Improve User Experience Research Articles

End-to-end elevator voice command recognition system based on improved convolutional neural network

Abstract As a common life scenario, the use of voice control in elevator operation can effectively reduce public health risks and improve user experience in the post-epidemic era. However, due to the characteristics of its closure and public space, it is difficult to deploy the elevator. To solve this problem, an end-to-end speech command recognition algorithm based on a new deep neural network is proposed. The algorithm uses the self-built corpus for training, and uses the speech segment interception algorithm to obtain audio segments under the premise of speech stream as input, puts them into the network model in real time for reasoning, and drives the elevator to run according to the output results. The results show that compared with the recognition model using MFCC-CNN, the network has achieved about 10% improvement in accuracy under the premise of smaller computation, and compared with the DTW algorithm, the network has improved the accuracy by about 25%. Finally, a typical deployment environment is constructed to prove the correctness and effectiveness of the method in practical application.

Application of intelligent internet of things and interaction design in Museum Tour

The rapid development of information technology and the continuous improvement of human spiritual and cultural level have made people's acquisition of knowledge and information increasingly urgent. The information transmission method of traditional navigation systems is single and passive. This study aims to improve user experience and enhance the interactivity of museum visits. This article combines interactive technology in the Internet of Things to conduct in-depth research on the tour guide system in museums. By utilizing IoT technology, optimize the interaction design of the intelligent navigation system, and enhance the convenience and experience of visitors. In the experimental analysis, this article compared the main ticket purchasing methods of tourists, the level of understanding of museum staff about the collection, the amount of information obtained by tourists, and the satisfaction of tourists. The comparison results show that the proportion of electronic ticket purchases has increased by 35 % in terms of ticket purchasing methods. The staff's understanding of the collection has increased by 19 %, the amount of information received by tourists has increased by 12.7 %, and tourist satisfaction has also increased by 24.3 %. The conclusion indicates that the interactive design navigation system based on intelligent Internet of Things and interactive design can effectively meet the needs of different users and provide the public with a richer and deeper cultural experience.

Leaning-Based Interfaces Improve Simultaneous Locomotion and Object Interaction in VR Compared to the Handheld Controller.

Physical walking is often considered the gold standard for VR travel whenever feasible. However, limited free-space walking areas in the real-world do not allow exploring larger-scale virtual environments by actual walking. Therefore, users often require handheld controllers for navigation, which can reduce believability, interfere with simultaneous interaction tasks, and exacerbate adverse effects such as motion sickness and disorientation. To investigate alternative locomotion options, we compared handheld Controller (thumbstick-based) and physical walking versus a seated (HeadJoystick) and standing/stepping (NaviBoard) leaning-based locomotion interface, where seated/standing users travel by moving their head toward the target direction. Rotations were always physically performed. To compare these interfaces, we designed a novel simultaneous locomotion and object interaction task, where users needed to keep touching the center of upward moving target balloons with their virtual lightsaber, while simultaneously staying inside a horizontally moving enclosure. Walking resulted in the best locomotion, interaction, and combined performances while the controller performed worst. Leaning-based interfaces improved user experience and performance compared to Controller, especially when standing/stepping using NaviBoard, but did not reach walking performance. That is, leaning-based interfaces HeadJoystick (sitting) and NaviBoard (standing) that provided additional physical self-motion cues compared to controller improved enjoyment, preference, spatial presence, vection intensity, motion sickness, as well as performance for locomotion, object interaction, and combined locomotion and object interaction. Our results also showed that less embodied interfaces (and in particular the controller) caused a more pronounced performance deterioration when increasing locomotion speed. Moreover, observed differences between our interfaces were not affected by repeated interface usage.

