Long Short-term Memory Techniques Research Articles

Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles.

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants

Developing models to assess the nutrient status of plants at various growth stages is challenging due to the dynamic nature of plant development. Hence, this study encoded spatiotemporal information of plants within a single time-series model to precisely assess the nutrient status of aquaponically cultivated lettuce. In particular, the long short-term memory (LSTM) and deep autoencoder (DAE) approaches were combined to classify aquaponically grown lettuce plants according to their nutrient status. The proposed approach was validated using extensive sequential hyperspectral reflectance measurements acquired from lettuce leaves at different growth stages across the growing season. A DAE was used to extract distinct features from each sequential spectral dataset time step. These features were used as input to an LSTM model to classify lettuce grown across a gradient of nutrient levels. The results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). The hybridization of deep autoencoder and long short-term memory (DAE-LSTM) obtained the highest overall classification accuracy of 94%. The suggested methodology presents a pathway to automating the process of nutrient status diagnosis throughout the entire plant life cycle, with the LSTM technique poised to assume a pivotal role in forthcoming time-series analyses for precision agriculture.

A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China.

The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal-spatial-mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases.

Multidimensional Forensic Analysis of Onion Sites Using LSTM and Fuzzy Encoded Techniques: Insights and Applications in Digital Investigations

When it comes to digital investigations, the fact that onion sites make people anonymous makes investigative research much harder. For multidimensional forensic analysis of onion sites, this study suggests a new method that combines Long Short-Term Memory (LSTM) networks with fuzzy encoding methods. When you use the Tor network to get to onion sites, both users and services become anonymous, which makes it harder to do regular investigations. LSTM networks are used to look at trends in how people use the site and when material is updated over time. These networks are good at modeling sequential data. Fuzzy encoding methods work with LSTM to handle the uncertainty that comes with analyzing anonymous data, which makes investigative results more accurate. The method involves keeping time logs of what people do on onion sites and turning them into fuzzy sets to show how user interactions and content changes change over time. LSTM models are taught on these recorded sequences to find patterns that point to suspicious activity, like sudden changes in content or strange patterns of user access. This method can be used to find illegal activities, find secret services that host illegal content, and make profiles of how users behave in an anonymous network. Investigators can better track actions across onion sites when they use LSTM's ability to see how events depend on each other over time and fuzzy encoding's ability to deal with inaccurate data. Real-world onion site data used for experiments shows that the proposed method can find secret behaviors and patterns that are hard for traditional investigative methods to find. The results show that the accuracy and efficiency of detecting are much better than with traditional methods.

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

Real-time Speech Emotion Recognition using Machine Learning

This project is centred around examining spoken language emotion, a critical aspect of human-computer interaction and artificial intelligence. Leveraging advanced techniques such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, we aim to decipher and understand the nuanced expressions of emotions conveyed through speech. The investigation draws from diverse datasets, including RAVDESS, SAVEE, TESS, and CREAM-D, each carefully chosen to encapsulate a broad spectrum of emotions and real-world speech scenarios. The project unfolds by elucidating the methodologies employed in dataset selection, the prepossessing of raw audio data, and the intricacies of utilizing CNN and LSTM techniques for speech emotion analysis. The main objective is to create a robust and reliable outcome model capable of accurately classifying and interpreting emotions in speech across various contexts. Practical applications of this Research extend to fields such as sentiment analysis and potential contributions to mental health monitoring. Through this project, we contribute valuable insights to the evolving landscape of speech emotion analysis, addressing the inherent challenges and exploring opportunities for enhancing human-computer interaction and emotional intelligence in artificial systems.

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.

Predictive Data Analysis: Leveraging RNN and LSTM Techniques for Time Series Dataset

Recurrent neural networks (RNNs) and long short-term memory (LSTM) models have demonstrated tremendous effectiveness in modeling time series data due to the ability they possess to capture temporal dependencies. Stock market data is an example of time series data. Stock price data is very volatile and dynamic therefore one of the most turbulent areas to invest in might be the stock market. The choice to buy or sell stocks is heavily influenced by external circumstances and statistical analysis of previous stock performance. Stock price index forecasting has been a major area of research for many years and many machine learning and deep learning methods have been proposed to simplify this hard process, but little success has been discovered so far.In this paper, the application of RNNs and LSTMs on a stock price dataset to predict future values is explored. We start with Recurrent neural networks (RNNs) to predict the values but one of the major challenges with it is the "vanishing gradient" problem, which makes it difficult for the network to learn long-term dependencies in the data. To overcome this long short-term memory (LSTM) was used which eliminates the vanishing gradient problem. Data were normalized and divided into time steps to determine the relationship between past values and future values to make accurate predictions. The outcomes show that the MLS LSTM method significantly outperforms existing algorithms, achieving a prediction accuracy of 98.1% on the training data set and a 91.97% accuracy on the testing data set.

Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today’s world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature.

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.