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Deep learning has made identifying breast cancer lesions in mammography images an easy task in modern medicine, which has helped improve the diagnosis efficiency, sensitivity and accuracy by precisely identifying breast cancer from mammography images, contributing to timely detection and maintaining consistent performance. This paper presents the steps and strategies to develop a deep learning (DL) model to detect lesions in mammography images, based on U-Net architecture for precise segmentation, which has been developed for biomedical image segmentation, and incorporating ResNet34 as its encoder to extract features. Next, we employ the FastAI library, which simplifies and accelerates the model training tasks. For the data, studies and available resources lead us to INbreast, which is built with full-field digital mammograms contrary to other digitized mammograms. We obtained a high accuracy of 98% on the INbreast database, which is very challenging compared to state-of-the-art results.

Stock forecasting is a complicated and daily challenge for investors because of the non-linearity of the market and the high volatility of financial assets such as stocks, bonds and other commodities. There is a need for a powerful and adaptive stock prediction model that handles complexities and provides accurate predictions. The support vector regression (SVR) model is one of the most prominent machine learning models for forecasting time series data. An ensemble hyperbolic tangent kernel SVR (HTK-SVR-BO) is proposed in this paper, combining Tanh and inverse Tanh kernels with Bayesian optimization. Combining the strengths of multiple kernels using the ensemble technique and then using optimization to identify the optimal values for each SVR model to enhance the ensemble model performance is possible. Our proposed model is compared with an ensemble SVR model (LPR-SVR-BO), which uses well-known SVR kernel types, including linear, polynomial and radial basis function (RBF). We apply the proposed models to Microsoft Corporation (MSFT) stock prices. The mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2 score (model accuracy) and mean absolute percentage error (MAPE) are the regression metrics used to compare the effectiveness of each ensemble model. In our comparison, HTK-SVR-BO performs better in terms of regression metrics compared to LPR-SVR-BO and achieves results of 0.27424, 0.13392, 0.36595, 0.99997 and 5.2331 respectively. According to the analysis, the proposed model is more predictive and may generalize to previously unknown data more effectively, so it can be accurate when forecasting future stock prices.

This editorial summarises the special issue entitled “Future Trends of Machine Intelligence in Science and Industry”, which brings together several pieces of research that showcase the transformative impact of deep learning and intelligent systems across various domains, including healthcare, security and communication networks. By exploring advanced methodologies and innovative applications, this collection highlights significant strides in medical imaging, mental health diagnosis, biometric identification, smart grid management and adaptive e-learning. The featured articles delve into topics such as breast cancer detection using UNET architecture, psychodiagnosis prediction with deep learning, and blockchain-secured IoT systems for healthcare. Additionally, the issue covers revolutionary approaches in historical manuscript analysis, and contactless palm-print recognition. Through these comprehensive studies, we aim to inspire further advancements and cross-disciplinary collaborations, pushing the boundaries of what is achievable with modern technology.

Blockchain is regarded as a significant innovation and shows a set of promising features that can certainly address existing issues in real time applications. Decentralization, greater transparency, improved traceability and secure architecture can revolutionize healthcare systems. With the help of advancement in computer technologies, most healthcare institutions try to store patient data digitally rather than on paper. Electronic health records are regarded as some of the most important assets in healthcare system and are required to be shared among different hospitals and other organizations to improve diagnosis efficiency. While sharing patients’ details, certain basic standards such as integrity and confidentiality of the information need to be considered. Blockchain technology provides the above standards with features of immutability and granting access to stored information only to authorized users. The examination approach depends on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (or PRISMA) rules and an efficient planned search convention is utilized to look through multiple scientific databases to recognize, investigate and separate every important publication. In this paper, we present a solid systematic review on the blockchain and healthcare domain to identify the existing challenges and benefits of applying blockchain technology in healthcare systems. More than 150 scientific papers published in the last ten years are surveyed, resulting in the identifications and summarization of observations made on the different privacy-preserving approaches and also assessment of their performances. We also present a significant architectural solutions of blockchain to achieve interoperability. Thereby, we attempt to analyse the ideas of blockchain in the medical domain, by assessing the advantages and limitations, subsequently giving guidance to other researchers in the area.

