درخت حوزه‌های تخصصی

Cloud computing is an emerging concept of information technology that in many countries has an influence on many companies. The research was conducted to evaluate cloud computing adoption in Saudi Arabia; Benefits and hindrances for small and medium-sized enterprises (SMEs). The qualitative research approach is performed by interviews with the management of a variety of SMEs active in the information and communication technology (ICT) industry. This paper illustrates a significant positive correlation between the use of cloud computing and organizational quality performances. The paper concluded that the knowledge level of SMEs on the accessibility of cloud services is below average scale. The greatest challenges about the cloud service are privacy and security in the cloud among providers and users for the Saudi Arabian firms.

The evolution of today's application technologies requires a certain level of robustness, reliability and ease of integration. We choose the Fields Programmable Gate Array (FPGA) hardware description language to implement the facial recognition algorithm based on "Eigen faces" using Principal Component Analysis. In this paper, we first present an overview of the PCA used for facial recognition, then use a VHSIC Hardware Description Language (VHDL) simulation and design platform, which is the ISE. We describe the operation of each block and implement, thereafter, the computation of the global centered images. This corresponds to the first step of the PCA algorithm to assess its performance. The comparison of the results of this implementation with that of MATLAB confirmed the operability and effectiveness of this method for centralizing images. We also implemented the last part of this algorithm which is the computation of the Manhattan distance. The tests have given very satisfactory results.

Online reviews have become essential aspect in E-commerce platforms due to its role for assisting customers’ buying choices. Furthermore, the most helpful reviews that have some attributes are support customers buying decision; therefore, there is needs for investigating what are the attributes that increase the Review Helpfulness (RH). This research paper proposed novel model called inclusive review helpfulnessmodel (IRHM) can be used to detect the most attributes affecting the RH and build classifier that can predict RH based on these attributes. IRHM is implemented on Amazon.com using collection of reviews from different categories. The results show that IRHM can detect the most important attributes and classify the reviews as helpful or not with accuracy of 94%, precision of 0.20 and had excellent area under curve close to 0.94.

It has been proved that in the era of globalization and the development of information technologies, the need to optimize the supply chain of services increases, since the integration of participants in this chain is performed without the use of modern management information systems. In the paper, with the help of logic-cognitive, structural-functional methods, structural-logical and imitation modelling, a management approach to simulation of business interaction in the integration of enterprises with elements of ERP-system and CBIP is developed and proposed, which allows you to create a flexible, adaptive and secure IT infrastructure , which increases the efficiency of the enterprise and allows you to optimize the management of relations with other participants in the service supply chain. As a result of this application of modern supply chain management systems, a synergistic effect is achieved that allows you to minimize the number of technological operations, shorten the production cycle and reduce the cost of services both for each individual enterprise and in the service supply chain as a whole.

Smart applications with interconnected intelligent devices for sharing services arise serious security problems to the stability of this IoT complex and heterogeneous environment. Unless security considerations are analyzed and implemented properly in real time then IoT cannot be perceived as a pervasive network for the possible stakeholders. Current state of the art has analyzed trust-based security solutions as additional feature to application layer of the system which can identify and filter out the malicious nodes. In this paper we are proposing holistic trust management with edge computing mechanism to create trustworthy zones comprising different clusters, where Gateway on behalf of clusters will initiate migration of their nodes if falls below the defined Zone trust threshold level. The created zones are self-resilient against any malicious attacks and saves lots processing usage time and energy to address the security issues. By analyzing our proposed algorithm with other contemporary approaches to handle IoT security issues using trust mechanism, this approach is more precise in terms of protecting system against incurring malicious behavior, and also prolong the application operation duration by reducing communication and processing overhead.

Agovernmentshouldusesocialmediaforcommunicatingwithitscitizen.Theengagement index score is one of the methods for assessing the rate of governmental success in using social media as a tool in establishing interactive relationships with its citizen. In general, the engagement index score is obtained by calculating the number of posts, number of likes and comments, and so forth on a single social media account. Therefore, we propose an integrated engagement index score for three social media: Facebook, Youtube and Twitter. In this work, we carry out a study for local governments in Indonesia. The engagement index score was adopted from the previous research. However, we modified the formula to get a better distribution score.Our modified formula generates the same ranking sequences with previous research.Also, Facebook and Youtubes’ reaction are considered in this work to analyzes the quality of sentiments to a Facebook fan page and Youtube channel of localgovernments.

The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models.

Information Management and Analysis (IMA) implies the single data and knowledge base of the business organization whose algorithm and software complex is designed to solve functional problems of operating and dispatch management, marketing, financial arrangements, accounting, strategic planning and organizing e-document flow between the sources and users of information. The “information – communication - control” vector is directed at the achievement of corporate objectives. Assessment of IMA implementation efficiency is complicated by the fact that implementation of managerial (informational) solutions is possible indirectly, with the flows of material, financial and informational resources; the IMA algorithms are valid for a certain range of situations and modes, whose frequency and duration of action are not known in advance. The study is aimed at formalizing compatibility conditions for a set of economic and technical, actual, and probabilistic indicators applied for justifying the IMA implementation efficiency for industrial entrepreneurship. The IMA effectiveness is expected to be calculated as a total of network (communication) effects per each block of IMA algorithms, factored the presentation of information operability; to calculate cost-effectiveness (recoupment of investment) it is supposed to modernize the structural capital evaluation procedure according to A.Puliс. The article contains an introduction, concept of IMA, application of system efficiency theory to IMA, building the structure of IMA efficiency indicators, and conclusion. The main method of study: system approach.

Falls are a serious concern among the elderly due to being a major cause of harm to their physical and mental health. Despite their potential for harm, they can be prevented with proper care and monitoring. As such, the motivation for this research is to implement an algorithmic solution to the problem of falls that leverages the benefits of Machine Learning to detect falls in the elderly. There are various studies on fall detection that works on one dataset: wearable, environmental, or vision. Such an approach is biased against low fall detection and has a high false alarm rate. According to the literature, using two datasets can result in high accuracy and lower false alarms. The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. In addition, it has proposed a multimodal approach by training two separate classifiers using both Machine and Deep Learning and combining them into an overall system using sensor fusion in the form of a majority voting approach. Each trained model outputs an array comprising three percentage numbers, the average of the numbers in the same class from both arrays is then computed, and the highest percentage is the classification result. The working system achieved results were 97% accurate, with the highest being achieved by the Convolutional Neural Network algorithm. These results were higher than other state-of-the-art research conducted in the field.

Buying online is a trend especially for young customers aged less than 30 years old. A lot of studies have been conducted to investigate this phenomenon but hitherto no cohesive model has been established. Therefore, the present study is meant to investigate online shopping factors that contribute to customers’ purchase decision. Five factors were identified to have potential influence on online purchase decision and one factor is expected to be a mediator to affect the relationship. From 384 data collected from online customers who visited popular online shopping sites, it was found that all five factors (perceived convenience, perceived risk, perceived competence, perceived benevolence, and perceived integrity) lead to trust and at the same time trust acts as a quasi mediator to affect the relationship between these factors and online purchase decision. The findings provide the actively sought after evidence to support the claims on the role of these factors in affecting online purchase decision. Online businesses should incorporate the findings in order to facilitate customers’ decision making process while visiting their websites. The websites must contain or portray all the elements as found in this study.

According to Gartner’s Hype Cycle, gamification is poised for large-scale adoption in the coming years. The construct of gamification has received considerable interest, especially in the context of learning. Over the past five years, research in the area has grown. The current study looks at consolidating the conducted research and attempts to provide a snapshot of the work carried out in the last five years. The chosen studies have explored the role of gamification in learning activities and have attempted to provide empirical evidence to support gamified learning. The purpose of the study is to review existing literature, in terms of the outcomes, outcome variables, context of the study, and research methodology used. This would not only add to the knowledge in the field but would also guide future research. Additionally, practitioners would benefit from a consolidated view of five years of research into the practice of gamification.