Search Results (17)

Competition in Taiwan’s banking industry is becoming fierce. Banks’ traditional income based on interest rates is insufficient to support their growth. Therefore, banks are eager to expand their wealth management business to increase profits. The fee income from the sale of mutual funds is one of the major sources of banks’ wealth management business. The problem is how to look for the right customers and contact them effectively. Therefore, it is necessary to develop classification prediction models for these banks to evaluate their customers’ potential to buy mutual fund products sold by commercial banks and then deploy marketing resources on these customers to increase banks’ profits. Recently, the XGBoost algorithm has been widely used in conducting classification tasks. Therefore, using the eXtreme Gradient Boosting algorithm, a mutual fund marketing prediction model is developed based on a commercial bank’s data in this study. The results show that whether a customer has an unsecured loan, a customer’s amount of assets in the bank, the number of months for transactions, a place of residence, and whether the bank is the main bank for the total amount of credit card bills in the past six months are the top five factors for the models, providing valuable information for effective wealth management and marketing. Full article

In recent years, the rapid development of the Taishan tea industry has become a business card of local specialty agriculture. However, as consumers’ demands for Taishan tea product quality and safety continue to improve, the centralized database traceability system that the traditional Taishan tea industry relies on shows insufficient information credibility and core data security risks, making it difficult to match the diversified expectations of the market and consumers. In order to solve this problem, this paper proposes a trusted traceability model for Taishan tea based on blockchain technology, which utilizes blockchain technology and data hierarchical uploading mechanism to ensure data accuracy and transparency, and, at the same time, improves data uploading efficiency. The optimized SM2 encryption algorithm is introduced, and the execution efficiency of the encryption algorithm is improved by the concurrent processing framework, which guarantees the security and transmission speed of the data. The experimental results show that the blockchain-based trusted traceability model for Taishan tea significantly improves the data security, query, and writing speed, and greatly optimizes the problems of traditional traceability methods. With this research, the results in this paper not only help to improve the quality and safety of Taishan tea products but also provide technical support for the production enterprises to enhance their brand competitiveness. Full article

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain. Full article

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data. Full article

The Millennials and Generation Z use online shopping for a holistic experience and buy more expensive or better-quality products with buy now pay later payment methods for their highly demanding needs. The authors aimed to deepen understanding of this phenomenon by finding related knowledge fields and discovering the type of economy that will represent an increasing market share for the method of domestic e-commerce payments. The methodology used combined computer-assisted review, descriptive statistics, and linear regression to explain the market share of 23 economies worldwide. Student credit card use, myopic consumer law, buying tendencies. and dark financial triangles were identified as related topics. Logistics performance, ease of doing business, and postal development were found to be significant factors. Finally, economies with medium ranks are inclined to adopt this kind of payment easily. Hence, major implications, both managerial and academic, must be addressed. High responsibility should be borne by industry associations, which should run information campaigns by collaborating with public institutions. From the point of view of theoretical implications, studying the buy now and pay later concept and its outcomes might deepen understanding of consumer behaviour, decision-making processes, risk perception mitigation, debt behaviours, and credit adoption. Full article

The rapid development of information and communication technology has fostered a natural integration of technology and design. As a result, there is increasing interest in Augmented Reality (AR) business card systems that leverage digital media. This research aims to advance the design of an AR-based participatory business card information system in line with contemporary trends. Key aspects of this study include applying technology to acquire contextual information from paper business cards, transmitting it to a server, and delivering it to mobile devices; facilitating interactivity between users and content through a screen interface; providing multimedia business content (video, image, text, 3D elements) via image markers recognized by users on mobile devices, while also adapting the type and method of content delivery. The AR business card system designed in this research enhances traditional paper business cards by incorporating visual information and interactive elements and automatically generating buttons linked to phone numbers, location information, and homepages. This innovative approach enables users to interact and enriches their overall experience while adhering to strict quality control measures. Full article

Business organizations have huge potential to increase their customer base by offering e-commerce services, especially in the post-pandemic era. Ensuring secure e-commerce applications plays an important role in increasing customer base. To develop appropriate policies and secure technological infrastructures, business organizations first need to establish an understanding of the reservations of their customers toward e-commerce, as well as their perception of security and privacy of e-commerce applications. In this paper, we present the results of an empirical study of e-commerce customers conducted in Pakistan to gain an insight into their mindset on using e-commerce applications. An online questionnaire was set up to collect data, which were analyzed using the partial least squares method with SmartPLS software. The empirical findings highlight that customers’ concerns about credit card usage, concerns over information security, motivational factors for shopping offered by business organizations, customer trustworthiness, and user’s feelings about the reputation of e-commerce impact their perception of security of online data and trust in an e-commerce application. The results of this study can help organizations in Pakistan to develop policies and improve technological infrastructures by adopting emerging technologies and digital forensics. Full article