The main goal of the paper is to identify the causes of non-realization of project prediction and to propose a new framework for project prediction. A secondary goal is to explain why the approaches to project prediction used currently do not provide satisfactory results. The research was realised in the form of qualitative research using semi-structured interviews. The findings reveal that the main causes of non-realization of project prediction are follows: there is no methodology that could be practically used; simplified approaches to project prediction usually have low reliability for which reason they are generally unusable; suitable input data and information for project prediction are not available. The main contribution made by the paper is the identification of causes of non-realization of project prediction and the proposal of a new framework for project prediction that respects changing conditions during the lifecycle of the project and changes in the way of thinking in project prediction. A prerequisite for its application is a functioning system of knowledge management in projects, including the realization of post-project analysis.

Social media, blogs, review sites and forums can produce large volumes of data in the form of users’ emotions, views, arguments and opinions about various political events, brands, products and social problems. The user's sentiment expressed on the web influences readers, politicians and product vendors. These unstructured social media data are analysed to form structured data, and for this reason sentiment analysis has recently received the most important research attention. Sentiment analysis is a process of classifying the user’s feelings in different manners such as positive, negative or both. The major issue of sentiment analysis is insufficient data processing and outcome prediction. For this, deep learning-based approaches are effective due to their autonomous learning ability. Emotion identification from the text in natural language processing (NLP) provides more benefits in the field of e-commerce and business environments. In this paper, emotion detection-based text classification is used for sentiment analysis. The data collected are pre-processed using tokenization, stop word discarding, stemming and lemmatization. After performing data pre-processing, the features are identified using term frequency and inverse document frequency (TF-IDF). Then the filtered features are turned into word embeddings by documents as a vector (Doc2Vec). Then, for text classification, a deep learning (DL) based model called convolutional bidirectional long short-term memory (CBLSTM) is used to differentiate the sentiments of human expression into positive or good and negative or bad emotions. The neural network hyper-parameters are optimized with a meta-heuristic algorithm called the frog leap approach (FLA). The proposed CBLSTM with FLA uses four review and Twitter datasets. The experimental results of this study are compared with the conventional approaches LSTM-RNN and LSTM-CNN to prove the efficiency of the proposed model. Compared to LSTM-RNN and LSTM-CNN, the proposed model secures an improved average accuracy of 98.1% for review datasets and 97.5% for Twitter datasets.

Person identification through chest X-ray radiographs stands as a vanguard in both healthcare and biometrical security domains. In contrast to traditional biometric modalities, such as facial recognition, fingerprints and iris scans, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual's rib cage, lungs and heart, chest X-ray images emerge as a focal point for identification, even in scenarios where the human body is entirely damaged. Concerning the field of deep learning, a paradigm is exemplified in contemporary generations, with promising outcomes in classification and image similarity challenges. However, the training of convolutional neural networks (CNNs) requires copious labelled data and is time-consuming. In this study, we delve into the rich repository of the NIH ChestX-ray14 dataset, comprising 112,120 frontal-view chest radiographs from 30,805 unique patients. Our methodology is nuanced, employing the potency of Siamese neural networks and the triplet loss in conjunction with refined CNN models for feature extraction. The Siamese networks facilitate robust image similarity comparison, while the triplet loss optimizes the embedding space, mitigating intra-class variations and amplifying inter-class distances. A meticulous examination of our experimental results reveals profound insights into our model performance. Noteworthy is the remarkable accuracy achieved by the VGG-19 model, standing at an impressive 97%. This achievement is underpinned by a well-balanced precision of 95.3% and an outstanding recall of 98.4%. Surpassing other CNN models utilized in our research and outshining existing state-of-the-art models, our approach establishes itself as a vanguard in the pursuit of person identification through chest X-ray images.

