Network Traffic Prediction Based on Decomposition and Combination Model

Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.

People post their emotional expressions about specific events through social media platforms, such as praise and complaints about city bus travel services. In this paper, we propose a social media-based method to analyze bus service satisfaction. Firstly, we collect microblogs related to bus service from Sina Weibo, and pre-process the collected microblogs. Then the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model is combined with Bi-directional Long Short-Term Memory (BiLSTM) model to form BERT- BiLSTM model, using the pre-processed data to train the BERT-BiLSTM model to realize the classification of text data into positive and negative sentiments, and finally using the method of this paper to analyze and verify the satisfaction of Guangzhou bus services system in 2010. Comparing the method of this paper with traditional machine learning and deep learning methods, the experiments show the effectiveness of the BERT-BiLSTM model. KeywordsSocial mediaBus serviceSentiment analysisBERT-BiLSTMBinary classification

Consumer opinions on purchased products significantly influence the number of purchases on ecommerce platforms. Sentiment analysis of these opinions assists both consumers and companies in making informed decisions. Currently, consumers judiciously evaluate different aspects of a product to guarantee it meets their requirements. Consequently, performing sentiment analysis on various product aspects can aid consumers select the precise product and support companies in meeting on areas that prerequisite upgrading. This study recommends a systematic and expandable method to aspect-based sentiment analysis of user reviews for electrical devices like computers, mobile phones, and headphones. Through an amalgamation of advanced preprocessing, sentence-level segmentation by means of transformer-based models, and similarity-based aspect detection, the system is competent of extracting fine-grained sentiment for each product aspect. A hybrid sentiment classification strategy employing both BERT and VADER certifies robustness and accurateness in sentiment detection, while the structured transformation of data into a multi-label format permits for efficient model training. The proposed Bi-LSTM architecture with an integrated attention mechanism boosts sentiment prediction by concentrating on the utmost pertinent parts of each sentence, directing to better-quality performance across evaluation metrics, including accuracy, precision, recall, and F1-score. The exploration offers a comparative study of the performance of aspect-based sentiment analysis using a standard Bi-LSTM model versus a Bi-LSTM model with an attention mechanism, applied to analyse the reviews of three diverse products. The outcomes explains that the Bidirectional Long Short-Term Memory (Bi-LSTM) model with an attention mechanism outpaces the Bidirectional Long Short-Term Memory (Bi-LSTM) in terms of accuracy, precision, recall, and F1-score. The investigation validates the efficiency of merging NLP techniques and deep learning to deliver understandings from product reviews, presenting beneficial assistance to consumers by aiding to make informed decisions and focus on companies directing to improve their offerings based on targeted feedback.

According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption of automated and accurate models for heart disease detection is lacking since conventional methods rely on human analysis, which is time-consuming and error-prone. This work covers the crucial topic of heart disease diagnosis, especially ECG data analysis for cardiovascular disease detection. The integration of the Deep-Convolutional Neural Network (Deep-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) model with an Attention Mechanism enhances the accuracy and reliability of heart disease categorisation. The Deep-CNN component efficiently extracts features from capture spatial linkages, while the Bi-LSTM layers handle temporal dependencies to identify patient health patterns over time. The model is evaluated on 303 patient records with 14 clinical characteristics from the University of California, Irvine (UCI) Cleveland Heart Disease dataset. The suggested technique has 97.23% accuracy, 97.72% recall, precision, and 96.90% F1 score. These findings show that the proposed architecture improves diagnostic performance more than boosting ensemble approaches and hybrid models.

