Long Short-term Memory Techniques Research Articles

Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.

Rumors detection in social networks using dynamic graph-structured bi-directional long-short term memory technique

Rumors detection in social networks using dynamic graph-structured bi-directional long-short term memory technique

Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques

This study introduces a highly effective technique to address the load flow challenge in Radial Distribution Networks (RDNs). The proposed approach leverages two matrices derived from the topological features of distribution networks to provide an optimal solution to handle load flow challenges. To assess the efficacy of this technique, simulations were executed on an IEEE 33-bus radial distribution system using MATLAB. Deep Learning (DL) has become a powerful artificial intelligence technique that excels at interpreting power grid datasets. Thus, a data-driven methodology is presented that incorporates an advanced Long-Short-Term-Memory (LSTM) network. Employing the Recurrent Neural Network with the LSTM (RNN-LSTM) technique based on these simulations, the study precisely identifies the optimal placement of an integrated PV generator within the radial network. The application of DL techniques, specifically LSTM networks, exemplifies the potential of data-driven approaches in enhancing decision-making processes. The results of this study highlight the potential of RNN-LSTM for the optimal integration of PV generators and for ameliorating the reliability of RDNs.

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.

An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application

Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.

Integrating LSTM and NLP techniques for essay generation

This research explores the potential of Long Short-Term Memory (LSTM) and Natural Language Processing (NLP) for automated essay generation. The goal of the study is to create a model that can produce high-quality essays that are not only grammatically correct but also semantically meaningful and contextually relevant. The rise of NLP and Deep Learning has made it possible to generate text that is coherent and semantically sound. In this research, there is an advantage by leveraging the ability of LSTMs to capture long-term dependencies and context within the text, and combining it with NLP techniques, such as word embeddings, to process and encode textual data. The results of experiments show that the proposed model can effectively generate essays that are coherent, contextually relevant, and semantically meaningful. This is a significant advancement in the field of text generation and has potential applications in areas such as education, content creation, and language translation. In education, for example, the model could be used to generate essays for language proficiency tests or as a writing aid for students. In content creation, it could be used to generate articles, blog posts, and other written content. In language translation, the model could be used to generate essays in the target language that are semantically and contextually equivalent to the source language essay. The findings of this study contribute to the advancement of NLP and deep learning techniques in the area of text generation and open up new avenues for future research. Short after the proposed model was deployed, it was discovered that it outscored Multi-Topic Aware LSTM (MTA-LSTM), Topic-Attention LSTM (TAT-LSTM), and Topic-Averaged LSTM (TAV-LSTM) in human evaluation by 6.84 percent, 25.40 percent, and 34.94 percent, respectively. Furthermore, it enhanced automatic BLEU score evaluation scores by 11.68 percent, 26.23 percent, and 54.11 percent in MTA-LSTM, TAT-LSTM, and TAV- LSTM, respectively.

LieWaves: dataset for lie detection based on EEG signals and wavelets.

This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.

Stock Price Forecasting with Deep Learning Techniques

In this study, LSTM (Long-Short Term Memory) and GRU (Gated Recurrent Unit) techniques of deep learning, which are among the latest advanced technologies, were applied in the Google Colab software program for stock price forecasting. The dataset used in the study was obtained from Yahoo Finance and covers the dates between 02/01/2013 and 30/12/2022. Forecast models were created by considering 5 companies belonging to the XELKT (Electricity Market in Borsa Istanbul) index, which is part of BIST (Borsa Istanbul). Subsequently, the success of these forecast models was tested with the calculated model performance criteria, aiming to determine whether the techniques used were successful in stock price forecasting. Additionally, based on the results of MSE (Mean Squared Error) and MAPE (Mean Absolute Percentage Error) among the calculated model performance criteria, the techniques used were compared with each other, aiming to determine which of these techniques provided forecasts with less error. Then, through the analysis conducted on four different days, an attempt was made to identify the day that yielded the most successful forecasts. As a final step, the goal was to find a model with the least error based on techniques, epoch number, and the number of days forecasted, considering both MSE and MAPE for stocks. Since the model performance criteria outputs obtained from these analyses are below 1 for MSE and below 5% for MAPE, it can be concluded that both techniques demonstrate successful stock price forecasting. Consequently, in the comparison between these two techniques, it is observed that the LSTM technique is slightly more successful than the GRU technique.

