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Journal articles on the topic 'Personalized User Interface'
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NA, Qianwen, and Hisaya TANAKA. "Personalized SSVEP-BCI Text Spelling User Interface." International Symposium on Affective Science and Engineering ISASE2024 (2024): 1–4. http://dx.doi.org/10.5057/isase.2024-c000003.
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Jiming Liu, Chi Kuen Wong, and Ka Keung Hui. "An adaptive user interface based on personalized learning." IEEE Intelligent Systems 18, no. 2 (2003): 52–57. http://dx.doi.org/10.1109/mis.2003.1193657.
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Wasilewski, Adam. "Functional Framework for Multivariant E-Commerce User Interfaces." Journal of Theoretical and Applied Electronic Commerce Research 19, no. 1 (2024): 412–30. http://dx.doi.org/10.3390/jtaer19010022.
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Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.
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Tagirova, L. F., and N. G. Semenova. "Design of Personalized User Interfaces of Intelligent Training Systems Based on Neural Network Technologies." Informacionnye Tehnologii 29, no. 9 (2023): 473–84. http://dx.doi.org/10.17587/it.29.473-484.
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The article is devoted to the problem of developing adaptive interfaces of application programs, taking into account the individual characteristics of users. The novelty of the study is the use of two types of artificial neural networks (INS), which implement the creation of a personalized prototype of the interface, depending on the individual characteristics of the user. The first is a convolutional INS used to assess gender and age characteristics, as well as the emotional state of the user, based on recognition of his face. Second, deep INS is used to select the menu components of the adaptive interface prototype. The results of the experimental operation of the developed intelligent training system (IOS) with an adaptive interface showed an increase in the quality and efficiency of students' work when studying the material presented in the IOS.
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Youngho Lee, Y., and W. Woontack Woo. "Interactive edutainment system with enhanced personalized user interface framework." IEEE Transactions on Consumer Electronics 53, no. 2 (2007): 424–32. http://dx.doi.org/10.1109/tce.2007.381711.
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Ünlü, Sudenaz Ceren. "Enhancing User Experience through AI-Driven Personalization in User Interfaces." Human Computer Interaction 8, no. 1 (2024): 19. http://dx.doi.org/10.62802/m7mqmb52.
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Artificial intelligence (AI) has revolutionized user interface (UI) design by introducing personalization techniques that cater to individual user preferences, behaviors, and contexts. This research explores the integration of AI-driven personalization in user interfaces to enhance user experience (UX), focusing on adaptive design, predictive analytics, and real-time customization. By leveraging machine learning algorithms and behavioral data, AI enables interfaces to evolve dynamically, aligning with the unique needs of each user. This study investigates the role of personalization in improving engagement, satisfaction, and efficiency across various applications, such as e-commerce platforms, healthcare systems, and educational tools. Additionally, it examines the challenges of implementing personalized interfaces, including privacy concerns, data ethics, and algorithmic bias. By addressing these challenges, the research aims to develop best practices for ethical AI integration in user-centered design. The findings contribute to the growing body of knowledge on AI’s transformative potential in creating intuitive, efficient, and user-friendly interfaces, ultimately redefining the standards for digital interaction.
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Huynh, Brandon, Adam Ibrahim, Yun Suk Chang, Tobias Höllerer, and John O’Donovan. "User Perception of Situated Product Recommendations in Augmented Reality." International Journal of Semantic Computing 13, no. 03 (2019): 289–310. http://dx.doi.org/10.1142/s1793351x19400129.
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Augmented reality (AR) interfaces increasingly utilize artificial intelligence systems to tailor content and experiences to the user. We explore the effects of one such system — a recommender system for online shopping — which allows customers to view personalized product recommendations in the physical spaces where they might be used. We describe results of a [Formula: see text] condition exploratory study in which recommendation quality was varied across three user interface types. Our results highlight potential differences in user perception of the recommended objects in an AR environment. Specifically, users rate product recommendations significantly higher in AR and in a 3D browser interface, and show a significant increase in trust in the recommender system, compared to a web interface with 2D product images. Through semi-structured interviews, we gather participant feedback which suggests AR interfaces perform better due to their ability to view products within the physical context where they will be used.
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Yi, Minzhe, Ying Wang, Xiaoxue Tian, and Huichao Xia. "User Experience of the Mobile Terminal Customization System: The Influence of Interface Design and Educational Background on Personalized Customization." Sensors 21, no. 7 (2021): 2428. http://dx.doi.org/10.3390/s21072428.
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The study verified the role that different interface designs and users’ educational backgrounds play in the task performance and subjective evaluation of mobile terminal customization system. Interface type (based on scroll, alternative, and attribute) and user group (college students and industrial workers) were employed as the variables. A total of 72 users were included in the study, and an analysis of 3 × 2 between-participants design indicated that (1) Different interface designs of customization systems had a significant difference in task performance, the alternative based interface had the best results in the task performance, and there was no significant difference between the attribute-based and scroll-based interfaces in task performance; (2) The matching between educational background and interface type will affect the users’ evaluation on system usability. Industrial workers thought that the scroll-based and alternative-based interfaces were more useable, while college students preferred attribute-based interface design; (3) Different interfaces had a significant difference in user task load. The scroll-based interface had the lowest mental demand on the users, while alternative-based had the lowest physical demand on the users, though it consumed more effort; (4) Different educational backgrounds had a significant difference in user task load. Industrial workers showed lower effort in the scroll-based and alternative-based interfaces, while college students had lower effort in the attribution-based interface; (5) A correlation analysis showed that there was a significant negative correlation between the system usability score and the effort in task load. This study results have a positive significance for interface design. With educational background and layout as two important factors in our interface design, we may obtain the most appropriate design principles for enhancing the online customization experiences of different groups of consumers. The more important is that this study is based on the actual needs of the industry. For the first time, we take suitcase as an online customized product, which may not only help local manufacturers to extend their traditional offline distribution channels to online, but also provide a constructive thinking concerning interface design for customization of a single product.
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Murdalova, Iman E. "AUTOMATION OF INTERFACE GENERATION BASED ON USER DATA AND MACHINE LEARNING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 9/15, no. 150 (2024): 141–47. https://doi.org/10.36871/ek.up.p.r.2024.09.15.017.
