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Recently, Heart diseases is considered as the one of deadliest diseases which has resulted in the increased death rates across the globe. Predicting heart diseases requires vast experiences along with advanced knowledge. IoT and AI are two emerging technologies that help in heart disease prediction. High diagnostic accuracy with minimal processing overhead, however, continues to be a design problem for researchers. To address this problem, this paper develops the Intelligent IoT structure for the better prediction of cardiac diseases employing Harris Hawk Optimized Gated Modified Recurrent Units (HHO-M-GRU). The paper also proposes the real time data collection using IoT wearable test beds which comprises of electrocardiography sensors (ECG) interfaced with MICOTT Boards & ESP8266 transceivers. For later processing, the acquired data are saved on the cloud. The proposed deep learning network is utilized for evaluating the received heart data and used for predicting the heart diseases. Additionally, the suggested HHO-GRU is trained with the versatile datasets which consist of normal and abnormal stages of heart diseases. By calculating the suggested model's performance measures, including accuracy, precision, recall, specificity, and F1-score, a thorough experiment is conducted. The proposed framework was implemented in Keras libraries with Tensorflow 2.1.1 as backend. Furthermore, prediction performance and complexity overhead is compared using the other cutting-edge deep learning algorithms already in use to demonstrate the model's superiority. in predicting the heart diseases. The suggested approach beats previous models for learning with respect to of accurate prediction (99%) and minimal computing overhead, according to the results. [ABSTRACT FROM AUTHOR]

To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems.

Abstract Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

Facial expression recognition (FER) is one of the trending topics in the research field in recent years. Facial expressions are the direct way in which humans communicate their emotions and identifying these emotions requires complex cognitive skills. Because of continual advances in image processing and machine learning techniques, several novel strategies are presented each year. Their performance still needs to be enhanced, and it is a challenging task. So, we aim to propose a novel method called Ensemble Convolutional Recurrent Neural Network (CRNN), and this model is proposed to enhance the prediction accuracy of facial expressions. The methods such as GRU, LSTM, and CNN prediction outputs are incorporated. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is constructed as the ensemble model's topmost layer to adequately examine the few sub-models. The final output is predicted by the fuzzy inference system. The facial Recognition Dataset and EMOTIC dataset are utilized in the method for evaluating the results. The various performance metrics are utilized to test the efficiency of the proposed model such as precision, False Positive Rate, F1-score, True Positive Rate, and accuracy. Our proposed method achieves 99.52% for precision and 99.35% for F1-score. Compared with other existing such our proposed method achieves 99.75% and the value of the Area under the ROC curve (AUC) is 0.95. [ABSTRACT FROM AUTHOR]

Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action. [ABSTRACT FROM AUTHOR]

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The current study presents a novel, non-invasive method for estimating both systolic and diastolic blood pressure by combining photoplethysmogram (PPG) signals with physiological data, such as sex, age, weight, height, heart rate, and BMI, using two Gated Recurrent Units (GRUs) models. The first model processes dynamic patterns in PPG signals, while the second model incorporates physiological parameters. Both models are connected through a series of dense layers. To prepare the datasets for the GRU framework, rigorous preprocessing was conducted. This resulted in a robust architecture capable of accurately predicting systolic and diastolic blood pressure. The proposed method achieved a Mean Absolute Error (MAE) of 1.458 for systolic and 1.164 for diastolic blood pressure. These findings demonstrate the potential of this approach for continual and non-intrusive blood pressure monitoring in wearable health technology. The study’s results also make a significant contribution to the field of medical monitoring technology. The proposed solution addresses a major limitation in traditional blood pressure measurement practices and paves the way for advancements in personalized health monitoring, particularly for managing hypertension and cardiovascular conditions.