Efficient Prediction Model for Cardiovascular Disease Using Deep Learning Techniques

Cardiovascular illness is becoming more commonplace every day, which makes early identification of the condition worrisome and essential. Making a cardiac diagnosis is a challenging procedure that has to be finished fast and expertly. The identification and prediction of cardiovascular illness are vital medical responsibilities that help cardiologists appropriately diagnose and treat their patients. Deep Learning (DL) algorithms have found increasing usage in the medical industry because of their ability to recognize patterns in data. By applying machine learning to categorize the occurrence of cardiovascular illness, clinicians can reduce the rate of misdiagnosis. The goal of this work is to develop an ML model for cardiovascular disease (CVD) forecast based on correlated problems. This research develops a model that can accurately anticipate CVD illnesses, which will reduce the fatality rate from these ailments. This work employs a variety DL approaches to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. We used a benchmark dataset of UCI Heart disease prediction for this work, which consists of 14 different heart disease-related parameters. As DL models, there are Convolutional Nueral Network (CNN), Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). When compared to DL techniques, the result shows that LSTM offers superior prediction accuracy in less time. 91% accuracy was attained using this LSTM technique. This model performs better on training and testing data. The models were fitted to the test dataset as well as trained on the training dataset to see which fared best. The matrices acquired during this process were accuracy, specificity, sensitivity, Area Under Curve (AUC) and the Receiver Operating Characteristic Curve (ROC).

Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management.

Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals

To identify epilepsy, Electroencephalography (EEG) is an important and common tool used to study the electrical activity of the human brain. The machine learning-based classifier is utilized to detect the seizure by manually extracting the features from the EEG signals in previous works. Though, these effective benefits have been proved already with automatic feature extraction, they are unable to achieve the classification of multiple classes. Meanwhile, real-time epileptic seizure detection is unable to keep the capacity because the identifiable EGG is too long. Hence, this paper is decided to develop an enhanced deep learning architecture with EEG signal for performing automatic epileptic seizure detection. The EEG signal is collected from the standard datasets. The Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform (FBSE-EWT) method is used to decompose the signal from the gathered data. In feature extraction, the decomposed signals are used and while extracting the signal features, the autoencoder is involved in that process. To reduce the computational overload, the relief-F feature ranking method is used for choosing the important signal features. The Long Short Term Memory (LSTM) and Multi-Scale Atrous-based Deep Convolutional Neural Networks (MSA-DCNN) are used that is named as Hybrid Deep Scheme (HDS) to detect epileptic seizures with the top-ranked features for epileptic seizure classification. The Black Widow Optimization (BWO) and the Spider Monkey Optimization (SMO) are combined to generate Adaptive Spider Monkey Black Widow Optimization (ASMBWO) that is employed to perform the parameter tuning into a classification technique. Using different measures, the experimental analysis is done between the conventional epileptic seizure detection and the proposed model to establish an enhanced performance of the proposed model.