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The article discusses an approach to automating the generation of user interfaces based on user data and machine learning algorithms. The relevance of the topic is due to the need to create personalized interfaces that take into account individual preferences and user behavior, which is especially important in the context of a growing volume of digital products. The proposed system uses methods for analyzing user data, including behavioral and contextual data, to create adaptive interfaces using generative neural networks. The experiments showed that the generated interfaces provide high adaptation accuracy (91%), a 20% reduction in user task completion time, and an increase in satisfaction to 8.7 points out of 10. The results confirm that interface generation automation can reduce time and resource costs while maintaining high quality and personalization.
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Liu, Yingchia, Hao Tan, Guanghe Cao, and Yang Xu. "Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces." World Journal of Innovation and Modern Technology 7, no. 5 (2024): 1–21. http://dx.doi.org/10.53469/wjimt.2024.07(05).01.
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This paper presents a comprehensive study on developing and evaluating an adaptive UI/UX framework to enhance user engagement in mobile applications through personalized interfaces. The research investigates key factors influencing user engagement, including demographics, cognitive abilities, and contextual variables. A context-aware adaptation engine was designed to adjust interface elements based on real-time user data dynamically. The proposed framework was implemented in a mobile learning application and subjected to rigorous usability testing and user engagement analysis. Results demonstrated significant improvements in task completion rates, user satisfaction, and overall engagement metrics compared to non-adaptive interfaces. This study contributes valuable insights into the design and optimization of adaptive mobile interfaces, emphasizing the importance of personalization in creating compelling user experiences.
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Xu, Zhen, and Shan Wang. "Interactive Design of Personalized Website Search Interface Based on Visual Communication." Computational Intelligence and Neuroscience 2022 (May 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/2125506.
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Aiming at the problems of low user satisfaction and long search time in the traditional interactive design method of the personalized website search interface, a personalized website search interface interactive design method based on visual communication is proposed. Under the analysis of personalized website users' search behavior, the interactive personalized website search interface is designed through a navigation module, search module, link module, interactive layout module, and visual rendering module; in the visual rendering module, the advanced texture mapping method is used to render the personalized website search interface; on the personalized website search interface, the disturbance function is imported along the normal vector, the simplified new normal vector is intelligently calculated through the concave convex texture mapping algorithm, the normal vector is solved to generate the intersection point of the high-precision interface, the illumination brightness value of each pixel of the interface is intelligently calculated, the visual communication rendering model is constructed, and the visual communication effect of the interface is improved. The simulation results show that the website interface search time of this method is within 4.9 s and the user satisfaction is up to 100%, indicating that the interaction effect of the personalized website search interface designed by this method is good.
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Yingchia Liu, Hao Tan, Guanghe Cao, and Yang Xu. "Enhancing user engagement through adaptive UI/UX Design: A study on personalized mobile app interfaces." Computer Science & IT Research Journal 5, no. 8 (2024): 1942–62. http://dx.doi.org/10.51594/csitrj.v5i8.1457.
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This paper presents a comprehensive study on developing and evaluating an adaptive UI/UX framework to enhance user engagement in mobile applications through personalized interfaces. The research investigates key factors influencing user engagement, including demographics, cognitive abilities, and contextual variables. A context-aware adaptation engine was designed to adjust interface elements based on real-time user data dynamically. The proposed framework was implemented in a mobile learning application and subjected to rigorous usability testing and user engagement analysis. Results demonstrated significant improvements in task completion rates, user satisfaction, and overall engagement metrics compared to non-adaptive interfaces. This study contributes valuable insights into the design and optimization of adaptive mobile interfaces, emphasizing the importance of personalization in creating compelling user experiences. Keywords: Adaptive UI/UX, User Engagement, Context-Aware Adaptation, Mobile Application Design.
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Patil, Omkar. "Real Time Inventory Management System powered by Generative User Interface." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32623.
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This research explores the developing a real time inventory management system powered by a generative user interface. We are leveraging large language models like GPT4, Claude 3, and Google Gemini that support tool calling or function calling, and integrating it with the modern frontend frameworks like Next js that support streaming React Server Component (RSC), the proposed system enables interaction with the inventory through natural language prompts. We are using PostgreSQL as a choice of database and server actions are used to interact with the database in real time. The system composes and renders appropriate react components based on user prompt, providing a personalized user experience. The research discusses the system's architecture, implementation, and potential impact on inventory management systems. It showcases the potential of Large Language Models (LLMs) and conversational interfaces in enhancing enterprise software user experiences. Key Words: Inventory Management System, Generative User Interface, Generative AI, Large Language Models, Conversational Interface, Natural Language Processing
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Chaus, I. V., and T. A. Marusenkova. "FRONT-END FRAMEWORK FOR BUILDING APPLICATIONS WITH ADAPTIVE USER INTERFACES USING MACHINE LEARNING METHODS." Computer systems and network 6, no. 2 (2024): 250–66. https://doi.org/10.23939/csn2024.02.250.
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The article examines approaches to developing a front-end framework for creating web applications with an adaptive graphical interface that dynamically adjusts to the individual needs of users through machine learning algorithms. The relevance of the problem lies in the need to develop interfaces capable of simultaneously meeting the needs of different demographic groups, which requires flexibility in customizing the user experience (UX) and user interface (UI) of modern websites. Traditional interface design methods do not always account for the specific needs of each user, which reduces the effectiveness of interaction with the site. The article proposes an approach that utilizes reinforcement learning algorithms to analyze user interaction with the interface and automatically adapt the interface based on behavioral data. This enhances the accuracy of interface personalization and improves the overall user experience. The goal of the work is to develop a tool that enables the automated restructuring of the graphical interface of web applications based on individual user needs to improve their user experience. The research develops algorithms to optimize user interaction with web application pages and improve interface efficiency. The research results demonstrate the framework's ability to dynamically respond to user behavior, assess their level of interaction, and make informed decisions regarding interface parameter adaptation, which in turn helps developers to reduce amount of work needed to implement personalized interface by eliminating the need to manually develop website variants. Using this approach the estimated code base reduction is 40-50%. Keywords: adaptive interface, front-end, machine learning, user experience, web design.
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Chaus, I. V., and T. A. Marusenkova. "FRONT-END FRAMEWORK FOR BUILDING APPLICATIONS WITH ADAPTIVE USER INTERFACES USING MACHINE LEARNING METHODS." Computer systems and network 6, no. 2 (2024): 252–67. https://doi.org/10.23939/csn2024.02.252.
