Patch-Based Discriminative Learning for Remote Sensing Scene Classification

Because of the challenges associated with the difficulty of accurately labeling the remote sensing (RS) scene images and the need to identify new scene classes, few-shot learning has shown significant advantages in addressing the remote sensing scene classification (RSSC) tasks, leading to a growing interest. However, due to the scale variations of targets and irrelevant complex background in scene images, the current few-shot methods exist the following problems: the problem of the extraction capability of feature extractor in the few-shot mechanism; the problem of the separability of few-shot RS scene images classifier. To solve the above problems, an approach, called few-shot RSSC via subspace based on multiscale feature learning is introduced in this work. We first design a multiscale feature learning technique to address scale variations of the targets in the scene images. Concretely, different branches are utilized to learn scene features at various scales. The self-attention mechanism is embedded in each branch to incorporate the understanding of the global information in the different scale features. After that, a multiscale feature fusion operation, incorporating channel attention, will be devised to effectively merge the different scale features, so as to obtain a more precise feature representation of RS scene images. Furthermore, the subspace is utilized to capture the shared characteristics of each category, to reduce the impact of the complex irrelevant backgrounds in the scene images. The results of our experiments conducted on the public available RS scene datasets demonstrate the strong competitiveness of our approach.

PurposeOne of the paramount challenges in the field of structural health monitoring (SHM) is the precise assessment of structural damage. Recently, many methods have been proposed to diagnose damage in structures using time series data. Nevertheless, these techniques are restricted in their ability to capture the bidirectional temporal and spatial relationships among the data. To remedy these shortcomings, this study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN.Design/methodology/approachThis study proposes an advanced deep learning (DL) technique that integrates one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks, termed BiLSTM-1DCNN. The novelty of BiLSTM-1DCNN comes in its effective fusion of 1DCNN’s local feature extraction and BiLSTM’s capacity to capture long-term information from both directions (forward and backward).FindingsValidation using a dataset from the Nam O steel truss bridge demonstrates that the proposed method surpasses conventional models, including 1DCNN (87.9%), LSTM (72.5%), BiLSTM (76.1%) and LSTM-1DCNN (91.1%), achieving an accuracy rate of 96.4%. This approach provides a significant advancement in SHM, paving the way for more accurate and reliable damage detection.Research limitations/implicationsThe study’s limitations include potential overfitting due to the model’s complexity and the high computational resources required. The BiLSTM-1DCNN model’s performance is also influenced by the quality and quantity of the training data as well as the specific characteristics of the dataset. Additionally, while the model performs well in detecting damage in the Nam O steel truss bridge, its generalizability to other types of structures and environmental conditions remains untested.Practical implicationsThe BiLSTM-1DCNN model offers significant practical benefits for structural health monitoring by enhancing damage detection accuracy. Its ability to effectively capture both local features and long-term temporal dependencies makes it a valuable tool for real-time monitoring and early detection of structural issues. This can lead to more timely maintenance actions, potentially reducing repair costs and improving safety. The model’s high accuracy in detecting damage can also support infrastructure management decisions, contributing to better resource allocation and enhanced overall safety of critical structures.Social implicationsThe BiLSTM-1DCNN model’s advanced damage detection capabilities have positive social implications, such as improving the safety and reliability of public infrastructure like bridges. Enhanced structural health monitoring can reduce the risk of catastrophic failures, protecting lives and reducing accidents. By providing more accurate and timely assessments, the model can also lead to more efficient use of maintenance and repairs, ensuring that resources are allocated effectively.Originality/valueThe originality of the BiLSTM-1DCNN model lies in its innovative integration of one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This unique combination leverages 1DCNN’s strength in local feature extraction and BiLSTM’s capacity to capture temporal dependencies in both directions, addressing the limitations of existing methods. The model’s high accuracy in detecting structural damage, as demonstrated by its performance on the Nam O steel truss bridge dataset, highlights its value in advancing structural health monitoring technologies and offers a significant improvement over traditional approaches.