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection. [ABSTRACT FROM AUTHOR]

Martial arts involve highly complex motions with intricate poses and maneuvers. Current video analysis methods face challenges in robustly scaling across diverse data while accurately modeling the nuances of techniques and forms. This research develops a deep learning framework integrating convolutional neural networks (CNN) and Gated Recurrent Units (GRU) on Apache Spark to model martial arts poses and motions. The CNN extracts hierarchical visual features, highlighting athletes and movements in multimedia data. The GRU layers leverage temporal dynamics by processing pose sequences and actions via gating mechanisms. The GRU maintains an internal state, updating it based on current inputs and previous states. Its gates selectively retain context enabling the GRU to summarize variable-length time series into fixed pose encodings. The framework concatenates CNN visual features and GRU sequential states to provide interpretable pose representations. This granular encoding captures subtle posture details for fine-grained technique analysis. Comparative experiments validate the approach through accurate classification on UCF101 and precise pose estimation on the MADS dataset. The Spark integration enables scalable distributed training on these datasets. Specifically, the approach achieves 94% validation accuracy on martial arts action recognition. It also demonstrates robust performance with low MSE of 2.48 and MAE of 1.89 for pose estimation. The validated framework powers applications such as augmented coaching, optimized training regimes, progress tracking, and skill assessment to enhance martial arts mastery. Overall, the research pioneers an interpretable deep learning solution tailored for fine-grained martial arts training through scalable spatio-temporal modeling of complex motions. [ABSTRACT FROM AUTHOR]

The popularity of cryptocurrencies has been on the rise with the emergence of blockchain technologies. There have been enormous investments in the cryptocurrency market over the past few years. However, the volatile nature and significant price fluctuations in cryptocurrency have resulted in a high investment risk of these assets. In this paper, an improved neural network (NN) ensemble-based approach is proposed with the help of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (LSTM), i.e., CNN-BiLSTM for long-term price prediction of cryptocurrencies using both live data API and historical data. The CNN learns internal representation of each cryptocurrency independently and extracts useful features. On the other hand, the LSTM layers are used to predict time-series data and recognize the long as well as short-term dependencies efficiently and accurately. The proposed ensemble of CNN-BiLSTM consists of three layers of CNN and two layers of Bi-LSTM to improve the accuracy of the predictions. Moreover, MLP, GRU, CNN and LSTM models are also implemented and compared with the proposed model on the test datasets followed by error evaluation. For evaluating the error of each model, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) scores are analyzed for four cryptocurrencies Bitcoin, Ethereum, Dogecoin and Litecoin of historical and live data API. It is observed that the proposed CNN-BiLSTM ensemble model has the lowest RMSE score of 0.164 for live data API for Bitcoin and 0.166 for historical dataset for Dogecoin. The MSE score of 0.027 is observed for both Bitcoin and Dogecoin cryptocurrencies for live data API and 0.027 for Dogecoin for historical dataset. Thus, RMSE and MSE scores of the proposed approach are very less as compared to MLP, GRU, CNN and LSTM models for cryptocurrency price prediction for both the datasets. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual's educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual's ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN–LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU–CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models. [ABSTRACT FROM AUTHOR]

Mutations in genes can alter their DNA patterns, and by recognizing these mutations, many carcinomas can be diagnosed in the progression stages. The human body contains many hidden and enigmatic features that humankind has not yet fully understood. A total of 7539 neoplasm cases were reported from 1 January 2021 to 31 December 2021. Of these, 3156 were seen in males (41.9%) and 4383 (58.1%) in female patients. Several machine learning and deep learning frameworks are already implemented to detect mutations, but these techniques lack generalized datasets and need to be optimized for better results. Deep learning-based neural networks provide the computational power to calculate the complex structures of gastric carcinoma-driven gene mutations. This study proposes deep learning approaches such as long and short-term memory, gated recurrent units and bi-LSTM to help in identifying the progression of gastric carcinoma in an optimized manner. This study includes 61 carcinogenic driver genes whose mutations can cause gastric cancer. The mutation information was downloaded from intOGen.org and normal gene sequences were downloaded from asia.ensembl.org, as explained in the data collection section. The proposed deep learning models are validated using the self-consistency test (SCT), 10-fold cross-validation test (FCVT), and independent set test (IST); the IST prediction metrics of accuracy, sensitivity, specificity, MCC and AUC of LSTM, Bi-LSTM, and GRU are 97.18%, 98.35%, 96.01%, 0.94, 0.98; 99.46%, 98.93%, 100%, 0.989, 1.00; 99.46%, 98.93%, 100%, 0.989 and 1.00, respectively. [ABSTRACT FROM AUTHOR]