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The article examines approaches to developing a front-end framework for creating web applications with an adaptive graphical interface that dynamically adjusts to the individual needs of users through machine learning algorithms. The relevance of the problem lies in the need to develop interfaces capable of simultaneously meeting the needs of different demographic groups, which requires flexibility in customizing the user experience (UX) and user interface (UI) of modern websites. Traditional interface design methods do not always account for the specific needs of each user, which reduces the effectiveness of interaction with the site. The article proposes an approach that utilizes reinforcement learning algorithms to analyze user interaction with the interface and automatically adapt the interface based on behavioral data. This enhances the accuracy of interface personalization and improves the overall user experience. The goal of the work is to develop a tool that enables the automated restructuring of the graphical interface of web applications based on individual user needs to improve their user experience. The research develops algorithms to optimize user interaction with web application pages and improve interface efficiency. The research results demonstrate the framework's ability to dynamically respond to user behavior, assess their level of interaction, and make informed decisions regarding interface parameter adaptation, which in turn helps developers to reduce amount of work needed to implement personalized interface by eliminating the need to manually develop website variants. Using this approach the estimated code base reduction is 40-50%. Keywords: adaptive interface, front-end, machine learning, user experience, web design.
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Yamaji, Tokiya, Hiroyuki Nakamoto, Hideo Ootaka, Ichiro Hirata, and Futoshi Kobayashi. "Rapid Prototyping Human Interfaces Using Stretchable Strain Sensor." Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9893758.
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In the modern society with a variety of information electronic devices, human interfaces increase their importance in a boundary of a human and a device. In general, the human is required to get used to the device. Even if the device is designed as a universal device or a high-usability device, the device is not suitable for all users. The usability of the device depends on the individual user. Therefore, personalized and customized human interfaces are effective for the user. To create customized interfaces, we propose rapid prototyping human interfaces using stretchable strain sensors. The human interfaces comprise parts formed by a three-dimensional printer and the four strain sensors. The three-dimensional printer easily makes customized human interfaces. The outputs of the interface are calculated based on the sensor’s lengths. Experiments evaluate three human interfaces: a sheet-shaped interface, a sliding lever interface, and a tilting lever interface. We confirm that the three human interfaces obtain input operations with a high accuracy.
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Sui, Jinchi, and Mohd Mustafa Mohd Ghazali. "User Behavior Analysis and Optimization Countermeasures in Shopping APP Interface Design." International Journal of Education and Humanities 16, no. 2 (2024): 405–8. http://dx.doi.org/10.54097/wrae3p80.
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In this study, the interface design and marketing strategy of shopping APP were deeply discussed, and a series of optimization countermeasures were put forward. The principles of interface design and user behavior are analyzed, and the design problems such as information overload, complicated navigation, low usage frequency and high user loss are pointed out. The countermeasures of interface design optimization, user behavior guidance and marketing strategy adjustment are put forward, including simplifying interface, optimizing navigation, personalized recommendation, incentive mechanism, brand building and marketing promotion. These measures are aimed at improving user experience, enhancing user stickiness and enhancing market competitiveness. Through this research, it provides theoretical basis and practical guidance for shopping APP interface design optimization and market strategy adjustment.
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JANG, S. "Framework for Personalized User Interface by Sharing User-Centric Context between Real andVirtual Environments." IEICE Transactions on Information and Systems E89-D, no. 5 (2006): 1694–701. http://dx.doi.org/10.1093/ietisy/e89-d.5.1694.
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Zheng, Meng. "Research on user experience optimization strategy of e-commerce platform with biomechanics principle—Analysis based on data mining." Molecular & Cellular Biomechanics 22, no. 4 (2025): 1602. https://doi.org/10.62617/mcb1602.
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This study discusses the strategy of combining biomechanics principle and data mining technology to optimize the user experience of e-commerce platform. Through biomechanical principles, the interface design and interaction mode are optimized, and the operation comfort and efficiency are improved. Data mining technology deeply analyzes user behavior data, reveals user needs and pain points, and provides decision support for personalized service and interface design. The study proposes specific optimization strategies, such as personalized recommendation, interaction process simplification, response speed improvement and equipment adaptation, and emphasizes the importance of user feedback and continuous optimization mechanism. These strategies effectively improve the user experience and enhance the user stickiness and market competitiveness of the platform.
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Kiourexidou, Matina, Andreas Kanavos, Maria Klouvidaki, and Nikos Antonopoulos. "Exploring the Role of User Experience and Interface Design Communication in Augmented Reality for Education." Multimodal Technologies and Interaction 8, no. 6 (2024): 43. http://dx.doi.org/10.3390/mti8060043.
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Augmented Reality (AR) enhances learning by integrating interactive and immersive elements that bring content to life, thus increasing motivation and improving retention. AR also supports personalized learning, allowing learners to interact with content at their own pace and according to their preferred learning styles. This adaptability not only promotes self-directed learning but also empowers learners to take charge of their educational journey. Effective interface design is crucial for these AR applications, requiring careful integration of user interactions and visual cues to blend AR elements seamlessly with reality. This paper explores the impact of AR on user experience within educational settings, examining engagement, motivation, and learning outcomes to determine how AR can enhance the educational experience. Additionally, it addresses design considerations and challenges in developing AR user interfaces, drawing on current research and best practices to propose effective and adaptable solutions for educational AR applications. As AR technology evolves, its potential to transform educational experiences continues to grow, promising significant advancements in how users interact with, personalize, and immerse themselves in learning content.
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Xiaoan, Zhan, Xu Yang, and Liu Yingchia. "Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience." International Journal of Engineering and Management Research 14, no. 5 (2024): 134–47. https://doi.org/10.5281/zenodo.14190245.
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This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental results demonstrate significant improvements in layout quality, personalization accuracy, and user satisfaction compared to existing approaches. A customer research study with 200 participants from different cultures proves the effectiveness of the personalization model in real situations. The system achieves a personalization accuracy of 0.89 ± 0.03 and a transfer speed of 1.2s ± 0.1s, the most efficient state-of-the-art UI personalization system. In addition, we discuss the theoretical implications of our approach to UI/UX design principles, potential business applications, and ethical considerations around AI-driven identity. This research contributes to advancing adaptive interface design and opens up new ways to integrate deep learning with UI/UX processes.
