Scandinavian Journal of Information Systems http://sjis.scandinavian-iris.org/index.php/sjis <h2 align="justify">Welcome to SJIS</h2> <div> <div align="justify">Scandinavian Journal of Information Systems (SJIS) is the journal of the IRIS Association, which is the Scandinavian chapter of the Association for Information Systems.</div> </div> <div> <div align="justify"> </div> </div> <div> <div align="justify">The IRIS (Information Systems Research in Scandinavia) Association is a non-profit organization aiming to promote research and research education in the use, development, and management of information systems in Scandinavia, and making that research known in the international research community and among practitioners.<br />The IRIS Association has as its major purpose to develop and maintain the following: <ul> <li>The Scandinavian Journal of Information Systems</li> <li>The IRIS conference</li> </ul> </div> </div> <p align="justify">SJIS publishes research on development and use of information systems and related organizational and societal issues.<br />The journal publishes two issues per year, comprising regular research papers, reflection notes, and special issues. All papers are available in the free .</p> <div> <p align="justify">The reflection notes consist of an invited essay authored by one or more senior scholars, and a number of invited response papers from key researchers within the focused area. As an example, the reflection notes in issue 28(2) focused on the smart machine.</p> </div> <div align="justify">The special issues are edited by a team of guest editors based on submission of a special issue proposal to the coordinating editor. The special issues cover important and timely topics within the scope of the journal. Examples of recent SJIS special issues include:<br /> <ul> <li>Distributed participatory design</li> <li>Genre theory in IS</li> <li>Development and use of web-based information systems</li> <li>Scandinavian Researcher Career Retrospectives</li> <li>Rejuvenating Enterprise Systems</li> <li>Sustainable Development Goals in IS Research</li> </ul> </div> <div> </div> <div align="justify"><strong>eISSN 1901-0990</strong></div> Department of Mathematics and Computer Science, Institute of Electronic Systems, University of Aalborg en-US Scandinavian Journal of Information Systems 1901-0990 Behaviour Based Credit Card Fraud Detection Design And Analysis By Using Deep Stacked Autoencoder Based Harris Grey Wolf (Hgw) Method http://sjis.scandinavian-iris.org/index.php/sjis/article/view/223 <p>A country's economic development and progress are greatly influenced by its banking and financial industry. Recent years have seen a dramatic growth in the number of customers making purchases with plastic, whether they do it online or in a brick-and-mortar store. Many problems are being caused by fraudsters to clients, banks, and other financial institutions. As more and more people gain access to high-speed Internet, online banking expands to become a crucial trading platform. Users' confidence and safety can be compromised by fraudulent transactions and other forms of fake banking activity. Further, with the rise of sophisticated frauds like malware infection, scams, and bogus websites, businesses lose a great deal of money to fraud. In order to detect fraudulent activity in credit card transactions, this study develops three new contributions. The first enhancement is a data-balanced Deep stacking autoencoder for fraud detection based on the Harris Grey Wolf (HGW) network. Harris Grey Wolf is proposed to train Deep stacked autoencoder in this case (HGW). The suggested HGW-based Deep stacked autoencoder provides the most effective method for uncovering frauds by applying a fitness function, adapting to produce minimal error, and computing the optimal solution over a series of iterations. Using transaction data, we select the most important features to use for detection since they increase the rate and accuracy of detection. Features were selected because of their usefulness in providing crucial data and improving detection accuracy. This is how the efficacy of fraud detection systems is validated and distinguished from genuine systems. Accuracy is 0.92, sensitivity is 0.76, and specificity is 0.92 for HGW Deep Stacked autoencoder across a range of performance parameters</p> Govind Prasad Buddha GRADXS, Dr. NAGAMALLESWARA RAO Copyright (c) 2023 2023-02-03 2023-02-03 35 1 1 8 An Improved Optimization Techniques For High Dimensions Data Analysis http://sjis.scandinavian-iris.org/index.php/sjis/article/view/224 <p>&nbsp;</p> <p>Modern data analytics systems have many potential real-time applications, such as managing student grades, reading tax returns, processing zip codes and checks, and analyzing medical data. In general, multivariate sequence overlap, separation, concatenation, feature selection, and feature extraction for data with multivariate sequence length have been analyzed by various researchers. Previous methods are planned to predict unconstrained numbers that include all four preprocessing phases’ beforehand classification. The most difficult task of this predictive model is feature extraction and classification. These phases invented to offer improved classification accuracy. Since the number and strings of data are usually unknown, determining the optimal boundary between them is very complicated. Some existing feature extraction methods rely on heuristic models inspired by nature to generate latent features for classification. Therefore, the optimal choice of features is difficult to predict due to the variability of features. Some common methods in feature extraction improved the classification results and produced the best results. The method consists of three basic modules. Dataset acquisition, classification module as Convolutional Neural Network (CNN) and optimization module (squirrel search optimization). Likewise, performance metrics such as precision, recall, precision and execution time are evaluated and compared to various existing methods.