درخت حوزه‌های تخصصی

Background: The Internet of things (IoT) is a new technology that expanding rapidly and has led to the emergence of new services in the banking industry. Although IoT is an emerging technology in Iran, it is expected to become a paradigm in the future. IoT applications in banking have helped to provide high quality and fast responses to customers anytime, anywhere. Purpose: This research aims to identify and prioritize IoT applications in the Iranian banking industry based on business continuity indicators. Methods: First, based on the comprehensive literature review and practical experiences, IoT applications in the banking industry are identified. Then, each indicator's weight was calculated using the Grey Decision-making trial and evaluation laboratory (DEMATEL). Finally, IoT-based applications in the Iranian banking industry have prioritized using the multi-attribute utility theory (MAUT). Due to the unstable economic conditions for the financial sector, business continuity indicators are employed in this study. Also, the reason for using MAUT is to consider the utility of banking experts for performing IoT banking services. Findings: According to results, IoT-based services in the banking industry, including Transmit instant reports, smart ATMs, non-contact electronic payments, and electronic checks, are identified for Iranian banks. Then, These appropriate services are prioritized with the capabilities of Iranian banks.

World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter. Sentiment analysis of the text uses Natural language processing, a machine learning technique to tell the orientation of opinion of a piece of text. This system extracts attributes from the piece of writing such as a) The polarity of text, whether the speaker is criticizing or appreciating, b) The topic of discussion, subject of the text. A comparison of the work done so far on sentiment analysis on tweets has been shown. A detailed discussion on feature extraction and feature representation is provided. Comparison of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine, XGBoost and Random Forest, based on their accuracy depending upon type of feature, is shown. Moreover, this paper also provides sentiment analysis of political views and public opinion on lockdown in India. Tweets with ‘#lockdown’ are analysed for their sentiment categorically and a schematic analysis is shown.

Nowadays, the tourism industry accounts for approximately 10% of the global GDP, while it only contributes 3% of the economy in Iran. Since the pressure of US sanctions increases day after day on the Iranian economy, the necessity of paying attention to this industry as a source of foreign currency is felt more than ever. The purpose of this research is to analyze the reviews of users of social commerce websites by using a combination of text mining and data mining techniques. For this purpose, the database of TripAdvisor website (TripAdvisor.com) was evaluated, and all profile information of users who commented on hotels in Iran was collected. These comments on all the content of the website, such as hotels, restaurants, and attractions, were then extracted and analyzed. The optimal number of clusters was considered four clusters by calculating the Davies-Bouldin index, namingly water therapy tourists, boutique hotels style and Iran urban tourists, travelholics and food tourists, business and health tourists. Every single cluster possesses unique attributes and features. Afterward, the association rules were further identified for each cluster according to the characteristics of each cluster and the information in the users' profiles. Finally, a solution is proposed to increase the participation of the users on the website, and targeted promotional plans are expressed in accordance with the well-known features of each cluster.

The progression of Internet as a vehicle to penetrate and develop markets is far reached. Advancement in telecommunication and technology are very rapid in Malaysia. Further, the spur is supported and promoted by the government whilst Malaysian’s are receptive to this. However, Malaysian retailers seem to channel much of its turnover through the old avenue. This study is a litmus test on Malaysians awareness on the local apparel sites. Some of the highlighted rationale to shop for apparels online is the experience, convenience and availability. On the flip side, non-user friendly, safety and non-sensory environment are underscored. These variables are analyzed using Chi-Square analysis to test its statistical independence, followed by correlation and further regressed. The research is focused on university students within Klang Valley. There are limited resources to previous studies done on this subject as source of reference. Therefore, further studies can bring forth insights on retailers in Malaysia and the consumer’s behavior; or either one, as the catalyst influence awareness on our local apparel sites. Rhetorical as it may sound, no one is prepared to assume risks in a questionable market

Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.