EvalSeer: An Intelligent Gamified System for Programming Assignments Assessment

In traditional financial performance evaluation models, parameter settings are often too large or too small, resulting in significant model errors. To address this issue, an improved artificial bee colony algorithm was proposed and applied to optimize the parameters of performance evaluation models. This method first constructs a corporate financial performance evaluation system, and then improves the artificial bee colony algorithm with differential evolution algorithm to optimize the parameters of the long short-term memory network, in order to improve the accuracy of the long short-term memory network in corporate financial performance evaluation. The results showed that the improvement of the ABC algorithm was effective. The improved ABC algorithm converged on the Ackley function in the 800th iteration, and the ABC algorithm converged in the 1400th iteration. The evaluation error of the proposed method is the lowest, with the algorithm having the lowest four errors of -0.0121, 0.0453, 0.0683, and 0.0047, respectively. Among the other algorithms, the comprehensive error of the financial performance evaluation model based on Long Short Term Memory (LSTM) network is relatively low, but still lower than the algorithm proposed in the study. The research proposes a long short-term memory network optimized based on improved artificial bee colony algorithm, which can accurately evaluate the financial performance of enterprises, help them review their own development level, and clarify their future development direction.

Background: Cardiac arrhythmias are one of the leading causes of heart failure. In particular, atrial fibrillation (AFib) is a kind of arrhythmia that can lead to heart stroke and myocardial infarction. It is very important and crucial to predict AFib at an early stage to prevent heart disease. Electrocardiogram is one of the premium diagnostic tools which is used by most of the researchers for predicting irregular heartbeats. There are many works carried out in finding heart disease using machine learning classifiers. Aims and Objectives: Deep learning based hybrid Long Short Term Memory (LSTM) network is hybridized with Enhanced Whale Optimization (EWO) to minimize the network optimization and configuration issues faced in the existing models and proposed to increases the accuracy of predicting AFib. Materials and Methods: The proposed LSTM network is hybridized with a EWO technique for predicting AFib. This study uses a hybrid LSTM EWO network for classifying the various output labels of heart disease. EWO is used to predict the most relevant features from the raw dataset. Then, the LSTM model is used to predict the AFib of a patient from normal ECG data. Results: The DL based LSTM EWO achieves better results in all the performance metrics by analyzing the optimized features in feature space, training, and testing phase and successfully obtains better performance in an effective manner. LSTM improves the accuracy by reducing the number of units in the hidden layer which optimizes the network configuration. The proposed model achieves 96.12% accuracy which is 12.81% higher than RF, 15.01% higher than GB, 28.04% higher than CART, and 16.92% higher than SVM. Conclusion: The proposed model hybrid LSTM network integrated EWO for predicting the AFib. The EWO is applied for selecting the most appropriate features needed for the model to learn and produce improvised performance. The optimization and network configuration problems faced in the existing studies are avoided by choosing the suitable number of LSTM units and the size of the time window. This has been implemented through LSTM units and their window size. In addition, we made a statistical examination to prove the importance of proposed work against other models. It is observed that the experimental results attained with 96% of accuracy, better than conventional models.

This paper explains prediction of share market trends of organizations using Artificial Neural Network (ANN). The Long Short Term Memory (LSTM) incorporated with a simple neural network gives the result of the movement of company's stock prices in the share market. LSTM is used for processing the time-series data. LSTM is a type of Recurrent Neural Network (RNN). In this work, layers of LSTM networks called stacked LSTM is a core component that process the huge volume of time series data. LSTM model works like a human brain because of the power to have a short term and long term memory. During data processing in the training stage, the model keeps a short term memory of the relation between the date and stock prices which is available in the data. It then starts keeping track of the relations from the successive dates and stock prices since the inception of the company. In this stage, the model tries to find a pattern or a trend in the stock price movement. This is kept in the long term memory. As the model processes further data, it finds an accurate pattern in the stock price movement. The exact date or a number of days is given as input and the stock price is given as output from the model

Wind power forecast based on improved Long Short Term Memory network

Hydrate plugging and pipeline leak can impair the normal operation of natural gas pipeline and may lead to serious accidents. Since natural gas pipeline safety monitoring based on active acoustic excitation can detect and locate not only the two abnormal events but also normal components such as valves and pipeline elbows, recognition and classification of these events are of great importance to provide maintenance guidance for the pipeline operators and avoid false alarm. In this article, long short-term memory (LSTM) network is introduced and applied to classify detection signals of hydrate plugging, pipeline leak, and elbow. Adaptive moment estimation (Adam) algorithm is introduced and utilized to accelerate the long short-term memory network convergence in training. Experimental results demonstrate that the network with three layers and 64 units per cell performs the best. The cross-entropy loss in training is 0.0005, and classification accuracies are all 100% in training, validation, and testing which verify the validity of the long short-term memory network. Therefore, the method based on the long short-term memory network and adaptive moment estimation algorithm can work efficiently on pipeline events classification and has great guiding significance for safety assurance of natural gas transmission.

