User Behavior Analysis Research Articles

Virtual reality applied as a teaching tool: 3D virtual tour at IFMT Tangará da Serra

This work aims to develop a virtual tour of the IFMT Tangará da Serra campus facilities using 3D modeling and Virtual Reality (VR) to be applied in the discipline of introduction to Computer Graphics of the technical course of Maintenance and Support in Informatics integrated to High School. VR allows the interaction and navigation of users in 3D environments maintained by computer, using mapping channels and analysis of user behavior, enabling the exchange of information between the virtual environment and the user, affecting one or more human senses. It is an interaction and entertainment technology that can be successfully applied to assist in teaching strategies in high school subjects. With the evolution of education, that is, the improvement of the processes of exploration, discovery, observation and construction of knowledge, new teaching tools have been emerging, among which virtual learning environments stand out because they allow people to conduct experiments or perform tasks in a new and different way that they could not do in the physical/real world, such as flying, visiting places that do not exist or are difficult to access through the manipulation and analysis of the object of study itself. Thus, VR will be fundamental in this process of educational evolution, where the use of digital tools such as ICTs (Information and Communication Technologies) is increasingly sought for the successful application of active teaching methodologies in the classroom. Therefore, the objective of this project refers to the development of an interactive VR system to be used with 3D VR glasses, designed, modeled and rendered using the free software Blender, to present a virtual tour of the facilities of the IFMT Tangará da Serra campus. This tool will be very useful for demonstrating computer graphics concepts to students, as well as to the external community, as a form of institutional promotion and marketing.

LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT Applications

LIME-Mine: Explainable Machine Learning for User Behavior Analysis in IoT Applications

Research on the Implementation of Financial Risk Control Models and Algorithms Based on Machine Learning

Nowadays, machine learning has been rapidly applied to the financial services industry. With the applications of machine learning, it will bring challenges to the stable development of the financial industry. This paper studies the user behavior analysis algorithm based on operator big data, and provides financial risk control mechanism for financial enterprises. Taking finance as the data collection object, in the user behavior prediction algorithm, the experiment uses the spectral clustering algorithm to cluster the non-convex data samples, and converts the samples to the Laplacian feature space. It not only reduces the dimension, but also clusters the data, and obtains the global optimal solution. The proposed algorithm is analyzed experimentally, and the tool prototype is realized. The developed gadgets have also been applied in enterprises. The research provides a method for the construction of a risk control model in the financial industry, thereby helping financial enterprises to better control the risk of lending. This paper also provides reference for the performance improvement of the model.

Internet behavioral models for improving internet quality of service or user profiling: a systematic literature review

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience.

IoT-Enabled Innovative Environment with Efficient Routing for the Digital Library Services to Examine the Behavior of Users

An IoT-enabled innovative environment with efficient routing is proposed for digital library services to analyze user behavior. This conceptual framework leverages the Internet of Things (IoT) to create a smart library ecosystem where connected devices collect and share data in real-time. The emphasis on efficient routing ensures seamless access to digital resources, optimizing the user experience. This study introduces an IoT-enabled innovative environment aimed at optimizing digital library services by examining user behavior, incorporating the Clustered Centered Routing Cryptography Scheme (CCRCS). The proposed framework leverages the Internet of Things (IoT) to create a dynamic ecosystem within digital libraries, facilitating efficient data collection and analysis. By implementing the CCRCS, data transmission is secured through clustered-centered routing, ensuring the integrity and confidentiality of user interactions and resource access. Through the IoT infrastructure, libraries can monitor user behavior in real-time, capturing valuable insights into preferences, browsing patterns, and resource utilization. This holistic approach enables libraries to adapt their services to better meet user needs, optimize resource allocation, and enhance the overall user experience. The integration of IoT technologies with robust cryptographic protocols represents a significant advancement in digital library management, offering unparalleled opportunities for data-driven decision-making and personalized service delivery.