The technological advancements in the field of E-healthcare have resulted in unprecedented generation of medical data which increases the risk of data security and privacy. Ensuring the privacy of Electronic Health Records (EHR) has become challenging due to outsourcing of healthcare information in the cloud. This increases the chance of data leakage to unauthorized users and affects the privacy and integrity of the user data. It requires a trustworthy central authority to protect the sensitive patient information from both internal and external attacks. This paper presents a blockchain based privacy preservation framework for securing EHR data. The proposed framework integrates the immutability and decentralized nature of blockchain with advanced cryptographic techniques to ensure the confidentiality, integrity and availability of EHR. The EHR data are stored in an InterPlanetary File System (IPFS) which is encrypted using a hybrid cryptographic algorithm. In addition, a novel smart contact based patient-centric access control is designed in this paper using a blockchain-based SHA-256 hashing algorithm to protect the privacy of patient data. The experimental results show that the proposed framework enables secure sharing of health information between network users with improved data privacy and security. Furthermore, the optimized search process reduces the time and space complexity compared to the traditional search process. Through the utilization of smart contracts, this framework enforces patient-centric access controls and allows patients to manage and authorize access to their medical data.

The objectives of this study are to analyse the content of publications focused on the area of information ethics and discover patterns, knowledge and thematic trends. The main research question is: What is the intellectual and topical structure of the field of information ethics? We apply bibliometric analytical methods, including co-citation analysis (41 most cited authors out of 9947), co-word analysis (127 keywords), visualizations (maps) and analysis of time periods in strategic diagrams. These methods are interpreted with the use of previous content analyses and results of a Delphi study. The dataset covers publications between 1988 and 2023 collected from Web of Science using the search term “information ethics” in titles, keywords and abstracts (469 records). The study presents the research background and objectives, related research review, research methods and findings. Results are visualized in maps of topics and trends. We investigate the intellectual and thematic structure of information ethics, including numbers of publications, main disciplines, the intellectual structure (authors, topics, trends) and identify four time periods (1988-2005, 2006-2012, 2013-2019, 2020-2023) visualized by strategic diagrams. The study reveals the multidimensionality and multidisciplinary dynamic evolution of information ethics. The main trends are the topics of ethics of artificial intelligence and algorithms, data ethics, ethics of information literacy, informational privacy and dis/misinformation. We find that information ethics studies are embedded in wider contexts of the information crisis and design of public digital services. We propose education and information literacy courses related to ethical sensitivity, data ethics and the use of AI tools. The study contributes to bridging the gap between information ethics studies and human information interactions. Our results confirm the increasing interest in ethics of artificial intelligence.

Artificial intelligence methods, especially deep learning, have seen increasing application in analysing personality and occupational data to identify individuals with psychological and neurological disorders. Currently, there is a great need for effectively processing mental healthcare with the integration of artificial intelligence such as machine learning and deep learning. The paper addresses the pressing need for accurate and efficient methods for diagnosing psychiatric disorders, which are often complex and multifaceted. By exploiting the power of convolutional neural networks (CNN), we propose a novel CNN-based natural language processing method without removing stop words for predicting psychiatric diagnoses capable of accurately classifying individuals based on their psychological data. Our proposal is based on keeping a richer linguistic and semantic context to accurately predict psychiatric diagnosis. The experiment involves two datasets: one gathered from a private clinic and the other from Kaggle, called the Human Stress Dataset. The outcomes from the first dataset demonstrate a remarkable accuracy rate of 98.51% when employing CNN, showcasing their superior performance compared to the standard machine learning techniques such as logistic regression, k-nearest neighbours and support vector machines. With the second dataset, our model achieved an impressive area under the receiver operating characteristic curve (AUROC) of 0.87. This result surpasses those achieved by existing state-of-the-art methods, further highlighting the efficacy of our CNN-based approach in discerning subtle nuances within the data and making accurate predictions. Moreover, we have compared our model with three other programs on the same dataset and the accuracy reached 78.52%. The results are promising to aid parents or clinicians in early and rapidly predicting the ill individual.

With the vast success of e-recruitment, online job offers have increased. Therefore, there is a number of job portals and recommendation systems trying to help users filter this massive amount of offers when searching for the right job. Until today, most of these systems' searching techniques are confined to using keywords such as job titles or skills, which also returns many results. This paper proposes a job recommender system that exploits the candidate's resume to select the appropriate job. Our system, AnnoJob, adopts a semantic annotation approach to: (1) intelligently extract contextual entities from resumes/offers, and (2) semantically structure the extracted entities in RDF triples using domain ontology, providing a unified presentation of the content of the documents. Furthermore, to select the suitable offer, we propose a novel semantic matching technique that computes the similarity between the resume/offers based on identifying the semantic similarity and relatedness between the RDF triples using the domain ontology and Wikidata, which enhance job-ranking results over existing information retrieval approaches. We evaluate our system using various experiments on data from real-world recruitment documents.