Gesture recognition technology: a new dimension in human-computer interaction interface

Development of an interface for intelligent gesture control to improve user experience and increase the efficiency of interaction with a computer. This paper proposes a gesture recognition system based on artificial intelligence using convolutional neural networks (CNN). The system comprises three stages: pre-processing, optimal frame determination, and gesture category identification. The extracted features used are independent of movement, scaling, and rotation, providing greater flexibility to the system. The suggested gesture control technology, known as Kazakh Sign Language (KSL) for Kazakh alphabets, eliminates the need for additional devices, enabling users to interact with the system naturally. Experiments demonstrated that the proposed KSL system can accurately recognize Kazakh language alphabet letters with a high precision of 97.3%, owing to the utilization of artificial intelligence and CNN to enhance the accuracy and effectiveness of gesture control. Gestures, a type of visual formation, are perceivable by computers through machine learning models. The selection of methods and systems for recognizing Kazakh sign language gestures was accompanied by addressing various challenges related to language-specific orthographic and gestural features. The developed gesture control interface for human-computer interaction is applied in the field of inclusive education, aiming to assist deaf and hard-of-hearing children in learning sign language.

Revolutionizing enterprise resource planning: A transformation journey in the Albanian telecommunication industry

This paper explores the critical aspects of ERP (Enterprise Resource Planning) transformation in the Albanian telecommunications industry. The focus is on SAP tools that include asset modules, the platform for managing ERP stock and inventory, and a new frontline platform for selling device and accessories for all the channels by supporting both mobile and fixed devices. The telecom industry has evolved significantly, and the need for efficient operations, better integration, enhanced security, and improved user experiences has become dominant. Cloudification of the SAP solution running in GCP is part of the revolution. The technical implementation is strongly related to the advantages of an ERP transformation within the telecommunications industry. These benefits include enhanced efficiency and productivity, seamless integration, and impeccable data accuracy, bolstered security and compliance, and an overall enhancement in user experiences. Furthermore, this implementation also navigates the challenges and critical considerations essential in this transformative journey. Re-usability is a key component since the successful implementation in a single market for a single local market, can be replicated in the other markets as well by justifying the high implementation cost of 3 million Euros and the involvement of 32 local stakeholder and 45 global stakeholders.

Efficient Exploration and Exploitation for Sequential Music Recommendation

Music streaming services heavily rely upon recommender systems to acquire, engage, and retain users. One notable component of these services are playlists, which can be dynamically generated in a sequential manner based on the user’s feedback during a listening session. Online learning to rank approaches have recently been shown effective at leveraging such feedback to learn users’ preferences in the space of song features. Nevertheless, these approaches can suffer from slow convergence as a result of their random exploration component and their session-agnostic exploitation component. To overcome these limitations, we propose a novel online learning to rank approach which efficiently explores the space of candidate recommendation models by restricting itself to the orthogonal complement of the subspace of previous underperforming exploration directions. Moreover, we propose a session-aware exploitation component which leverages the momentum of the current best model during updates. Our thorough evaluation using simulated listening sessions from two large Last.fm datasets demonstrates substantial improvements over state-of-the-art approaches in terms of early-stage performance, which results in an improved user experience during online learning. In addition, we demonstrate that long-term convergence can be further enhanced by adaptively relaxing exploration constraints along the way.

Analysis of recommendation systems based on knowledge graphs

Due to the rapid development of internet technology, recommendation systems have played a crucial role in improving user experience and enhancing user retention. Knowledge graphs (KG), a technique capable of capturing complex semantic relationships and contextual information, are gradually included in recommendation systems to augment their accuracy and intelligence. This paper reviews the application of knowledge graphs in recommendation systems, analyzing their unique advantages in handling user-item relationships. This paper comprehensively analyzes embedding methods based on tensor decomposition and translation, which primarily designed for static knowledge graphs. This study thoroughly explains the advantages and disadvantages of these methods in practical application. Moreover, this paper discusses the challenges faced by dynamic knowledge graphs, such as temporal data processing, real-time updates, and inference, proposing potential solutions and future research areas. Integrating knowledge graphs with machine learning techniques enhances the ability of social media recommendation systems to understand user preferences and deliver highly personalized recommendations effectively. This study provides theoretical support and practical guidance for the implementing of knowledge graphs in recommendation systems, holding significant academic value and practical significance.

