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Electric vehicles (EVs) have become a prominent alternative to fossil fuel vehicles in the modern transportation industry due to their competitive benefits of carbon neutrality and environment friendliness. The tremendous adoption of EVs leads to a significant increase in demand for charging infrastructure. But, the scarcity of charging stations (CSs) concerns efficient and reliable EV charging. Existing studies discussed EV energy consumption prediction schemes at the CS without analyzing the affecting parameters such as energy demand, weather, day, etc. In this regard, we have proposed an energy consumption and distribution framework for EVs in a smart grid environment for efficient EV charging after analyzing the affecting parameters such as location, weekday, weekend, and user. Moreover, we have considered EV dataset to perform a detailed and deep analysis of energy consumption patterns based on the aforementioned parameters such as CS (Station ID) within the location (Location ID), weekday, weekend, and user (UserID). The main aim is to understand the smart grid-based electricity distribution to the CS by analyzing energy consumption patterns for reliable EV charging. We have done different analysis on different parameters and present their graphical representations.

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 \times 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.

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods.

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%.

In general situation, the high intensive tasks linked to computation can be provisioned either through dedicated servers or can be properly filtered in virtual platforms. The major constraint in such situation can be associated with obtaining decision in process initiated as well as in the cost of data transmission preserving security. Sometimes some specific issues are required to be resolved during utilization of Internet of Things in specified applications expecting feasible solutions. Often it has been observed that the traditional computing mechanisms linked with the devices like routers equipped with specific infrastructures as well as services may not be adequate for implementation due to lack of flexibilities. In such situation, it may be difficult for data acquisition and processing. In fact, this complexity can be due to constrain in operations linked to computational resources especially in distributed environments. Sometimes also it is required to focus on specific data retrieved from different IoT distributed components linked to virtual machines. Accordingly, the techniques should be enabled on proper accumulation of data with accurate prediction prioritizing the causal factors and data sharing mechanisms. Though it is equally important to handle large scaled data related to issues of multi domain applications, it is essential to enhance the modularity, flexibility as well as scalability of the data and to maintain the optimal accuracy. Also to address these issues, specific computational approaches especially ant colony optimization technique can be the support to make commonalities and obtain close association of the resources with the relevant data. The implementation mechanism in virtual machines also supports integration of complex data and provisions privacy with security.

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).

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.

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.

The paper contains a description of a research conducted in collaboration with some Erasmus for Young Entrepreneurs program Romanian students, concerning the use and implementation of ICT (Information and Telecommunication Technology), especially in distance managing Small and Medium Enterprises (SMEs). The paper is a response to the nature of contemporary business management, with the constantly increasing amount of work, bureaucracy and the necessity to travel on business combined with the need to manage the company being away. In the research the students assumed the roles of the employees and entrepreneurs simultaneously, to gain the perspective from both points of view – supervised by the authors. The aim was to find and test the effectiveness of the available ICT remote management instruments in the context of SMEs. The aim was to create a study of implementation in real-life conditions, considering the advantages and disadvantages of the solutions tested, including possible future trends. The result was a set of recommendations for business people. Apart from the educational value of the research, the unique, dual perspective assumed by the students participating in it, provided them with a practical insight and skills to better understand and make a better use of the conditions of managing and being managed. The results described here both practical and academic value. The resulting recommendations may constitute the basis of positive changes and improvement in the use of advanced technology in business activities. It is worth noting with its international character the study focuses not only on selected Polish enterprises, but the resulting contents reflects the situation in SMEs in the EU as well, thus it may be a starting point for analyzing other regions worldwide.