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Shrivastava, Hritvik. "Enhancing Personalization and Contextual Information Retrieval in Vehicles." East African Scholars Journal of Engineering and Computer Sciences 7, no. 06 (2024): 57–60. http://dx.doi.org/10.36349/easjecs.2024.v07i06.001.
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This paper presents a novel virtual vehicle interactive interface device designed to enhance the driving experience by providing personalized, real-time, and contextually relevant information to vehicle users. Unlike traditional information retrieval systems that rely on static algorithms, our device uses a dynamic, agent-based approach to adapt to user preferences and real-time data. The system integrates advanced voice interfaces, extensible storage, and multiple sensors to offer a more interactive and responsive experience. Experimental results demonstrate the device's capability to handle complex queries, adapt to evolving user needs, and improve overall user satisfaction through personalized responses and learning mechanisms.
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He, Bo. "Personalized Web Data Mining System." Advanced Materials Research 219-220 (March 2011): 183–86. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.183.
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Most of Web data mining systems did not construct user profiles and could not support personalized Web data mining. Aiming at the shortcomings, the paper defined and established user profiles. On the base of this, the paper designed a personalized Web data mining system, namely PWDMS. PWDMS consisted of user interface module, data preprocessing module and data mining module. In addition, this paper discussed the key technology of PWDMS. It is proved that applying personalized technology to Web data mining is efficient.
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Aage, Preeti, Mayur Pawar, Sayali Sarang, and Rakhi Patil. "Stock Tracking and Analysis for Personalized Trading Advice Using Adaptive User Interface." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (2015): 64–70. http://dx.doi.org/10.53555/nncse.v2i3.489.
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The Stock Tracker is an adaptive recommendation system for trading stocks that automatically acquires content based models of user preferences to tailor its buy and sell advice. The system incorporates an efficient algorithm that exploits the fixed structure of user models and relies on unobtrusive data-gathering techniques. In this paper, we describe our approach to personalized recommendation and its implementation in this domain. We also discuss experiments that evaluate the system's behaviour on both human subjects and synthetic users. The results suggest that the Stock Tracker can rapidly adapt its advice to different types of users.
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S K, Radhika. "Spotify Clone – Melodix : A Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41242.
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This paper explores the development of a Spotify clone app, focusing on its core features and functionality to provide a seamless music streaming experience. By integrating user-friendly interfaces, personalized playlists, and advanced search capabilities, the app aims to replicate Spotify's success while offering customizable options. The study examines the technical architecture, including the backend, database management, and music recommendation algorithms. It also highlights the importance of scalability, security, and user engagement, suggesting that such an app has the potential to revolutionize the way users interact with music platforms and consume content. Key Words : Spotify Clone, Music Streaming, Personalized Playlists, User Interface, Search Functionality.
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Wang, Dingwen, Shibang Zhang, and Xuanchen Shi. "UI design of mobile terminal based on user experience." Highlights in Art and Design 1, no. 2 (2022): 11–13. http://dx.doi.org/10.54097/hiaad.v1i2.1891.
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With the development of science and technology and the development of functions in mobile communication tools represented by mobile phones, the channels for people to receive information and visual elements are constantly expanding. At this time, the visual appreciation in the UI interface is extremely important, which can effectively help users obtain information, obtain visual elements, and build a good interactive environment. In this era, people not only pursue personalized design concepts and sensory pleasure in visual aesthetics, but also interface design has become the most convenient way to show characteristics, emotions and ideas. This paper designs and discusses the mobile visual software from the perspective of facilitating the audience's visual acquisition, so as to explore the UI interface design of excellent visual software.
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FAUZIA, PARVEEN M.F.RAHMAN, and CHOLE VIKRANT. "VOICE ENABLE PERSONALIZED WEB SEARCH." JournalNX - a Multidisciplinary Peer Reviewed Journal 3, no. 6 (2017): 38–41. https://doi.org/10.5281/zenodo.1421019.
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The technology of voice browsing is rapidly evolving these days. It is because the use of cell phones is increasing at a very high rate, as compared to connected PCs. Speech interface integrated browser is a web browser that helps users by using an interactive voice user interface ,useful to those who have difficulties in seeing and reading a web content. Listening and speaking are the natural modes of communication and information gathering. As a result we are now heading towards a more voice based approach of browsing rather than operating on textual mode. A voice browser or speech browser will take and presents the information in the form of voice as well as text, using text to speech and speech to text conversion to render information. People want to get accurate and appropriate data at the top of search results in a user friendly manner. People also want to get a personal space over the internet when they are browsing on web, from this arises a need of personalization of the search history. Thus there is a need of a highly efficient and effective ranking algorithm that provides search results according to user preferences. This paper concentrates on this new technique, voice browsing, which unites speech recognition and speech synthesis with better personalized search that can be very fruitful in the coming years. In this paper we provide personalization by creating individual search history for each user on the browser and also focused on the search results to get customized according to the user demand. https://journalnx.com/journal-article/20150349
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Jylhä, Henrietta, and Juho Hamari. "Development of measurement instrument for visual qualities of graphical user interface elements (VISQUAL): a test in the context of mobile game icons." User Modeling and User-Adapted Interaction 30, no. 5 (2020): 949–82. http://dx.doi.org/10.1007/s11257-020-09263-7.
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Abstract Graphical user interfaces are widely common and present in everyday human–computer interaction, dominantly in computers and smartphones. Today, various actions are performed via graphical user interface elements, e.g., windows, menus and icons. An attractive user interface that adapts to user needs and preferences is progressively important as it often allows personalized information processing that facilitates interaction. However, practitioners and scholars have lacked an instrument for measuring user perception of aesthetics within graphical user interface elements to aid in creating successful graphical assets. Therefore, we studied dimensionality of ratings of different perceived aesthetic qualities in GUI elements as the foundation for the measurement instrument. First, we devised a semantic differential scale of 22 adjective pairs by combining prior scattered measures. We then conducted a vignette experiment with random participant (n = 569) assignment to evaluate 4 icons from a total of pre-selected 68 game app icons across 4 categories (concrete, abstract, character and text) using the semantic scales. This resulted in a total of 2276 individual icon evaluations. Through exploratory factor analyses, the observations converged into 5 dimensions of perceived visual quality: Excellence/Inferiority, Graciousness/Harshness, Idleness/Liveliness, Normalness/Bizarreness and Complexity/Simplicity. We then proceeded to conduct confirmatory factor analyses to test the model fit of the 5-factor model with all 22 adjective pairs as well as with an adjusted version of 15 adjective pairs. Overall, this study developed, validated, and consequently presents a measurement instrument for perceptions of visual qualities of graphical user interfaces and/or singular interface elements (VISQUAL) that can be used in multiple ways in several contexts related to visual human-computer interaction, interfaces and their adaption.
