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Summary: In recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher. [ABSTRACT FROM AUTHOR]

Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT's sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%. [ABSTRACT FROM AUTHOR]

In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., F-LSTM utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of F-LSTM , we ran different empirical simulations. Our findings demonstrate that F-LSTM outperforms conventional approaches and machine learning techniques by achieving a loss minimal of 2.3 × 10 − 4 . Furthermore, the F-LSTM uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the F-LSTM requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (F-LSTM). In terms of data privacy and accuracy, F-LSTM addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction. [ABSTRACT FROM AUTHOR]

Recently, industrial Internet of things becomes more popular and it involves a group of intelligent devices linked to create systems which observe, gather, communicate, and investigate data. In this view, the demand for compression techniques in remote sensing images is increasing since low complexity technique is required in spacecraft. Deep learning, for instance, convolutional neural network (CNN) has gained more attention in the domain of computer vision, particularly for high‐level applications like detection along with interpretation. At the same time, it is difficult to resolve the low‐level applications like image compression and it is investigated in this article. This article presents an optimal compression technique using CNNs for remote sensing images. The proposed method uses CNN for learning the compact representation of the original image which held the structural data and was then coded by Lempel Ziv Markov chain algorithm. Next, the encoded image was reconstructed to retrieve the original image with high reconstructed image quality. The proposed optimal compression technique is compatible with the available image codec standards. Wide range of experiments was carried out and the results were compared with binary tree and optimized truncation, JPEG, and JPEG2000 in terms of compression efficiency, reconstructed image quality, and space saving (SS). The obtained results apparently proved the effectiveness of the presented method, which attains an average peak signal to noise ratio of 49.90 dB and SS of 89.38%. [ABSTRACT FROM AUTHOR]

The paper concerns the problem of the emulgamation of blockchain technology with business. Blockchain seems to be the one of the most recent technological innovations. This technology can be used in industry, the public sector, health care, education and business. The potential of this technology is not yet fully examined. Initially, blochchain was mainly used for cryptocurrencies, but this is only a part of the possible applications. Large possibilities of practical use result, among others, from the fact that making any change in historical records is impossible. There is no central server that could be vulnerable to cyber attacks. This technology allows to execute immediate financial transactions without intermediaries. This article consists of an introduction after which the definitions of blockchain have been presented. The most important part of the paper presents the results of analysis related to blockchain implementation in business. In addition, the work includes assumptions regarding further development of this technology with particular emphasis on business. This article has been elaborated within the DIMBI project ("Developing the Innovative Methodology of Teaching Business Informatics", performed within the Erasmus+ program KA2 - Cooperation for innovation and the exchange of good practices; project number: 2015-1-PL01-KA203-0016636). [ABSTRACT FROM AUTHOR]

Today we are having a huge information explosion across the world. Earlier the amount of information was increasing arithmetically, but today, information has expanded in geometric series. The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. Big Data isn't just for large enterprises with large budget. Today, small companies can have the benefits of the monumental amounts of digital data to make right and fast decisions to develop their enterprises. In fact, over the last couple of years, small and mid-size companies have seen more big date deployments than the big competitors. In India and Poland, the data boom isn't just limited to big enterprises, the growth of big data startup/technology vendors, is helping SMEs in scaling up infrastructure capabilities and driving insights from data. The increased availability of accessible, cheap data centres delivered by cloud vendors, has brought down the costs of upfront investment for small businesses, thereby reducing the market entry barrier. It is the question of choosing the right analytics vendors that fits the bill for small businesses. This paper aimed at designing a framework of Big Data implementation in SMEs. The reason for selecting these two countries is that there are international tie-ups between two universities of both countries. [ABSTRACT FROM AUTHOR]

The work concerns the problem of the management of security issues in Poland, especially in the perspective of Small and Medium Enterprises. The growth of Information and Telecommunication Technology has caused major impacts on business. Thus, serious security questions may be raised concerning small companies. Significant aspects of security issues are discussed, e.g. types of targets, types of attackers, legal issues and some recommendations for businessmen. The research is based on a review of works published in recent literature. The main method used in this inquiry was a case study. Additionally, the paper contains assumptions regarding further development of methods to secure information and communication systems. The results of the research reported here can be important both for business and academia. The recommendations may constitute the basis of improvement in the security area of activities supported by computers. [ABSTRACT FROM AUTHOR]

The paper contains a brief description of analysis done in literature addressing the problem of using carpooling systems. Significant economic, technical and legal aspects have been discussed. It may be observed that certain possibilities and financial profits lead to the fact that carpooling solutions are commonly used, especially by young people. After a short introduction into the subject, examples of carpooling systems such as BlaBlaCar, Inonecar, Autem.pl, JedziemyRazem.pl have been characterized and analyzed. The next part consists of a presentation of the economic aspects. Part three consists of the technical aspects. In part four there is a description of the legal situation of the passenger and of the driver from the corporate, civil, insurance, tax and criminal law perspectives. Finally, a case study has been presented. The authors have used a selected system to travel to conferences in countries in Europe: Poland, Slovakia, Hungary, Serbia, Bulgaria, Romania, Germany, Sweden. Brief conclusions end the paper. The main method used in this research was case study. In addition, the work contains assumptions regarding the further development of carpooling systems, both among individuals and business people. Carpooling systems may be used by individuals and business people to save money and time, however the disadvantages should be taken into consideration as well. [ABSTRACT FROM AUTHOR]

In the last several years a new technology known as Big Data has been developing. Data may now be perceived as "big", but that does not mean it is exclusively for large enterprises. It is a tool now known for being better and better accessible to small and medium-sized enterprises (SMEs) too. Enjoying easier and cheaper access to servers and data centres, delivered through cloud vendors, SMEs now face fewer constraints on upfront investment, the challenges presenting themselves as organisational and strategic by nature. Data is collected and analysed to provide new information and knowledge for useful business. Similar theoretical assumptions were the basis of business intelligence systems. Therefore, whether something new or the evolution of technology is concerned, Business Intelligence will always evolve. The issues outlined above will be analysed in this paper. It consists of a short introduction, after which the concepts and definitions of Big Data are presented. The next section presents results of analysis related to Big Data in different business areas with particular emphasis on applications dedicated to SMEs. The entire discussion ends with a brief conclusion. [ABSTRACT FROM AUTHOR]

Globalization, the Knowledge based Economy and Information and Communications Technologies (ICT) have caused major changes in Small Medium Enterprises (SMEs). Currently, the digital economy is driven by modern information systems and new ICT tools which offer employees and employers access to new work opportunities. The purpose of this article is to analyze the emerging trends and approaches in technology, as well as their impact on SMEs. In the first part of the paper, the author presents the influence of globalization on SME's business. The next part of the paper is devoted to significant aspects of using modern ICT in SMEs. Moreover, various aspects regarding changes in the work system with the use of new ICT solutions both by employees and employers have been discussed. Also, virtualization and mobile technologies issues in SMEs are an important element, taken into account by the author of this paper. The resulting recommendations may constitute a spark for positive changes and improvement in the area of knowledge based business activities [ABSTRACT FROM AUTHOR]