Conversational and generative artificial intelligence and human–chatbot interaction in education and research

AbstractArtificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

Content Classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using Vector Space Model (VSM)

The official website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) is an important platform for disseminating information to a diverse audience. Efficiently categorizing the vast amount of content available on the website is essential for enhancing user experience and optimizing information retrieval. These categories will also become an identifier and topic classification based on the content inside the article. This study presents a systematic approach to content classification of the Official Website of the Ministry of Foreign Affairs of the Republic of Indonesia (MoFA RI) using the Vector Space Model (VSM). The methodology involves preprocessing the text data, constructing a term-document matrix, and implementing cosine similarity to measure the relevance of documents to predefined categories. The study demonstrates the effectiveness of VSM in accurately classifying content, thus facilitating streamlined access to information for users navigating the website. Furthermore, the findings offer insights into enhancing the organization and accessibility of governmental online platforms, contributing to improved user experience and information dissemination.

Company Profile Application Design as Promotional Media Using Laravel

Keratip Design, a design agency specializing in creative services such as graphic design, encounters intense competition in the expanding design market. Presently, corporations depend on social media as a means of promotion. However, the outcomes could be more optimal. Moreover, Keratip Design encounters challenges in engaging with prospective customers, impeding the company's capacity to establish robust connections with potential clients. In order to address these problems, it is imperative to create and construct a contemporary, engaging, and appealing company profile application utilizing Laravel as the development framework. By utilizing Laravel as the underlying framework for constructing the company profile application, Kereatif Design can enhance the program's performance, bolster its security measures, and facilitate future expansion of its functionality. The app will allow Keratip Design to effectively showcase its portfolio, outline its design services, and furnish clients with the company's contact information in an immersive and dynamic manner. This project aims to develop a corporate profile application utilizing Laravel as a promotional instrument for Keratip Design. By utilizing this program, Keratip Design aims to enhance its online presence, attract prospective consumers, and improve user experience. This study's results can significantly enhance a company's visibility, enhance company reporting, foster more substantial engagement with potential consumers, and enhance presentations.

Semi-autonomous touch method merging robot’s autonomous touch and user-operated touch for improving user experience in robot touch

The demand for therapeutic robots to alleviate mental health problems is growing. Studies have shown that people’s mental health improves when they are touched. Consequently, therapeutic robots are designed to alleviate stress through robot’s autonomous touch. However, robot’s autonomous touch can sometimes cause discomfort to recipients. This paper proposes a semi-autonomous touch method that merges robot’s autonomous touch with user-operated touch to mitigate discomfort while maintaining the sensation of being touched by another person. We conducted an experiment involving 24 participants who were touched on the neck by robots under three conditions: robot’s autonomous touch, user-operated touch, and the proposed semi-autonomous touch method condition. Additionally, the study investigated participants’ impressions of the robot in each condition. The results showed that semi-autonomous touch condition mitigated discomfort more effectively compared with the robot’s autonomous touch method condition. It also enhanced the feeling of being touched by another person entity and suppressed interaction boredom compared with the user-operated touch method condition. Participants reported higher trustworthiness and perceived friendliness in robots utilizing the semi-autonomous touch method compared to those with autonomous touch method condition. These findings indicate that robots featuring the proposed semi-autonomous touch method can provide a comforting experience, leveraging the therapeutic benefits of being touched by another person, and underscore their potential in mental health applications.

Multi-source auxiliary information tourist attraction and route recommendation algorithm based on graph attention network

Abstract In the field of tourism recommendation systems, accurately recommending scenic spots and routes for users is one of the hot research directions. In order to better consider the complex interaction between user preferences and attraction features, as well as the potential connections between different information sources, this study constructed a graph attention network model using knowledge graphs for tourist attraction and route recommendations, and extracted features from visual images using visual geometry group-16. The results indicate that, in Xian, when the learning rate is 0.01, the area under the curve value is 0.916. The area under the curve of New York is 0.909, and the learning rate is 0.001. The area under the curve value of the Tokyo dataset is 0.895. When the learning rate is moderate, the model quickly stabilizes in the first 16 rounds and reaches its optimal state in 26–30 rounds. When the propagation depth is 2, the accuracy is 0.920, 0.905, and 0.895, respectively. After introducing visual features, the accuracy, recall, and F1 score improved by 10 to 15.7%. The multi-layer perceptron further increased the effect by 4–6%. These experimental data fully demonstrate the effectiveness and accuracy of the recommendation algorithm. This study provides a powerful tool for tourism recommendation systems, which helps to further improve user experience.