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Kliesch, Sven, Josef Chalupper, Thomas Lenarz, and Andreas Büchner. "App-Based Self-Adjustment—User Behavior and Adjustment Practices of Cochlear Implant Users in Everyday Life." Applied Sciences 14, no. 24 (2024): 11708. https://doi.org/10.3390/app142411708.
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This study evaluated the effectiveness and usage of a smartphone application for adjusting the hearing impression of cochlear implants or hearing aids in everyday contexts. Building on previous laboratory research that demonstrated subjective auditory perception improvements through personalized settings, this research aimed to confirm these findings in real-life scenarios. Additionally, it analyzes user behavior and adaptation practices. Over a period of six months, 15 participants, including both bimodal and bilateral cochlear implant recipients, employed a self-adjustment app in their everyday lives. This application enabled them to modify the auditory experience through two distinct user interfaces and store their configurations as accessible programs. The collected data indicated that all participants were able to operate the app, and 13 of 15 participants successfully used the app throughout the whole study. In contrast to our previous findings, the acoustic environment did not affect frequency settings but did influence the choice of user interface. Bimodal users more frequently adjusted the settings for each ear independently compared to bilateral users. Most participants stated that they would also use the app outside of this study.
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Fazlollahtabar, Hamed, and Amir Muhammadzadeh. "A Knowledge-Based User Interface to Optimize Curriculum Utility in an E-Learning System." International Journal of Enterprise Information Systems 8, no. 3 (2012): 34–53. http://dx.doi.org/10.4018/jeis.2012070103.
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The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia formed in a Web-based educational system makes learning a task-driven process, motivating learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. Many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and to adaptively provide learning paths. Although most personalized systems consider learner preferences, interests and browsing behaviors when providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended curriculums are matched to each other. Therefore, the authors proposed approach is based on an integer program (IP) to optimize user curriculum accompanying with fuzzy logic approach which analyze the effective criteria by linguistic variables in a knowledge based system. The effectiveness of the proposed framework is shown by numerical illustrations which are inferenced from the designed user interface.
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Liu, Yuzhao, Yuhan Liu, Shihui Xu, Kelvin Cheng, Soh Masuko, and Jiro Tanaka. "Comparing VR- and AR-Based Try-On Systems Using Personalized Avatars." Electronics 9, no. 11 (2020): 1814. http://dx.doi.org/10.3390/electronics9111814.
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Despite the convenience offered by e-commerce, online apparel shopping presents various product-related risks, as consumers can neither physically see nor try products on themselves. Augmented reality (AR) and virtual reality (VR) technologies have been used to improve the shopping online experience. Therefore, we propose an AR- and VR-based try-on system that provides users a novel shopping experience where they can view garments fitted onto their personalized virtual body. Recorded personalized motions are used to allow users to dynamically interact with their dressed virtual body in AR. We conducted two user studies to compare the different roles of VR- and AR-based try-ons and validate the impact of personalized motions on the virtual try-on experience. In the first user study, the mobile application with the AR- and VR-based try-on is compared to a traditional e-commerce interface. In the second user study, personalized avatars with pre-defined motion and personalized motion is compared to a personalized no-motion avatar with AR-based try-on. The result shows that AR- and VR-based try-ons can positively influence the shopping experience, compared with the traditional e-commerce interface. Overall, AR-based try-on provides a better and more realistic garment visualization than VR-based try-on. In addition, we found that personalized motions do not directly affect the user’s shopping experience.
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Atuweni, Yamiko Maonga, and Mkandawire Mtende. "AI-aided design studio: Enhancing graphic design and user interface with machine learning." i-manager's Journal on Artificial Intelligence & Machine Learning 2, no. 2 (2024): 10. http://dx.doi.org/10.26634/jaim.2.2.20736.
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The challenge in a design studio lies in helping graphic designers create dynamic and personalized designs efficiently. To address this, the AI-Aided Design Studio is a web-based application that leverages machine learning algorithms to assist graphic designers in producing more vibrant and customized designs. By integrating artificial intelligence and machine learning techniques, this application seeks to empower designers with tools that can generate design suggestions, such as design elements, color schemes, and layout improvements, automate repetitive tasks, and provide personalized design recommendations based on user preferences and historical data. The development will also involve creating an interactive software application that allows users to collaboratively design and customize graphics and user interface elements with AI-driven enhancements. Through this innovative approach, the application not only offers practical utility in design but also showcases the potential of artificial intelligence to transform traditional creative processes into more efficient and personalized experiences. The AI-Aided Design Studio will also incorporate a sophisticated image-to-text generator that utilizes state-of-the-art machine learning algorithms. This generator enables designers to analyze and interpret visual elements, automatically extracting relevant textual data from images. This process not only expedites the initial phase of design research but also facilitates a seamless transition between the visual and textual aspects of the creative process.
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M, Mohanapriya. "Integrated Athlete Pathway Interface." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47788.
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ABSTRACT The Integrated Athlete Pathway Interface is a user-focused sports development app designed to centralize opportunities from various sources such as leagues, training camps, and competitions. It uses APIs to gather real-time data from trusted platforms and is structured to support future API integrations for extended functionality. The app includes a smart search bar that helps athletes discover relevant training paths and set clear goals, while a built-in guidance tool assists in navigating the application processes for events. Users can bookmark preferred opportunities through a favorites system for quick access later. For organizing practice matches, the platform features automated scheduling and live updates, ensuring smooth interaction between players and organizers. Developed with Flutter on the frontend and Python on the backend, the system combines performance, adaptability, and automation to offer a personalized and scalable environment for athlete progression and goal-oriented pathway tracking. Keywords: Athlete development, real-time data, API integration, opportunity discovery, training updates, competition access, search tools, favourites system, practice scheduling, automation, Flutter frontend, Python backend, personalized growth, sports navigation, user-centered design.
