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Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting.

Sentiment analysis is the most trending and ongoing research in the field of data mining. Nowadays, several social media platforms are developed, among that twitter is a significant tool for sharing and acquiring peoples’ opinions, emotions, views, and attitudes towards particular entities. This made sentiment analysis a fascinating process in the natural language processing (NLP) domain. Different techniques are developed for sentiment analysis, whereas there still exists a space for further enhancement in accuracy and system efficacy. An efficient and effective optimization based feature selection and deep learning based sentiment analysis is developed in the proposed architecture to fulfil it. In this work, the sentiment 140 dataset is used for analysing the performance of proposed gated attention recurrent network (GARN) architecture. Initially, the available dataset is pre-processed to clean and filter out the dataset. Then, a term weight-based feature extraction termed Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) model is used to extract the sentiment-based features from the pre-processed data. In the third phase, a hybrid mutation-based white shark optimizer (HMWSO) is introduced for feature selection. Using the selected features, the sentiment classes, such as positive, negative, and neutral, are classified using the GARN architecture, which combines recurrent neural networks (RNN) and attention mechanisms. Finally, the performance analysis between the proposed and existing classifiers is performed. The evaluated performance metrics and the gained value for such metrics using the proposed GARN are accuracy 97.86%, precision 96.65%, recall 96.76% and f-measure 96.70%, respectively.

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

With the advancement of deep neural networks and physics‐data‐driven coupling methods, significant new opportunities have emerged for improving watershed hydrological simulations. Attention mechanisms (AM) and physical process‐wrapped recurrent neural networks (PRNN) enhance model performance by dynamically highlighting key hydrological features and integrating physical concepts to learn complex processes. However, in PRNN models where runoff relies on storage‐driven hydrological processes, the potential of employing AM to quantify and interpret respective contributions of aquifer‐delineated storage layers remains underexplored. This study addresses this gap by integrating an AM into a PRNN based on the Hydrologiska Byråns Vattenbalansavdelning, yielding a diagnostic framework to assess whether the model's internal representations are consistent with established hydrological principles. Validated on 451 catchment attributes and meteorology for large‐sample studies basins, the proposed PRNN‐θaf‐A variant yields a median Nash‐Sutcliffe efficiency of 0.72. The diagnostic analysis reveals two key findings: (a) the soil moisture layer (S3) is the dominant contributor to runoff generation, accounting for a mean annual attention weight of 0.53; (b) the model's seasonal behavior is systematically linked to basin climate, demonstrated by a strong correlation (R2 = 0.68) between the seasonal attention shift for snowpack (S1) and the basin snow fraction. These results illustrate the potential of the framework to produce internal representations that are physically plausible and consistent with established hydrological principles, thereby bridging the gap between process‐based understanding and data‐driven modeling.

Recently, due to the advance and impressive results of deep learning techniques in the fields of image recognition, natural language processing and speech recognition for various long-standing artificial intelligence (AI) tasks, there has been a great interest in applying towards security tasks too. This article focuses on applying these deep taxonomy techniques to network intrusion detection system (N-IDS) with the aim to enhance the performance in classifying the network connections as either good or bad. To substantiate this to NIDS, this article models network traffic as a time series data, specifically transmission control protocol / internet protocol (TCP/IP) packets in a predefined time-window with a supervised deep learning methods such as recurrent neural network (RNN), identity matrix of initialized values typically termed as identity recurrent neural network (IRNN), long short-term memory (LSTM), clock-work RNN (CWRNN) and gated recurrent unit (GRU), utilizing connection records of KDDCup-99 challenge data set. The main interest is given to evaluate the performance of RNN over newly introduced method such as LSTM and IRNN to alleviate the vanishing and exploding gradient problem in memorizing the long-term dependencies. The efficient network architecture for all deep models is chosen based on comparing the performance of various network topologies and network parameters. The experiments of such chosen efficient configurations of deep models were run up to 1,000 epochs by varying learning-rates between 0.01-05. The observed results of IRNN are relatively close to the performance of LSTM on KDDCup-99 NIDS data set. In addition to KDDCup-99, the effectiveness of deep model architectures are evaluated on refined version of KDDCup-99: NSL-KDD and most recent one, UNSW-NB15 NIDS datasets.

Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing architectures used in text classification. Traditional approaches combine the strengths of these two networks by straightly streamlining them or linking features extracted from them. In this article, a novel approach is proposed to maintain the strengths of RNN and CNN to a great extent. In the proposed approach, a bi-directional RNN encodes each word into forward and backward hidden states. Then, a neural tensor layer is used to fuse bi-directional hidden states to get word representations. Meanwhile, a convolutional neural network is utilized to learn the importance of each word for text classification. Empirical experiments are conducted on several datasets for text classification. The superior performance of the proposed approach confirms its effectiveness.

Recurrent neural networks (RNNs) have achieved state-of-the-art performances on various applications. However, RNNs are prone to be memory-bandwidth limited in practical applications and need both long periods of training and inference time. The aforementioned problems are at odds with training and deploying RNNs on resource-limited devices where the memory and floating-point operations (FLOPs) budget are strictly constrained. To address this problem, conventional model compression techniques usually focus on reducing inference costs, operating on a costly pre-trained model. Recently, dynamic sparse training has been proposed to accelerate the training process by directly training sparse neural networks from scratch. However, previous sparse training techniques are mainly designed for convolutional neural networks and multi-layer perceptron. In this paper, we introduce a method to train intrinsically sparse RNN models with a fixed number of parameters and floating-point operations (FLOPs) during training. We demonstrate state-of-the-art sparse performance with long short-term memory and recurrent highway networks on widely used tasks, language modeling, and text classification. We simply use the results to advocate that, contrary to the general belief that training a sparse neural network from scratch leads to worse performance than dense networks, sparse training with adaptive connectivity can usually achieve better performance than dense models for RNNs.

Multi-variate time-series (MTS) forecasting is the prediction of future for a sequence of data. The process of analysing obtained data can benefit the community financially and securely, for instance observing stock exchange trends and predicting malicious attacks whenabout. MTS forecasting models face many problems including data and model complexity, energy constraints and computational cost. These problems could affect budget allocation, latency and carbon emission. Recurrent neural networks are one of these models, which are known for their computational complexity due to slow learning process which requires more energy to train. Contributing to green AI, in this paper, we propose a competitive and energy-efficient lightweight recurrent neural network based on a hybrid neural architecture that combines Random Neural Network (RaNN) and Simple Recurrent Network (SRN), namely Random Simple Recurrent Network (RSRN). We consider RaNN for its distinctive probabilistic properties and SRN for adding lightweight recurrent ability to the RaNN to process sequential data. The paper shows how RSRN is trained using adapted and optimised versions of back propagation (BP), back propagation through time (BPTT) and truncated BPTT (TBPTT). The latter two algorithms use penalised gradient descent to prevent gradient explosion problems by employing the average of total gradient over time. Evaluated on several datasets, RSRN achieves best performance when using TBPTT. Moreover, we performed a comparative study against well-known recurrent models showing its superiority compared to the state-of-the-art models, while requiring much less computational time and training parameters. In addition, we investigated the multi-layer architecture and its properties.

Agriculture is the most important sector and the backbone of a developing country’s economy. Accurate crop yield prediction models can provide decision-making tools for farmers to make better decisions. Crop yield prediction has challenged researchers due to dynamic, noisy, non-stationary, non-linear features and complex data. The factors that influence crop yield are changes in temperature and rainfall, plant disease, pests, fertilizer, and soil quality. The paper discusses the factors affecting crop yield, explores the features utilized, and analysis deep learning methodologies and performance metrics utilized in crop yield prediction.

Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method.