Sensing Human Emotion using Emerging Machine Learning Techniques

Human emotion recognition using machine learning is a new field that has the potential to improve user experience, lower crime, and target advertising. The ability of today's emotion detection systems to identify human emotions is essential. Applications ranging from security cameras to emotion detection are readily accessible. Machine learning-based emotion detection recognises and deciphers human emotions from text and visual data. In this study, we use convolutional neural networks and natural language processing approaches to create and assess models for emotion detection. Instead of speaking clearly, these human face expressions visually communicate a lot of information. Recognising facial expressions is important for human-machine interaction. Applications for automatic facial expression recognition systems are numerous and include, but are not limited to, comprehending human conduct, identifying mental health issues, and creating artificial human emotions. It is still difficult for computers to recognise facial expressions with a high recognition rate. Geometry and appearance-based methods are two widely used approaches for automatic FER systems in the literature. Pre-processing, face detection, feature extraction, and expression classification are the four steps that typically make up facial expression recognition. The goal of this research is to recognise the seven main human emotions anger, disgust, fear, happiness, sadness, surprise, and neutrality using a variety of deep learning techniques (convolutional neural networks).

Factors Influencing Consumer Purchase Intention and Adoption Onwards of 5G Mobile Phone in Malaysia

The use of 5G technology signifies a significant progression in mobile telecommunications, offering improved speed, connection and efficiency. The objective of this research is to determine the variables that impact customer purchase intention and subsequent adoption of 5G mobile phones, specifically from the viewpoint of popular shopping platforms in Malaysia. The theoretical framework can be found pursuant to the Technology Acceptance Model (TAM) and the Diffusion of Innovations (DOI) theory. Utilising quantitative surveys to collect extensive data from a varied sample of Malaysian customers who are Shopee subscribers. A total of 413 samples was successfully collected using convenience sampling of Malaysians in the Klang Valley. Findings suggest that factors such as perceived ease of use, subjective norm, and attitude serve a crucial role in determining purchase intention. The online purchasing of 5G mobile phones is greatly influenced by both intention and actual behaviour. The study emphasises the need of using targeted marketing tactics that specifically target these diverse characteristics. Online merchants and mobile phone manufacturers may greatly boost customer trust and adoption rates by improving user experience, offering comprehensive product information, and implementing strong security measures. The study offers vital information for competitors seeking to take advantage of the continuously growing 5G industry in Malaysia.

Re-imagining the future state of the ventricular assist device controller interface through human-centered design.

Ventricular assist devices (VADs) are effective therapy for patients with end-stage heart failure. Current VAD controllers offer improved interactivity, yet limitations of the visual, tactile, and auditory interface persist that impact patient experience and quality of life (QoL). This study explores how VAD controllers can be redesigned using a human-centered design approach to enhance the emotional and functional experience of the device for patients. VAD patients (n = 21), caregivers (n = 4) and healthcare practitioners (n = 24) were interviewed to uncover design opportunities. From this, a series of realistic scenarios to design for emerged. A "design by analogy" method took inspiration from existing consumer products to ideate new functionality for the VAD wearable system, creating concepts for a controller interface and paired wearable device. An additional 15 patients and 2 caregivers were engaged to explore current VAD controller experiences and evaluate the future-state concepts. This research validated the need for increased automation and emergency functionality in VAD controllers, including remote monitoring of data, accurate communication of battery status, and automated medical alerts for critical device alarms. "Manage my health," "Feeling normal," "Social belonging," "Feeling safe," and "Sense of control" emerged as key patient concerns to be met by future VAD controller designs. The study demonstrated an innovative and relevant approach to improve usability of future VAD peripherals. By considering both emotional and functional perspectives in the design of lifesaving medical devices such as VADs, device manufacturers can uncover new opportunities to improve patient QoL through improved user experiences.

