN and RNN variants. In this study, we proposed speaker identification using hybrid two-dimensional CNN (2DCNN) and bidirectional gated recurrent unit (BiGRU) to improve performance. The proposed model integrates the advantage of 2DCNN and BiGRU layers to improve the performance of the model. 2DCNN layers have the advantage of extracting short-term spatial features from input data and it has a limited number of parameters for computation. BiGRU layers have an advantage in extracting long-term temporal dependency between the features in both directions (i.e. backward and forward) and it is efficient in achieving convergence during training. Spectrograms of the speech were used as input in our proposed model because of the rich acoustic features of the speaker. To compare the performance of the proposed model, additional experiments are conducted using the models 2DCNN, CNN-LSTM, CNN-BiLSTM and CNN-GRU. The experiments were conducted on the VoxCeleb1 audio dataset, which consists of 153,516 utterances collected from the 1251 speakers. The accuracy, precision, recall and f1 score of the proposed model are 98.28%, 99.08%, 98.92% and 98.97% respectively. The proposed model was compared with the existing works to show the effectiveness of the proposed model. The experiment results and the comparison with the existing works show that the proposed model has higher performance than both existing works and other models experimented in this study.

Computational modelling of an optical character recognition system for Yorùbá printed text images

Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning

This research deals with the industrial financial forecasting in order to calculate the yearly expenditure of the organization. Forecasting helps in estimation of the future trends and provides a valuable information to make the industrial decisions. With growing economies, the financial world spends billions in terms of expenses. These expenditures are also defined as budgets or operational resources for a functional workplace. These expenses carry a fluctuating property as opposed to a linear or constant growth and this information if extracted can reshape the future in terms of effective spending of finances and will give an insight for the future budgeting reforms. It is a challenge to grasp over the changing trends with an effective accuracy and for this purpose machine learning approaches can be utilized. In this study Long Short-Term Memory (LSTM), which is a variant of Recurrent Neural Network (RNN) from the family of Artificial Neural Networks (ANN), is used for forecasting purposes along with a statistical tool IBM SPSS for comparative analysis. In this study, the experiments are performed on the data set of Pakistan GDP by type of expenditure at current prices - national currency (1970-2016) produced by Economic Statistics Branch of the United Nations Statistics Division (UNSD). Results of this study demonstrate that the proposed model predicted the expenses with better accuracy than that of the classical statistical tools.

Stocks are one of the most popular investment instruments among the public due to their potential for long-term returns through price appreciation and dividend distributions; however, stock price movements are heavily influenced by various factors such as macroeconomic conditions, market sentiment, and corporate actions, making accurate forecasting essential for investors to minimize risk and maximize profit. PT Bank BJB Tbk (ticker code: BJBR), a major bank in Indonesia that operates both conventional and Sharia-based services, has shown high volatility over the past few. Therefore, this research aims to develop a stock price prediction model for BJBR using the Long Short-Term Memory (LSTM) approach, a variant of Recurrent Neural Networks (RNN) well-suited for time series data. Historical closing price data from January 2020 to June 2025 were collected, preprocessed through normalization, dataset division, and transformation into supervised learning format, and then used to train an LSTM model with a two-layer architecture and dropout layers to prevent overfitting. The model was trained using the Adam optimizer and evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Evaluation results showed that the model achieved a high level of accuracy, with an R² value of 0.9643 on the test data, while visualizations of predicted versus actual prices demonstrated a strong alignment, proving that the LSTM model is effective in capturing temporal patterns in financial time series data and can serve as a valuable tool for data-driven investment decision-making.

Missing values are a common problem of time series such as sensor-generated data due to issues of data collection, transmission, and storage. To deal with this problem, various data imputation methods have been proposed. Among these methods, variants of recurrent neural networks (RNNs) have attracted a lot of attention as they provide a natural representation of temporal dynamics. Though such methods have been shown to achieve higher imputation accuracy, they mostly focus on the modeling of temporal dynamics from a short-term perspective while ignoring the long-term features such as trend and seasonal information embedded in time series data. In this paper, we investigate different paradigms of time series imputation methods to capture temporal dynamics from both long-term and short-term perspectives. In particular, we propose a new learning paradigm called MuSDRI which combines the time series decomposition method Seasonal Trend decomposition using Loess (STL) with RNNs. Specifically, STL is used to decompose long sequences of time series into trend, seasonal, and remainder components such that long-term dynamics can be revealed; RNNs are used to capture local patterns from the remainder component extracted by STL such that short-term dynamics can be refined. Moreover, MuSDRI is able to handle time series with multiple seasonal patterns by learning their contributions with respect to the imputation task. We evaluate the proposed imputation methods on three real-world datasets. Experimental results show that the imputation methods considering both the long-term and short-term dynamics achieve higher imputation accuracy, and among them MuSDRI in general achieves the best performance.