The World Wide Web (the Web) has become part of people’s daily lives. Although the Web, like the Internet itself, was designed as a decentralized network, hand in hand with the increase in its interactivity Web users gradually concentrated on a limited number of platforms. As a result, providers of these large international platforms have become centres of power that can easily influence users’ behaviour and what information they can access. This paper is based on an integrative literature review and its aim is to describe the development of the Web from its beginnings to the present. This development is viewed from the perspective of centralization of the Web and the reactions that this centralization has provoked, especially the current trends towards the so-called re-decentralized Web. More specifically, the paper focuses on the privacy imperative that might act as a driving force for the re-decentralized Web, and on the technological innovations enabling development of truly decentralized platforms and applications. This paper contributes to the discussion of implications that a wider adoption of the re-decentralized Web could bring in the near future.

Blockchain is transforming the banking sector and offering opportunities for significant cost reduction and efficient banking services. However, implementing blockchain is a challenge due to lack of adequate knowledge and skills on how to implement the technology. As a result, there are very few market-ready blockchain banking products and organisations are unable to realise the promised value. This paper presents an overview of the banking sector’s blockchain use cases, design and implementation considerations and techniques. The aim is to offer an evidence-based primer to guide researchers and practitioners. The study relies on the systematic literature review method and reviews a total of 45 papers comprising 26 peer-reviewed scholarly articles and 19 technical reports from the banking industry. Leximancer software is used to support the thematic data analysis. The results show for the banking sector an increase in experimentation efforts geared towards the development of payment systems. The results also indicate key considerations from a technological, organisational and environmental perspective. The study highlights that platform selection, scalability and resilience are some of the critical technical considerations for implementing blockchain banking systems. Organisational considerations include collaboration and governance-related challenges. From an environmental perspective, the study notes several legal and regulatory considerations. This study contributes to the existing literature on blockchain adoption in banking, which is still in the nascent stage. The study also offers a research agenda for further understanding of blockchain implementation in the banking sector. Opportunities for further research are noted in the areas of interoperability, governance, security and privacy.

In the present times, the healthcare sector has seen an enormous growth in the usage of technology ranging from EHRs (electronic health records) to personal health trackers. Currently, there is a need for managing EHRs effectively with respect to storage, privacy and security measures. State-of-art technologies such as blockchain and artificial intelligence (AI) are applied in the healthcare domain. Innovation in AI is steadily advancing and is finding its place in different industries. The integration of blockchain and AI looks promising as there are several benefits. Blockchain can make the AI more secure and autonomous whereas AI can drive the blockchain with intelligence. The objective of this article is to explore the uses of blockchain as well as AI technology in the field of healthcare. We aim to survey the advantages, issues and challenges of integrating blockchain with AI technology, including future research directions in the healthcare domain. In this study, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) rules and an efficient searching protocol were used to examine several scientific databases to recognize and investigate every important publication. A solid systematic review was carried out on integration of blockchain and AI in the healthcare domain to identify existing challenges and benefits of integrating these two technologies in healthcare. Our study found that the integration of AI and blockchain technology has a potential to provide several benefits in terms of performance and security which conventional EHRs lack. The inherent benefits of blockchain and AI together are limitless, but the bare outcomes based on blockchain powered by AI technology are yet to be obtained. In addition, the outcome of our detailed study may aid researchers to carry out further research.

