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Purpose: This systematic literature review (SLR) paper presents the overview and analysis of the existing ontologies application in the SE domain. It discusses the main challenges in terms of its ontologies development and highlights the key knowledge areas for subdomains in the SE domain that provides a direction to develop ontologies application for SE systematically. The SE is not as straightforward as the traditional economy. It transforms the existing economy ecosystem through peer-to-peer collaborations mediated by the technology. Hence, the complexity of the SE domain accentuates the need to make the SE domain knowledge more explicit. Design/methodology/approach: For the review, the authors only focus on the journal articles published from 2010 to 2020 and mentioned ontology as a solution to overcome the issues specific for the SE domain. The initial identification process produced 3,326 papers from 10 different databases. Findings: After applying the inclusion and exclusion criteria, a final set of 11 articles were then analyzed and classified. In SE, good ontology design and development is essential to manage digital platforms, deal with data heterogeneity and govern the interoperability of the SE systems. Yet the preference to build an application ontology, lack of perdurant design and minimal use of the existing standard for building SE common knowledge are deterring the ontology development in this domain. From this review, an anatomy of the SE key subdomain areas is visualized as a reference to further develop the domain ontology for the SE domain systematically. Originality/value: With the arrival of the Fourth Industrial Revolution (4IR), the sharing economy (SE) has become one of the important domains whose impact has been explosive, and its domain knowledge is complex. Yet, a comprehensive overview and analysis of the ontology applications in the SE domain is not available or well presented to the research community. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Enabling business sustainability for stock market data using machine learning and deep learning approaches" published in the Annals of Operations Research. The original article contained an incorrect funding statement, which has now been updated. The correction has been made by the authors and the article has been corrected. The publisher, Springer Nature, remains neutral in terms of jurisdictional claims and institutional affiliations. The authors of the article are S. Divyashree, Christy Jackson Joshua, Abdul Quadir Md, Senthilkumar Mohan, A. Sheik Abdullah, Ummul Hanan Mohamad, Nisreen Innab, and Ali Ahmadian. [Extracted from the article]

This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniques to enhance the effectiveness of stock trading and investment decisions by accurately anticipating stock prices and providing valuable insights to investors and businesses. The study begins by analyzing the complexities and challenges of stock market analysis, considering factors like political, macroeconomic, and legal issues that contribute to market volatility. To address these challenges, we proposed the methodology called AlphaVision, which incorporates various machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Boosting, K-Nearest Neighbors, and Support Vector Machine, alongside deep learning models such as Multi-layer Perceptron (MLP), Artificial Neural Networks, and Recurrent Neural Networks. The effectiveness of each model is evaluated based on their accuracy in predicting stock prices. Experimental results revealed that the MLP model achieved the highest accuracy of approximately 92%, outperforming other deep learning models. The Random Forest algorithm also demonstrated promising results with an accuracy of around 84.6%. These findings indicate the potential of machine learning and deep learning techniques in improving stock market analysis and prediction. The AlphaVision methodology presented in this research empowers investors and businesses with valuable tools to make informed investment decisions and navigate the complexities of the stock market. By accurately forecasting stock prices based on news updates and ROI values, the model contributes to better financial management and business sustainability. The integration of machine learning and deep learning approaches offers a promising solution for enhancing stock market analysis and prediction. Future research will focus on extracting more relevant financial features to further improve the model’s accuracy. By advancing decision support models for stock price prediction, researchers and practitioners can foster better investment strategies and foster economic growth. The proposed model holds potential to revolutionize stock trading and investment practices, enabling more informed and profitable decision-making in the financial sector. [ABSTRACT FROM AUTHOR]

The Covid outbreak necessitated the use of an automated method for treating patients with critical symptoms. Increasing use of the Internet of Things (IoT) and smart devices requires access to real-time data over robust and secure network infrastructures. During the lifetime of healthcare applications, the data must be transferred in a controlled manner. In this context, this study proposes, an IoT-based decision support system for the analysis of Covid-related data in healthcare systems. Through in-vehicle monitoring, it is intended to safely send important data and patient updates to attending physicians. The suggested approach uses feature extraction methods to identify patients' crucial states while protecting their private information. To improve feature extraction, two techniques are combined: ensemble classification and cipher substitution for secure Json Web Tokens (JWTs). By incorporating the Secure Hashing Algorithm-2 (SHA-2) methods into the Feature Extraction based Logistic Regression (FELR), security protections are substantially reinforced. The system uses the Message Queuing Telemetry Transport (MQTT) protocol to provide secure communication between devices and access to the extracted characteristics from rescue vehicles. The system analyzes passengers, traffic data, and medical issues, which are continually updated with cloud-based security, by using vehicle-based communication networks. The secure movement of patient data inside the hospital network is ensured by this cloud-based system. By using sophisticated regression analysis and ensemble learning approaches, our suggested methodology has shown significant advantages compared to traditional models such as Logistic Regression (LR) and Mixed Regression (MR). Our technique demonstrates exceptional performance with an accuracy rate of 96%, a recall rate of 91%, and an F-measure of 91%. We also precisely compared the performance of our proposed FELR algorithm with hybrid prediction model using deep learning, the extra tree convolutional neural network-based ensemble model, ensemble-based random forest, ensemble transformation learning, and machine learning with regression. Significantly, FELR algorithm has demonstrated exceptional superiority by consistently surpassing the predictive capabilities of all these algorithms. The system has exceptional performance and exhibits improved efficiency and secure transmission capabilities. The unique model that we propose offers a feasible and effective approach for detecting crucial states in patients within healthcare facilities. The success of our framework is shown via a well-planned approach that includes important processes such as data preprocessing, feature extraction, ensemble model construction, and evaluation. The proposed framework offers a promising avenue for aiding healthcare professionals in making informed decisions regarding the critical health trajectories of COVID-19 patients. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing recognition among competent city developers and researchers regarding the critical importance of integrating healthcare data analysis into smart urban planning. This research study focuses on enhancing the healthcare system using a cognitive-based cloud framework. We employ the Internet of Things (IoT) to collect patient data and utilize a cloud-based cognitive framework for real-time patient monitoring. We aim to achieve cost-effective and high-quality healthcare. Deep learning techniques are applied to assess the health status and obtain experimental results. Specifically, we conduct pathology detection and classification using Electroencephalography (EEG). Multimodal patient health data, including EEG signals, are recorded using an EEG sensor. Intelligent IoT devices transmit the EEG signals from patients to the cloud, where they undergo processing and are forwarded to a cognitive module. The system tracks various sensor readings, such as facial expressions, speech, EEG, movements, and gestures, to determine the patient's condition. To classify the EEG signals as pathological or normal, our proposed method employs a Transfer Learning-based Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and the Kruskal-Wallis (KW) method. We demonstrate the effectiveness of our approach, outperforming existing methods on the same dataset, achieving an impressive accuracy of 95.13% in identifying EEG pathologies. [ABSTRACT FROM AUTHOR]