Enhancing building energy efficiency underscores the critical need for innovative predictive models to mitigate environmental issues from high energy consumption, especially in residential areas with air-conditioning and heating ventilation systems. This study introduces the use of Long Short-Term Memory (LSTM) networks for early prediction of residential electric consumption, representing a significant innovation in the field. Unlike traditional Deep Neural Network (DNN) and Artificial Neural Network (ANN) models, Long Short-Term Memory networks efficiently process time-series data, predicting future energy usage with unmatched accuracy. The Long Short-Term Memory model exhibited superior training efficiency, requiring only 2.69 s for over 500 test cases, outperforming Deep Neural Network and Artificial Neural Network models, which took 5.26 and 3.88 s, respectively. Its predictive accuracy, evidenced by an R-squared value of 0.97, surpasses the 0.95 and 0.92 of Deep Neural Network and Artificial Neural Network models, respectively. This breakthrough enables accurate predictions of annual energy usage before construction starts and aids in identifying energy efficiency improvements early in the design process. Applying Long Short-Term Memory networks in this context marks a substantial advancement in predictive modeling for building energy consumption, equipping architects and engineers with a vital tool for designing energy-efficient buildings from the beginning. The innovation and quantitatively proven effectiveness of the Long Short-Term Memory model highlight its potential to revolutionize early-stage building design strategies, filling a crucial gap in the existing literature.

Abstract. Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information about the learned relationships that map inputs to outputs, and do these mappings represent known hydrological concepts? Small-scale experiments have demonstrated that the internal states of long short-term memory networks (LSTMs), a particular neural network architecture predisposed to hydrological modelling, can be interpreted. By extracting the tensors which represent the learned translation from inputs (precipitation, temperature, and potential evapotranspiration) to outputs (discharge), this research seeks to understand what information the LSTM captures about the hydrological system. We assess the hypothesis that the LSTM replicates real-world processes and that we can extract information about these processes from the internal states of the LSTM. We examine the cell-state vector, which represents the memory of the LSTM, and explore the ways in which the LSTM learns to reproduce stores of water, such as soil moisture and snow cover. We use a simple regression approach to map the LSTM state vector to our target stores (soil moisture and snow). Good correlations (R2>0.8) between the probe outputs and the target variables of interest provide evidence that the LSTM contains information that reflects known hydrological processes comparable with the concept of variable-capacity soil moisture stores. The implications of this study are threefold: (1) LSTMs reproduce known hydrological processes. (2) While conceptual models have theoretical assumptions embedded in the model a priori, the LSTM derives these from the data. These learned representations are interpretable by scientists. (3) LSTMs can be used to gain an estimate of intermediate stores of water such as soil moisture. While machine learning interpretability is still a nascent field and our approach reflects a simple technique for exploring what the model has learned, the results are robust to different initial conditions and to a variety of benchmarking experiments. We therefore argue that deep learning approaches can be used to advance our scientific goals as well as our predictive goals.

Most Graph Neural Networks (GNNs) follow the message-passing scheme. Residual connection is an effective strategy to tackle GNNs' over-smoothing issue and performance reduction issue on non-homophilic networks. Unfortunately, the coarse-grained residual connection still suffers from class-imbalanced over-smoothing issue, due to the fixed and linear combination of topology and attribute in node representation learning. To make the combination flexible to capture complicated relationship, this paper reveals that the residual connection needs to be node-dependent, layer-dependent, and related to both topology and attribute. To alleviate the difficulty in specifying complicated relationship, this paper presents a novel perspective on GNNs, i.e., the representations of one node in different layers can be seen as a sequence of states. From this perspective, existing residual connections are not flexible enough for sequence modeling. Therefore, a novel node-dependent residual connection, i.e., Long Short-Term Graph Memory Network (LSTGM) is proposed to employ Long Short-Term Memory (LSTM), to model the sequence of node representation. To make the graph topology fully employed, LSTGM innovatively enhances the updated memory and three gates with graph topology. A speedup version is also proposed for effective training. Experimental evaluations on real-world datasets demonstrate their effectiveness in preventing over-smoothing issue and handling networks with heterophily.