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Liu, Yingchia, Yang Xu, and Shiji Zhou. "Enhancing User Experience through Machine Learning-Based Personalized Recommendation Systems: Behavior Data-Driven UI Design." Applied and Computational Engineering 112, no. 1 (2024): 42–46. http://dx.doi.org/10.54254/2755-2721/2024.17905.
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The application of artificial intelligence (AI) continues to expand across various industries, especially in enhancing user experience and optimizing business processes. Through deep learning and machine learning algorithms, companies are able to analyze user behavior data and provide personalized recommendations, which effectively improve customer satisfaction and loyalty. This data-driven approach enables businesses to stand out in a highly competitive market. This paper explores the key role of machine learning-based personalized recommendation systems in improving user experience and highlights the importance of behavioral data-driven UI design for business success. Research shows that successful recommendation systems not only rely on advanced technology applications, but also need to deeply understand user needs to optimize user interface design and promote effective user interaction. As technology continues to advance, personalized recommendation systems will become more intelligent, and companies should actively explore these innovative ways to increase user engagement and brand loyalty to achieve sustainable business growth.
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Fei, Fei, Quan Yin Zhu, Cheng Jie Xu, Lei Zhou, and Xiang Li. "Personalized Customization on Science and Technology Intelligence." Applied Mechanics and Materials 631-632 (September 2014): 1149–52. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.1149.
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In order to suite customized demand of enterprises on scientific and technological information, a Web-based Front Platform Management System of Scientific and Technological Information is designed and realized. Customizable front platform page adopts HTML5+CSS3+jQuery to realize dynamic customization of display mode and contents, meeting the diversified demand of different users. Upon design and improvement, the system interface basically realizes customized operation on scientific and technological information, providing a front platform interface of good user-experience for the customized platform of scientific and technological information for enterprises.
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Guguloth, Praveen Kumar. "AI-Driven Design Systems: The Future of Scalable UI Frameworks." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 15–22. https://doi.org/10.37745/ejcsit.2013/vol13n221522.
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The integration of artificial intelligence in design systems has revolutionized user interface development, marking a paradigm shift in digital product creation. AI-driven design systems enhance workflow efficiency through automated component generation, pattern recognition, and accessibility compliance monitoring. These advanced systems leverage deep learning models, neural networks, and computer vision technologies to process user interactions and adapt interfaces dynamically. Across various sectors, including SaaS, financial services, and healthcare, the implementation of AI-powered design systems has demonstrated significant improvements in development cycles, user engagement, and cost efficiency. The automation of design processes enables teams to focus on strategic initiatives while maintaining consistency across platforms. Machine learning algorithms optimize user experiences through personalized interface delivery and automated testing frameworks. The transformation extends beyond operational metrics, encompassing enhanced accessibility compliance, reduced technical debt, and improved cross-team collaboration. This technological evolution represents a fundamental advancement in how organizations approach interface design and user experience optimization.
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Fodor, Milán András, Hannah Herschel, Atilla Cantürk, Gernot Heisenberg, and Ivan Volosyak. "Evaluation of Different Visual Feedback Methods for Brain—Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP)." Brain Sciences 14, no. 8 (2024): 846. http://dx.doi.org/10.3390/brainsci14080846.
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Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.
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Santana, Jonas Machado, Bruno Duarte Silveira, Crescencio Lima, et al. "Design and Implementation of an Interactive System for Service Robot Control and Monitoring." Sensors 25, no. 4 (2025): 987. https://doi.org/10.3390/s25040987.
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This project aims to develop an interactive control system for an autonomous service robot using an ROS (robot operating system). The system integrates an intuitive web interface and an interactive chatbot supported by Google Gemini to enhance the control experience and personalization for the user. The methodology includes the integration of an API (application programming interface) to access a database storing user preferences, such as speed and frequent destinations. Furthermore, the system employs facial recognition, people groups’ recognition, and adaptive responses from the chatbot for autonomous navigation, ensuring a service tailored to the individual needs of each user. To validate the proposal, it was implemented on an autonomous service robot, integrated into a motorized wheelchair. Tests demonstrated that the system effectively adjusts the wheelchair’s behavior to user preferences, resulting in safer and more personalized navigation. The use of facial recognition and chatbot interaction provided more intuitive and efficient control. The developed system significantly improves the autonomy and quality of life for wheelchair users, proving to be a viable and efficient solution for autonomous and personalized control. The results indicate that integrating technologies like ROS, intuitive web interfaces, and interactive chatbots can transform the user experience of autonomous wheelchairs, better meeting the specific needs of users.
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Gajalakshmi N, Andalpriya C, Raja Lakshmi K, and Nuttrenai V. "Fit AI-Personalized Diet and Fitness Planner." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 508–13. https://doi.org/10.47392/irjaem.2025.0080.
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Maintaining a healthy lifestyle is becoming increasingly challenging due to hectic schedules, unhealthy eating habits, and the lack of personalized diet and fitness guidance. Generic health plans often fail to address individual requirements, leading to ineffective results and poor adherence. To overcome these challenges, FitAI: Personalized Diet and Fitness Planner is developed as an AI-powered web application that provides customized diet and fitness recommendations based on user-specific data. The system collects key user inputs, including age, height, weight, gender, exercise frequency, dietary preferences, and existing health conditions, to generate tailored health plans. By leveraging machine learning algorithms, FitAI analyzes this data to offer dynamic and adaptive suggestions that evolve based on user progress. Unlike static diet charts or generic fitness apps, FitAI continuously refines recommendations to match changing user needs. The application is developed using Python, Streamlit, and Mediapipe, ensuring a seamless, interactive, and intelligent user experience. Streamlit provides an intuitive web interface, simplifying user interactions, while Mediapipe enables real-time fitness tracking, ensuring correct posture and exercise execution. The AI-driven recommendation system continuously learns from user habits, improving the accuracy and relevance of health suggestions. The system was evaluated based on accuracy, efficiency, and user satisfaction, demonstrating a 90% alignment with expert health plans and 85% positive user feedback. The Mediapipe-based posture tracking effectively improved exercise form, while the Streamlit interface enhanced accessibility and engagement. However, challenges such as tracking accuracy variations and limited real-time health monitoring indicate areas for future improvement. FitAI bridges the gap between generic health recommendations and personalized wellness solutions, empowering individuals to take control of their fitness and nutrition. By integrating machine learning, adaptive AI models, and user-friendly web technologies, FitAI presents a smart, data-driven solution for individuals seeking effective and sustainable health management. Future enhancements may include integration with wearable devices and advanced deep learning models for more precise and real-time health tracking.
