Recurrent Neural Network Approach Research Articles

Movie Recommendation System Using Optimized RNN Approach.

This paper proposes a movie recommendation system that utilizes an optimized Recurrent Neural Network (RNN) approach. The proposed system is designed to provide users with personalized movie recommendations based on their previous movie preferences and the sentiments. The system works by taking user input, analyzing their movie preferences using content-based filtering techniques, and generating a list of recommended movies. The RNN architecture used in this system is optimized using a combination of techniques such as dropout regularization, early stopping, and parameter tuning. The proposed optimization techniques aim to reduce overfitting, improve convergence speed, and increase the model's overall accuracy. To evaluate the effectiveness of the proposed approach, we conducted experiments on the Movie Lens dataset. The results indicate that the optimized RNN-based movie recommendation system outperforms other existing recommendation systems such as collaborative filtering, content-based filtering, and standard RNN models. Furthermore, the proposed system achieved a significant improvement in accuracy and provided highly personalized recommendations to users. Overall, the proposed movie recommendation system using optimized RNN approach is a promising solution for providing personalized movie recommendations to users. It can be implemented in various platforms such as movie streaming websites, social media, and other movie-related platforms to improve the user experience and increase engagement

A recurrent neural network approach for nonconvex interval quadratic programming

In the paper, a new approach is presented to tackle an interval optimization problem with nonconvex interval objective function and affine interval equality and inequality constraints utilizing a recurrent neural network. The approach involves converting the nonconvex interval quadratic programming into a cubic optimization via convex combinations and considering its weight as a decision variable. The recurrent neural network is then established to solve the cubic optimization through subgradient techniques and an exterior penalty function method. Moreover, it is proven that there is a unique global solution to the recurrent neural network, and its trajectory can reach the feasible region in a finite time and remain there. Additionally, it is demonstrated that the trajectory of recurrent neural network converges exponentially or in finite time towards a singleton belonging to the set of critical points of the cubic optimization. This approach is beneficial for interval quadratic programming problems where the weight is unknown or challenging to determine in practical applications. Finally, the effectiveness of this approach is verified through two numerical examples.

Short-term wind power forecasting through stacked and bi directional LSTM techniques.

Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem. This, in turn, improves training performance. The RNN model is developed based on stack LSTM and bidirectional LSTM. The parameters like mean absolute error (MAE), standard deviation error (SDE), and root mean squared error (RMSE) are utilized as performance measures for comparison with recent state-of-the-art techniques. Results showed that the proposed technique outperformed the existing techniques in terms of RMSE and MAE against all the used wind farm datasets. Whereas, a reduction in SDE is observed for larger wind farm datasets. The proposed RNN approach performed better than the existing models despite fewer parameters. In addition, the approach requires minimum processing power to achieve compatible results.

Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks

Artificial neural networks (ANNs), one of the deep learning techniques that has sparked a lot of attention recently for its exceptional modelling capabilities of nonlinear systems, are an essential candidate for model-based control systems. In particular, recurrent neural networks (RNN) have shown remarkable capacity to forecast the dynamic behaviour of nonlinear processes using time-series data. In order to regulate the rate at which chemical species are produced in intricate processes, RNNs have successfully served as the predictive model in model-based controllers. However, the necessity for massive amounts of data to capture complex processes is a drawback of typical RNN models. With the goal of efficient utilisation of the available data, a different, transfer learning-based training approach for RNNs is presented in this work. The transfer learning strategy is taken into consideration to overcome the challenges stemming from lack of data for the construction of RNN models. In particular, weight-sharing RNNs are developed using a priori physical knowledge. Next, a comprehensive analysis of a complex chemical process on a large scale is conducted to showcase the benefits of the suggested approach across various data sizes and its effectiveness when combined with MPC in contrast to conventional RNNs.