Arrhythmia is a kind of cardiac conduction disorder those result in irregular heartbeats. The electrocardiograph (ECG) signal may identify conduction system abnormalities. However, its visual analysis is challenging and time-consuming. An automated system for cardiac disorder detection may help in early and prompt diagnosis of diseases. In this paper, stationary wavelet transform (SWT) was used for pre-processing of the raw ECG signal before the segmentation and normalization process. Thereafter, recurrent neural network (RNN), gated recurrent units (GRU), bi-directional long short-term memory (Bi-LSTM) have been implemented for classification of normal, left bundle branch block (L-BBB), right bundle branch block l(R-BBB), premature atrial contraction (PAC), and premature ventricular contraction (PVC) beats. Bi-LSTM networks have shown best accuracy of 99.72% among all three implemented models. This demonstrates that this model is appropriate for computer-aided diagnosis of heartbeats. [ABSTRACT FROM AUTHOR]

The proliferation of artificial intelligence (AI) has opened up new avenues for the modeling of power electronics with ultra-fast transient responses, such as wide-bandgap (WBG) devices. This article highlights the significance of ultra-fast transient device-level hardware emulation for the DC railway microgrid (DRM) in real-time. To this end, the proposed approach partitions the DRM power system by transmission line method (TLM) and employs gated recurrent unit (GRU) and electromagnetic transient (EMT) modeling techniques for system-level subsystems. Meanwhile, for WBG devices, gallium nitride (GaN) high electron mobility transistors (HEMT) and silicon carbide (SiC) insulated gate bipolar transistors (IGBT) are modeled using a novel physical feature neuron network (PFNN), which offers high flexibility with a variable time-step (as low as 1 ns), thereby improving the accuracy, efficiency and accelerating the emulation on the field-programmable gate array (FPGA). The effectiveness of the proposed approach is confirmed by comparing the emulation results with offline simulation results obtained from PSCAD/EMTDC for system-level and SaberRD for device-level transients. The proposed PFNN approach provides strong versatility, ultra-fast transient emulation capability, and significantly improved accuracy, which bodes well for the future of power electronics device-level emulation.

With the continuing active research on deep learning, research on stock price prediction using deep learning has been actively conducted in the financial industry. This paper proposes a method for predicting stock price movement using stock and news data. The stock market is affected by many variables; thus, market volatility should be considered for predicting stock price movement. Because stock markets are efficient, all kinds of information are quickly reflected in stock prices. We create a new fusion mix by combining price and text data features and propose a hybrid information mixing module designed using two map blocks for effective interaction between the two features. We extract the multimodal interaction between the time-series features of the price data and the semantic features of the text data. In this paper, a multilayer perceptron-based model, the hybrid information mixing module, is applied to the stock price movement prediction to conduct a price fluctuation prediction experiment in a stock market with high volatility. In addition, the accuracy, Matthews correlation coefficient (MCC) and F1 score for the stock price movement prediction were used to verify the performance of the hybrid information mixing module.