Research on the application and promotion of the carbon neutral concept based on the attention mechanism in football under the end-to-end architecture

IntroductionIn light of escalating concerns regarding global warming and environmental pollution, the pursuit of carbon neutrality has emerged as a pivotal strategy to address climate change on a global scale. As society becomes increasingly conscious of its ecological impact, various sectors, including sports, are urged to embrace environmental responsibility. This study seeks to explore the integration of a carbon neutral framework utilizing artificial intelligence's attention mechanism within the realm of football, with the aim of contributing to football's adoption of carbon neutrality.MethodsThe study commences by introducing an end-to-end architectural framework capable of unifying and optimizing all facets of football to realize a comprehensive carbon-neutral objective. This architecture serves as a consolidated platform for enhancing carbon emission reduction within football pedagogical activities, fostering synergy among diverse constituents while concurrently assessing the equilibrium between carbon reduction and pedagogical effectiveness. Subsequently, attention mechanisms are leveraged to heighten the efficacy and comprehensibility of carbon-neutral strategies. The application of attention mechanisms enables the model to autonomously focus on attributes or regions closely associated with carbon neutrality objectives, thereby facilitating precision and efficacy in recommending carbon neutral strategies. By employing attention mechanisms in football, a more thorough understanding of carbon emissions' dynamics is attained, allowing for the identification of pivotal emission contributors and tailored suggestions for emission mitigation. Furthermore, the Long Short-Term Memory (LSTM) method is employed to analyze football time-series data. Given football's intricate sequence of actions, the LSTM technique adeptly captures long-term dependencies, offering improved analysis and optimization of carbon emissions during football activities.ResultsThe integrated end-to-end architectural framework offers a holistic approach to carbon-neutral football strategies. Attention mechanisms effectively enhance the focus and interpretation of carbon-neutral strategies, contributing to precise and impactful recommendations. Employing LSTM for time-series analysis aids in comprehending carbon emission dynamics, enabling the identification of efficacious carbon neutral strategies. The study underscores the potential of AI-driven attention mechanisms and LSTM in fostering carbon neutrality within football.DiscussionThe study's findings underscore the viability of integrating AI-driven methodologies, specifically attention mechanisms and LSTM, to promote carbon neutrality within the football domain. The end-to-end architecture serves as a foundational platform for comprehensive carbon emission reduction, offering potential for broader application in other sectors. The combination of attention mechanisms and LSTM engenders deeper insights into carbon emissions' intricate temporal dynamics, informing the development of targeted strategies for emission mitigation. The study's outcomes provide theoretical underpinnings for advancing sustainable football practices and inspire the broader adoption of carbon neutrality principles across diverse domains.

A Double-Layer Indemnity Enhancement Using LSTM and HASH Function Technique for Intrusion Detection System

The Intrusion Detection System (IDS) is the most widely used network security mechanism for distinguishing between normal and malicious traffic network activities. It aids network security in that it may identify unforeseen hazards in network traffic. Several techniques have been put forth by different researchers for network intrusion detection. However, because network attacks have increased dramatically, making it difficult to execute precise detection rates quickly, the demand for effectively recognizing network incursion is growing. This research proposed an improved solution that uses Long Short-Term Memory (LSTM) and hash functions to construct a revolutionary double-layer security solution for IoT Network Intrusion Detection. The presented framework utilizes standard and well-known real-time IDS datasets such as KDDCUP99 and UNSWNB-15. In the presented framework, the dataset was pre-processed, and it employed the Shuffle Shepherd Optimization (SSO) algorithm for tracking the most informative attributes from the filtered database. Further, the designed model used the LSTM algorithm for classifying the normal and malicious network traffic precisely. Finally, a secure hash function SHA3-256 was utilized for countering the attacks. The intensive experimental assessment of the presented approach with the conventional algorithms emphasized the efficiency of the proposed framework in terms of accuracy, precision, recall, etc. The analysis showed that the presented model attained attack prediction accuracy of 99.92% and 99.91% for KDDCUP99 and UNSWNB-15, respectively.

