scholarly journals A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

2021 ◽  
Vol 3 (4) ◽  
pp. 946-965
Author(s):  
Sourav Malakar ◽  
Saptarsi Goswami ◽  
Bhaswati Ganguli ◽  
Amlan Chakrabarti ◽  
Sugata Sen Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Energy ◽  
Weather Conditions ◽  
Feature Representation ◽  
Energy Prediction ◽  
Pv Systems ◽  
Proposed Model ◽  
Energy Domain ◽  
Learning Architectures ◽  
Gated Recurrent Unit

Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India.

Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level

2021 ◽  
Vol 20 (6) ◽  
pp. 1-17
Author(s):  
B. Premjith ◽  
K. P. Soman
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Short Term ◽  
Long Short Term Memory ◽  
Target Languages ◽  
Main Components ◽  
Learning Architectures ◽  
Gated Recurrent Unit ◽  
Character Sequence

Morphological synthesis is one of the main components of Machine Translation (MT) frameworks, especially when any one or both of the source and target languages are morphologically rich. Morphological synthesis is the process of combining two words or two morphemes according to the Sandhi rules of the morphologically rich language. Malayalam and Tamil are two languages in India which are morphologically abundant as well as agglutinative. Morphological synthesis of a word in these two languages is challenging basically because of the following reasons: (1) Abundance in morphology; (2) Complex Sandhi rules; (3) The possibilty in Malayalam to form words by combining words that belong to different syntactic categories (for example, noun and verb); and (4) The construction of a sentence by combining multiple words. We formulated the task of the morphological generation of nouns and verbs of Malayalam and Tamil as a character-to-character sequence tagging problem. In this article, we used deep learning architectures like Recurrent Neural Network (RNN) , Long Short-Term Memory Networks (LSTM) , Gated Recurrent Unit (GRU) , and their stacked and bidirectional versions for the implementation of morphological synthesis at the character level. In addition to that, we investigated the performance of the combination of the aforementioned deep learning architectures and the Conditional Random Field (CRF) in the morphological synthesis of nouns and verbs in Malayalam and Tamil. We observed that the addition of CRF to the Bidirectional LSTM/GRU architecture achieved more than 99% accuracy in the morphological synthesis of Malayalam and Tamil nouns and verbs.

scholarly journals Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

2021 ◽  
Vol 297 ◽  
pp. 01072
Author(s):  
Rajae Bensoltane ◽  
Taher Zaki
Keyword(s):  
Deep Learning ◽  
Vector Representation ◽  
Rule Based ◽  
External Resources ◽  
Unit Model ◽  
Proposed Model ◽  
Feature Based ◽  
The Impact ◽  
Gated Recurrent Unit ◽  
Machine Learning Models

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.

Deep Learning based Models for Solar Energy Prediction

2021 ◽  
Vol 6 (1) ◽  
pp. 349-355
Author(s):  
Imane Jebli ◽  
Fatima-Zahra Belouadha ◽  
Mohammed Issam Kabbaj ◽  
Amine Tilioua
Keyword(s):  
Deep Learning ◽  
Solar Energy ◽  
Energy Prediction

scholarly journals Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 739 ◽  
Author(s):  
Jin-Young Kim ◽  
Sung-Bae Cho
Keyword(s):  
Deep Learning ◽  
Energy Demand ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Electric Energy ◽  
Representation Learning ◽  
The State ◽  
Efficient System ◽  
Energy Prediction ◽  
Proposed Model

As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 minutes with 60-minute demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

scholarly journals An efficient encode-decode deep learning network for lane markings instant segmentation

2021 ◽  
Vol 11 (6) ◽  
pp. 4982
Author(s):  
A. Al Mamun ◽  
P. P. Em ◽  
J. Hossen
Keyword(s):  
Deep Learning ◽  
Environmental Conditions ◽  
False Negative ◽  
Distinct Type ◽  
Weather Conditions ◽  
Driver Assistance Systems ◽  
Traffic Condition ◽  
Computational Framework ◽  
Proposed Model ◽  
Deep Learning Network