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

Continuous emotion recognition is a challenging task and a key part of human-computer interaction, especially multimodal emotion recognition can effectively improve the accuracy and robustness of recognition. But there are limited emotion data sets, and it is difficult to extract emotion features. We present a multi-level segmented decision level fusion emotion recognition model to improve the performance of emotion recognition. In this paper, we predict multi-modal dimensional emotional state on AVEC2017 dataset. Our model uses Bidirectional Long Short-Term Memory (BLSTM) as multi-level segmented emotional feature learning model, and uses the SVR model as fusion model of the decision layer. The BLSTM can model different forms of emotional information in time, and can also consider the impact of previous and later emotional features on current results. The SVR model can compensate for the redundant information of emotion recognition. At the same time, we also consider annotation delay and temporal pooling in our multi-modal dimensional emotion recognition model. Our multi-modal emotion recognition model achieves significant recognition improvements and provide the robustness. Finally, we compare the baseline methods which used the same dataset, and find that the CCC performance of our method is the best on arousal, which is 0.685. Our research shows that the proposed multi-layer segmentation decision level fusion emotion recognition model is conducive to improving performance.

Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security risks in CPS settings. The suggested approach makes use of deep learning (DL) and reinforcement learning (RL) techniques. To guarantee data consistency, pre-processing procedures such as mean-based imputation and min–max scaling come after data collection. ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. Detecting attack patterns with high sensitivity (0.984), specificity (0.983), and accuracy (0.991626) for dataset 1, the accuracy of dataset 2 is 0.985 for 70% of training and 0.988 for 80% of training, and the Proposed-DBID-Net architecture enhances CPS security in Industry 4.0. The evaluation phase emphasizes how crucial feature selection is to maximize the model’s accuracy. In conclusion, this study offers a thorough and flexible method for protecting CPS in Industry 4.0, guaranteeing accuracy and scalability across changing cyber threats.

In cybersecurity, the rise of fileless malware poses a significant challenge to endpoint security. Traditional detection methods often fail against these sophisticated attacks, necessitating advanced techniques like deep learning models. This study highlights the limitations of Bi-Directional Long Short-Term Memory (BLSTM) models in dynamic malware analysis and proposes enhancements through Convolutional Long Short-Term Memory (ConvLSTM) architecture. BLSTM models process input sequences in forward and backward directions, combining the results into one output. While this dual-layer approach improves analysis, it is time-consuming, potentially increasing the risk of fileless malware attacks. A key limitation of BLSTM is the lack of parameter sharing between forward and backward directions. This reduces its ability to capture spatial and temporal features simultaneously, hindering effectiveness in detecting fileless malware. To address this, the ConvLSTM model consolidates feature extraction within a single LSTM cell layer. ConvLSTM breaks down samples into subsequence and uses timesteps for additional feature extraction, enabling spatial-temporal data analysis and improving malware prediction accuracy. The model was tested using a dynamic malware dataset. Unlike traditional LSTM, ConvLSTM integrates convolutional layers, allowing parameter sharing across both spatial and temporal dimensions. This reduces computational complexity and improves model performance in handling multidimensional data. The research re-simulated prior work with BLSTM using the same malware dataset. The Spyder app ran the event simulator and the ConvLSTM model's results replaced BLSTM's using identical parameters. Time, accuracy and loss were the main performance metrics. ConvLSTM outperformed BLSTM, achieving 98% detection accuracy compared to BLSTM's 90%. It also significantly reduced processing time, averaging 10 seconds, while BLSTM took 22 seconds. ConvLSTM experienced lower losses, averaging 10% per epoch versus BLSTM's 20%. In conclusion, ConvLSTM offers superior performance over BLSTM in fileless malware detection. Its enhanced computational efficiency and ability to quickly mitigate threats make it a robust solution for fortifying endpoint security against evolving cyber threats. ConvLSTM holds potential in strengthening defence mechanisms against sophisticated malware attacks, providing a proactive approach to safeguarding networks and data.

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient.