Data-Driven Personalized Marketing in E-commerce: Practical Applications

Data-driven personalized marketing is crucial for enhancing user experience and increasing sales in e-commerce. This paper explores the practical applications of personalized marketing, focusing on algorithms, user behavior analysis, and associated risks. Also, the paper illustrates how data-driven personalized marketing is implemented and its impact on user engagement and sales. The collection and processing of large amounts of user data are essential for personalized marketing strategies. Various algorithms, including collaborative filtering and deep learning, are employed for personalized recommendation systems. Real-time data analysis techniques enable e-commerce companies to adjust marketing strategies rapidly. Combining information from several platforms, including social media, mobile apps, and websites helps in creating comprehensive user profiles for effective personalized marketing. Analyzing user behavior is essential to comprehending user requirements and preferences. However, there are risks and challenges associated with data-driven personalized marketing, including data privacy and compliance issues, over-personalization risks, and data quality concerns.

Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.

Identifying Key Influencers on X: A Network Analysis of User Behavior through Social Relationships

Identifying Key Influencers on X: A Network Analysis of User Behavior through Social Relationships

AI 스피커의 지속 사용을 위한 사용자 행동 복합 분석 연구

AI 스피커의 지속 사용을 위한 사용자 행동 복합 분석 연구

Online Room Booking System for HotelVilla18

ABSTRACT: The hospitality industry has undergone a significant transformation with the advent of online booking platforms, reshaping the way travelers search for and book hotel accommodations. This research paper investigates the role of hotel booking web development in enhancing user experience and business efficiency within the hospitality sector. The study focuses on evaluating the user interface, usability, real-time availability, pricing transparency, secure payment processing, and booking management systems of hotel booking websites or applications. A mixed-method approach is employed, combining quantitative analysis of user behavior and system performance metrics with qualitative insights from user surveys, interviews with industry experts, and case studies of successful booking platforms. The findings contribute to understanding best practices, user preferences, technological advancements, and business strategies in hotel booking web development. The outcomes of this study are valuable for hoteliers, web developers, policymakers, and researchers aiming to optimize the online booking experience, drive customer satisfaction, and achieve business success in the digital era. Keywords– Online Room Booking, Hotel Reservation System, Instant Hotel Booking, Secure Hotel Booking.

Photography portfolio Website

Abstract— The V2S2photography website stands as a pinnacle of excellence in Indian wedding photography, showcasing an extensive array of services tailored to capture the intricate beauty and cultural richness of matrimonial celebrations. From the enchanting moments of engagement ceremonies to the breathtaking landscapes of pre-wedding shoots, and from the grandeur of wedding festivities to the cinematic storytelling of short films, V2S2photography epitomizes creativity, professionalism, and artistry. This research paper delves into the multifaceted role of the V2S2photography website, examining its impact on client perception, brand positioning, and business success. Through an in-depth analysis of user behavior, feedback, and conversion metrics, the study aims to uncover actionable insights for optimizing website performance and enhancing the overall client experience. Keywords: Indian wedding photography, V2S2photography, website development, client interaction, brand positioning, user experience, engagement ceremonies, pre-wedding shoots, wedding festivities, short films, cultural richness, matrimonial celebrations.

Personalized Recommendation Research Based on Logistic Regression Algorithm for Amazon Product Reviews

<p class="MsoNormal" align="justify"><span style="font-family: Times New Roman;">This study explores personalized recommendation strategies within Amazon's product review system using the Logistic Regression algorithm. By analyzing user behavior and review data, a predictive model was developed to forecast user preferences for specific products. The research employed extensive real-world data and validated the model's effectiveness and accuracy through empirical analysis. Findings indicate that the proposed personalized recommendation system significantly enhances user experience and increases product sales. The study contributes an effective recommendation algorithm for e-commerce platforms, offering practical implications for enhancing user engagement and optimizing marketing strategies in competitive markets.</span></p>

Analysing Multidimensional Strategies for Cyber Threat Detection in Security Monitoring