Studying the ways in which people construct their identities in online environments is a pressing contemporary concern. The research reported in this article was designed to examine the uses of, and influences on, Vietnamese respondents’ identity formation on Facebook. Data were collected by means of a social survey and the application of the Zaltman metaphor elicitation technique, a procedure that searches for customers’ thoughts and emotions by digging deep into the visual and non-visual illustrations that customers collect or make on their own. The findings show how Vietnamese Facebook users present themselves and how they thereby facilitate their self-expansion and maintain their sense of self-esteem. According to the analysis, it can be suggested that Facebook is where adults portray their socially conformed versions against social reflection. They use this platform to seek validation, demonstrate their professional side to make them look better in the eyes of society. Drivers of online identity formation are revealed through negotiating with conflicts in their existing identities. The level of self-modification amongst respondents is adjusted in relation to their social vigilance, conformity and motivations in and between social categorization.

With the COVID-19 pandemic experiences of Filipino students, the face-to-face mode of instruction in the classroom has been phased out in exchange for online learning platforms such as Google Classroom (GCR) among some K-12 learners. As advised by the Commission on Higher Education (CHED), most colleges and universities had to try available learning management systems; hence, this research study aimed to investigate the role of trialability and purposefulness towards GCR acceptance among tertiary institutions following the CHED educational policy. The researchers came up with eight hypotheses, which suggested that purposefulness may influence educational policy and acceptance of GCR. Trialability of GCR may influence educational policy and technical access. One thousand sixty-six (1066) respondents from six public higher institutions of learning were given online questionnaire; however, only 913 users were considered for the structural equation modelling and indirect effect of the suggested factors in this study. Using SmartPLS 3.0, the findings revealed that except for the hypothesis on institutional willingness (p< 0.054), all the hypotheses were highly supported at the level of significance p < 0.00 to p< 0.005. Thus, this study proves that GCR is an appropriate platform for colleges and universities. Trialability and purposefulness are two great factors that contributed to the acceptance and adoption of GCR in higher institutions of learning. Future researchers are therefore encouraged to replicate this study and validate the findings since the use of GCR is relatively new among Filipino teachers and learners.

Automatic gender recognition is one of the frequently solved tasks in computer vision. It is useful for analysing human behaviour, intelligent monitoring or security. In this article, gender is recognized based on multispectral images of the hand. Hand (palm and back) images are obtained in the visible spectrum and thermal spectrum; then a fusion of images is performed. Some studies say that it is possible to distinguish male and female hands by some geometric features of the hand. The aim of this article is to determine whether it is possible to recognize gender by the thermal characteristics of the hand and, at the same time, to find the best architecture for this recognition. The article compares several algorithms that can be used to solve this issue. The convolutional neural network (CNN) AlexNet is used for feature extraction. The support vector machine, linear discriminant, naive Bayes classifier and neural networks were used for subsequent classification. Only CNNs were used for both extraction and subsequent classification. All of these methods lead to high accuracy of gender recognition. However, the most accurate are the convolutional neural networks VGG-16 and VGG-19. The accuracy of gender recognition (test data) is 94.9% for the palm and 89.9% for the back. Experiments in comparative studies have had promising results and shown that multispectral hand images (thermal and visible) can be useful in gender recognition.

With the development of modern data processing, mining and collection technologies, various companies and institutions will have more opportunities to make these data operations faster and more efficiently. From the economic perspective, processing personal data is evidently lucrative and companies would therefore like to obtain as much data as possible. This paper analyses and summarizes existing and emerging social and economic trends, implications and issues caused by clashes between European legislation on personal data protection (the GDPR) and current data processing practices. Utilizing both quantitative and qualitative data, the article attempts to scrutinize the implications of the conflict between the rising demand for privacy and personal data protection on the one hand and the ever-growing need to process and store personal data, especially by commercial organizations, on the other. Analysing databases, legislation, reports, statistical data and surveys, the paper attempts to provide an estimate of the value of personal data and the consequences of poor handling of personal data.

Model-Driven Engineering (MDE) is a software development paradigm that uses models as the main artifacts in the development process. MDE uses model transformations to propagate changes between source and target models. In some development scenarios, target models should be updated based on the evolution of source models. In such cases, it is required to re-execute all transformation rules to update the target model. Incremental execution of transformations, which partially executes the transformation rules, is a solution to this problem. The Epsilon Transformation Language (ETL) is a well-known model transformation language that does not support incremental executions. In this paper, we propose an approach to support the incremental execution of ETL transformations. Our proposal includes a process, as well as a prototype, to propagate changes to the target model. In the proposed approach, all the changes in the source model are detected to identify and re-execute the rules which deal with computing the required elements for updating the target model. We evaluated the correctness and performance of our approach by means of a case study. Compared to the standard ETL, the results are promising regarding the correctness of target models as well as faster execution of the transformation.