In this study, the Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to optimize the unbalanced allocation mechanism in the electricity market, and an empirical analysis is made based on the Nord Pool dataset. It is found that the Bi-LSTM model is significantly better than the traditional method in reducing the total deviation power and unbalanced cost. Specifically, the proposed model reduces the total deviation power by 16.5% and the unbalanced cost by 12.3%. The incentive compatibility index shows that the incentive effect of the proposed model reaches 27.0%. It is higher than that of the F1 mechanism (23.5%) and the F2 mechanism (25.5%). In terms of cost allocation efficiency, the mean square error of the proposed model is 0.018. This shows higher cost allocation accuracy compared with 0.028 in the F1 mechanism and 0.022 in the F2 mechanism. These results show that the proposed model has obvious advantages in improving market operation efficiency, encouraging market participants to reduce deviation electricity, and realizing fair cost allocation. The research conclusion provides a new technical path for the design of the allocation mechanism in the power market and provides strong support for future market management and optimization.

The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.

The proliferation of misinformation, as insidious and pervasive as water, presents an unprecedented challenge to public discourse and comprehension. Often propagated to further specific ideologies or political objectives, misinformation not only misleads the populace but also fuels online advertising revenue generation. As such, the urgent need to pinpoint and eliminate misinformation from digital platforms has never been more critical. In response to this dilemma, this paper proposes a solution built on the backbone of massive data generation in today’s digital landscape. By leveraging advanced technologies, such as AI-driven systems with deep learning models and natural language processing capabilities, we can monitor and analyze an extensive scope of social media data. This, in turn, facilitates the identification of misinformation across multiple platforms and alerts users to potential propaganda. Central to our study is the development of misinformation classifiers based on a deep bi-directional long short-term memory (Bi-LSTM) model. This model is further enhanced by employing a genetic algorithm (GA), which automates the search for an optimal neural architecture, thereby significantly impacting the training behavior of the deep learning algorithm and the performance of the model being trained. To validate our approach, we compared the efficacy of our proposed model with nine traditional machine learning algorithms and a deep learning model rooted in long short-term memory (LSTM). The results affirmed the superiority of our GA-tuned Bi-LSTM model, which outperformed all other models in detecting misinformation with remarkable accuracy. Our intention with this paper is not to present our model as a comprehensive solution to misinformation but rather as a technological tool that can aid in the process, supplementing and bolstering the existing methodologies in the field of misinformation detection.

The rapid growth of AI-generated content from ChatGPT, Gemini, and many others poses significant challenges in digital media. There are increasing instances of text generated by machine learning models being indistinguishable from human-written text. This calls for an effective way of detecting and distinguishing text generated by AI from human text. Using a Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a Bidirectional Long Short-Term Memory (Bi-LSTM) model for classifying AI-generated text in a digital media context was developed. A carefully curated dataset of human and AI-generated text samples was used. The Bi-LSTM model without an embedding layer was implemented, optimizing the model to capture complex linguistic patterns apparent in each text type. An experimental setup was used to evaluate the effectiveness of the model. It was noted that the model achieved a remarkable test accuracy of 99.79%, with a loss of 0.009. To facilitate practical implementation, we developed a web application using Next.js with our model served from a Flask server that enables real-time AI text detection. Our results highlight the model’s ability to accurately identify AI-generated text, providing valuable insights into deploying such models to verify content on media platforms. This study highlights the potential of neural network-based classifiers to address the pressing need for automated AI text detection in an increasingly AI-influenced digital ecosystem.

Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts

For many natural language processing applications, part of speech (POS) tagging remains as a preliminary task. Marathi, is observed as a popular language in India but it only has limited tools and corpus for NLP applications. An accurate POS tagger is essential for many NLP tasks like sentiment analysis, named entity recognition, dependency parsing, etc. This research work proposes a deep learning model and bidirectional long short-term memory (Bi-LSTM) model to perform POS tagging for Marathi text. We achieved an accuracy of 85% for the deep learning model and 97% for the Bi-LSTM model. Our contribution here is based on three folds - building a deep learning model, building the Bi-LSTM model, comparison with machine learning techniques for the same dataset.