Lung pressure predictive model using LSTM: A deep learning techniques

<p>The human body relies on controlled breathing to ensure oxygen reaches all cells while filtering out contaminants to protect the lungs. However, infections like the Delta virus and SARS-CoV2 (COVID-19) have led to Acute Respiratory Distress Syndrome (ARDS), requiring urgent medical care, including mechanical ventilation. The overwhelming number of patients has strained healthcare organizations and workers, necessitating advancements in automated healthcare technology. To address this challenge, we propose a novel solution to predict pressure in mechanical ventilation (MV) for various lung illnesses. The goal is to accurately predict the pressure within the respiratory circuit, which poses a challenging sequence prediction issue. To tackle this, we employ a cutting-edge deep learning approach known as Long Short-Term Memory (LSTM), which exhibits remarkable performance in selectively recalling patterns over time. While traditional recurrent neural networks (RNNs) can handle short-term patterns well, the introduced LSTM technique excels in managing complex sequence prediction problems. Comparing the proposed method with four existing algorithms, the researchers demonstrate that their approach achieves significantly higher accuracy. The impressively low error rate of 1.85 × 10<sup>−7</sup> showcases a substantial improvement over existing system. This groundbreaking advancement has the potential to alleviate the pressure on the current healthcare infrastructure and significantly improve care for patients in need of mechanical ventilation due to respiratory issues.</p>

Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro

Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. The focus of this paper is applying and comparing the performance of Support Vector Machine and hybrid LSTM regression models for air pollution prediction. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, our approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution of this paper is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures.

Hydrogen production via renewable-based energy system: Thermoeconomic assessment and Long Short-Term Memory (LSTM) optimization approach

A clean and carbon-free energy market can be more sustainable and safer that can be approaches by investing on hydrogen energy. Further, a renewable energy-driven energy production cycle coupled with an energy storage option is of major significance. In the present article, a hybrid process based on geothermal and solar energies is conceptually designed and simulated. The process is comprised of a solar-organic Rankine cycle (ORC) unit, a geothermal cycle, an electrolysis unit (based on alkaline electrolyzer stack, AES), and a nanomaterial-based unit (i.e., alkaline fuel cell stack, AFC) to store hydrogen. Combining nickel nanoparticles thermally enclosed in multiwalled carbon nanotubes (MWCNTs) with a composite thermoset anion-exchange membrane enables alkaline water splitting. When the grid demands are less than generated electricity rate, additional electricity is fed into an AES to generate hydrogen fuel. Further, when the grid demands are more than generated electricity rate, stored hydrogen and oxygen are fed into an AFC to back convert the chemical energy in the fuel into electrical energy. The thermodynamic and thermo-economic evaluations of the considered hybrid process are comprehensively presented. Furthermore, multi-objective optimization of the introduced process has been developed in accordance with the genetic optimization technique to maximize the rate of electricity and minimize the cost rate. Additionally, the Long Short-Term Memory (LSTM) -based optimization technique is established and discussed. The outcomes indicated that under the LSTM-based genetic optimization technique the output electricity and electricity and hydrogen costs of the considered hybrid process can be improved by 34.55% and reduced by 40.3% and 26.8%, respectively. It was also found that the estimations obtained from the LSTM technique are very reliable, and can be relied on with acceptable accuracy. The estimation based on LSTM has an accuracy level of more than 98.1%.

A case study of Gulf Securities Market in the last 20 years: A Long Short‐Term Memory approach

Various researches have been conducted on forecasting stock prices. Several tools ranging from statistical techniques to quantitative methods have been used by researchers to forecast the market. But so far, very little research has been done on forecasting the stock markets of the Gulf countries such as Saudi Arabia, United Arab Emirates, Oman, Kuwait, Bahrain, and Qatar. Our approach is to predict the market indices of the Gulf countries using Long Short‐Term Memory (LSTM) techniques. Thereafter, we optimized the hyperparameters of the LSTM technique using various optimization methods such as Grid Search and Bayesian Optimization with Gaussian Process and found out the best‐suited hyperparameter for the LSTM model. We tried the LSTM method for predicting the indices using data from the last twenty years.