Fingerprint Identification Method Based on Convulsional Neural Networks

The article presents an advanced method of fingerprint identification based on convolutional neural network (CNN) technology. This work elaborately describes the development and implementation process of a specialized CNN architecture for detecting and verifying the authenticity of fingerprints. Utilizing the comprehensive Socofing dataset allowed for an in -depth analysis of the model’s ability to distinguish between genuine and fabricated fingerprints, where the model demonstrated impressive accuracy – up to 98.964%. Special attention is given to error analysis, including the false discovery and omission rates, pointing towards potential directions for further improvement. Besides highlighting the technical aspects and high identification accuracy, the article also addresses potential challenges and limitations that the method might encounter. This includes issues related to the imbalance and diversity of data in the Socofing set, as well as limitations associated with computational resources when training deep neural networks. Potential pathways for model optimization are discussed, particularly focusing on reducing the false omission rate, which could improve user experience in authentication. The concluding section of the article emphasizes the importance of the presented work for the security sector, where precise authentication of fingerprint images is critically needed. The obtained results can be considered a solid foundation for future scientific developments in this direction. Additionally, the need for systematic updates and modifications of the model is highlighted to adapt it to continually improved imitation techniques, ensuring its long-term relevance and effectiveness.

The Effect of Automated Teller Machine (ATM) On Customer Satisfaction of Digital Services Users at Afghanistan Selected Commercial Banks (Afghanistan International Bank, Azizi Bank and Islamic Bank of Afghanistan)

This study investigates the impact of Automatic Teller Machines (ATMs) on customer satisfaction among users of digital services at commercial banks in Afghanistan, specifically Afghanistan International Bank, Azizi Bank, and Islamic Bank of Afghanistan. The banking sector in Afghanistan requires further development in expanding ATM infrastructure and enhancing customer satisfaction through improved user experience and support services. Utilizing a quantitative approach with multiple linear regression analysis, the research examines primary data collected through questionnaires as well as secondary data sources. The findings reveal a significant positive relationship between ATM usage and customer satisfaction. To enhance ATM availability and customer satisfaction, commercial banks in Afghanistan should strategically install and maintain ATMs, focusing on key locations in Kabul. Investing in ATM facilities can improve various dimensions of customer satisfaction.

Enhancing User Experience through Recommendation Systems: A Case Study in the E-commerce Sector

The significance of recommendation system techniques in improving user experience on e-commerce platforms should be considered as data-driven technologies progress. Conventional recommendation systems frequently need help dealing with issues such as data sparsity, cold start problems, and scalability, which significantly affect the ability of e-commerce platforms to offer accurate and relevant recommendations to consumers. This study evaluates the effectiveness of different recommendation system methodologies, including Averaging, Content-Based Filtering, Collaborative Filtering, Matrix Factorization, and Hybrid Approaches. Utilizing an extensive dataset from an e-commerce company, which encompasses consumer and product information such as ProductID, ProductName, Category, Price, CustomerID, and RatingReview, the dataset is divided into training and testing sets to assess the accuracy of the recommendation system models. Metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, Precision, and Recall are used for this evaluation. Preliminary results indicate that applying advanced recommendation system techniques, especially after optimizing hyperparameters, dramatically improves the accuracy of recommendations and increases user satisfaction compared to more straightforward methods. These findings can revolutionize recommendation tactics on e-commerce platforms, leading to more tailored and gratifying user experiences. The outcomes of this research are expected to enhance the optimization of recommendation systems and broaden the current knowledge on this topic within e-commerce.

Artificial Intelligence in Design and Impact on Electronic Marketing in Companies

E-marketing refers to the use of technology and artificial intelligence to analyze and interpret marketing data. In addition to providing recommendations and guidance to improve marketing campaigns, improve user experience, and increase sales. These tools include data analytics, machine learning, data classification and aggregation, voice and image recognition, and natural language. Therefore, this study aimed to identify the effect of artificial intelligence (AI) in Design on E-Marketing in Companies. This study used a survey design with a quantitative approach, with the target group being workers of E-Marketing companies. A 198 surveys were also gathered from Jordanian E-Marketing companies, however, 187 questionnaires were judged viable for study. Where the results indicate AI positively affects E-Marketing, and natural language processing, analytical models, content marketing, and digital marketing affect E-Marketing.

Multimodal Technologies for Remote Assessment of Neurological and Mental Health.

Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. We first present an overview of the various modalities of health information-speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural-which can each be extracted from various signal sources-audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation.