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Purpose: In contemporary times, augmented reality is a cornerstone technology within the tourism sector. Nonetheless, the inclination towards embracing and utilizing augmented reality applications in tourism hinges upon multifaceted determinants, meriting examination through the integrated lens of technology acceptance and use theory. Concurrently, these determinants sway individuals' hedonistic motivations to engage with such apps. This juncture holds significance for managerial considerations, delineating pivotal factors influencing the adoption of this technology. Hence, this present inquiry explores the nexus between the technology acceptance and use theory and the hedonistic motivations for tourism augmented reality apps on mobile platforms within the Iranian context.Method: The current research's statistical population consists of users of tourism augmented reality apps. The sample size is 201 users. The data collection tool is a questionnaire. A structural equation test was used to analyze the data.Findings: The research findings indicate that four factors, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, have a positive and significant impact on the hedonic motivation to use tourism augmented reality apps. Moreover, all four factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) positively influenced the intention to use augmented reality applications on mobile phones. Additionally, hedonic motivation was shown to positively and significantly affect the intention to use tourism augmented reality apps on mobile phones. Notably, hedonic motivation is a crucial mediator between the Unified Theory of Acceptance and Use of Technology (UTAUT) dimensions and the intention to use augmented reality applications. However, the mediating role of hedonic motivation was not supported for performance expectancy and behavioral intention.Conclusion: This research investigates the factors influencing the motivation to use augmented reality (AR) apps in tourism. It finds that performance expectancy, effort expectancy, social influence, and facilitating conditions positively impact hedonic motivation for AR app usage. The study validates the Unified Theory of Acceptance and Use of Technology (UTAUT) for predicting AR app adoption. Notably, hedonic motivation mediates between UTAUT dimensions and intention to use AR technology, suggesting implications for enhancing AR app design in the tourism sector.

Cloud computing technology could bring about cost-effectiveness, scalability, usage easiness, and flexibility against environmental changes. Despite several advantages of this technology, the acceptance and movement to the cloud computing environment occurs slowly, particularly in several organizations in developing countries. Cloud computing allows an individual to share distributed sources and services. This study aims to identify the optimal security framework in cloud computing, focusing on the Infrastructure Communications Company as a case study. The method used in this study is descriptive (non-test) and the correlative research scheme is PLS (partial least squares). The participants of the current study were selected among the Tehran Communication Infrastructure Company employees. Based on the Cochran formula and the research population which was 600, 234 were selected using the random sampling method. To measure the security framework in cloud computing, the questionnaires related to Joils and Opria (2017), Tari (2014), Qio and Gayi (2017), and Sing et al. (2016) were used. Findings indicate that the impacts of technological and technical factors, experienced human resources, financial resource provision and secure infrastructures on information technology security were significant (0.01). Moreover, information technology management and supporting regulations on security risk assessment have significant effects. The assessment of security risk has a significant effect on information security. Additionally, information security management and supporting regulations have a significant effect on Goal setting (0.01). Goal setting has a significant effect on environmental and physical security. Based on the results, the evaluated model of the research could explain 0.53 of information security management, 0.50 of security evaluation, 0.36 of goal setting, 0.55 of information security, and 0.35 of environmental security.

  In the modern technological age, laptops are widely used for doing various day-to-day activities and getting updates all around us. The COVID-19 situation is playing a vital role in a dynamic shift in buyer behavior with multiple personal computing devices at home. Prioritizing and selecting appropriate laptop devices is difficult because there are several options of laptops that are available in the market, and these are equipped with the latest features to do gaming, designing, attending online classes, and performing office and other everyday tasks. There are multiple selection criteria that are complex in nature. MCDM (Multiple Criteria Decision Making) approaches can handle and analyze these complicated criteria. By using MCDM techniques, decision-making can be done to select the top-ranked alternative from among the available alternatives. This paper exhibits a group of two MCDM techniques; Best Worst Method (BWM) and Analytical Hierarchy Process (AHP), which have been used to evaluate relative weights of considered conflicting criteria such as brand, price, storage capacity, RAM, processor, weight, touch screen, Bluetooth, and screen size, and these weights are used in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking and selecting the best product of laptops.

Fog computing is considered a promising solution to minimize processing and networking demands of the Internet of things (IoT) devices. In this work, a model based on the energy consumption evaluation criteria is provided to address the deployment issue in fog computing. Numerous factors, including processing loads, communication protocols, the distance between each connection of fog nodes, and the amount of traffic that is exchanged, all have an impact on the re-search system's overall energy consumption. The power consumption for implementing each com-ponent on the fog node as well as the power consumption for information exchange between the fog nodes are taken into account when calculating each fog node's energy use. Each fog node's energy consumption is closely correlated to how its resources are used, and as a result, to the average normalized resource utilization of a fog node. When the dependent components are spread across two distinct fog nodes, the transfer energy is taken into account in the computations. The sum of the energy used for transmission and the energy used for computational resources is the entire amount of energy consumed by a fog node. The goal is to reduce the energy consumption of the fog network while deploying components using a novel metaheuristic method.  Therefore, this work presents an enhanced water strider algorithm (EWSA) to address the problem of deploying application components with minimum energy consumption. Simulation experiments with two scenarios have been conducted based on the proposed EWSA algorithm. The results show that the EWSA algorithm achieved better performance with 0.01364 and 0.01004 optimal energy consumption rates.

What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions

Nowadays, many companies cannot see the digital investment that plays a main role in the IR 4.0. Therefore, this study is investigating the study of investment as plays a critical role in an analytical activity to assess the benefits and costs of an investment and can be used as an investment justification. Traditional investment appraisal uses a financial approach where the benefits and costs are quantified in a certain amount of value for money and then compared in value. Moreover, this study is revealed the fruitful outcomes because revealed the investment valuation method with NPV (Net Present Value) and ROV (Real Option Valuation). ROV is an alternative to financial valuation. Seeding from the same philosophy as Financial Option, ROV has advantages in handling the flexibility, risk, and volatility that may occur from an investment. Thus, ROV is considered more able to appreciate an investment that has these characteristics. Investment appraisal with ROV is better able to appreciate investment than traditional financial methods, as shown by ROV's NPV results in the case of marketing with Loyalty points through email communication as a digital investment that are greater than ordinary NPV. This is because ROV can appreciate flexibility in investments that have choices of investment plans in the future

The condition of the hotel's competitiveness is a strong brand. The introduction of digital marketing in the strategy of hotel branding creates new opportunities for hotels when interacting with guests through digital channels. The purpose of this study is to develop theoretical and practical measures to improve the effectiveness of marketing strategies in hotel branding using digital tools. To achieve the goal of the study was conducted research on targeted branding some of the largest hotel chains. The results of the analysis showed that in the process of branding at each stage the corresponding goals have achieved by means of advertising, marketing, public relations management, personnel selection, corporate culture.This study substantiates the main tools of the strategy of the Digital Marketing and Sales. The brands need to constantly monitor changes in market positions and audience sentiment using all the features and channels. The priorities should be implemented by performing key tasks, in particular such astimely measurements as far as brand experience has a positive effect on customer satisfaction and loyalty.

The purpose of this study is the development of IT-consulting in the system of foreign economic activity of the Black Sea region. The relevance of this study is due to the need to address the development of consulting services in the system of foreign economic activity of the agro-industrial complex in the region. It is established that today many companies are interested in reducing the risks associated with running their own business. This has led to a rapid growth of consulting companies. The most popular are IT consulting services, market research, financial consulting and auditing with the largest market share, while environmental consulting is only gaining momentum. Institutional priorities of IT-consulting services in the system of foreign economic development of agro-food production of the region are established. The mechanism of development of the system of regional IT-consulting for stimulation of foreign economic activity of agro-food sphere is offered. The basic elements of development and functioning of the international infrastructure of regulation and self-regulation of IT-consulting services are investigated. It is determined that in the long run the services will be developed on the basis of innovative concepts and management tools to ensure the strategic competitiveness of customers. The consumers served are metallurgy, energy, construction, telecommunications, chemical industry, food industry, trade and distribution, media companies and the financial sector. In general, the consulting industry in Ukraine has some space, and domestic consulting companies also have a significant share of reserves in the market of consulting services. The estimated value of exports of the Black Sea region according to the models in 2022 is projected at the level of 2989.242 million dollars, and in 2023 - at the level of 3146.893 million dollars. It is proved that for further intensification of foreign economic activity of enterprises two main problems should be solved: to overcome excessive dependence on export of plant products (and other products with low added value) and to intensify export capacities of Kherson region.