Automated prediction of diabetes mellitus using infrared thermal foot images: recurrent neural network approach

Millions of people worldwide are affected by Diabetes Mellitus (DM), which is a chronic disease. Evaluation of the DM indicator, namely blood glucose level, requires invasive methods such as glucometer or blood tests, which cause discomfort to the patient. Automated noninvasive monitoring methods are urgently needed to ensure consistency and better treatment. The regular monitoring of DM can prevent or delay the onset of complications. Thermal foot images have been proposed as noninvasive methods for the prediction of DM. Thermograms were acquired at Mittal Eye Hospital, Sangrur, India, from 50 participants in the diabetic (without neuropathic conditions) and non-diabetic groups using a thermal camera (FLIR E-60). This study proposes an automated prediction system for DM using thermal foot images and Recurrent Neural Network (RNN) approach. The proposed system processes the thermal images and extracts relevant features using a CNN (Convolutional Neural Network). The extracted features were then fed to the RNN to predict the presence or absence of the DM. The experimental results demonstrate that the proposed framework attains an accuracy of (97.14 ± 1.5)%, surpassing the predictive capabilities of light-weight convolutional neural network (Lw-CNN), which only achieves an accuracy of (82.9 ± 3) % in predicting DM. This performance outperformed other state-of-the-art methods in the field. Our approach has the potential to be used as prediction tool for DM. Therefore, the proposed system has the potential for prediction of DM and improve patient outcomes by enabling timely intervention. Future work should focus on evaluating the proposed system on a larger dataset and integrating it with clinical decision support systems for personalized care. This study holds the promise of transforming DM screening and diagnosis, leading to enhanced patient outcomes.

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation.

Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations.

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

Optimal Reconstruction of Probabilistic Boolean Networks.

In gene regulatory networks (GRNs), it is important to model gene regulation based on a priori information and experimental data. As a useful mathematical model, probabilistic Boolean networks (PBNs) have been widely applied in GRNs. This article addresses the optimal reconstruction problem of PBNs based on several priori Boolean functions and sampled data. When all candidate Boolean functions are known in advance, the optimal reconstruction problem is reformulated into an optimization problem. This problem can be well solved by a recurrent neural network approach which decreases the computational cost. When parts of candidate Boolean functions are known in advance, necessary and sufficient conditions are provided for the reconstruction of PBNs. In this case, two types of reconstruction problems are further proposed: one is aimed at minimizing the number of reconstructed Boolean functions, and the other one is aimed at maximizing the selection probability of the main dynamics under noises. At last, examples in GRNs are elaborated to demonstrate the effectiveness of the main results.

Vessel trajectory prediction with recurrent neural networks: An evaluation of datasets, features, and architectures

Maritime situational awareness tasks such as port management, collision avoidance, and search-and-rescue missions rely on accurate knowledge of vessel locations. The availability of historical vessel trajectory data through the Automatic Identification System (AIS) has enabled the development of prediction methods, with a recent focus on trajectory prediction via recurrent neural networks (RNNs) and other deep learning architectures. While these methods have shown promising performance benefits over kinematic and clustering-based models, comparing among RNN-based models remains difficult due to variations in evaluation datasets, region sizes, vessel types, and numerous other design choices. As a result, it is not clear whether recent methods based on highly-sophisticated network architectures are necessary to achieve strong prediction performance. In this work, we present a simple fusion-based RNN approach to vessel trajectory prediction that allows for easy incorporation of exogenous variables. We perform an extensive ablation study to measure the impact of various modeling choices, including preprocessing, loss functions, and the choice of features, as well as the first usage of surface current information in vessel trajectory prediction. We demonstrate that our approach achieves state-of-the-art performance on three large regions off the United States coast, obtaining an improvement of up to 0.88 km over competing methods when predicting three hours into the future. We conclude that our simple architecture can outperform more complicated architectures while incurring a lower memory cost. Further, we show that the choice of loss function and the inclusion of surface current information both have significant impact on prediction performance.

The Impact of the COVID-19 Pandemic on Stock Market Performance in G20 Countries: Evidence from Long Short-Term Memory with a Recurrent Neural Network Approach.

In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.

