Improving Web Platform Performance Through Predictive Cloud Resource Management

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

The rapid development of Internet information technology has made e-commerce enterprises face complex and changing financial problems. Combining artificial intelligence algorithms and dynamic monitoring of financial risks has been a current research hotspot. Based on this, this paper conducts an empirical study with a sample of listed Chinese e-commerce enterprises from 2012 to 2022. Firstly, using factor analysis (FA) to obtain the common factors between the original financial and non-financial indicators has the effect of reducing the overfitting risk of the model. Secondly, the mean square error (MSE) of the output and predicted values of the Long Short-Term Memory neural network (LSTM) is used as the fitness function of the intelligent swarm optimization algorithm, and then the Particle Swarm Optimization (PSO) algorithm is used to optimize the learning rate (LR) and the number of hidden layer neurons in the Long Short-Term Memory (LSTM) neural network. Finally, a financial risk prediction model based on FA-PSO-LSTM deep learning is constructed, and multiple benchmark models are introduced for comparative analysis on each evaluation index. The study shows that for nonlinear multivariate data with complex structure, the fused deep learning model proposed in this paper achieves the lowest values in mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This indicates that the model has the best prediction effect, which is helpful to help managers make relevant decisions efficiently and scientifically and make the enterprise sustainable.

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations.

Time series forecasting is important for cloud computing because it helps keep services running, ensures that Quality of Service (QoS) is maintained at a high level even when workloads change, and makes sure that resources are used well. In the actual world, cloud workloads do not stay the same all the time. Rather, they evolve throughout time, exhibiting cyclical trends, nonlinear fluctuations, and periods of elevated demand. This complicates the utilization of conventional forecasting methodologies. This study presents a hybrid ensemble-based approach for short-term cloud workload prediction that enhances adaptability through workload-aware modeling to tackle this issue. The method employs feature extraction and K-means clustering, complemented by a dynamically weighted ensemble of Prophet, Long Short-Term Memory (LSTM), and Linear Regression models. NASA HTTP server logs and ClarkNet WWW datasets are used to test the architecture with different window sizes and prediction horizons. Experimental results indicate that modest observation windows and short-term perspectives yield the optimal correlation between response time and prediction stability. On the other hand, the hybrid ensemble always does better than single models in different types of workloads. These results show how effectively the suggested technique works for proactive provisioning of cloud resources and reliable auto-scaling in environments that are cloud-native.

Language models based on Long Short-Term Memory (LSTM) neural networks have been widely applied to automatic speech recognition, natural language processing research. However, current LSTMs are trained by employing the cross-entropy loss function, which only considers the target category without considering the competing categories during the training processes. Thus, current training methods cannot fully exploit the discriminative information provided by the data labels. To tackle this problem, we propose a Large Margin Long Short-Term Memory Neural Network (LMLSTM) model in this paper. Our model employs the large margin discriminative principle as a heuristic term to navigate the convergence process during training. Our model has improved the discriminative ability of the original LSTM while maintaining the capability when generating sequential data. Our proposed large margin term was tested on the Penn Treebank corpus language modelling task. Experimental results demonstrate that the proposed LMLSTM model outperforms current LSTM models in terms of accuracy and perplexity without increasing the depth.

This study constructs a model suitable for studying the performance of a Japanese speech recognition translation system based on a long short-term memory (LSTM) neural network. The impact of the matrix on the accuracy and recognition ratio of the speech recognition system is considered. This study provides a general introduction to the LSTM neural network, gives an overview of a Japanese speech recognition translation system, and presents experimental data to evaluate its performance. Then, the LSTM neural network is recommended for studying the Japanese speech translation recognition system, and a suitable model is established to conduct experiments on the system. By comparing example analyses, the experimental results show that the optimized PLSTM (Part-Aware Long Short-Term Memory) achieved an 8.09% improvement over an unoptimized LSTM network while maintaining recognition accuracy.

Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network

The airfoil inverse design is a crucial engineering challenge in the field of aerospace. The feature extraction capability of deep learning provides a new approach to address airfoil inverse design problems. This paper investigates the application of the long short-term memory (LSTM) neural network to airfoil inverse design under given local pressure coefficients (Cp) and friction coefficients (Cf). The database contains 625 samples for National Advisory Committee for Aeronautics airfoils at Mach numbers (Ma) from 0.2 to 0.5. Primarily, the mean relative error (MRE) for prediction is less than 0.94% when the sequence length parameter (λ) is set to 20%, suggesting the high precision of the LSTM model in airfoil inverse design. Then, the effects of the predicted location are analyzed in the present model. Subsequently, several sub-LSTM models are integrated, inspired by the Mixture of Experts approach. This multi-position long and short-term memory model (MP-LSTM) is aimed at addressing incomplete geometric predictions and fully utilizing the upstream and downstream information in the aerodynamic sequence, further reducing the MRE by 23%–0.724%. Finally, the MP-LSTM model is proven to reduce prediction errors up to 64% and 72% compared with previous convolutional neural network and conditional generative adversarial network models, respectively. This study presents an accurate local prediction model in airfoil inverse design rather than other global models, which is more flexible for further airfoil optimization.

Our work proposes a hardware architecture for a Long Short-Term Memory (LSTM) Neural Network, aiming to outperform software implementations, by exploiting its inherent parallelism. The main design decisions are presented, along with the proposed network architecture. A description of the main building blocks of the network is also presented. The network is synthesized for various sizes and platforms, and the performance results are presented and analyzed. Our synthesized network achieves a 251 times speed-up over a custom-built software network, running on an i7–3770k Desktop computer, proving the benefits of parallel computation for this kind of network.

A Predictive Energy Management Strategy for Multi-mode Plug-in Hybrid Electric Vehicle based on Long short-term Memory Neural Network

The success of Intelligent Driver Assistance (IDA) depends on the system's ability to accurately model the state of traffic surrounding the ego vehicle and predict driving behavior of the surrounding vehicles in order to help the ego driver make the best informed decisions in real-time. The ability to predict acceleration behavior is crucial as a first step towards modeling traffic patterns. In this paper, we show that Long Short-Term Memory (LSTM) neural networks are capable of producing acceleration distributions from which accurate future acceleration values can be sampled. Furthermore, state values calculated from these acceleration predictions are used as input for future predictions, showing that these networks are capable of generating realistic simulated vehicle trajectories over short prediction horizons.

Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.

Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.

Taking well-known tourist villages in Heilongjiang Province as the research object, we constructed a rural tourism system resilience assessment framework with the dimensions of “environment, institution, economy, society, and culture”. Using a geographical detector to analyze driving factors, an LSTM neural network model was constructed to predict the evolution trend of the rural tourism system resilience of these villages. The resulting insights included the following: ① The rural tourism system resilience of the well-known tourist villages in Heilongjiang Province is at a medium level, with a relatively good degree of development in the environmental dimension and the lowest degree in the economic dimension. ② The existence of financial support, water supply guarantee, domestic waste treatment, livestock manure treatment, and tourism development satisfaction are core driving factors for rural tourism system resilience; there is a non-linear or two-factor enhancement effect among these factors, and the interaction between domestic waste treatment and tourism development satisfaction has the strongest influence, while policy support particularly improves rural tourism system resilience and interacts most frequently with other driving factors. ③ Compared to the backpropagation (BP) neural network, the long short-term memory (LSTM) neural network has better stability and prediction accuracy.

The emission of NOx in thermal power plants would cause serious pollution to the environment, and effective prediction of NOx has important significance. With the development of deep learning, many methods can effectively solve the prediction problem. In this paper, a long short-term memory (LSTM) neural network based on the generalized correntropy (GC) loss function, which is called the GC-LSTM neural network here, is proposed to predict the NOx emission. In order to make the characteristics of high-dimensional data have the same measurement scale, normalization is used to preprocess data firstly. Then, the processed historical data is used as training data to train the GC-LSTM neural network. After that, the emission of the NOx at each time instant can be predicted according to the online measured relevant data based on the established GC-LSTM neural network. Finally, prediction results under the LSTM neural network with GC, mean square error (MSE) and the mean absolute error (MAE) loss functions are given. And according to the simulation results, it can be seen that the loss function curve of the LSTM network based on GC is more stable and has better convergence. It can be seen that the proposed GC-LSTM prediction is more accurate, by comparing the probability density function (PDF) and root mean square error (RMSE) of the error between the actual values and the prediction values.