<span lang="EN-US">Nowadays, advanced driver assistance systems (ADAS) has been incorporated with a distinct type of progressive and essential features. One of the most preliminary and significant features of the ADAS is lane marking detection, which permits the vehicle to keep in a particular road lane itself. It has been detected by utilizing high-specialized, handcrafted features and distinct post-processing approaches lead to less accurate, less efficient, and high computational framework under different environmental conditions. Hence, this research proposed a simple encode-decode deep learning approach under distinguishing environmental effects like different daytime, multiple lanes, different traffic condition, good and medium weather conditions for detecting the lane markings more accurately and efficiently. The proposed model is emphasized on the simple encode-decode Seg-Net framework incorporated with VGG16 architecture that has been trained by using the inequity and cross-entropy losses to obtain more accurate instant segmentation result of lane markings. The framework has been trained and tested on a vast public dataset named Tusimple, which includes around 3.6K training and 2.7 k testing image frames of different environmental conditions. The model has noted the highest accuracy, 96.61%, F1 score 96.34%, precision 98.91%, and recall 93.89%. Also, it has <span>also obtained the lowest 3.125% false positive and 1.259% false-negative value, which transcended some of the previous researches. It is expected to</span> assist significantly in the field of lane markings detection applying deep neural networks.</span>

scholarly journals Bidirectional Nonnegative Deep Model and Its Optimization in Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xianhua Zeng ◽  
Zhengyi He ◽  
Hong Yu ◽  
Shengwei Qu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Nonnegative Matrix ◽  
Feature Representation ◽  
Nonlinear Fitting ◽  
Feature Representations ◽  
Deep Model ◽  
Proposed Model ◽  
Out Of Sample ◽  
Narrow Signal

Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

scholarly journals Weibo Text Sentiment Analysis Based on BERT and Deep Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10774
Author(s):  
Hongchan Li ◽  
Yu Ma ◽  
Zishuai Ma ◽  
Haodong Zhu
Keyword(s):  
Deep Learning ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Monitoring Network ◽  
Feature Representation ◽  
Vector Representation ◽  
Text Data ◽  
Proposed Model ◽  
Sentiment Dictionary ◽  
Text Sentiment Analysis

With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

scholarly journals An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chirag Roy ◽  
Satyendra Singh Yadav ◽  
Vipin Pal ◽  
Mangal Singh ◽  
Sarat Kumar Patra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Confusion Matrix ◽  
Wireless Systems ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Proposed Model ◽  
Gated Recurrent Unit ◽  
Deep Learning Model

With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from −20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones.

Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques

2021 ◽  
Vol 17 (2) ◽  
pp. 72-95
Author(s):  
Justice Kwame Appati ◽  
Ismail Wafaa Denwar ◽  
Ebenezer Owusu ◽  
Michael Agbo Tettey Soli
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Absolute Error ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Learning Techniques ◽  
Proposed Model ◽  
Bidirectional Lstm ◽  
Gated Recurrent Unit

This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.

scholarly journals Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoyan Wang ◽  
Gaokui Xu
Keyword(s):  
Energy Source ◽  
Deep Learning ◽  
Solar Energy ◽  
Carbon Emissions ◽  
Weather Conditions ◽  
Solar Panels ◽  
Environmental Friendliness ◽  
Wind Generators ◽  
Pv Panel ◽  
Sensing Model

Traditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photovoltaic (PV) solar panels are used as an energy source because of free and environmental friendliness. However, this technology has become a source of inspiration for many researchers. The proposed method suggests to extract useful features from PV and wind generators and then train the system to control them and update the inputs according to prediction results. Solar energy produces energy that varies according to the external influences and the immediate changes in weather conditions. Solar panels are connected through an inverter with the grid, through which the work of the solar panels is monitored using the Internet. It is worth using neural networks (NN) to control variables and adopt system output of previous iteration in processing operations. Use of deep learning (DL) in the control of solar energy panels helps reduce the direct surveillance of the system online. Solar power generation systems mainly depend on reducing the pollution resulting from carbon emissions. Saving CO2 emission is the main purpose of PV panel cells, so smart monitoring can achieve better result in that case.

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