Recurrent Neural Network Model Research Articles

A Secured Keystroke-Based Model for Preventing Social Engineering Attacks using Recurrent Neural Network

Among several authentication problems, preventing social engineering attacks using behavioural biometric approach has not received the required attention especially with focus on keystroke dynamics. This study aims to leverage the power of deep learning for more accurate and robust continuous authentication based on typing patterns. The proposed framework for this study utilized deep learning algorithm for behavioural biometrics authentication using Keystroke dynamics. The deep learning model was developed using Recurrent Neural Network (RNN) algorithm and was optimized was to obtain a better performance with Bayesian optimization which, eventually enhanced the model's accuracy. The dataset was split into training and testing in the model design phase and some hyperparameters such as dense, activation, batch size, sigmoid, filament, input size and epoch were used and optimized for building the deep learning algorithm. The RNN model is used to generate the evaluation metrics such as log loss, accuracy, precision and recall. The result presented the accuracy, precision, recall, and loss function as 100%, 100%, 100%, and 36% respectively for optimized model. The cost metrics yielded 0.0032, 0.0032, and 0.0006 MAE, MSE, and RMSE respectively. The developed KBB shows high level of social engineering attacks mitigation in comparison with the existing solution from the performance measure results.

ENHANCING NATURAL LANGUAGE PROCESSING USING WALRUS OPTIMIZER WITH SELF-ATTENTION DEEP LEARNING MODEL IN APPLIED LINGUISTICS

Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches.

Hybrid dung beetle optimization based dimensionality reduction with deep learning based cybersecurity solution on IoT environment

The Internet of Things (IoT) interconnects various devices and objects through the Internet to interact with corresponding devices or machines. Now, consumers can purchase many internet-connected products, from automobiles to refrigerators. Extending network capacities to every aspect of life can save money and time, increase efficiency, and enable greater access to digital experiences. Cybersecurity analysts often refer to this as increasing the attack surface from which hackers can benefit. Implementing the proper security measures is crucial since IoT devices can be vulnerable to cyberattacks and are often built with limited security features. Securing IoT devices involves implementing security measures and best practices to secure them from potential vulnerabilities and threats. Deep learning (DL) models have recently analyzed the network pattern for detecting and responding to possible intrusions, improving cybersecurity with advanced threat detection abilities. Therefore, this study presents a new Hybrid Dung Beetle Optimization-based Dimensionality Reduction with a Deep Learning-based Cybersecurity Solution (HDBODR-DLCS) method on the IoT network. The primary goal of the HDBODR-DLCS technique is to perform dimensionality reduction with a hyperparameter tuning process for enhanced detection results. In the primary stage, the HDBODR-DLCS technique involves Z-score normalization to measure the input dataset. The HDBO model is used for dimensionality reduction, which mainly selects the relevant features and discards the irrelevant features. Besides, intrusions are detected using the attention bidirectional recurrent neural network (ABiRNN) model. Finally, an artificial rabbits optimization (ARO) based hyperparameter tuning process is performed, enhancing the overall classification performance. The empirical analysis of the HDBODR-DLCS method is tested under the benchmark IDS dataset. The simulation outcomes indicated the HDBODR-DLCS method's improved abilities over existing approaches.

Predicting chlorophyll-a dynamics in the Ashtamudi estuary using ANN and ANFIS techniques

Excessive nutrients and associated high chlorophyll-a concentration are of growing concern to the public health due to the occurrence of eutrophication in aquatic bodies. Continuous monitoring of chlorophyll-a has limitations, thereby necessitating the development of prediction models. Even though chlorophyll-a prediction models are numerous, they rarely fit into estuarine conditions, which is encountered with dynamic mixing of freshwater and sea water. Thus, the present study identifies the most appropriate driving factors that are relevant for estuarine conditions through chlorophyll-a prediction models developed using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS) approaches in the Ashtamudi estuary, India. Salinity, which is an indirect measure of the estuarine mixing intensity, has been used to replicate the dynamic mixing in the estuary. The results indicate that the model incorporating salinity along with total nitrogen, total phosphorous and turbidity well-predicted chlorophyll-a concentration with a coefficient of determination ranging from 0.73 to 0.99. General Regression Neural Network (GRNN) and ANFIS models outperformed Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models. Of the various ANFIS models, the ones that used Gaussian and Generalized Bell membership functions were most appropriate for the prediction of chlorophyll-a than the models with triangular and trapezoidal membership functions. The study identified that the models that considered salinity were less sensitive and can be used for modelling chlorophyll-a. The chlorophyll-a was observed to have a direct influence of salinity at salinity values greater than 5‰. The study highlighted that estuarine mixing further enhances the primary productivity through increased penetration of light leading to higher chlorophyll-a concentration. Further the study emplasized that the predictive models are important tools fitting well within estuarine environments due to its replicability.