Exploring deep learning architectures for urban sound classification

Urban sound monitoring is an essential part of city planning as it helps to understand the acoustic environment and its impact on citizens. Machine learning is a valuable tool in this field, providing important insights into the city's soundscape and supporting informed decision making, which can be useful for implementing noise mitigation measures and ensuring regulation compliance using sound source identification. Low-cost acoustic sensors have proven to be a highly effective way to acquire input data, offering scalable and widespread acoustical coverage of a city. The objective of this project is to improve upon existing sound classification models by comparing the performance of deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and the hybrid Recurrent Convolutional Neural Network (RCNN) approach. This study will also explore more sophisticated architectures, which incorporate dual channel feature extraction and temporal-frequency attention mechanisms (TFCNN). By evaluating the effectiveness of these models, the project aims to drive the field forward and support the development of smart, sustainable cities. The goal is to provide decision-makers with the insights and tools they need to create a better acoustic environment for city dwellers.

Recurrent Session Approach to Generative Association Rule based Recommendation

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot. In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability.

Billiard based optimization with deep learning driven anomaly detection in internet of things assisted sustainable smart cities

Internet of Things (IoT) technology involves a network of interconnected devices and sensors that gather and exchange information. In smart cities, IoT devices were utilized in several fields including energy, transportation, waste management, healthcare, etc., to improve the overall quality of life and sustainability of the populace. But, as the usage of IoT increases, the cybersecurity and data privacy become a concern of safety. An anomaly detection system helps to identify possible data breaches or cyber-attacks by identifying abnormal data patterns. Deep learning (DL) driven anomaly detection has emerged as an effective and powerful method for identifying abnormal behaviours or patterns in the data domain. This technique leverages the abilities of a deep neural network for automated learning of complex patterns and representations from data, which make it better for anomaly detection task where irregularities cannot be easily defined by handcrafted rules. This paper establishes a new Billiard Based Optimization with Deep Learning Driven Anomaly Detection and Classification (BBODL-ADC) technique in IoT-assisted Sustainable Smart Cities. The goal of the BBODL-ADC technique lies in the proper recognition and classification of anomalies in the IoT-assisted smart city. To obtain that, the BBODL-ADC system applies a binary pigeon optimization algorithm (BPEO) algorithm for the effectual selection of features. Besides, the BBODL-ADC technique utilizes Elman recurrent neural network (ERNN) approach for the recognition and classification of anomalies. Moreover, the BBO system can be used for better parameters chosen by the ERNN algorithm. The stimulation value of the BBODL-ADC algorithm can be executed benchmark database. The achieved outcomes demonstrate the remarkable outcome of the BBODL-ADC methodology of 95.69% and 99.21% compared to existing models under dataset-1 and dataset-2.

A data driven recurrent neural network approach for reproduction of variable visuo-haptic force feedback in surgical tool insertion

The surge of interest in haptic technology is due to inspirational advances in the robotic-assisted surgical system, where haptics has the role of delivering tactile feedback for enhancement of user experience. This work presents a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN) framework with Dimensionality Reduction (DR) and a Cyclical Learning Rate (CLR) optimizer for reproducing variable forces produced in different skin layers during the performance of various surgical procedures. This paper deals with online estimation of the force parameters of original porcine skin, and the same has been tested in real-time and Visuo-haptic environment for training surgeons. The proposed model has processed both spatial and temporal information acquired from three different dataset, surgical tools and manipulator. The results of proposed framework RNN-LSTM + DR + CLR show a 9.23 % & 3.8 % improvement in force prediction accuracy in real-time and 7.11 % & 1.68 % improvement in Visuo-haptic simulation compared to the RNN and RNN-LSTM prediction frameworks, respectively. The sensitivity analysis shows that torque (97.62 %), position (94.54 %), deformation (93.20 %), stiffness (89.23 %), tool diameter (87.25 %), rotation (63.21 %), and orientation (62.56 %) features have a respective impact on the predicted force. The performance of RNN-LSTM was better when the network was optimized with dimensionality reduction, loss function as Root Mean Square Error (RMSE), and learning rate as Cyclical Learning Rate (CLR). The research outcomes show the effectiveness of the method for estimating the force on the surface and internal layers of the skin. Also, the method has applications in real-time surgical tasks and surgeon training.