Abstract. Rainfall time series prediction is essential for monitoring urban hydrological systems, but it is challenging and complex due to the extreme variability of rainfall. A hybrid deep learning model (VMD-RNN) is used in order to improve prediction performance. In this study, variational mode decomposition (VMD) is first applied to decompose the original rainfall time series into several sub-sequences according to the frequency domain, where the number of decomposed sub-sequences is determined by power spectral density (PSD) analysis. To prevent the disclosure of forthcoming data, non-training time series are sequentially appended for generating the decomposed testing samples. Following that, different recurrent neural network (RNN) variant models are used to predict individual sub-sequences, and the final prediction is reconstructed by summing the prediction results of sub-sequences. These RNN variants are long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU), which are optimal for sequence prediction. In addition to three common evaluation criteria, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), the framework of universal multifractals (UMs) is also introduced to assess the performance of predictions, which enables the extreme variability of predicted rainfall time series to be characterized. The study employs two rainfall time series with daily and hourly resolutions, respectively. The results indicate that the hybrid VMD-RNN model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure LSTM model without decomposition.

Recurrent transform learning

<div class="section abstract"><div class="htmlview paragraph">Connected vehicle data unlock compelling solutions for vehicle owners and fleet managers. In selecting machine learning algorithms for use in predicting a connected vehicle signal value, time series dependency is critical to understand. With little to no time series dependency, conventional machine learning models may be used with a feature set that has few or no lag variables. If there is a lot of time series dependency including long-term dependencies, deep learning architectures like variants of recurrent neural networks (RNN) may be a better approach. Further, at any time step, RNN features may be specified to use some number of past time steps to predict the latest value. This paper seeks to identify time series dependency of connected vehicle signals, and selection of the number of time steps to look back in the features set to minimize error.</div></div>

Reservoir computing (RC), a variant of recurrent neural networks (RNNs), is well-known for its reduced energy consumption through exclusive focus on training the output weight and its superior performance in handling spatiotemporal information. Implementing these networks in hardware requires devices with superior fading memory behavior. Unlike filament-based two-terminal devices, those relying on ferroelectric switching demonstrate improved voltage reliability, while three-terminal transistors provide additional active control. HfO2-based ferroelectric materials such as Hf0.5Zr0.5O2 (HZO), have garnered attention for their scalability and seamless integration with CMOS technology. This study implements a RC hardware based on MoS2-HZO integrated device structure with enhanced spontaneous polarization field. By adjusting the oxygen vacancy concentration, the devices exhibit consistent responses to both identical and nonidentical voltages, making them suitable for diverse RC applications. The high accuracy of MNIST handwritten digits recognition highlights the rich reservoir states of the traditional RC architecture. Additionally, the impact of masks on RC implementation is assessed, showcasing the device's capability for spatiotemporal signal analysis. This development paves the way for implementing energy-efficient and high-performance computing solutions.

Prediction concept needs human-like thinking and ideas. ANN helps make a program for describing the solutions to the prediction problems. To gain accurate prediction, there is a need for deep thinking in the knowledge system discovered. Deep learning helps form such next-level type of ANN thinking. Deep learning models do not require humans for programming the problems. It can determine the prediction by itself by learning the dataset. This paper describes the prediction by considering sequential model for continuous accessing of data with the help of multi-layers. Deep learning is very helpful for predicting larger dataset. This paper proposes an algorithmic model known as a Comprehensive Deep Recurrent Artificial Neural Networks (CDRANN) by hybridizing LSTM (long short-term memory) with RNN (recurrent neural network) backtrack solver for better future stock prediction. This paper also explains the need for hybridizing evolutionary model CDRANN to create a novel change in future prediction.KeywordsCDRANN (Comprehensive Deep Recurrent Artificial Neural Networks)LSTM (long short-term memory)RNN (recurrent neural network)Stock marketPrediction

BackgroundEntity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years. In recent years, recurrent neural network (RNN), one of deep learning methods that has shown great potential on many problems including named entity recognition, also has been gradually used for entity recognition from clinical texts.MethodsIn this paper, we comprehensively investigate the performance of LSTM (long-short term memory), a representative variant of RNN, on clinical entity recognition and protected health information recognition. The LSTM model consists of three layers: input layer – generates representation of each word of a sentence; LSTM layer – outputs another word representation sequence that captures the context information of each word in this sentence; Inference layer – makes tagging decisions according to the output of LSTM layer, that is, outputting a label sequence.ResultsExperiments conducted on corpora of the 2010, 2012 and 2014 i2b2 NLP challenges show that LSTM achieves highest micro-average F1-scores of 85.81% on the 2010 i2b2 medical concept extraction, 92.29% on the 2012 i2b2 clinical event detection, and 94.37% on the 2014 i2b2 de-identification, which is considerably competitive with other state-of-the-art systems.ConclusionsLSTM that requires no hand-crafted feature has great potential on entity recognition from clinical texts. It outperforms traditional machine learning methods that suffer from fussy feature engineering. A possible future direction is how to integrate knowledge bases widely existing in the clinical domain into LSTM, which is a case of our future work. Moreover, how to use LSTM to recognize entities in specific formats is also another possible future direction.

Recurrent neural networks (RNNs) have enjoyed great success in speech recognition, natural language processing, etc. Many variants of RNNs have been proposed, including vanilla RNNs, LSTMs, and GRUs. However, current architectures are not particularly adept at dealing with tasks involving multi-faceted contents. In this work, we solve this problem by proposing Multiple-Weight RNNs and LSTMs, which rely on multiple weight matrices in an attempt to mimic the human ability of switching between contexts. We present a framework for adapting RNN-based models and analyze the properties of this approach. Our detailed experimental results show that our model outperforms previous work across a range of different tasks and datasets.