The escalating risk of cyber threats requires continuous advances in security monitoring techniques. This survey paper provides a comprehensive overview of recent research into novel methods for cyber threat detection, encompassing diverse approaches such as machine learning, artificial intelligence, behavioral analysis and anomaly detection. Machine learning plays a central role in cyber threat detection, highlighting the effectiveness of deep neural networks in identifying evolving threats. Their adaptability to changing attack patterns is emphasized, underlining their importance for real-time security monitoring. In parallel, ensemble learning is explored, combining multiple models to improve overall detection accuracy and create a robust defense against a spectrum of cyber threats. The literature reviewed highlights the importance of behavioral analysis, with a novel approach that integrates user behaviour profiling with anomaly detection. This has proven effective in identifying suspicious activity within a network, particularly insider threats and stealthy attacks. Another behavioral framework using User and Entity Behavior Analytics (UEBA) is presented for enhanced anomaly detection, highlighting the importance of context-aware monitoring in improving threat detection accuracy. Collaborative defense mechanisms emerge as a major focus of the research papers reviewed, exploring the potential of sharing threat information between organisations to enhance collective security monitoring. Their findings underscore the importance of a collaborative approach to staying ahead of rapidly evolving cyber threats. Some types of cyber-attacks are also analysed in the context of a security operations centre (SOC) monitoring environment using a security information and event management (SIEM) tool - Splunk. In conclusion, this survey paper synthesizes recent advances in cyber threat detection methods in security monitoring that integrate machine learning, behavioral analysis, and collaborative defense strategies. As cyber threats continue to evolve, these novel methods provide valuable insights for researchers, practitioners, and organisations seeking to strengthen their cybersecurity defenses. This concise overview emphasises the multi-dimensional approach required to secure digital ecosystems, providing a concise yet comprehensive guide to modern cyber threat detection strategies.

Privileged User Behavior Analytics (PUBA) for Insider Threat Detection

Privileged User Behavior Analytics (PUBA) for Insider Threat Detection

Optimization and User Behavior Analysis of Accounting Online Informatization Service Platform Using UTAUT

Optimization and User Behavior Analysis of Accounting Online Informatization Service Platform Using UTAUT

Analysis of user behavior in urban parking under different level of information scenarios provided by smart devices or connected cars

Due to the issues of land redevelopment and changes of use within urban areas, many cities must adopt measures to reorganise and optimise parking space. This paper proposes a methodology to study one of them by implementing parking information systems (PIS). This solution offers users a competitive advantage by allowing them to know about the free parking spaces at the moment of decision-making. To achieve this goal, microscopic simulations are conducted to analyse the effects of various scenarios involving the implementation of PIS. The data used in these simulations is obtained from the Santander area in Spain. For the evaluation of results, a methodology has been developed that combines the evaluation of social factors for citizens and operational impacts for decision-makers. The results show significant improvements with increasing user information rate, e.g., the number of unsuccessful parking attempts before finding a final parking space is reduced by 55%, and 37% less particulate pollutants are emitted into the atmosphere.

Simulation of E-learning video recommendation based on virtual reality environment on English teaching platform

E-learning video is an effective teaching tool, and virtual reality technology can enhance the learning experience. However, the current English teaching platform lacks the personalized and real-time feedback video recommendation system. Therefore, this study proposes an E-learning video recommendation system based on virtual reality environment, which aims to provide a personalized, interactive and real-time feedback learning experience. In this paper, the prototype of E-learning video recommendation system based on virtual reality environment is designed, and the simulation experiment is carried out on the English teaching platform. In the environment of the E-learning platform, human–computer interaction and entertainment media can enhance students’ learning process and assist in teaching work. This article analyzes the simulation of E-learning video recommendation based on virtual reality environment on English teaching platforms. The E-learning video recommendation system can create user profiles on the website based on their basic information and behavior, fully tap into their preferences, and provide personalized learning services for them. The recommendation system can be divided into two parts: offline and online. Offline systems are mainly responsible for creating video related content, while online systems store data in databases, allowing for real-time investigation and retrieval of data; Online systems can respond to user requests in real-time, and combining online extraction with user behavior analysis can generate the final recommendation results. While constructing an artificial intelligence platform for English teaching, it is also necessary to propose application strategies for English teaching and achieve modernization of curriculum teaching.