Plastic Constitutive Training Method for Steel Based on a Recurrent Neural Network

The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive datasets of steel were constructed using the Gaussian stochastic process. The RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) were used as models for training. The effects of the data pre-processing method, neural network structure, and training method on the model training were analyzed. The prediction ability of the model for different scale series and the corresponding data demand were evaluated. The results show that LSTM and the GRU are more suitable for stress–strain prediction. The marginal effect of the stacked neural network depth and number gradually decreases, and the hysteresis curve can be accurately predicted by a two-layer RNN. The optimal structure of the two models is A50-100 and B150-150. The prediction accuracy of the models increased with the decrease in batch size and the increase in training batch, and the training time also increased significantly. The decay learning rate method could balance the prediction accuracy and training time, and the optimal initial learning rate, batch size, and training batch were 0.001, 60, and 100, respectively. The deep learning plastic constitutive model based on the optimal parameters can accurately predict the hysteresis curve of steel, and the prediction abilities of the GRU are 6.13, 6.7, and 3.3 times those of LSTM in short, medium, and long sequences, respectively.

Comparison of Deep Learning and Machine Learning Model for Phishing Email Classification

Email is a medium in business communication that people use everyday and not only holds sensitive information but also identity for the user and organization. The uniqueness of information contained make phishing detection and remedy cannot be applied generally. Existing method applied in common on security software such as blacklisting keyword or email address is not enough to outpace evolving phishing method. Nowadays the implementation of machine learning and deep learning grow rapidly fast and the method used by machine learning and deep learning that can learn pattern of inputs and natural language processing making classification to detect email phishing promising, this study present proposed method of phishing mail classification by comparing the use of machine learning and deep learning model, The algorithm used in this paper are recurrent convolutional neural network and random forest with tf-idf and fasttext word embedding. The Dataset contain phishing and legitimate of an email gathered from a trading company in Indonesia. The dataset will be split into 70% for training and 30% for testing. As a result of this comparison, the random forest model with tf-idf word embedding achieve highest accuracy of 100% for the dataset used and the highest accuracy for recurrent convolutional neural network model with fasttext is 98.21%.

Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention

Predicting Neonatal Hypoglycemia Using AI Neural Networks in Infants from Mothers with Gestational Diabetes Mellitus.

BACKGROUND This study aimed to develop a predictive model for the association between maternal and neonatal anthropometric data and neonatal hypoglycemia based on data from mothers with gestational diabetes mellitus (GDM) and their neonates. MATERIAL AND METHODS We included 106 pregnant women with GDM (based on the World Health Organization International Association of Diabetes and Pregnancy Study Groups) and their neonates. Neonatal hypoglycemia was defined as a threshold of 2.5 mmol/L. Neonatal blood glucose levels were performed at 0, 0.5, 1, 3, and 24 h after birth. An artificial neural network (ANN) and recurrent neural network (RNN) were developed to predict the neonate blood concentrations and investigate the relative contribution of maternal and neonate clinical variables to neonatal hypoglycemia. RESULTS Of 106 mothers with GDM, 85% had obesity, and 78% had vaginal deliveries, with neonates averaging a birth weight of 3335.83 g. The ANN model, based on the clinical data from mothers and neonates, predicted blood glucose levels with a high degree of accuracy, achieving a coefficient of determination of 0.869 and a root mean square error (RMSE) of 0.274. Neonatal birth weight and maternal body mass index were the 2 most significant factors in predicting neonatal hypoglycemia, contributing 18.6% and 15.9%, respectively. The RNN model similarly forecasted glucose levels effectively, addressing the dynamic changes in blood glucose with 0.63 mmol/L RMSE and 0.53 mmol/L mean absolute error. CONCLUSIONS ANN and RNN models effectively predict neonatal hypoglycemia in infants of mothers with GDM, highlighting the critical role of maternal and neonatal factors.

Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.67 and 0.64, respectively) and use explainable artificial intelligence methods to interpret model predictions and understand salinization drivers. These models are applied to the Delaware River Basin, a developed watershed with diverse land uses that experiences anthropogenic salinization from winter deicer applications. These models capture seasonality for the winter first flush of deicers, and the streams with elevated predictions correspond well with indicators of deicer application. This result suggests that these models can be used to identify potential salinity-impaired streams for winter best management practices. Daily salinity predictions are driven primarily by land cover (urbanization) trends that may represent anthropogenic salinization processes and weather at time scales up to three months. Such modeling approaches are likely transferable to other watersheds and can be applied to further understand salinization risks and drivers.

A LSTM algorithm-driven deep learning approach to estimating repair and maintenance costs of apartment buildings

PurposeThis study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models.Design/methodology/approachRepair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors.FindingsThe LSTM model’s learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models’ learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects.Originality/valueGathering a real-world high-quality data set of apartment’s repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively.

Exploring Recent NLP Advances for Tamil: Word Vectors and Hybrid Deep Learning Architectures

The advancements of deep learning methods and the availability of large corpora and data sets have led to an exponential increase in the performance of Natural Language Processing (NLP) methods resulting in successful NLP applications for various day-to-day tasks such as Language translation, Voice to text, Grammar checking, Sentiment analysis, etc. These advancements enabled the well-resourced languages to adapt themselves to the digital era while the gap for the low-resource languages widened. This research work explores the suitability of the recent advancements in NLP for Tamil, a low-resource language spoken mainly in South India, Sri Lanka, and Malaysia. From the literature survey, it has been found that there is a lack of comprehensive study on the effect of the recent advancements of NLP for the Tamil text. To fill this gap, this research work analysed the performance of deep learning based text representation and classification approaches namely word embedding, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for Tamil text classification tasks. Different dimensional pretrained word2Vec and FastText word vectors were built for Tamil and their effectiveness on Text classification was evaluated. The study found that the pre-trained 300- dimensional FastText word vector showed better performance than other pre-trained word vectors for Tamil text classification. Further, in this study, four simple hybrid CNN and Bi-GRU models were proposed for Tamil text classification and their performances were evaluated. The study found that hybrid CNN and Bi-GRU models perform better compared to the classical machine learning models, individual CNN and RNN models, and the Multilingual BERT model. These results confirm that the jointly learned embeddings with different deep learning architectures like CNN and RNN can achieve remarkable results for Tamil text classification, thus ensuring that the deep learning approaches can be successful for NLP on Tamil text.

Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecasting

Solar energy (SE) is vital for renewable energy generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) is essential to address these challenges. The current research presents an innovative forecasting approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network for encoding and a Gated Recurrent Unit (GRU) network for decoding, incorporating attention mechanisms and positional encoding. This model is proposed to enhance SI forecasting accuracy by effectively utilizing meteorological weather data, handling overfitting, and managing data outliers and data complexity. To evaluate the model’s performance, a comprehensive comparative analysis is conducted, involving five algorithms: Artificial Neural Network (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, and Transformer models. The findings indicate that employing the TIR model leads to superior accuracy in the analyzed area, achieving R2 value of 0.9983, RMSE of 0.0140, and MAE of 0.0092. This performance surpasses those of the alternative models studied. The integration of BiLSTM and GRU algorithms with the attention mechanism and positional encoding has been optimized to enhance the forecasting of SI. This approach mitigates computational dependencies and minimizes the error terms within the model.

