Hyperspectral Image Classification Based on Fractional Fourier Transform

Hyperspectral remote sensing image (HRSI) can provide additional spectral information of objects and have been widely used in many fields. However, due to the complex environment of the HRSI gathering area, collecting the labeled samples of HRSI is time-consuming and labor-intensive. The scarcity of labeled samples is one of the major difficulties for HRSI analysis and processing. In this letter, a spatial–spectral prototypical network (SSPN) for HRSI is proposed for solving the problem of lack of labeled samples. The contribution of this letter is threefold. First, we design a novel local pattern coding algorithm to combine the spatial and spectral information of HRSI pixels based on spatial neighborhood correlation. Then, a spatial–spectral feature extraction algorithm based on 1-D convolutional neural network (1-D-CNN) is suggested to learn the spatial–spectral metric space where HRSI pixels can be correctly classified with only a few labeled samples. Finally, a novel prototype representation for HRSI in spatial–spectral metric space is proposed to better classify the mixed pixels existing in HRSI. The experimental results on three popular HRSI data sets demonstrate that the proposed SSPN is significantly better than the traditional algorithms.

Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.

Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.

Online leakage current classification using convolutional neural network long short-term memory for high voltage insulators on web-based service

In this paper, we propose an emotion recognition model based on convolutional neural network (CNN), long short term memory (LSTM) and channel attention mechanism, aiming at the low classification accuracy of machine learning methods and the uneven spatial distribution of electroencephalogram (EEG) electrodes. This model can effectively integrate the frequency, space and time information of EEG signals, and improve the accuracy of emotion recognition by adding channel attention mechanism after the last convolutional layer of the model. Firstly, construct a 4-dimensional structure representing EEG signals. Then, a CLSTM model structure combining CNN and LSTM is designed. CNN is used to extract frequency and spatial information from 4-dimensional input, and LSTM is used to extract time information. Finally, the channel attention module is added after the last convolutional layer of CLSTM model structure to allocate the weight of different electrodes. In this paper, an emotion recognition model based on CLSTM and channel attention mechanism was proposed from the perspective of integrating the frequency, space and time 3-dimensional information of EEG signals. The average classification accuracy of the model on SEED public data set reached 93.36%, which was significantly improved over the existing CNN and LSTM emotion recognition models.

Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situationculminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. Thepandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern,forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, havingknowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view toaccordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemicto both public health and economic sectors. Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory(LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm ConvolutionalNeural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, SouthAfrica and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developedfor 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAEand RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesserMSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africaand Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infectioncases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but alsoin terms of lesser MSE, MAE and RMSE.

The classification of hyperspectral remote sensing images (HSI) is an important task in the processing and application of hyperspectral images. Convolutional neural network (CNN) has significant advantages in the extraction and fusion of hyperspectral image spectrum and spatial features, so it has become a common method in the field of HSI classification. However, the spectral domain feature redundancy of hyperspectral images is very high, and the spatial domain feature structure is complex, which makes CNN more time-consuming and higher memory requirements in the feature extraction process. Based on this problem, lightweight networks with fewer parameters have gradually become the main application method in the field of HSI classification. In order to reduce the computational complexity of deep feature extraction and take into account the shallow features, a high-order nonlinear Ghost module is proposed on the basis of the original Ghost linear transformation module. Furthermore, in view of the independent characteristics of each dimension of HSI, a Trip-GhostNet is proposed, which simultaneously extracts and fuses features from three dimensions in a lightweight manner. According to the distribution characteristics of HSI, the influence of attention embedding methods in the high-order Ghost module of each branch on the feature extraction is compared and analyzed. The results show that the proposed model can reduce model calculations and improve classification accuracy, and is suitable for HSI classification problems.

Human identification using camera-based surveillance systems is a challenging research topic, especially in cases where the human face is not visible to cameras and/or when humans captured on cameras have no clear visual identity owing to environments with low-illumination. With the development of deep learning algorithms, studies that are based on the human gait using convolutional neural networks (CNNs) and long short-term memory (LSTM) have achieved promising performance for human identification. However, CNN and LSTM-based methods have the limitation of having higher loss of temporal and spatial information, respectively. In our approach, we use shallow CNN stacked with LSTM and deep CNN followed by score fusion to capture more spatial and temporal features. In addition, there have been a few studies regarding gait-based human identification based on the front and back view images of humans captured in low-illumination environments. This makes it difficult to extract conventional features, such as skeleton joints, cycle, cadence, and the lengths of walking strides. To overcome these problems, we designed our method considering the front and back view images captured in both high- and low-illumination environments. The experimental results obtained using a self-collected database and the open database of the institute of automation Chinese academy of sciences gait dataset C show that the proposed method outperforms previous methods.

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

Emotion is the most important gear for human textual communication with each other via social media. Nowadays, people use text for reviewing or recommending things, sharing opinions, rating their choices or unlikeness, providing feedback for different services, and so on. Bangladeshi people use Bangla to express their emotions. Current research based on sentiment analysis has got low-performance output by using several approaches on detecting sentiment polarity and emotion from Bangla texts. In this study, we have developed four models with the hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) with various Word Embeddings including Embedding Layer, Word2Vec, Global Vectors (Glove), and Continuous Bag of Words (CBOW) to detect emotion from Bangla texts (words, sentences). Our models can define the basic three emotions; happiness, anger, and sadness. It will make interaction lively and interesting. Our comparisons are bestowed against CNN, LSTM with different Word Embeddings, and also against some previous researches with the same dataset based on classical Machine Learning techniques such as Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors (K-NN). In our proposed study, we have used Facebook Bangla comments for a suitable dataset. In our study, we have tried to detect the exact emotion from the text. And in result, the best model integrating Word2Vec embedding layer with a hybrid of CNN-LSTM detected emotions from raw textual data with an accuracy of 90.49% and F1 score of 92.83%.

Emotion recognition (ER) is crucial for understanding human behaviours, social interactions, and psychological well-being. Electroencephalography (EEG) has emerged as a promising tool for capturing the neural correlates of emotions. This work is a systematic review of articles in ER using EEG signals. A total of 120 articles from 1041 articles were selected based on PRISMA guidelines using defined inclusion and exclusion criteria, published between 2018 and 2024. This article aims to provide an in-depth understanding of the current landscape of ER from EEG signals utilizing deep learning (DL). This review offers valuable guidance for researchers and practitioners seeking more refined and reliable emotion classification systems. To explore the effectiveness of DL models in EEG-based ER, several potential DL models, such as convolutional neural network, long short-term memory (LSTM), gated recurrent unit (GRU), hybrid bidirectional LSTM (BiLSTM), bidirectional GRU, and advanced DL models such as convolutional recurrent neural network and EEG-Conformer models are applied to two popular datasets, SEED and GAMEEMO, respectively, to depict the full process of ER. Additionally, the performance of DL models is also compared with the performance of basic machine learning (ML) models such as SVM, k-nearest neighbors, logistic regression, and boosting algorithms such as AdaBoost, XGBoost and LightGBM. Through extensive experiments and performance evaluations, the performance of different models when applied to the datasets mentioned above is compared. The accuracy, precision, recall, and F1-scores are analysed to determine the most effective model for EEG-based ER. The findings of this study demonstrate that the performance of hybrid DL models is more efficacious than that of ML models. The best-performing model (BiLSTM) classified the emotions, with an accuracy of 90.54% when applied to the GAMEEMO dataset. This research contributes to the growing body of literature on ER and provides insights into the feasibility of using EEG signals to understand emotional states, and presents a structured roadmap for future exploration. The findings can aid in the development of more accurate and reliable ER systems, which can have wide-ranging applications in psychology, social sciences, and human-computer interactions.

In this paper, a software comparative analysis of two neural network models is presented, namely, Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM) neural network. The evaluation is performed using the famous deep learning database the “MNIST” to check the accuracy, model size, speed and complexity of the two models for future digital realization on reconfigurable hardware. In addition to that, we optimize the size of the two models by quantizing the weights width to 8-bits instead of 32-bits. The results show an extensive reduction in the size of each model (by 10X) with a slight drop in the accuracy. The results also show that the CNN is more accurate and much faster than LSTMs making it the best model to be implemented on reconfigurable hardware.

ABSTRACTIn this paper, the convolutional recurrent neural network (ConvLSTM) architecture is proposed to predict individual stock prices. The characteristics of stock data are automatically extracted through convolutional neural network (CNN). Afterward, the extracted features are inputted into a long short-term memory (LSTM) model with memory characteristics for prediction. Since CNN has been a representation learning model, it is quite appropriate for automatic feature extraction. Besides, the LSTM architecture of the recurrent neural network could effectively surmount the issues of gradient disappearance and expansion of the time series data. Ten stock historical data had been collected in the experimental data set. Furthermore, many commonly used technical indicators are calculated in advance for the expansion of the dimension of the training samples. The experimental results obtain a 3.449 RMSE (root-mean-square error) in average.