E-commerce consumer privacy protection and immersive business experience simulation based on intrusion detection algorithms

In the process of e-commerce shopping, immersive service experiences and entertainment shopping models are increasingly popular among consumers. This article analyzes the privacy protection and immersive business experience simulation of e-commerce consumers based on intrusion detection algorithms.After studying intrusion detection algorithms, this article delves into personal privacy protection models related to public network e-commerce consumption. This article first explores the significance and current status of intrusion detection algorithms, as well as the research status of deep learning in this field, and conducts comparative experiments on improved intrusion detection algorithms. Then, after analyzing the requirements of the evaluation support system for privacy protection algorithms, the overall framework of the personal privacy protection system was designed and studied. Intrusion detection algorithm identifies possible privacy threats by monitoring and analyzing user behavior data, and then proposes an immersive business experience simulation framework, which includes virtual reality technology and personalized recommendation system, enabling users to immerse themselves in the virtual environment of e-commerce platform and enjoy personalized shopping experience. Interactive experiences can provide e-commerce consumers with more shopping pleasure, and at the same time, businesses need to ensure that consumer privacy is effectively protected during the consumption process. In the end, a new personal privacy protection model was successfully constructed, which verified the great advantages of the algorithm in terms of running time, and concluded that using intrusion detection algorithms can greatly protect the personal privacy of e-commerce consumers using public networks.

Review of Visualization in User Behavior Analysis

User behavior visualization as a powerful tool can help analysts better understand user behavior data. Previous research such as Information Architecture believes that a user journey map is the regular way to visualize user behavior patterns. The technique is a diagram that shows the steps, touch-points, and emotions that users go through when using their products or services. Current studies mainly focus on the procedure and framework of applying user behavior data into visualization tools and its benefit to business development. Studies also found that visualization in user behavior analysis has difficulty in dealing with complex data. However, few studies have discussed the possible solution for the current limitation of visualization in user behavior analysis. With the popularization of the Internet and mobile devices, the scale and scale of user behavior data and the complexity will continue to increase, and user behavior visualization will be an important tool for analyzing this data, helping to tap potential business opportunities and improve the efficiency of data analysis. Therefore, we would make the review on visualization in user behavior analysis. We will first provide the background of the user behavior and its visualization and then introduce the definition of user behavior. Afterward, we will discuss the current process of visualization in user behavior analysis and its current limitations. Next, we will propose the possible solution of introducing machine learning, deep learning, predicting the data with a sequence model, and using cluster analysis with usability analysis tools to contribute to the future development of visualization in user behavior analysis. We believe that with data amount and scale keep increasing, the visualization tools need to develop themselves in diversity and data processing speed.

IMPROVING THE USER EXPERIENCE OF FINANCIAL TECHNOLOGY IT SERVICES BASED ON UX/UI RESEARCH

In today’s dynamic fintech environment, user requirements are constantly changing, and digital services need to be developed and adapted to meet their expectations and needs. UI/UX research technologies and methods provide rich opportunities to study user preferences and identify problems and shortcomings in existing digital services. The main aspects of improving the user experience of the mobile bank were reviewed. Recommendations were made for the further development of the IT service. The purpose of this article is to examine the main aspects of improving the user experience and to formulate recommendations for the development of IT services in the banking sector. Research methodology – at the first stage, an analysis of user behavior in fintech was carried out: a study of user expectations and preferences in the field of digital financial services. Then user experience was analyzed and UI/UX studies were conducted for the ‘Notification Center’ of the bank’s mobile application. The final stage of the study was to evaluate the effectiveness of the redesign project of the Notification Centre section of the bank's mobile application and to formulate recommendations for the further development of the mobile application.The research conducted has shown the practical importance of improving the user experience of the bank’s mobile application, which consists of improving usability, increasing customer satisfaction and improving the overall impression of banking services. This leads to increased conversion, customer retention and increased loyalty to the bank.Improving the user experience of the bank’s mobile application makes it possible to increase the efficiency of the bank’s employees, reduce the burden on the customer support department and reduce the number of errors when performing routine operations or actions. This not only optimises the bank’s internal processes, but also helps to improve the overall quality of service, which has a particular impact on the bank’s reputation, competitiveness and financial performance.