Due to the lack of music knowledge extraction tools and public datasets, there are relatively few studies on music entity relation extraction. The Bidirectional Encoder Representations from Transformer (BERT) model considers contextual information and provides more dynamic, richer vector representations of generated words. Bidirectional Gated Recurrent Units (BGRU) has longdistance learning capability, which can obtain rich contextual semantic feature. Therefore, this paper proposes a deep learning model based on BERT and BGRU combined with an attention mechanism for Chinese music relation extraction. Experimental results show that the proposed method obtains good results when extracting the relation between musical entities. [ABSTRACT FROM AUTHOR]

The aim of this paper is to address the current situation where business units in smart grid (SG) environments are decentralized and independent, and there is a conflict between the need for data privacy protection and network security monitoring. To address this issue, we propose a distributed intrusion detection method based on convolutional neural networks–gated recurrent units–federated learning (CNN–GRU–FL). We designed an intrusion detection model and a local training process based on convolutional neural networks–gated recurrent units (CNN–GRU) and enhanced the feature description ability by introducing an attention mechanism. We also propose a new parameter aggregation mechanism to improve the model quality when dealing with differences in data quality and volume. Additionally, a trust-based node selection mechanism was designed to improve the convergence ability of federated learning (FL). Through experiments, it was demonstrated that the proposed method can effectively build a global intrusion detection model among multiple independent entities, and the training accuracy rate, recall rate, and F1 value of CNN–GRU–FL reached 78.79%, 64.15%, and 76.90%, respectively. The improved mechanism improves the performance and efficiency of parameter aggregation when there are differences in data quality. [ABSTRACT FROM AUTHOR]

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection.

Vehicles that run on petrol face competition from electric vehicles (EVs), which are more environmentally friendly and consume less energy than gasoline-powered automobiles. If we can predict the states that have an effect on charging, we might be able to estimate how much charging electric vehicle owners will require in the future. It is also capable of operating and managing charging infrastructure, in addition to providing users with individualised charge capacity statistics based on where they are precisely at the moment. As a result of this, developing a reliable model that can accurately predict the charging state of an electric vehicle has become an important issue. Based on the findings of this study, it is recommended to employ a combination of machine learning and deep learning in order to guarantee that the charging process is both secure and dependable, and that the battery does not become overcharged or over-drained. It has been suggested that a process of feature extraction using Recursive Neural Networks (RNNs) be utilised in order to obtain sufficient feature information regarding the battery. The bidirectional gated recurrent unit framework (GRU) was then established in the research project in order to make an educated guess as to the state of the electric vehicle. It is because of the information that the GRU obtains from the output of the RNNs that the model is significantly more useful. As a result of its more straightforward structure, the RNN-GRU is less effective when it comes to computing. In light of the findings of the tests, it is clear that the GRU method is capable of accurately monitoring the mileage of an electric vehicle. Based on the results of numerous tests conducted in the real world, it has been demonstrated that a mixed deep learning-based prediction method has the potential to provide a faster convergence speed and a lower error rate than the conventional method of obtaining an estimate of the state of charge.

To meet the progressively stringent demands for trajectory tracking performance in precision/ ultra-precision industry, multiple advanced feedforward compensation strategies have been reported in recent years. However, there is no fair and comprehensive performance comparison of advantages and drawbacks of various methods under different industrial working conditions. In this paper, a comparative study of several performance-oriented feedforward strategies represented by standard iterative learning control (ILC), cascaded ILC (CILC), gated recurrent units based feedforward compensation (GRU-FFC), and real-time iterative compensation (RIC) has been systematically conducted by theoretical clarification and experimental verification. Various trajectory tracking tasks are accomplished on a precision mechatronic motion stage, and the tracking performances to be examined include (i) tracking accuracy; (ii) extrapolation capability for non-repetitive trajectories; (iii) disturbance rejection ability. Experimental results are sufficiently summarized with the combination of underlying control mechanism analysis, which can provide a comprehensive and reliable selection guidance of feedforward compensation strategies in different practical industrial scenarios.

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew's correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen's kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual’s educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual’s ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN–LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU–CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models.