The object of the research is the economic dynamic of a depressed low-productive economy, as exemplified by the modern Ukrainian economy. With the purpose to analyze the main causes and consequences of such an economy, the characteristic tendencies of its functioning, activation of the processes of reformation and growth, there was developed the dynamic economic model with significant share of consumer import and export of raw materials, with the use of IT technology. The model also takes into account the socio-economic clustering of a society. It is formalized in the space of economic variables, which describes respectively the savings (liquid capital) of the main social groups involved in the economy, the prices of consumer products in the domestic and foreign markets, the price of exported raw materials for the exchange rate.The model is intended for qualitative analysis of the processes occurring in the economy described by it, and for conducting computational experiments on the basis of information technologies in simulation mode with its parameters, which allows to identify the most characteristic features of the object of the research. The results of experimental researches with the model allowed to establish the main periods of economic dynamics represented by the dynamic variables model, its specificity and social consequences for the society, which is characterized by a bimodal distribution of savings. There was investigation the dependence of the mode solution from it parameters which means in practice the dependence of economic dynamics from the change of individual conditions and factors of economic development.The obtained results are important for informational and analytical support of decision making processes in order to bring the being studied economy out of a low-productive state.

Microfinance institutions in India provide a set of financial services to the economically weaker sections. Recently, a large number of microfinance institutions have emerged in India and they have favorable impact for poverty reduction. The impact of these institutions on entrepreneurship and society, needs to be explored in greater depth. The objective of this study is to apply machine learning techniques to explore this impact. The research uses a MIX dataset for three successive years, namely 2017, 2018, and 2019. This dataset comprises eight variables centered on gross loan portfolio. Principal Component Analysis (PCM) has been applied on the sample dataset for dimensionality reduction, resulting in two main components and each component consist of fraction from eight variables. Then, the sample dataset has been labelled with the help of clustering using K-means clustering technique. Further, classification models based on K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) are applied to predict the appropriate category of entrepreneurship. The experiment result shows that the machine learning techniques have been found effective and useful tools for estimating the impact of microfinance on entrepreneurship in India.

Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.5 algorithm to extract six subcategories of emotions over three classes of sentiments based on word-based analysis of text. The proposed approach has yielded superior results with seven existing approaches in terms of parametric values, with an accuracy of 0.96, precision of 0.92, sensitivity of 0.94, and an f1-score of 0.92.

Online reviews play a crucial role in the consumer decision-making process in the glamping industry. Some reviews are misleading; therefore, users need to identify credible reviews to form objective opinions. This study examined dimensions of perceived review credibility and its influence on purchase intention within the glamping business. Online surveys were conducted with respondents with relevant travel experiences to examine the key credibility factors. Findings identified that review length, amount of detail, writing style, and travelers’ images; as well as mixed, moderate, and two-sided reviews influence perceived review credibility. It was also found that perceived review credibility influences purchase intention; that management response impacts perceived company credibility and purchase intention; and that personalized management response is valuable for the perceived credibility and purchase intention. A revised conceptual framework was developed to demonstrate the sources of perceived credible online reviews and the role of management responses in the reviews. In addition to the theoretical contribution, this study can have practical marketing implications for businesses when creating online promotional material for their products and engaging with customers

Without utilization of computer and its related technology, modern day’s life cannot be headway. It has also transformed into an incredibly troublesome task. The genuine challenges included are shorter life cycles, cost effective and higher software quality goals. Despite these challenges the software developers have started to give cautious thought on to the procedure to develop software, testing and reliability investigation of software and to reinforce the method. Developer most fundamental decisions related to the perfect release time of Software. Software development method incorporates a piece of vulnerabilities and ambiguities. We have proposed a multi objective software release time issue under fuzzy environment using a software reliability growth model to overcome such vulnerabilities and ambiguities. Further we have discussed the fuzzy environment framework to deal with the issue. Considering model and issue, we can especially address the issue of when to release software under these conditions. Results are illustrated numerically.

In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research