Sharing of Electronic Health Records (EHRs) is of significant importance in health care. Lately, a cloud-based electronic health record sharing scheme has been used extensively to share patient records among various healthcare organizations. However, cloud centralization may compromise patients’ privacy and security. Due to the special features of blockchain, it is important to see this technology as a promising solution to resolve these issues. This article proposes a privacy-preserving, secure EHR sharing and access control framework based on blockchain technology. The proposal aims to implement EHR blockchain technology and ensure that electronic records are stored safely by specifying user access permissions. We emulate the cryptographic primitives and use smart contracts to describe the relationships between the EHR owner and EHR user through the proposed system on the Ethereum blockchain. We assess the proposal results based on encryption and decryption time and the costs of the smart contract. The encryption and decryption times are proportional to the size of the EHR, which varies from 128 KB to 128 MB. When it comes to encryption, the smallest EHR takes 0.0012 s to encrypt, while the largest EHR, which is 128 MB, takes 1.4149 s. On the other hand, a 128 KB EHR takes 0.0013 s to decrypt, whereas a 128 MB EHR requires 1.6284 s. As a result, performance evaluation and security analysis confirm that the proposal is secure for practical application.

Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level. Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency. However, the users’ reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language. The dataset was collected manually from two online agencies (Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%. The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.

This editorial aims to summarize the special issue entitled “Sustainable Solutions for Internet of Things Using Artificial Intelligence and Blockchain in Future Networks”, which deals with the impacts of recent infrastructure development using the Internet of things. This special issue consists of four scientific articles.

The proliferation of smartphones and sensor-based networks has led to a greater need for context-aware applications and pervasive business processes. One of the key approaches that seek to satisfy this need is context-aware service composition. Service composition can be achieved in two ways, i.e., service choreography and service orchestration. Embedding the context into an orchestrated composite service enhances its flexibility, but makes its development and execution more complicated. This study aims to reduce this complexity by introducing the CA-BPEL approach. Our proposed approach enables developers to turn a standard orchestrated service into a context-aware orchestrated service, consistent with the standard WS-BPEL language. This study applies the Design Science Research Methodology, in which we evaluate CA-BPEL by using a tourism demonstration along with the conduction of a usability survey that shows the convenience of the proposed approach. We also compare our proposed approach with 14 related studies. Our investigations suggest that CA-BPEL has much potential to facilitate the development and execution of context-aware service compositions.

At the end of 2019, COVID-19 (Coronavirus 2019) emerged in Wuhan, China, and spread rapidly worldwide. The use of virtual social networks, especially Twitter, has increased due to the present condition. The purpose of the present systematic literature review is to review the investigations on Twitter's role in the COVID-19 crisis. For this purpose, an appropriate search strategy was used to extract the studies conducted in the Web of Science and PubMed databases. In the end, 24 articles were reviewed. The results indicate that in the period of the COVID-19 pandemic, the content and tweets posted on Twitter were affected by this crisis, and various people such as the general public, health professionals, and politicians were sharing opinions, emotions, personal experience, and educational content about exposure to COVID-19 on this social media. Therefore, the speed of providing information to people has been one of the main advantages of Twitter during the crisis of COVID-19; however, the risk of using invalid information without scientific citation is also one of the most important concerns of using Twitter among people as well as health and governmental organizations. Thus, users should evaluate information accuracy more carefully and pay attention to the quality and validity of information before employing or sharing it. Governments and professionals can also prevent this disease's contagion even in similar future crises by employing Twitter correctly in the period of crisis and using the useful experience gained from applying social networks in the outbreak of COVID-19.