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Mansor, Marzita, Wan Adilah Wan Adnan, and Natrah Abdullah. "Personalized Reading: Developing User-Describing Profile for Slow Learner Children." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 07 (2019): 103. http://dx.doi.org/10.3991/ijim.v13i07.10775.
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<p><span style="font-size: medium;"><span style="font-family: Times New Roman;">Personalization is a good supplement for learning process. It has been claimed that personalization has a huge potential of providing solution to facilitate the learning path based on children ability and preferences. Diverse research on personalized learning for children have been conducted which, are commonly concerns on the development and implementation of personalized learning products and services. However these researches have little emphasized in exploring slow learner personalized learning process particularly on their reading ability. With that, this paper aims to highlight two key important processes of personalization for slow learner children which are construction of user profile and scenario. The scope of this study is on personalization of reading for slow learner children. There were 13 slow learner children with reading difficulties from primary school participated in this study. The key findings from this study are the construction of user profile and scenario that represent the personalization for reading. These user profile and scenario construction then provide guidelines for the development of personalized interface design for slow learner reading application. </span></span></p><p><strong> </strong></p><p> </p>
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Zhan, Xiaoan, Yang Xu, and Yingchia Liu. "Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 463–78. https://doi.org/10.60087/jaigs.v6i1.270.
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This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental results demonstrate significant improvements in layout quality, personalization accuracy, and user satisfaction compared to existing approaches. A customer research study with 200 participants from different cultures proves the effectiveness of the personalization model in real situations. The system achieves a personalization accuracy of 0.89 ± 0.03 and a transfer speed of 1.2s ± 0.1s, the most efficient state-of-the-art UI personalization system. In addition, we discuss the theoretical implications of our approach to UI/UX design principles, potential business applications, and ethical considerations around AI-driven identity. This research contributes to advancing adaptive interface design and opens up new ways to integrate deep learning with UI/UX processes.
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Varun Pandey and Varun Awasthi. "How reinforcement learning can drive personalized financial wellness." International Journal of Science and Research Archive 15, no. 1 (2025): 1567–83. https://doi.org/10.30574/ijsra.2025.15.1.1244.
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Financial wellness is a pervasive challenge: many individuals struggle with saving, investing, and budgeting effectively. Traditional budgeting tools and Robo-advisors often provide generic advice, failing to account for an individual’s unique behavior and needs. This paper proposes a novel approach that integrates reinforcement learning (RL), behavioral analytics, and natural language processing to deliver real-time, personalized financial recommendations. We formulate personal finance management as a Markov Decision Process, using a Deep Q-Network (DQN) to learn optimal actions (such as saving or investment allocations) tailored to a user’s financial state. To incorporate user diversity, we apply unsupervised clustering (K-Means) on behavioral data to create distinct user personas, enabling the RL agent to adapt its strategy for different profiles. An interactive conversational agent powered by OpenAI’s GPT API serves as the user interface, translating the RL agent’s outputs into natural dialogue and handling user queries. We present an end-to-end implementation in Python, including synthetic data generation, persona clustering, RL training, and integration with OpenAI’s language model. Experimental results on a simulated personal finance environment demonstrate that the RL agent learns policies that significantly improve saving and investment outcomes compared to baseline strategies. The conversational interface provides personalized coaching, which can boost user engagement and trust. This interdisciplinary framework—combining RL for decision-making, clustering for personalization, and NLP for interaction—illustrates a promising direction for intelligent financial advisors that learn and communicate adaptively, ultimately empowering users to achieve better financial wellness.
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Starke, Alain, Martijn Willemsen, and Chris Snijders. "Promoting Energy-Efficient Behavior by Depicting Social Norms in a Recommender Interface." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (2021): 1–32. http://dx.doi.org/10.1145/3460005.
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How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, social norms and other nudges are studied to understand how people can be convinced to take action (e.g., towel re-use is boosted when stating that “75% of hotel guests” do so), but most of these nudges are not personalized. In contrast, recommender systems know what to recommend in a personalized way, but not much human-computer interaction ( HCI ) research has considered how personalized advice should be presented to help users to change their current habits. We examine the value of depicting normative messages (e.g., “75% of users do X”), based on actual user data, in a personalized energy recommender interface called “Saving Aid.” In a study among 207 smart thermostat owners, we compared three different normative explanations (“Global.” “Similar,” and “Experienced” norm rates) to a non-social baseline (“kWh savings”). Although none of the norms increased the total number of chosen measures directly, we show that depicting high peer adoption rates alongside energy-saving measures increased the likelihood that they would be chosen from a list of recommendations. In addition, we show that depicting social norms positively affects a user’s evaluation of a recommender interface.
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Yovanoff, Mary, David Pepley, Katelin Mirkin, Jason Moore, David Han, and Scarlett Miller. "Personalized Learning in Medical Education: Designing a User Interface for a Dynamic Haptic Robotic Trainer for Central Venous Catheterization." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 615–19. http://dx.doi.org/10.1177/1541931213601639.
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While Virtual Reality (VR) has emerged as a viable method for training new medical residents, it has not yet reached all areas of training. One area lacking such development is surgical residency programs where there are large learning curves associated with skill development. In order to address this gap, a Dynamic Haptic Robotic Trainer (DHRT) was developed to help train surgical residents in the placement of ultrasound guided Internal Jugular Central Venous Catheters and to incorporate personalized learning. In order to accomplish this, a 2-part study was conducted to: (1) systematically analyze the feedback given to 18 third year medical students by trained professionals to identify the items necessary for a personalized learning system and (2) develop and experimentally test the usability of the personalized learning interface within the DHRT system. The results can be used to inform the design of VR and personalized learning systems within the medical community.
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Ma, Wen Xuan, Shi Jie Wang, and Hong Wen Chen. "Model Base Construction and Transfer Analysis for the Cylindrical Gear of SEW-F Reducer." Advanced Materials Research 952 (May 2014): 231–34. http://dx.doi.org/10.4028/www.scientific.net/amr.952.231.
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To improve reducer products quality, continuously innovate and make them more personalized and customized, based on CATIA, researches are on extracting models feature parameter, setting up model base interface parameter, obtaining parametric design source code and writing program code for the cylindrical gear of SEW-F speed reducer. Process outside accessible way has been used for the second development of CATIA. User interface and model base connection can be made. And gear model base has been built. Through inputting gear design parameter in the model base user interface, the part model can be transferred automatically so as to develop the products soon.