</p> U.Indumathi, Dr.S.J.Sathish Aaron Joseph Copyright (c) 2023 2023-02-06 2023-02-06 35 1 9 16 Effective Evaluation Technique For Using A Photovoltaic System Based On A Fly-Back Converter To Improve Power Quality http://sjis.scandinavian-iris.org/index.php/sjis/article/view/225 <p>In this modern period, the need for power generation is increasing fast, and to supply the consumer's demand for power, voltage harvested from diverse renewable energy sources must be managed.&nbsp; Many solutions have been developed with the assistance of various theories and methodologies to raise and maintain the consistent output voltage. However, raising the voltage causes fluctuation and waves at the output during operation, increasing the cost of all equipment as well as maintenance.&nbsp; The proposed model is implemented with the intelligent control MPPT approach, which allows the controller to respond faster. The Fractional open-circuit voltage controller utilizes in harvesting the maximum amount of energy, despite being impacted by external factors. In terms of architecture and performance, fly-back converters are similar to booster conversions. It comprises an inductor similar to the primary winding of a transformer, and the secondary giving the output is another inductor similar to the secondary winding of a transformer. As a result, the fly-back converter is a power supply design that includes a mutually coupled inductor that stores energy as current travels through it and releases it when power is removed. This methodology is used by the fractional open circuit voltage-based tracking algorithm to provide maximum power point tracking. The input voltage of the converter, which is the same as the solar panel terminal voltage, is maintained at times its open circuit voltage with the aid of power converter modules. It makes use of a two-stage inverter, which is an open-loop circuit that converts the converter's DC output to AC voltage. As a result, the voltage created from the model has a sinusoidal waveform with decreased harmonics, which is obtained by using a low pass filter and then applied to the load.</p> Gokulkannan. K, Balaji. G, Muralibabu. B, Ramachandran. S, Surendiran. S, Rathinam. A Copyright (c) 2023 2023-02-06 2023-02-06 35 1 17 28 Analysis of the destructive impact of attack drones on critical civil infrastructure: a combined method of protection based on the application of an electromagnetic shield http://sjis.scandinavian-iris.org/index.php/sjis/article/view/226 <p>The article examines and analyzes the use of unmanned attack vehicles on critical infrastructure in the russian-Ukrainian war, tactical and technical characteristics, options for use and determination of priorities. The methods of protection against the destructive impact of unmanned aerial vehicles on critical infrastructure are analyzed. The article proposes the use of a combined (physical and electromagnetic) method of protecting critical infrastructure based on the use of electromagnetic shields. Tasks for further research.</p> Heorhii Dementiiuk, Maksym Iasechko , Serhii Bazilo , Igor Trofimov , Kostiantyn Horbachov, Serhii Riazantsev, Andrii Lutsyshyn, Ihor Zaitsev Copyright (c) 2023 2023-02-06 2023-02-06 35 1 29 37 Strategy to increase E-Customer Loyalty Through E-Customer Satisfaction in E-Commerce Business in Indonesia http://sjis.scandinavian-iris.org/index.php/sjis/article/view/227 <p>The purpose of this study was to measure the degree of electronic customer's electronic satisfaction and loyalty to online sales and shopping services in Indonesia. The study is classified as a research study using descriptive analysis methods, taking samples from the population and using questionnaires as the primary tool for data collection. The population in this study is all Indonesian online shopping consumers. Respondents were selected using probability sampling and systematic random sampling. The survey sample used consisted of 156 respondents. The survey sample was randomly selected for the first data, but specific intervals are used for the second data. The results of the analysis using the structural equation modeling (SEM) model are as follows; (1) The E-Marketing mix has a large positive effect on e-customer satisfaction. (2) E-Service quality (E SERVQUAL) has a significant positive impact on e-customer satisfaction; (3) E-Brand trust has a significant positive impact on e-customer satisfaction. (4) E-customer satisfaction has a significant positive impact on E-customer retention. This study will help online selling business stakeholders identify the factors that can affect e-customer loyalty.