Mutations in genes can alter their DNA patterns, and by recognizing these mutations, many carcinomas can be diagnosed in the progression stages. The human body contains many hidden and enigmatic features that humankind has not yet fully understood. A total of 7539 neoplasm cases were reported from 1 January 2021 to 31 December 2021. Of these, 3156 were seen in males (41.9%) and 4383 (58.1%) in female patients. Several machine learning and deep learning frameworks are already implemented to detect mutations, but these techniques lack generalized datasets and need to be optimized for better results. Deep learning-based neural networks provide the computational power to calculate the complex structures of gastric carcinoma-driven gene mutations. This study proposes deep learning approaches such as long and short-term memory, gated recurrent units and bi-LSTM to help in identifying the progression of gastric carcinoma in an optimized manner. This study includes 61 carcinogenic driver genes whose mutations can cause gastric cancer. The mutation information was downloaded from intOGen.org and normal gene sequences were downloaded from asia.ensembl.org, as explained in the data collection section. The proposed deep learning models are validated using the self-consistency test (SCT), 10-fold cross-validation test (FCVT), and independent set test (IST); the IST prediction metrics of accuracy, sensitivity, specificity, MCC and AUC of LSTM, Bi-LSTM, and GRU are 97.18%, 98.35%, 96.01%, 0.94, 0.98; 99.46%, 98.93%, 100%, 0.989, 1.00; 99.46%, 98.93%, 100%, 0.989 and 1.00, respectively.

Deep learning models are one of the widely used techniques for forecasting time series data in various applications. It has already been established that the Recurrent Neural Networks (RNN) such as the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), etc., perform well in analyzing sequence data for accurate time-series predictions. But, these specialized recurrent architectures suffer from certain drawbacks due to their computational complexity and also their dependency on short term historical data. Hence, there is a scope for further improvement. This paper analyzes the effects of various optimizers and hyper-parameter tuning, on the precision and time efficiency of different deep neural architectures. The analysis has been conducted on COVID-19 pandemic data. Since Convolutional Neural Networks (CNN) are known for their super-human ability in identifying patterns from images, the time-series data has been transformed into a slope-information domain for analyzing the slope patterns over time. The domain patterns have been projected on a 2D plane for further analysis using a restricted recursive CNN (RRCNN) algorithm. The experimental results reveal that the proposed methodology reduces the error over benchmarked sequence models by almost 20% and further reduces the training time by nearly 50%. The prediction models considered in this study have been evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE%). [ABSTRACT FROM AUTHOR]

Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars' colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet's weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. [ABSTRACT FROM AUTHOR]

Human activity recognition is concerned with detecting different types of human movements and actions using data gathered from various types of sensors. Deep learning approaches, when applied on time series data, offer promising results over intensive handcrafted feature extraction techniques that are highly reliant on the quality of defined domain parameters. In this paper, we investigate the benefits of time series data augmentation in improving the accuracy of several deep learning models on human activity data gathered from mobile phone accelerometers. More specifically, we compare the performance of the Vanilla, Long-Short Term Memory, and Gated Recurrent Units neural network models on three open-source datasets. We use two time series data augmentation techniques and study their impact on the accuracy of the target models. The experiments show that using gated recurrent units achieves the best results in terms of accuracy and training time followed by the long-short term memory technique. Furthermore, the results show that using data augmentation significantly enhances recognition quality. [ABSTRACT FROM AUTHOR]

The revolution of artificial intelligence (AI) is transforming major industries worldwide. With accurate inferencing, AI has caught the attention of many engineers and scientists. Promisingly, hardware-in-the-loop (HIL) emulation can adopt this type of modeling method as one of the alternatives after comprehensive investigation. This paper proposes an approach for emulating power electronic motor drive transients for advanced transportation applications (ATAs) using machine learning building blocks (MLBBs) without any traditional circuit-oriented transient solver. The more electric aircraft (MEA) power system is chosen as a case study to validate the real-time emulation performance of MLBBs. Inside MLBBs, neural networks (NNs) have been applied to build component-level, device-level, and system-level models for various equipment. These models are well trained in a cluster and transplanted into the field-programmable gate array (FPGA) based hardware platform. Finally, MLBB emulation results are compared with PSCAD/EMTDC for system-level and SaberRD for device-level, which showed high consistency for model accuracy and high speed-up for hardware execution.