The radiological features of computed tomography (CT) images and the sequence of radiomics signatures in continuous slices of lung CT lesions are helpful in identifying subtypes of lung adenocarcinoma. A model based on bidirectional long short-term memory (Bi-LSTM) and multihead attention can learn the rules of this sequence well. In this study, 421 patients with 427 lesions confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) were recruited from three hospitals. The radiomics signatures of the identified lesion regions in each CT image were extracted using 'PyRadiomics' software, and the corresponding radiological features were subsequently documented and collected. Then, the top 100 features were extracted by the minimum redundancy maximum relevance (mRMR) feature ranking method. A model based on the radiological and imaging features was established to classify the lesions using Bi-LSTM and multihead attention. The diagnostic performance of the model was measured by the area under the curve (AUC) of the receiver operating characteristic (ROC). The model combined radiological features and radiomics signatures. The AUCs of the model in the training, testing, and validation groups were 0.985, 0.94 and 0.981, respectively, and the accuracy was 0.92, 0.976 and 0.91, respectively. In addition, we trained two other models [convolutional neural network (CNN) + multihead attention, long short-term memory (LSTM) + multihead attention] and compared them using the testing dataset. The precision of the two models was 0.89 and 0.88, respectively, and the accuracy was 0.88 and 0.87, respectively. Bi-LSTM and multihead attention based on radiomics signatures and radiological features provide a way to distinguish AIS, MIA, and IAC.

Social media data are characterized by significant noise and non-standardization, thereby posing challenges for existing methods in recognizing named entities owing to the entity sparsity and insufficient semantic richness. Thus, to deal with these issues, this study proposes SEMFF-NER, a named entity recognition (NER) method in social media texts that integrates multi-scale features and syntactic information. First, global features are extracted using a Transformer-based encoder (XLNET) with embedded dependency syntactic relations to enhance semantic representation. Next, sliding windows of different lengths capture local features, which are input into a bi-directional long short-term memory (BiLSTM) to capture multi-level local features. Subsequently, the fusion-attention mechanism effectively integrates global contextual information with multiple local features to predict the optimal entity labels. Extensive experiments conducted on three datasets collected from English social media platforms (WNUT2016, WNUT2017, OntoNotes5.0_English) demonstrate the advantageous performance of our proposed method, and ablation experiments further confirm the method’s viability and effectiveness.

Prediction of PM2.5 concentration in urban agglomeration of China by hybrid network model

In the era of information explosion, named entity recognition (NER) has attracted widespread attention in the field of natural language processing, as it is fundamental to information extraction. Recently, methods of NER based on representation learning, e.g., character embedding and word embedding, have demonstrated promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information, e.g., capitalization, inter-word relations, keywords, and lexical phrases, from multilevel perspectives. Intuitively, multilevel features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel attentive multilevel feature fusion (AMFF) model for NER, which captures the multilevel features in the current context from various perspectives. It consists of four components to, respectively, capture the local character-level (CL), global character-level (CG), local word-level (WL), and global word-level (WG) features in the current context. In addition, we further define document-level features crafted from other sentences to enhance the representation learning of the current context. To this end, we introduce a novel context-aware attentive multilevel feature fusion (CAMFF) model based on AMFF, to fully leverage document-level features from all the previous inputs. The obtained multilevel features are then fused and fed into a bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) network for the final sequence labeling. Extensive experiments on four benchmark datasets demonstrate that our proposed AMFF and CAMFF models outperform a set of state-of-the-art baseline methods and the features learned from multiple levels are complementary.

Parts of Speech (pos) tagging corresponds to techniques of tagging accurate pos tags of each word of a sentence. It is a pivotal and indispensable part of sentiment analysis, synthesizing text to speech, analyzing textual data and many more Natural Language Processing (NLP) tasks. Though there are many researches done in the field of languages like English, Chinese etc., it is still comparatively unexplored for Bangla. An excellent pos tagger in Bangla language can accelerate NLP of Bangla textual data. In this study, we have developed a novel hybrid Deep Learning (DL) model with two parallel input networks with Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM), and an output network of CNN. CNN works well regarding the local dependencies of words in sentences, and BiLSTM works well with the global dependencies of words regarding the context in a sentence. No other work has been done with local features of textual data along with long term word dependencies in Bangla pos tagging studies. The model can work with any external Word Embeddings. In this study, it is implemented with both Embedding Layer and Word2Vec layer. In the evaluation process, Mathews Correlation Coefficient (MCC), Precision-Recall curve are used along with accuracy and F1-score due to the dataset's imbalanced characteristic. Our proposed model accompanied by Word2Vec layer has outperformed Vanilla Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), BiLSTM networks, and also one recent study using the same dataset with the highest accuracy of 0.974, F1-score of 0.883, and MCC value of 0.936.