</p> Ahmad Sopyan, Wenny Desty Febrian, Indra Sani, Muratin, Ade Risna Sari, Nia Sarinastiti, Ade Firdiana, Harryi Purwoko Copyright (c) 2023 2023-02-06 2023-02-06 35 1 38 46 Technologies used in the classroom as a means of transferring information across boundaries of online classes in order to improve learning outcomes of students http://sjis.scandinavian-iris.org/index.php/sjis/article/view/229 <p>The COVID-19 outbreak has caused a significant shift in people’s daily lives and teaching-learning processes all across the world. On the other hand, the global effort to maintain containment measures to prevent the spread of this deadly disease has equally harmed the education sector and caused economic hardship during the last two years. The purpose of this article is to explore teachers’ opinions on online learning techniques and platforms in Vietnam, as well as the concerns and problems that instructors have while moving to online platforms, understanding of online tools/platforms used to deliver lessons, and suggestions for refining the process for successful teaching. This study will provide survey data on the psychology of students during a period of social isolation. The results are then based on sample collection using a simple web-based questionnaire. Present some facts that might help improve the use of interactive learning among learners, notably instructors, who can then help with wider deployment. The findings also indicate a substantial need for professional development, with a focus on digital literacy skills, as well as a teacher community understanding of the importance of online platforms in the teaching process. Information and communication technology may be useful in the classroom as a new source of teaching resources and as a means of transferring information across boundaries. It also enables teachers to speak with their colleagues and build networks with other institutions, which aids in the growth of the university. These new digital technologies used in education encourage the development of new strategies and teaching approaches as a result of the shift in student learning processes.</p> Huynh Kim Cuong Copyright (c) 2023 2023-02-06 2023-02-06 35 1 47 52 Using the applications in order to promote street food: A case of Tiktok platform http://sjis.scandinavian-iris.org/index.php/sjis/article/view/230 <p>We live at a time when social networks, in particular, are greatly dominating the technological landscape. People of all ages now have their own social network accounts. Additionally, as people's living standards rise, they will tend to spend more time engaging in leisure activities, most notably traveling. People frequently research the destination they intend to visit before arriving, thus using social media to promote photographs enables us to reach clients more quickly than through traditional research. This is a combination of two contents that people are very interested in, giving tourism firms more knowledge about social media promotion channels from which to choose the right advertising strategy for their company. expanding Titok's user base. Specifically, to offer a useful search engine for tourist information to help people find an appropriate tourist destination.</p> Tran Khai Minh Phap Copyright (c) 2023 2023-02-06 2023-02-06 35 1 53 56 Enhancing Social Media Sentiment Analysis with a Deep Learning Language Model Ensemble http://sjis.scandinavian-iris.org/index.php/sjis/article/view/231 <p>The need for understanding users' actions is significant due to the fast growth of data caused by users' contributions on social media platforms, which is especially pertinent in light of the current epidemic caused by the coronavirus. The scope of the investigated dataset in this research is the thoughts included inside postings relating to the epidemic. It might be difficult to identify the categorization algorithms that are best suited for this sort of information. In this setting, models of deep learning for sentiment analysis have the potential to bring detailed representation capabilities and increased performance in comparison to feature-based methods that are already in use. In this study, we focus on enhancing the performance of sentiment classification by utilizing a specialized deep-learning model. This model combines an improved word embedding method with a long short-term memory (LSTM) network that we construct. Ultimately, our goal is to improve the accuracy of sentiment analysis. In addition, we present an ensemble model that combines our baseline classifier with other state-of-the-art classifiers that are used for conducting sentiment analysis. This model was created by combining our baseline classifier with other state-of-the-art classifiers. It is the goal of this model to be more accurate than any of the other models taken individually. This article made two types of contributions. (1) We provide a robust framework using word embedding and an LSTM network to learn contextual links among words and grasp unheard or unusual words in emergent circumstances like the coronavirus epidemic by detecting suffixes and prefixes from training data. Because it can learn word contexts, this framework can achieve this. This framework is able to do this task as a result of its capacity to learn the contextual relations between words and to learn the contextual connections between words. (2) We propose a hybrid ensemble model for sentiment analysis in order to capture and make use of the major discrepancies that exist among methods that are considered to be state-of-the-art. These discrepancies exist across various approaches to the problem of analyzing people's feelings about things. In a lot of the experiments that we run, we make use of our one-of-a-kind Twitter coronavirus hashtag dataset, as well as public review datasets from Amazon and Yelp. An inquiry based on statistics is carried out for the aim of drawing conclusions, and the findings of this research demonstrate that the performance of these recommended models beats that of other models with respect to the accuracy of categorization</p> Lateshwari, Dr. Sushil Bansal Copyright (c) 2023 2023-02-06 2023-02-06 35 1 57 67