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

[Display omitted] • BG prediction for 30,45 and 60 min PH with three DNNs using the OhioT1DM dataset. • 7 BG prediction models were obtained with 3 DNNs and their decision level fusions. • Hyper-parameters are selected for each neural network using grid search algorithm. • For 30 min history BG prediction, a lower RMSE value was obtained then other studies. Background and Objective: Diabetes mellitus is a chronic disease that requires regular monitoring of blood glucose in the circulatory system. If the amount of glucose in the blood is not regulated constantly, this may have vital consequences for the individual. For this reason, there are many studies in the literature that perform blood glucose (BG) prediction. Methods: Blood glucose prediction is generally performed by using many parameters. In this paper, it was attempted to predict the future blood glucose values of the patient by using only the blood glucose values of diabetes patients' history. For this purpose, Long short term memory (LSTM), WaveNet and Gated Recurrent Units (GRU) and decision-level combinations of these architectures were used to predict blood glucose. First of all, hyper-parameters were selected for the most efficient operation of these network architectures and experimental studies were conducted using the extended OhioT1DM data set which has blood glucose history of 12 diabetes patients. Results: Experimental studies using 30, 45 and 60 min prediction horizon (PH), the average lowest RMSE value were obtained by the fusion of three networks as 21.90 mg/dl, 29.12 mg/dl, 35.10 mg/dl respectively. Conclusions: When the obtained RMSE value compared to state-of-art studies in the literature, the results show that the proposed method is quite successful for short-term blood glucose prediction. In addition, the proposed fusion method gives a new perspective for future studies in the literature for BG prediction. [ABSTRACT FROM AUTHOR]

Energy storage systems will play a key role in the power system of the 21st century considering the large penetrations of variable renewable energy, growth in transport electrification, and decentralization of heating loads. Therefore, reliable real-time methods to optimize energy storage, demand response, and generation are vital for power system operations. This article presents a concise review of battery energy storage and an example of battery modeling for renewable energy applications and details an adaptive approach to solve this load leveling problem with storage. A dynamic evolutionary model based on the first-kind Volterra integral equation is used in both cases. A direct regularized numerical method is employed to find the least-cost dispatch of the battery in terms of the integral equation solution. Validation on real data shows that the proposed evolutionary Volterra model effectively generalizes a conventional discrete integral model taking into account both the state of health and the availability of generation/storage. [ABSTRACT FROM AUTHOR]

• Linear and nonlinear ensemble models are proposed for spring discharge forecasting. • Ensemble models can reduce bias and increase generalization capabilities. • Ensem-SVR-BO outperforms other models and achieves robust results. Forecasting karst spring discharge is crucial for groundwater resource management in karst aquifers. These aquifers, with their inherent heterogeneity and complexity influenced by a myriad of environmental factors and hydrological processes, often show nonlinear and nonstationary behaviors. This study introduces novel multivariate multi-step ensemble models, both linear and nonlinear, to forecast karst spring discharge based on predictions from long short-term memory (LSTM), gated recurrent units (GRU) and one-dimensional convolutional neural network (1D-CNN). Linear and nonlinear ensemble learners including simple average method (SAM), linear regression (LR) and support vector regression (SVR) with Bayesian optimization (BO) are used to aggregate obtained results from base models and produce the final ensemble results. These proposed models, named as Ensem-SAM, Ensem-LR and Ensem-SVR-BO, are implemented for daily spring discharge forecasting across various lead times and time steps at Barton Springs, Texas, USA. Notably, these ensemble models outperform the individual base models in prediction accuracy and consistency. The results demonstrate that the ensemble framework can effectively leverage the strengths of diverse deep learning models and complement their limitations, thereby gaining strong generalization capabilities and robust performance, especially with diversiform and nonlinear data. The most marked disparities between individual base models and ensemble models arise with a short time step or a long lead time. Among all, Ensem-SVR-BO exhibits the best generalization capability and delivers accurate and robust prediction results even when one or all base models stuck in local optima. [ABSTRACT FROM AUTHOR]