Problem definition: The intelligent transportation system (ITS) plays a vital role in the construction of smart cities. For the past few years, traffic flow prediction has been a hot study topic in the field of transportation. Facing the rapid increase in the amount of traffic information, finding out how to use dynamic traffic information to accurately predict its flow has become a challenge. Methodology: Thus, to figure out this issue, this study put forward a multistep prediction model based on a convolutional neural network and bidirectional long short-term memory (BILSTM) model. The spatial characteristics of traffic data were considered as input of the BILSTM model to extract the time series characteristics of the traffic. Results: The experimental results validated that the BILSTM model improved the prediction accuracy in comparison to the support vector regression and gated recurring unit models. Furthermore, the proposed model was comparatively analyzed in terms of mean absolute error, mean absolute percentage error, and root mean square error, which were reduced by 30.4%, 32.2%, and 39.6%, respectively. Managerial implications: Our study provides useful insights into predicting the short-term traffic flow on highways and will improve the management of traffic flow optimization.

Most of the current word segmentation methods are rule-based and traditional machine learning methods. Universal word segmentation tools do not work well in the field such as metallurgy. Domain-specific Chinese word segmentation is rarely studied. In recent years, with the development of deep learning, the neural network has been proved to be effective in Chinese word segmentation. However, this promising performance relies on large-scale training data. Neural networks with conventional architectures cannot achieve the desired results in low-resource datasets due to the lack of labeled training data. This paper takes the field of metallurgy as an example and proposes a domain-specific Chinese word segmentation based on Bi-directional long-short term memory (Bi-directional LSTM) model in the metallurgical field. First, the word segmentation model is obtained by using the Bi-directional LSTM model to train the internal and external domain knowledge. Then, a series of tuning parameters are carried out and the label probability of the word is combined with the weight. Finally, the result of word segmentation is obtained by label inference layer. The experimental results show that the proposed method can create a better word segmentation effect in the field of metallurgy.

With the advancement of cloud computing technologies, there is an ever-increasing demand for the maximum utilization of cloud resources. It increases the computing power consumption of the cloud’s systems. Consolidation of cloud’s Virtual Machines (VMs) provides a pragmatic approach to reduce the energy consumption of cloud Data Centers (DC). Effective VM consolidation and VM migration without breaching Service Level Agreement (SLA) can be attained by taking proactive decisions based on cloud’s future workload prediction. Effective task scheduling, another major issue of cloud computing also relies on accurate forecasting of resource usage. Cloud workload traces exhibit both periodic and non-periodic patterns with the sudden peak of load. As a result, it is very challenging for the prediction models to precisely forecast future workload. This prompted us to propose a hybrid Recurrent Neural Network (RNN) based prediction model named BHyPreC. BHyPreC architecture includes Bidirectional Long Short-Term Memory (Bi-LSTM) on top of the stacked Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Here, BHyPreC is used to predict future CPU usage workload of cloud’s VM. Our proposed model enhances the non-linear data analysis capability of Bi-LSTM, LSTM, and GRU models separately and demonstrates better accuracy compared to other statistical models. The effect of variation of historical window size and training-testing data size on these models is observed. The experimental result shows that our model gives higher accuracy and performs better in comparison to Autoregressive Integrated Moving Average (ARIMA), LSTM, GRU, and Bi-LSTM model for both short-term ahead and long-term ahead prediction.

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.

Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential level is challenging due to the higher uncertainties involved. Recently deep neural networks have been leveraged to address this issue. This paper investigates the capabilities of a bidirectional long short-term memory (BiLSTM) and a convolutional neural network-based BiLSTM (CNN-BiLSTM) to provide a day ahead (24 hr.) forecasting at an hourly resolution while minimizing the root mean squared error (RMSE) between the actual and predicted load demand. Using a publicly available dataset consisting of 34 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr. span, given the previous 24 hr. load data. The BiLSTM model achieved the lowest RMSE of 1.4842 for the overall daily forecast. In addition, standard LSTM and CNN-LSTM models are trained and compared with the BiLSTM architecture. The RMSE of BiLSTM is 5.60%, 2.85% and 2.60% lower than LSTM, CNN-LSTM and CNN-BiLSTM models respectively.