Machine learning (ML) has become a trending domain over the past few years, the accessibility of Graphical Processing Units (GPUs), Tensor Processing Units (TPUs) have given impetus for the same. Various applications like speech and face recognition, natural language processing, text analytics, big data analytics, regression analysis, pattern recognition and classification are based on the machine learning concept. Regression analysis evaluates the impact of a set of variables among themselves as well as the final formulation. Using this fitting of a particular theory for the real-world inputs can be evaluated. In this chapter regression analysis is performed on the COVID-19 data to predict the next values of the parameters. The Long Short Term Memory Networks (LSTMs) are used here for the prediction task, the LSTMs come under a special category of Neural Networks known as Recurrent Neural Networks (RNNs) which are used for this prediction task. The stateless and stateful implementation of LSTMs are designed and their performance is evaluated. The details of stateful and stateless architecture and their implementation in Keras framework is presented here. The results indicate that the LSTMs have better performance as compared to the RNNs.

This paper aims to predict the occurrence of pests and diseases for cotton based on long short term memory (LSTM) network. First, the problem of occurrence of pests and diseases was formulated as time series prediction. Then LSTM was adopted to solve the problem. LSTM is a special kind of recurrent neutral network (RNN), which introduces gate mechanism to prevent the vanished or exploding gradient problem. It has been shown good performance in solving time series problem and can handle the long-term dependency problem, as mentioned in many literatures. The experimental results showed that LSTM performed good on the prediction of occurrence of pests and diseases in cotton fields, and yielded an Area Under the Curve (AUC) of 0.97. The paper further verified that the weather factors indeed have strong impact on the occurrence of pests and diseases, and the LSTM network has great advantage on solving the long-term dependency problem.

This study proposes a traffic flow prediction method based on long short term memory (LSTM) network. Firstly, traffic date is preprocessed by time series method. Then a traffic flow prediction algorithm framework based on LSTM arm was proposed to improve the accuracy of traffic forecast and compare algorithm differences between LSTM, support vector machine (SVM) and radial basis function (RBF). In the last part, a reliable experiment was designed. The experimental results verify the superiority performance of LSTM over SVM and RBF in traffic flow prediction.

This study explores the use of Long Short Term Memory (LSTM) networks, a variant of Recurrent Neural Networks (RNNs), in the context of financial forecasting, specifically oil price prediction. The research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology and tests six different LSTM variants. The models are evaluated based on Mean Squared Error (MSE), aiming to determine the optimal parameter settings for each LSTM type. Among the variants tested, the Gated Recurrent Unit (GRU) emerged as the highest performer, achieving an MSE of 0.100. This was surprising, as simpler variants outperformed more complex ones, suggesting that simpler LSTM models may be better suited for financial time series forecasting, especially with simpler datasets. In addition to the model experiments, primary research, including interviews with industry professionals, was conducted to validate the results and gather suggestions for improving the methodology. It was concluded that future studies could improve the reproducibility and robustness of the findings by using a random seed for model training and implementing multiple code versions to gather a distribution of results, which would enhance the general reliability of the outcomes.

Neural Network-based approaches have recently produced good performances in Natural language tasks, such as Supertagging. In the supertagging task, a Supertag (Lexical category) is assigned to each word in an input sequence. Combinatory Categorial Grammar Supertagging is a more challenging problem than various sequence-tagging problems, such as part-of-speech (POS) tagging and named entity recognition due to the large number of the lexical categories. Specifically, simple Recurrent Neural Network (RNN) has shown to significantly outperform the previous state-of-the-art feed-forward neural networks. On the other hand, it is well known that Recurrent Networks fail to learn long dependencies. In this paper, we introduce a new neural network architecture based on backward and Bidirectional Long Short-Term Memory (BLSTM) Networks that has the ability to memorize information for long dependencies and benefit from both past and future information. State-of-the-art methods focus on previous information, whereas BLSTM has access to information in both previous and future directions. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short-Term Memory (LSTM) networks are more precise and successful than both unidirectional and bidirectional standard RNNs. Experiment results reveal the effectiveness of our proposed method on both in-domain and out-of-domain datasets. Experiments show improvements about (1.2 per cent) over standard RNN.

Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.

Artificial Neural Networks (ANNs) have been around for a while, and as technology has progressed, more people can have now access to Graphical Processing Units (GPUs), Tensor Processing Units (TPUs), and complex architectures. These days, deep neural networks are of the utmost significance in pattern recognition. One special application of the ANNs is the sequence classification and prediction. A special type of neural network with the capacity to remember patterns along with the temporal aspects have been widely used, they are the recurrent Neural Networks (RNNs). The Long Short Term Memory Networks (LSTMs) are improved versions of RNN with a better dealing of vanishing gradient problems. In this chapter, we discuss an LSTMs with their regular implementation as well as time distributed and bidirectional implementations for the purpose of sequence prediction.  Every day, new information about the COVID-19 pandemic's effects is released, and people all over the world are still dealing with its aftermath. Long Short-Term Memory (LSTM) networks are trained with this data in order to predict              estimates of the global impact of the COVID-19 pandemic. The LSTM architectures are discussed and compared with a vanilla RNN and the results are presented here. The results show the LSTMs outperform RNNs when the mean absolute error is compared for all the models.