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Song, Hao, Fangyuan Chen, Qingjin Peng, Jian Zhang, and Peihua Gu. "Improvement of user experience using virtual reality in open-architecture product design." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 13 (2017): 2264–75. http://dx.doi.org/10.1177/0954405417711736.
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User experience has a significant impact on the effective product design and improvement, especially for a personalized product to meet user’s individual need. The development of personalized products requires data from user experience in the evaluation of the product function and performance. The existing methods of Internet-based interactive platforms and direct market user surveys cannot provide users full experience of product features. This research proposes a user interactive system based on virtual reality technologies to provide users a close-real experience in the development of open-architecture products. The system provides users an interface built on the virtual environment. The users can review a product design by virtually operating and evaluating the product. The system records users’ operations and feedbacks for designers to improve the product. Food trucks designed using the open-architecture concept are used as applications to verify the proposed method. A user survey is conducted to examine the system effectiveness.
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García-Hidalgo, Miguel, Ángel M. García-Pedrero, Cristina Caetano-Sánchez, Marcos Gómez-España, Mario Lillo-Saavedra та José Miguel Olano. "ρ-MtreeRing: A Graphical User Interface for X-ray Microdensity Analysis". Forests 12, № 10 (2021): 1405. http://dx.doi.org/10.3390/f12101405.
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Wood microdensitometry provides an integrated measurement of inter and intra-annual changes in wood anatomy and lignification. Although it can be acquired through a wide array of techniques, X-ray-based techniques are still the standard. Conversion of a grayscale X-ray image to density and annual ring boundaries delimitation is performed through image analysis software. Proprietary software has dominated these applications, albeit Free Open Source Software (FOSS) has been developed recently. We present ρ-MtreeRing, a user-friendly FOSS that streamlines the entire microdensitometry analysis process through a graphical user interface based on Shiny R Software without any programming knowledge. We compared the results of this program with the most widely used commercial software (WinDendro), showing the validity of the results. ρ-MtreeRing can be personalized and developed by the microdensitometry research community.
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Lee, Jun, Younggwang Lee, and Sungjun Park. "Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects." Applied Sciences 12, no. 23 (2022): 12414. http://dx.doi.org/10.3390/app122312414.
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This paper proposes a pseudo-haptic interface that depicts the virtual weights of dumbbells in a virtual gymnasium. When a user performs a dumbbell biceps curl, he/she fixes the elbow joint as a standard joint and lifts the dumbbell, with its movement trajectory represented as a circular arc. The trajectories and velocity of dumbbell bicep curls differ depending on human physiological characteristics. Therefore, the proposed system provides an adaptable exercise area and force visualization of virtual dumbbells using a velocity-based pseudo-haptic interface and computer vision-based tracking method. The system recognizes the position and rotation of joints related to a dumbbell biceps curl with the implementation of density-based spatial clustering of applications with noise (a clustering algorithm) and resizes the radius and angle of an integrated force circular gauge. Furthermore, when a user lifts a dumbbell, the system recognizes, using linear regression, the current position and lifting force of the virtual dumbbell and visualizes the current lifting force with a guided movement trajectory to match the lifting force. Experimental results show that the proposed pseudo-haptic interface increased weight perception and usability by up to 30% compared to conventional methods (p < 0.05).
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B, Bhuvaneshwari. "AI-Driven Smart Food Ordering System with Personalized Nutrition Recommendations using Conversational Interface." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3108–22. https://doi.org/10.22214/ijraset.2025.68799.
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With the increasing popularity of online food ordering platforms, there remains a significant gap in delivering personalized and health-conscious food recommendations. This paper presents a Smart Food Ordering System that integrates AI-driven personalization based on individual user health data. The proposed system combines natural language processing (NLP) with FastText embeddings for intent classification and chatbot interaction, enabling users to place food orders through a conversational interface. Personalized recommendations are generated by analyzing user-specific health parameters such as age, weight, dietary restrictions, and fitness goals. The system is developed using Flask for the web interface and MongoDB for data storage, with additional modules for real-time order tracking, payment processing, and geolocation-based delivery validation. This integrated approach not only enhances user experience but also promotes healthier food choices. Experimental results demonstrate the system’s effectiveness in accurately understanding user intent and generating contextually relevant, healthoptimized food recommendations
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Osman, Mohamad Jahidi, Nurul Hawani Idris, Mohd Radhie Mohd Salleh, Zulkepli Majid, Zamri Ismail, and Nurain Othman. "Utilizing Machine Learning to Determine User Interface Elements in Agricultural Mobile Location-Based Services." IOP Conference Series: Earth and Environmental Science 1412, no. 1 (2024): 012025. https://doi.org/10.1088/1755-1315/1412/1/012025.
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Abstract The evolution of mobile location-based technology revolutionizes user experiences, providing personalized services and seamless navigation. This transformation influences how individuals interact with information and surroundings, enhancing daily life’s connectivity and convenience. However, creating interactive mobile location-based user interfaces (UI) for diverse users, especially in lower-middle-income countries, poses a significant challenge. Overcoming barriers to technology adoption within this diverse user group necessitates strategic planning to prevent exacerbating the digital divide. It is imperative to comprehend farmers’ preferences and requirements, especially in the context of user interface design. This study aims to determine preferred UI elements using machine learning based on respondents’ demographic characteristics. This investigation examines the user interface preferences of chili farmers in reporting pest and disease incidents through the implementation of a machine learning algorithm. Data was acquired through surveys and field experiments involving specified user interface elements for reporting chili crop issues in Batu Pahat and Johor Bahru. The findings of this study were analyzed using the Random Forest (RF) and Support Vector Machine (SVM) algorithms, and it shows that the image icon and radio button were the respondents’ preferences in stating the name of the disease. Based on the overall accuracy and kappa values, Random Forest is the more effective model for making predictions in this study. The findings support the transition of Malaysian farmers towards becoming intelligent farmers when designing the UI based on the demographic characteristics of farmers, aligning with the objectives of the Shared Prosperity Vision 2030 and endorsing the National Agrofood Policy 2021-2030 (NAP2.0). Additionally, this initiative aligns with the Food and Agriculture Organization of the United Nations (FAO) efforts to digitize farmers, including smallholders.