Deep learning approach for one-hour ahead forecasting of weather data

ABSTRACT Weather is made up of multiple parameters, including solar radiation (SR), atmospheric pressure (AP), soil temperature (ST), atmospheric temperature (AT), wind speed (WS), relative humidity (RH), and sunshine duration (SD). These factors are also crucial for the renewable energy sector, solar simulation, agriculture, air pollution, water supply and distribution, avalanche warning, forestry, and town and regional planning. A deep learning method based on a neural network with Long Short-Term Memory (LSTM) was employed in this investigation for one-hour-ahead weather data forecasting. The ability of the LSTM model was compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS) with that of the fuzzy c-means (FCM), Autoregressive Integrated Moving Average (ARIMA) model, and the Autoregressive Moving Average (ARMA) model. Mean absolute error (MAE), correlation coefficient (R), root means square error (RMSE), average bias, Nash – Sutcliffe efficiency coefficient (NSE), and mean absolute percentage error (MAPE) were selected as evaluation criteria. Results indicated that the proposed LSTM model presented good enough results compared to other used methods. 7 different types of meteorological data from a total of 4 years (35040 hours) were divided into 25% test data and 75% training data for the models. The best result was obtained for the hourly ST estimation of Adana province using the LSTM method, the MAE, RMSE, R, bias, NSE, and MAPE values were computed as 0.016°C, 0.078°C, 0.9999, −0.00018°C, 0.0805%, and 0.9999, respectively. On the other hand, the worst result was obtained for the hourly SD for Mardin province when ARIMA was used, and the statistical measures were derived as 0.128 hours for MAE, 0.215 hours for RMSE, 0.8851 for R, 0.00091 hours for bias, and 0.7657 for NSE. In this regard, it is demonstrated that the LSTM technique outperformed the other models in terms of all-weather data estimates and delivered highly sensitive outcomes.

Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique

Maximum Power Point Tracking (MPPT) is a technique used in photovoltaic (PV) systems to maximize the power output from the solar panel by constantly tracking and adjusting the optimal operating point. To achieve this, various algorithms have been developed, with Particle Swarm Optimization (PSO) being a widely used method. By adjusting the control system’s parameters, PSO can determine the optimal operating point of the solar panel and improve its overall performance. PSO employs swarm intelligence by simulating the behavior of particles to find the best solution for a given problem. Long Short-Term Memory (LSTM) belongs to the family of Recurrent Neural Networks (RNN) in machine learning and is designed to address the limitations of traditional RNNs in capturing long-term dependencies that exist in sequential data. The combination of PSO and LSTM techniques can result in an efficient MPPT algorithm that leverages the benefits of both. PSO is utilized to optimize the control parameters of the MPPT algorithm, while LSTM is used to predict the solar panel’s power output based on historical data. Consequently, this integration can lead to an accurate and efficient MPPT algorithm that can effectively track the solar panel’s maximum power point. In this research article, an effort has been made to control the duty cycle of the converter by suitably controlling the system gain. A Matlab-based Simulink model in conjunction with Python programming has been used to make the system more robust.

Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia

The Disease Control Division, Ministry of Health states that mortality data are a crucial tool for examining a community's health. Several studies have been conducted on developing a predictive model to predict the mortality rate but most of them are applying the standard machine learning models. Hence, this study is conducted with its main objective to develop the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models in predicting the general mortality rate in Malaysia and compare the predicted mortality rates between data with and without post-pandemic statistics. The death rate was investigated using the 2022 Revision of World Population Prospects, the twenty-seventh edition of official United Nations population estimates and projections issued by the Population Division of the United Nations Secretariat's Department of Economic and Social Affairs.  A Z-test was employed to investigate if the 2021 and 2022 mortality rates bring any significant difference to the predicted mortality rates. The findings confirm the superiority of the LSTM model in accurately predicting mortality rates compared to RNN with the inclusion of post-pandemic data did not significantly affect the model's predictions. Overall, this study contributes to the understanding of Malaysian mortality rates and provides a foundation for future investigations in this field.  The accuracy in predicting the future mortality rates, particularly in pandemic scenarios, remains challenging and requires further research.