The development of information technology, along with the high growth and diversification of consumer needs, has revolutionized the way in which business-to-consumer transactions occur. All this progress was boosted by the COVID-19 pandemic period in a different manner in each EU country, depending on different local aspects. The main goal of this paper is to determine the key characteristics of e-commerce in European Union countries in a pandemic context, based on Eurostat Digital Economy data for 2019–2020. Therefore, for an easier visualization, based on PCA, using 27 analyzed variables, new unique dimensions were revealed: 1. heavy online purchasers, 2. triggers for embracing digital purchasing, 3. perceived barriers against buying online (privacy concerns, security, or not having a card), 4. dynamics of online interaction with public authorities, and 5. enterprise online sharing. Moreover, clustering techniques set four groups of countries with different online commerce patterns that might require attention, according to their specificities, both from a government level and from a business perspective. Special attention is paid to Romania, which has one of the biggest e-commerce industries in Southeastern Europe, but with the share of e-commerce in total retail still quite low, despite this great increase. The models of other countries could be important in helping Romania to catch up with the most successful economies in terms of e-commerce. Full article

The rapid growth in e-commerce has resulted in an increasing number of people shopping online. These shoppers depend on credit cards as a payment method or use mobile wallets to pay for their purchases. Thus, credit cards have become the main payment method in the e-world. Given the billions of transactions that occur daily, criminals see tremendous opportunities to be gained from finding different ways of attacking and stealing credit card information. Fraudulent credit card transactions are a serious business issue, and such ‘scams’ can result in significant financial and personal losses. As a result, businesses are increasingly investing in the development of new ideas and methods for detecting and preventing fraud to secure their customers’ trust to protect their privacy. In recent years, learning algorithms have emerged as important in research areas aimed at developing optimal solutions to this issue. The core challenge currently facing researchers is that of the imbalanced credit card dataset, in which the data are highly skewed and the number of normal transactions is much higher than fraudulent transactions, which thus negatively affects the performance of credit card fraud detection. This paper reviews the sampling techniques and their importance in solving the imbalanced data problem. Past research is found to show that hybrid sampling techniques will produce excellent results that can improve the fraud detection system. Full article

Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as passwords, credit card ids, and personal information. Enormous intelligent attacks are launched dynamically with the aim of tricking users into thinking they are accessing a reliable website or online application to acquire account information. Researchers in cyberspace are motivated to create intelligent models and offer secure services on the web as phishing grows more intelligent and malicious every day. In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. BERT was used to extract the URLs’ text from the Phishing Site Predict dataset. Then, the natural language processing (NLP) algorithm was applied to the unique data column and extracted a huge number of useful data features in terms of meaningful text information. Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. Then, the data were classified into legitimate and phishing URLs. To evaluate the proposed method, a famous public phishing website URLs dataset was used, with a total of 549,346 entries. However, three scenarios were developed to compare the outcomes of the proposed method by using similar datasets. The feature extraction process depends on natural language processing techniques. The experiments showed that the proposed method had achieved 96.66% accuracy in the results, and then the obtained results were compared to other literature review works. The results showed that the proposed method was efficient and valid in detecting phishing websites’ URLs. Full article

This study aimed to assess the robustness and resilience of retail areas in Seoul, based on the changes in sales before and after the COVID-19 outbreak. The spatial range and temporal scope of the study were set as district- and community-level retail areas in Seoul, from January 2019 to August 2020, to consider the effect of the COVID-19 outbreak. The data used in this study comprised sales information from the retail sector, namely Shinhan Card sales data for domestic and foreigners by business type in Seoul, provided by Seoul Big Data Campus. We classified the retail areas based on the change in sales before and after the COVID-19 outbreak, using time series clustering. The results of this study showed that time series clustering based on the change in sales can be used to classify retail areas. The similarities and differences were confirmed by comparing the functional and structural characteristics of the district- and community-level retail areas by cluster and by retail area type. Furthermore, we derived knowledge on the decline and recovery of retail areas before and after a national crisis such as the emergence of a COVID-19 wave, which can provide significant information for sustainable retail area management and regional economic development. Full article

The COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact. For health reasons, such physical distancing requirements should be guaranteed in private and public spaces. In Spain, occupancy is restricted by law but, in practice, certain spaces may become overcrowded, existing law infringements in places that rely on occupancy estimations that are not accurate enough. For instance, although the number of passengers who enter a public transportation service is known, it is difficult to determine the actual occupancy of such a vehicle, since it is commonly unknown when and where passengers descend. Despite a number of counting systems existing, they are either prone to counting errors in overcrowded scenarios or require the active involvement of the people to be counted (e.g., going through a lathe or tapping a card when entering or exiting a monitored area) or of a person who manages the entering/exit process. This paper presents a novel IoT occupancy system that allows estimating in real time the people occupancy level of public spaces such as buildings, classrooms, businesses or moving transportation vehicles. The proposed system is based on autonomous wireless devices that, after powering them on, do not need active actions from the passengers/users and require a minimum amount of infrastructure. The system does not collect any personal information to ensure user privacy and includes a decentralized traceability subsystem based on blockchain, which guarantees the availability, security and immutability of the collected information. Such data will be shared among smart city stakeholders to ensure public safety and then deliver transparent decision-making based on data-driven analysis and planning. Full article

Positioning capabilities have become essential in context-aware user services, which make easier daily activities and let the emergence of new business models in the trendy area of smart cities. Thanks to wireless connection capabilities of smart mobile devices and the proliferation of wireless attachment points in buildings, several positioning systems have appeared in the last years to provide indoor positioning and complement GPS for outdoors. Wi-Fi fingerprinting is one of the most remarkable approaches, although ongoing smart deployments in the area of smart cities can offer extra possibilities to exploit hybrid schemes, in which the final location takes into account different positioning sources. In this paper we propose a positioning system that leverages common infrastructure and services already present in smart spaces to enhance indoor positioning. Thus, GPS and WiFi are complemented with access control services (i.e., ID card) or Bluetooth Low Energy beaconing, to determine the user location within a smart space. Better position estimations can be calculated by hybridizing the positioning information coming from different technologies, and a handover mechanism between technologies or algorithms is used exploiting semantic information saved in fingerprints. The solution implemented is highly optimized by reducing tedious computation, by means of opportunistic selection of fingerprints and floor change detection, and a battery saving subsystem reduces power consumption by disabling non-needed technologies. The proposal has been showcased over a smart campus deployment to check its real operation and assess the positioning accuracy, experiencing the noticeable advantage of integrating technologies usually available in smart spaces and reaching an average real error of 4.62 m. Full article

The rapid development of urbanization has increased traffic pressure and made the identification of urban functional regions a popular research topic. Some studies have used point of interest (POI) data and smart card data (SCD) to conduct subway station classifications; however, the unity of both the model and the dataset limits the prediction results. This paper not only uses SCD and POI data, but also adds Online to Offline (OTO) e-commerce platform data, an application that provides customers with information about different businesses, like the location, the score, the comments, and so on. In this paper, these data are combined to and used to analyze each subway station, considering the diversity of data, and obtain a passenger flow feature map of different stations, the number of different types of POIs within 800 m, and the situation of surrounding OTO stores. This paper proposes a two-stage framework, to identify the functional region of subway stations. In the passenger flow stage, the SCD feature is extracted and converted to a feature map, and a ResNet model is used to get the output of stage 1. In the built environment stage, the POI and OTO features are extracted, and a deep neural network with stacked autoencoders (SAE–DNN) model is used to get the output of stage 2. Finally, the outputs of the two stages are connected and a SoftMax function is used to make the final identification of functional region. We performed experimental testing, and our experimental results show that the framework exhibits good performance and has a certain reference value in the planning of subway stations and their surroundings, contributing to the construction of smart cities. Full article

The circular economy may help firms to maximize the value of their material resources and minimize the overall resource use, waste, pollution and emissions of their business activities. Implementing a circular economy program requires radical changes in product, business model and ecosystem innovation. Most research on circular oriented innovation takes a product or business model perspective. Few publications have explored how to innovate in ecosystems: how a group of loosely coupled organizations can change how they interact with each other to achieve a collective outcome. This study proposes the Circularity Deck: a card deck-based tool that can help firms to analyze, ideate and develop the circularity potential of their innovation ecosystems. The tool is based on a literature review of circular oriented innovation principles, and of practical examples that show how these principles have been applied. The principles are organized according to the intended circular strategy outcome that they pursue (i.e., narrow, slow, close, regenerate and inform material and energy flows), and the extent of the innovation perspective that is needed to operationalize a principle (i.e., product, business model, or ecosystem innovation). This review and categorization process first produced a novel analysis of the circular economy innovation landscape, using an ecosystem perspective. Second, these results served to develop the Circularity Deck, which was further developed and tested for ease of use and perceived usefulness in 12 workshops with 136 participants from 62 different organizations. The Circularity Deck provides an approach for future research and practice to integrate new principles and examples that can help firms to analyze, ideate and develop circular innovation ecosystems. Full article