Utilizing Multi-layer Perceptron for Esophageal Cancer Classification Through Machine Learning Methods

Aims This research paper aims to check the effectiveness of a variety of machine learning models in classifying esophageal cancer through MRI scans. The current study encompasses Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN), and Visual Geometry Group 16 (VGG16), among others which are elaborated in this paper. This paper aims to identify the most accurate model to facilitate increased, improved diagnostic accuracy to revolutionize early detection methods for this dreadful disease. The ultimate goal is, therefore, to improve the clinical practice performance and its results with advanced machine learning techniques in medical diagnosis. Background Esophageal cancer poses a critical problem for medical oncologists since its pathology is quite complex, and the death rate is exceptionally high. Proper early detection is essential for effective treatment and improved survival. The results are positive, but the conventional diagnostic methods are not sensitive and have low specificity. Recent progress in machine learning methods brings a new possibility to high sensitivity and specificity in the diagnosis. This paper explores the potentiality of different machine-learning models in classifying esophageal cancer through MRI scans to complement the constraints of the traditional diagnostics approach. Objective This study is aimed at verifying whether CNN, KNN, RNN, and VGG16, amongst other advanced machine learning models, are effective in correctly classifying esophageal cancer from MRI scans. This review aims at establishing the diagnostic accuracy of all these models, with the best among all. It plays a role in developing early detection mechanisms that increase patient outcome confidence in the clinical setting. Methods This study applies the approach of comparative analysis by using four unique machine learning models to classify esophageal cancer from MRI scans. This was made possible through the intensive training and validation of the model using a standardized set of MRI data. The model’s effectiveness was assessed using performance evaluation metrics, which included accuracy, precision, recall, and F1 score. Results In classifying esophageal cancers from MRI scans, the current study found VGG16 to be an adequate model, with a high accuracy of 96.66%. CNN took the second position, with an accuracy of 94.5%, showing efficient results for spatial pattern recognition. The model of KNN and RNN also showed commendable performance, with accuracies of 91.44% and 88.97%, respectively, portraying their strengths in proximity-based learning and handling sequential data. These findings underline the potential to add significant value to the processes of esophageal cancer diagnosis using machine learning models. Conclusion The study concluded that machine learning techniques, mainly VGG16 and CNN, had a high potential for escalated diagnostic precision in classifying esophageal cancer from MRI imaging. VGG16 showed great accuracy, while CNN displayed advanced spatial detection, followed by KNN and RNN. Thus, the results set new opportunities for introducing advanced computational models to the clinics, which might transform strategies for early detection to improve patient-centered outcomes in oncology.

An Enhanced Transportation System for People of Determination.

Visually Impaired Persons (VIPs) have difficulty in recognizing vehicles used for navigation. Additionally, they may not be able to identify the bus to their desired destination. However, the bus bay in which the designated bus stops has not been analyzed in the existing literature. Thus, a guidance system for VIPs that identifies the correct bus for transportation is presented in this paper. Initially, speech data indicating the VIP's destination are pre-processed and converted to text. Next, utilizing the Arctan Gradient-activated Recurrent Neural Network (ArcGRNN) model, the number of bays at the location is detected with the help of a Global Positioning System (GPS), input text, and bay location details. Then, the optimal bay is chosen from the detected bays by utilizing the Experienced Perturbed Bacteria Foraging Triangular Optimization Algorithm (EPBFTOA), and an image of the selected bay is captured and pre-processed. Next, the bus is identified utilizing a You Only Look Once (YOLO) series model. Utilizing the Sub-pixel Shuffling Convoluted Encoder-ArcGRNN Decoder (SSCEAD) framework, the text is detected and segmented for the buses identified in the image. From the segmented output, the text is extracted, based on the destination and route of the bus. Finally, regarding the similarity value with respect to the VIP's destination, a decision is made utilizing the Multi-characteristic Non-linear S-Curve-Fuzzy Rule (MNC-FR). This decision informs the bus conductor about the VIP, such that the bus can be stopped appropriately to pick them up. During testing, the proposed system selected the optimal bay in 247,891 ms, which led to deciding the bus stop for the VIP with a fuzzification time of 34,197 ms. Thus, the proposed model exhibits superior performance over those utilized in prevailing works.