A hybrid control topology for cascaded H-bridge multilevel inverter to improve the power quality of smart grid connected system: NBO-RERNN approach

This manuscript proposes a hybrid control technique for the grid-connected photovoltaic (PV) generation system with a cascaded multilevel inverter (CMLI). The proposed hybrid approach integrates the Namib beetle optimization (NBO) and recalling-enhanced recurrent neural network (RERNN) approach, known as the NBO-RERNN technique. The cascaded multilevel inverter (MLI) is intended to increase the optimum control signal using the proposed controller. The cascaded multilevel inverter (MLI) is designed by using the minimum count of switches. The key purpose of the proposed approach is to improve power regulation or maximal solar energy conversion system (SECS) and to achieve good power quality (PQ) of the system. The proposed NBO technique generates the optimum control signal dataset offline. Based on the fulfilled dataset, the RERNN generates the best CMLI control signals inonlinemanner. The resultant control signals are used to regulate cascaded multilevel inverter insulated gate bi-polar switches (IGBT). For this purpose, the proposed NBO-RERNN control topology defines converter switching states by modeling generation system operating modes. The parameter variations of the system and external disturbances are mitigated, and the load demands are fulfilled optimally by using this control technique. The NBO-RERNN control topology is implemented in MATLAB, and its performance is compared with existing approaches. The simulation reveals that the proposed method has lower THD than the existing one.

A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method

Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the stock market prediction problem has attracted significant attention from both researchers and practitioners. Conventional machine learning models often fail to interpret the content of financial news due to the complexity and ambiguity of natural language used in the news. Inspired by the success of recurrent neural networks (RNNs) in sequential data processing, we propose an ensemble RNN approach (long short-term memory, gated recurrent unit, and SimpleRNN) to predict stock market movements. To avoid extracting tens of thousands of features using traditional natural language processing methods, we apply sentiment analysis and the sliding window method to extract only the most representative features. Our experimental results confirm the effectiveness of these two methods for feature extraction and show that the proposed ensemble approach is able to outperform other models under comparison.

Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT

Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods.

Deep Learning-Assisted Citation Recommendation System Using Multi- cell RNN Approach

A process of retrieving relevant or suitable papers that are mostly related to the research topic while carrying research knowing the behavior and patterns of citation for mapping the disciplines and subjects. This is used increasingly for judging usually takes a huge amount of effort and time. The citation recommendation is used frequently for solving this issue on suggesting the list of candidate papers automatically which may match the references of user. On applying Deep learning (DL) models in the citation recommendation system, the performance enhancement is greatly visible in various recent research studies. Hence, in this work, a deep learning-based model is employed for the citation recommendation system. Primarily, the input dataset is taken and the function words are removed using stochastic matching pattern scheme at which the library is created. Then, the canonicalization of document is carried by means of probabilistic min-match algorithm. The data is classified finally using Multi-cell RNN (Recurrent neural network) approach after which the rank is set for citation. At last, the performance is assessed for various metrics like MAP, MRR, precision, recall, and F1-measure. The obtained results are then compared with traditional models to validate and show the enhancement of proposed model over existing schemes. It is revealed from the analysis that the proposed scheme shows improvement in results.

지자기센서를 이용하지 않는 6축 IMU 기반의 3차원 관절각 추정용 순환 신경망

Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach

Machine learning based approaches for automatic disease prediction is a novel research area in healthcare informatics. Electronic Health Records in medical settings improves early-stage illness diagnosis. However, when standard rule-based approaches, like doctor's prescription or laboratory test reports are employed for disease diagnosis, the advantages of EHRs are not accomplished adequately. As a result, there is a requirement of technology based solution which helps in prediction of psychological diseases in a more efficient way. The proposed research work offers a hybrid Hopfield recurrent neural network (H2RN2) approach to predict psychological diseases by using amorphous clinical EHRs taken from Kaggle database. The proposed model automatically learns inherent semantic characteristics from available clinical data items. It uses fivefold cross validation technique within a recurrent neural network which detracts over fitting of the model. In addition to effective learning during training of the model, the hybrid approach also helps in accurate prediction of the disease with improved accuracy. The proposed model is assessed using three measuring parameters, accuracy, recall and F1-score and yields an accuracy of 97.53% in experimental evaluation, which is superior to several existing approaches for psychological disease prediction. The results demonstrate that the proposed model outperforms several other techniques in predicting the risk of psychiatric disorders. In future, the similar approach may be employed to predict gender-based psychological diseases or to anticipate the risk of various physiological diseases.