The paper deals with an analysis and evaluation of an innovative visual design of the Czech Financial Administration application, which was launched in order to solve needs arising from the COVID-19 pandemic. The Financial Administration had not made use of information technologies for several years, which had prevented the streamlining of public services offered to taxpayers. The aim of the study is to compare the perception of the visual interface design of two Financial Administration applications: (a) the Tax Portal – launched before the pandemic, and (b) the application for the provision of a compensation bonus for self-employed – launched during the pandemic. The research is based on a sequential mixed method design, where findings from a focus group and an interview are used to define relevant properties of web design to be evaluated in a questionnaire survey. The difference between variables regarding the perception of the Financial Administration applications is determined using a paired t-test. The results show that the new application has brought a significant change in the appearance of the interface design. Positive results of the Financial Administration’s innovative approach can be beneficial for future development of different e-government projects.

In recent years, community detection is important because members of the same community share the same concepts. For efficient community detection in a social network, the influence node plays a vital role. A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community. Therefore, the status of a person is determined by the user’s influence strength. That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network. Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another. Effective community detection in a complex structure is a challenging task. Many studies have been done based on topological networks. The approaches are ineffective, inefficient and require more time to process. To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO). Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.

The surge in interest in online teaching increased not only due to the pandemic. It had been growing even before. The main objective of this study is therefore to explore how online teaching has changed. It addresses experience and opinions of educators of Czech and Slovak universities in the period from the first days of the COVID-19 lockdown (March 2020) till the peak of its second wave (May 2021). To examine the impact of disharmony, the authors investigated Czech and Slovak university educators’ activities and behaviour during their online teaching. A descriptive statistics approach was applied. A total of 172 educators participated in our online survey. Our results reveal that online teaching has become a fundamental component of their education. Our outcomes demonstrate their low preparation for this unexpected event as well as their quick adaptation to the new situation. Additionally, data indicate that their difficulties reconcile their previous experience and teaching practices with online teaching. Finally, they show that about half of them are still sceptical about the future of online education and dream of return to traditional teaching. Our results also indicate that universities should facilitate their efforts in developing online education methodology and overall support to their educators.

This editorial aims to summarise the special issue entitled “Perspectives of Social Informatics” that builds on the current international view of social informatics. The special issue consists of eight scientific articles and one book review.

The extant literature highlights that mobile banking offers various benefits for consumers. However, there is only a limited number of studies that investigate mobile banking adoption by the elderly. This study investigates the factors influencing the adoption of mobile banking by the elderly in a developing country context. The authors explore the enablers, barriers and perceptions of the elderly towards mobile banking adoption. Data were collected through interviews and focus group sessions with respondents from KwaZulu Natal Province in South Africa. The study relies on the Actor-Network Theory as a lens through which to understand the interrelated factors that influence the elderly’s perception and adoption of mobile banking. The results reveal a low adoption of mobile banking by the elderly. In addition, it was evident that the barriers that influence the adoption of mobile banking by the elderly include a lack of information and understanding, security and trust issues, demographic factors, language, the complexity of mobile banking applications, and resistance to change. The identified important enablers towards the adoption of mobile banking include convenience, unlimited access, cost-effectiveness. The study proposes a mobile banking adoption model for the elderly and highlights the interrelated technical and non-technical factors influencing mobile banking adoption. Additionally, it offers design guiding principles aligned to the elderly’s needs and perceptions of mobile banking.

The article presents a focused analysis of the Russian-language scientific literature of the first decades of the twenty-first century on the problems of the development of social informatics in Russia. The authors have shown that the multidimensionality of social informatics causes an increasing interdisciplinary research interest in the professional community and is divided into four directions. First, researchers refined the conceptual foundations of social informatics. The second direction is devoted to the study of information resources in their dynamics. The third direction considers and analyses digital traces and their use under the conditions of digital transformation. The fourth direction is devoted to the problems of children and young people in the information environment. The authors achieve intermediate results. Firstly, the clarification of the conceptual basis of the study of social informatics is due to the public demand for comprehensive informatization. Secondly, interest in information resources is due to the need for effective organization of data series in information systems. Thirdly, the accounting and analysis of digital traces and their use are due to the public need for collective security. Fourthly, it is necessary to observe the problems of perception and use of digital technologies by children and young people, as well as their socialization in the developing information society. Finally, the authors conclude that the potential of social informatics is increased by an interdisciplinary interest in qualitative changes in Russian society in the context of digital transformation.