Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis

Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007-2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format for further processing. advanced encryption Standard (AES) algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems. Finally, the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models. Herein different pre-trained deep learning architectures (Inception-ResNet-v2, VGG-19, ResNet-50) used transfer learning is adopted for feature extraction. The softmax of heterogeneous base classifiers assists to make individual predictions. As a meta-classifier, the ensemble approach is employed to obtain final optimal results. Disease predicted image is consigned to the recurrent neural network with long short-term memory (RNN-LSTM) for severity analysis, and the patient is directed to seek therapy based on the outcome. The proposed method achieved 98.6% accuracy, 0.978 precision, 0.982 recalls, and 0.974 F1-score on five class classifications. The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective. © 2023 CRL Publishing. All rights reserved.

The Optimization of ECG-Leads Based Cardiovascular Abnormal Detection Scheme

An electrocardiogram, often known as an ECG, is a diagnostic tool that measures the electrical activity of the heart in order to identify potential heart abnormalities. Although the normal 12-lead ECG is the dominant approach in cardiac diagnostics, it is still challenging to identify distinct heart illnesses using a single lead or a reduced number of leads. Automatic diagnosis of cardiac abnormalities via the ECG with a reduced lead system (less than the typical 12-lead system) may give a helpful diagnostic alternative to traditional 12-lead ECG equipment that is both simple to use and less expensive. This alternative uses fewer leads than the standard system. This study considers the use of Recurrent Neural Networks Long Short-Term Memory (RNN- LSTM) to identify the ability to use less standard ECG leads to detect cardiac abnormalities using various lead combinations, including 6, 4, 3, 2, 1, and 12 lead ECG data. The results of this investigation are presented in this article. Data pre-processing, model design, and hyperparameter tuning are all essential for RNN-LSTM multi-label classification. The initial step was to pre-process the ECG readings to eliminate the base-line wander noise for ECG signals;the next stage is lead combination selection and clipped to have an equal duration of 10 seconds at various used leads. The gathered results show a possibility of using a single lead instead of multiple leads for preliminary cardiovascular diseases (CVDs) identification. It is a critical issue, especially during emergencies such as the COVID- 19 pandemic or in crowded hospitals when medical resources are limited and online (internet-based) monitoring technologies are vital. © 2022 IEEE.

Docker Container based Crowd Control Analysis Using Dask Hadoop Framework

Crowd control is a public policy technique in which massive crowds are handled in order to avoid the emergence of possible issues or threats caused by COVID-19 and over-crowding. In this pandemic, social distancing is critical as there is a high chance of being infected in a crowd. With mounting fears about public disease transmission, the significance of crowd monitoring is crucial in these testing times. In the existing system, the model takes more time and resources to process the data from the crowd control application thus resulting in delayed prediction. Early prediction of the crowd level will help people and other government agencies to control and monitor the crowd. Hence, the main goal of the proposed system is to process a large amount of input from the crowd control application in minimal time using Dynamic Task Scheduling (Dask) based Hadoop framework in a multi-node docker cluster. The multi-node cluster processes the input data in different clusters. Each cluster data is fed to model for prediction and forecasting the count of crowd at a location. The models considered for evaluation are RNN_LSTM and ARIMA. The results shown that RNN_LSTM model has provided better accuracy of 97% compared to the ARIMA of 89%. The results show that the prediction performance of RNN_LSTM has shown 40% decrease in Mean Absolute Error (MAE) and 30% decrease in Root Mean Squared Error (RMSE) over the existing ARIMA model. The proposed system is available as an application to the public and enable them to decide whether to visit a particular place or not. © 2022 ACM.

Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARSCov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences, there is still limited methods on what new viruses can be generated in future due to mutation. This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM (Recurrent Neural Network-Long ShortTerm Memory) and RNN-GRU (Gated Recurrent Unit) so that in the future, several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus. After the process of testing the model, the F1-recall came out to be more than 0.95. The mutation detection???s accuracy of both the models come out about 98.5% which shows the capability of the recurrent neural network to predict future changes in the genome of virus.

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

Covid-19 pandemic and resulting lockdown has created a wide impact on social life, including sudden rise in residential load demand. Utilities, for better load scheduling and economic operations, rely on different prediction models among which neural networks proved to be more appropriate. For such unforeseen situations, the non-availability of prior predictions elevated the utility challenges. Moreover, the stringency of lockdowns caused due to mutated COVID-19 virus, necessitates accurate lockdown load predictions. This paper proposes a Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM) model, trained to produce such predictions for two areas of residential sector. The model uses real-time residential load data from the year 2020, with and without weather parameters. The correlation factor (R) of proposed method 0.9683 outperformed the ARIMA's value 0.703. The model is evaluated with correlation factors of 0.9683 and 0.9235 without temp;0.90361 and 0.913662 with temperature for Apurupa and Jyothi colonies respectively located in Hyderabad, India. In addition, the error metrics namely, Mean absolute percentage error (MAPE) and Mean absolute error (MAE) are 2.0464 and 138.576 for Apurupa colony;0.015 and 201.648 for Jyothi colony respectively. However, the prediction error metrics increased slightly with temperature data. The proposed framework will assist utilities for effective load predictions during situations such as pandemic lockdown.