Manual plant species classification requires more time, effort, and professional knowledge and also, and they are very costly. At present days, researchers utilized deep learning techniques for the classification of plants over plant images. The deep learning models attain great success, and then, the lack of interpretability set a limit on their application. To overcome these limitations, they utilized measurable, interpretable, and computer-aided features from plant leaf images. Image processing became very complex and crucial at the time of feature extraction. There are nearly 391,000 vascular plant species present in the world widely. So, the classification and identification of plants became complex and impractical for professionals. Most of the plant species have huge similarities and it consumes a huge amount of time for classification. So, it is essential to develop a computerized system for the identification and classification of plants. A great advancement is widely developed in science and technology for the recognition and classification of plant species in biological fields. Thus, the automatic plant leaf detection models are utilized to help professionals and botanists to classify plant species rapidly. This work proposes a novel automatic plant leaf image classification method through a deep learning algorithm. At first, the input images are gathered from the standard dataset, where the deep features from the gathered images are extracted using Resnet and VGG16. The extracted features are fused and fed to the classification stage. The plant leaf image classifications are done through the Bidirectional Long Short-Term Memory (Bi-LSTM). The empirical outcomes of the developed model have achieved better performance regarding precision and accuracy.

The COVID-19 pandemic has triggered a global crisis in health and economic sectors, causing profound impacts on sea transport and trade. This research paper investigates the ramifications of the pandemic on maritime shipping prices, and, hence, their subsequent influence on the stocks of shipping companies. This global upheaval disrupted international trade significantly, resulting in an unprecedented demand surge for shipping services and a substantial spike in freight rates. This study is intended to propose a predictive method based on Multivariate Multi-step convolutional neural network - Bidirectional Long Short-Term Memory (Multivariate Multi-step CNNBiLSTM) networks in order to forecast the prices of three of the most prominent stocks of big organizations operating in maritime transport. The proposed method is composed of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), where the research utilizes CNN to help for feature extraction from the inputted data, alongside BiLSTM that forecasts the closing stock price for the upcoming five days, utilizing the extracted feature data. Hence, stock price prediction can be realized by applying a novel optimization strategy, which was founded on the Multivariate Multi-step CNN-BiLSTM model and utilizing the Adam optimizer. Prediction accuracy can be assessed by incorporating four metrics into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error. Experimental findings demonstrate that Multivariate Multi-step CNN-BiLSTM yields the most dependable stock price forecasts with the highest accuracy. The proposed prediction method, correctly applied, can yield economic benefits at the macr and micro levels; the prediction accuracy can help policy makers make better future outlook estimates in relation to inflation, gross domestic product (GDP), and unemployment levels that might be impacted by the volatile, uncontrolled, or unexpected fluctuations of stock prices of some leading economic sectors that are closely connected to global shipping and supply chain operations; thus, leading to serious impacts at the microeconomic level in relation to costs, supply and demand, and behavior of individual consumers and companies.

This study investigates the application of advanced deep learning models to forecast fossil energy prices, a critical factor influencing global economic stability. Unlike previous research, this study conducts a comparative analysis of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) models. The evaluation metrics employed include Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results reveal that recurrent architectures, particularly GRU, LSTM, and Bi-LSTM, consistently outperform feedforward and convolutional models, demonstrating superior ability to capture temporal dependencies and nonlinear dynamics in energy markets. In contrast, the RNN and DNN show relatively weaker generalization capabilities. Additionally, visualizations of actual versus predicted prices for each model further emphasize superior forecasting accuracy of recurrent models. The results highlight the potential of deep learning in enhancing investment and policy decisions. Additionally, the results provide significant implications for policymakers and investors by emphasizing the value of accurate energy price forecasting in mitigating market volatility, improving portfolio management, and supporting evidence-based energy policies.