Generative AI with WGAN-GP for boosting seizure detection accuracy.

Imbalanced datasets pose challenges for developing accurate seizure detection systems based on electroencephalogram (EEG) data. Generative AI techniques may help augment minority class data to facilitate automatic epileptic seizure detection. This study investigates the impact of various data augmentation (DA) approaches, including Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), Vanilla GAN, Conditional GAN (CGAN), and Cramer GAN, on classification performance with Random Forest models. The best-performing GAN variant, WGAN-GP, was then integrated with a bidirectional Long Short-Term Memory (LSTM) architecture and compared against traditional and synthetic oversampling methods. The evaluation of different GAN variants for data augmentation with Random Forest classifiers identified WGAN-GP as the most effective approach. The integration of WGAN-GP with bidirectional LSTM yielded substantial performance improvements, outperforming traditional oversampling methods and achieving an accuracy of 91.73% on the augmented data, compared to 86% accuracy on real data without augmentation. The proposed generative AI approach combining WGAN-GP and recurrent neural network models outperforms comparative synthetic oversampling methods on metrics relevant for reliable seizure detection from imbalanced EEG datasets. Incorporating the WGAN-GP generative AI technique for data augmentation and integrating it with bidirectional LSTM elevates seizure detection accuracy for imbalanced EEG datasets, surpassing the performance of traditional oversampling and class weight adjustment methods. This approach shows promise for improving epilepsy monitoring and management through enhanced automated detection system effectiveness.

English Grammar Auto-Correction Robot based on Grammatical Error Generation Model

The traditional English grammar error correction system has problems such as poor error recognition precision and error correction success rate to be improved. Therefore, in this study, data augmentation techniques were used to transform and process error correcting English texts, and a rule-based and shallow neural network-based English text grammar correction model was constructed. The experimental results showed that the grammar error generation (GEG), rule-based (RB), classification-based (CB), and recurrent neural network (RNN) models achieved accuracy rates of 93.92%, 82.17%, 79.41%, and 88.09% in correcting grammar errors on 1702641 test sentences in the One Billion word corpus, respectively. The experimental results showed that the English grammar error correction model designed in this study had a strong error correction ability, but the computational efficiency was low. The research results significantly improved the accuracy and generalization ability of English grammar correction, optimized learning costs, and brought positive impacts to educational applications, providing strong support for the development of intelligent English grammar correction.

A dynamic solvent chamber propagation estimation framework using RNN for warm solvent injection in heterogeneous reservoirs

Warm solvent injection (WSI), injecting low-temperature solvent into formations to reduce the viscosity of heavy oil, is a clean technology for heavy oil production through reducing greenhouse gas emissions and water usage. The success of WSI operation depends on the uniform development and propagation of solvent chambers in reservoirs. However, reservoir heterogeneity stemming from shale barriers plays a detrimental role in the conformance of solvent chamber development and oil production rate. In this work, we developed a novel recurrent neural network (RNN)-based framework with the capability of efficiently tracking and estimating the solvent chamber positions in heterogeneous reservoirs based on only production time-series data. The developed estimation model utilizes the “sequence-to-sequence" mapping methodology to correlate observed production time-series sequence and solvent chamber edge sequence via a long short-term memory (LSTM) algorithm. The trained RNN models exhibit high accuracy, evidenced by the predicted dynamic solvent chamber locations match the corresponding true locations from numerical simulation, with a high coefficient of determination (R2) and a low mean squared error. Specifically, the achieved R2 values exceed 0.98 on both the training and testing data. The developed RNN-based workflow was tested via several cases from both regularly- and irregularly-shaped shale barriers, and the results were promising. The predicted solvent chambers showed strong agreement with those obtained from numerical simulations. The major benefits of this workflow include reducing computational time and saving overall monitoring and tracking costs for conventional techniques. The present work would provide a good demonstration of the capability of practical integration of machine learning methods in solving engineering problems.

Enhancing the Recognition of Handwritten Arabic Characters through Hybrid Convolutional and Bidirectional Recurrent Neural Network Models

Handwritten Arabic character recognition remains a challenging task in pattern recognition due to the inherent complexities of the cursive script and visual similarities between characters. While deep learning techniques have demonstrated promising results in this domain, further enhancements to the model architecture can drive even greater performance improvements. This study introduces a hybrid deep learning approach that combines Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). By leveraging the strengths of both convolutional and recurrent neural network components, the proposed models are able to effectively capture spatial features as well as model the temporal dynamics and contextual relationships present in handwritten Arabic text. Experiments conducted on the AHCD and Hijjaa benchmark datasets show that the CNN-Bi-GRU framework achieved state-of-the-art accuracy rates of 97.05% and 91.78% respectively, outperforming previous deep learning-based methods. These results demonstrate the significant performance gains that can be achieved by integrating specialized temporal modeling and contextual representation capabilities into the handwriting recognition pipeline, without the need for explicit segmentation. The findings of this research represent a crucial advancement in the continued development of sophisticated and precise deep learning systems for Arabic handwriting recognition, with broad applications across domains that rely on efficient text extraction from handwritten documents.

Cryptocurrencies Price Prediction Using Deep Learning Models (Gated Recurrent Unit And Recurrent Neural Network))

Background: With their volatile prices, cryptocurrencies have become valuable assets in the financial market. Predicting cryptocurrency prices accurately is essential for making well-informed investment decisions. Time series prediction models, like Gated Recurrent Unit (GRU) and Recurrent Neural Networks (RNN), are popular tools for financial data forecasting because they can capture sequential dependencies in data. Aim: This study aims to predict the average monthly closing prices of five major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Litecoin (LTC), and Ripple (XRP)—using GRU and RNN models and evaluate their performance in forecasting these prices. Method: Time series input sequences were produced and historical price data for the chosen cryptocurrencies were preprocessed using Min-Max Scaling. This data was divided into training and test sets, and it was used to train both the GRU and RNN models. The Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) were used to assess the performance of the model. Results: For the majority of cryptocurrencies, the RNN model exhibited better predicted accuracy and consistently outperformed the GRU model. For instance, the RMSE for Ripple was 0.06 for the RNN model and 0.09 for GRU. In a similar vein, the RNN model outperformed the GRU model with a MAPE of 12.97% for Ethereum. These results imply that RNN models are more suitable for financial forecasting in this sector, as they yield more accurate predictions for cryptocurrency values.

Improved RNN Model for Real-Time Human Activity Recognition

This study focuses on the automatic detection of human actions in video streams. The requirement to detect what human activities happen in videos is recognition of human action due to significant differences in people's visual and motion appearance and actions, camera perspective shifts, moving background, occlusions, noise, and a massive amount of video data. The human activity recognition challenge involves identifying physical activities carried out by individuals or groups based on traces of movements, including gestures, actions, interactions, and group activities. The detection of concepts usually requires additional annotations for the training dataset. In this paper, useful methods for categorizing human action recognition are discussed. The current models are an accurate deep learning method that is based on models that have been changed to be more useful. The large disparities that result from the backdrop and the size of the objects have prevented the identification of activities in videos from being fully and effectively addressed. The main objective is to achieve better accuracy for the Long Short-Term Memory (LSTM) method, which was used to improve the Recurrent Neural Networks (RNN) model. In this paper, LSTM is used to come up with models for different action recognition tasks. The model was made better by making the LSTM have four layers and putting 128 units, 64 units, 32 units, and 16 units in each layer, respectively. In addition, the performance evaluation of deep learning-based approaches has been compared to other related works. Therefore, an improved approach to RNN is proposed to recognize human actions. To classify the videos, a multilayer RNN with a specific type of LSTM is used to extract features from video sequences. The UCF-101 and UCF Sports human action recognition datasets are utilized in this study for both training and assessment. Test findings demonstrate that the suggested strategy achieved increased accuracy. Finally, the enhanced RNN model's total model accuracy in the UCF-101 dataset is 93.78% and 95.70% for the UCF Sport dataset.