Search Results (157)

In a world where artificial intelligence is constantly changing education, taking students’ feelings into account is a crucial framework for enhancing their engagement and academic performance. This article presents LearnerEmotions, an online application that employs machine vision technology to determine how learners are feeling in real time through their facial expressions. Teachers and institutions can access analytical dashboards and monitor students’ emotions with this tool, which is designed for use in both in-person and remote classes. The facial expression recognition model used in this application achieved an average accuracy of 0.91 and a loss of 0.3 in the real environment. More than 9 million emotional data points were gathered from an experiment involving 65 computer engineering students, and these insights were correlated with attendance and academic performance. While negative emotions like anger, sadness, and fear are associated with decreased performance and lower attendance, the statistical study shows a strong correlation between positive feelings like surprise and joy and successful academic performance. These results underline the necessity of technological tools that offer immediate pedagogical regulation and support the notion that emotions play an important role in the learning process. Thus, LearnerEmotions, which considers students’ emotional states, is a potential first step toward more adaptive learning. Full article

Transparency in business practices is essential for sustainability, ensuring that resources are used responsibly and that environmental and social impacts are properly measured and monitored, allowing the end consumer to make informed purchasing decisions without feeling cheated. The Digital Product Passport (DPP) promotes transparency by providing detailed information about a product’s origin, composition, and life-cycle activities, enabling more sustainable and responsible choices. The implementation of the DPP for textile and clothing items faces many challenges due to the large number and diversity of companies involved in the value chain of these products, combined with the large amount and variability of information that needs to be collected. Therefore, the integration and standardization of data from these companies is one of the largest present challenges. In this article, we study the use of Machine Learning (ML) algorithms for validating, in a homogeneous way, the quality of the data submitted by each company for the implementation of the DPP. We have studied four solutions that, using datasets organized in different ways and using different ML algorithms, enable selecting the solution that best suits each particular situation. Full article

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution. Full article

Urban waterfront streets are important mediators that reflect a city’s image and characteristics. They play a positive role in enhancing residents’ cohesion, mental and physical health, and social interactions. Human perceptions represent individuals’ psychological experiences and feelings toward the surrounding environment. Previous studies have explored the impact of urban street-built environmental factors on perceptions; however, research focusing on waterfront street environments and their impacts on human perceptions remains limited. Therefore, exploring the specific impact of waterfront street environmental characteristics on different dimensions of human perception is essential for guiding the development of livable cities. Based on Street View images (SVIs), this study applied artificial neural networks and machine learning semantic segmentation techniques to obtain physical feature data and human perception data of the Murasaki River waterfront line spaces in Kitakyushu, Japan. In addition, correlation and regression analyses were conducted to explore the specific impact of physical features on different dimensions of human perception in waterfront line spaces, and corresponding optimization strategies were proposed. The results show that street greenness significantly enhances perceptions of safety, wealth, and beauty, while effectively reducing boredom and depression. Furthermore, the building visual ratio contributes to increased street vitality. On the other hand, physical features such as openness, spatial indicators, and environmental color diversity have negative effects on positive perceptions, including safety and vitality. In particular, openness significantly increases boredom and depression. This study advances the exploration of urban waterfront street environments from the perspective of human perception, providing a theoretical foundation for improving the spatial quality of waterfront streets and offering references for human-centered urban planning and construction. Full article

Mental illness has emerged as a widespread global health concern, often unnoticed and unspoken. In this era of digitization, social media has provided a prominent space for people to express their feelings and find solutions faster. Thus, this area of study with a sheer amount of information, which refers to users’ behavioral attributes combined with the power of machine learning (ML), can be explored to make the entire diagnosis process smooth. In this study, an efficient ML model using Long Short-Term Memory (LSTM) is developed to determine the kind of mental illness a user may have using a random text made by the user on their social media. This study is based on natural language processing, where the prerequisites involve data collection from different social media sites and then pre-processing the collected data as per the requirements through stemming, lemmatization, stop word removal, etc. After examining the linguistic patterns of different social media posts, a reduced feature space is generated using appropriate feature engineering, which is further fed as input to the LSTM model to identify a type of mental illness. The performance of the proposed model is also compared with three other ML models, which includes using the full feature space and the reduced one. The optimal resulting model is selected by training and testing all of the models on the publicly available Reddit Mental Health Dataset. Overall, utilizing deep learning (DL) for mental health analysis can offer a promising avenue toward improved interventions, outcomes, and a better understanding of mental health issues at both the individual and population levels, aiding in decision-making processes. Full article

Cesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey conducted with women in Luxembourg, we first applied statistical methods to investigate the influence of various social and linguistic parameters on CS. Additionally, we explored how these factors relate to the feelings of happiness and respect women experience during childbirth. Subsequently, we employed four machine learning models to predict CS based on the survey data. Our findings reveal that women who speak Spanish have a statistically higher likelihood of undergoing a CS than women that do not report speaking that language. Furthermore, those who had CS report feeling less happy and respected compared to those with vaginal births. With both limited and augmented data, our models achieve an average accuracy of approximately 81% in predicting CS. While this study serves as an initial exploration into the social aspects of childbirth, it underscores the need for larger-scale studies to deepen our understanding and to inform policy-makers and health practitioners that support women during their pregnancies and births. This preliminary research advocates for further investigation to address this complex social issue comprehensively. Full article

The transition from Industry 4.0 to Industry 5.0 highlights recent European efforts to design intelligent devices, systems, and automation that can work alongside human intelligence and enhance human capabilities. In this vision, human–machine interaction (HMI) goes beyond simply deploying machines, such as autonomous robots, for economic advantage. It requires societal and educational shifts toward a human-centric research vision, revising how we perceive technological advancements to improve the benefits and convenience for individuals. Furthermore, it also requires determining which priority is given to user preferences and needs to feel safe while collaborating with autonomous intelligent systems. This proposed human-centric vision aims to enhance human creativity and problem-solving abilities by leveraging machine precision and data processing, all while protecting human agency. Aligned with this perspective, we conducted a systematic literature review focusing on trust and trustworthiness in relation to characteristics of humans and systems in human–robot interaction (HRI). Our research explores the aspects that impact the potential for designing and fostering machine trustworthiness from a human-centered standpoint. A systematic analysis was conducted to review 34 articles in recent HRI-related studies. Then, through a standardized screening, we identified and categorized factors influencing trust in automation that can act as trust barriers and facilitators when implementing autonomous intelligent systems. Our study comments on the application areas in which trust is considered, how it is conceptualized, and how it is evaluated within the field. Our analysis underscores the significance of examining users’ trust and the related factors impacting it as foundational elements for promoting secure and trustworthy HRI. Full article

This study introduces a novel construct, Spendception, which conceptualizes the psychological impact of digital payment systems on consumer behavior, marking a significant contribution to the field of consumer psychology and behavioral economics. Spendception reflects the reduced psychological resistance to spending when using digital payment methods, as compared to cash, due to the diminished visibility of transactions and the perceived ease of payments. This research aims to explore the role of Spendception in increasing consumer purchase behavior, whereas the role of impulse buying has been observed as a mediator. To test the proposed model, an extensive survey was performed by collecting 1162 respondents from all walks of life to get the real picture. We employed exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to validate the measurement of key constructs. To test the hypothetical relations among all the variables, we employed structural equation modeling (SEM). Furthermore, a machine learning technique was used to test the robustness of the model. Results showed that Spendception greatly boosted the consumer purchase behavior, with impulse buying partially mediating the relation. Gender was found to moderate the relationship, with female consumers being more susceptible to impulse buying caused by Spendception. The study showed that digital payment systems made buying feel less noticeable, which led to people spending more without realizing the financial impact. This study introduces Spendception, a novel construct that extends existing consumer behavior theories by explaining how digital payment systems reduce psychological barriers to spending. It bridges the gap between Spendception and the pain of paying, demonstrating that the lack of immediate visibility and physicality in digital payments alters consumers’ perceptions of spending, leading to impulse buying and higher purchase behavior. The findings also offer actionable insights for marketers in designing targeted campaigns that leverage the psychological effects of Spendception. The findings provide actionable insights for marketers to design targeted campaigns and for policymakers to promote financial literacy, ensuring ethical use of digital payment systems. Full article

This study enhances the music-listening experience and promotes Thai artists. It provides users easy access to Thai songs that match their current moods and situations, making their music journey more enjoyable. The system analyzes users’ emotions through text input, such as typing their current feelings, and processes this information using machine learning to create a playlist that resonates with their feelings. This study focuses on building a tool that caters to the preferences of Thai music listeners and encourages the consumption of a wider variety of Thai songs beyond popular trends. This study develops a tool that successfully creates personalized playlists by analyzing the listener’s emotions. Phrase and keyword recognition detect the listener’s emotions, generating playlists tailored to their feelings, thus improving their music-listening satisfaction. The classifiers employed in this study achieved the following accuracies: random forest (0.94), XGBoost (0.89), decision tree (0.85), logistic regression (0.79), and support vector machine (SVM) (0.78). Full article

The world prevalence of the two types of authorized and fraudulent transactions makes it difficult to distinguish between the two operations. The small percentage of fraudulent transactions, in turn, gives rise to the class imbalance problem. Hence, an adequately robust fraud detection mechanism must exist for tax systems to avoid their collapse. It has become significantly difficult to obtain any dataset, specifically a tax return dataset, because of the rising importance of privacy in a society where people generally feel squeamish about sharing personal information. Because of this, we arrive at the decision to synthesize our dataset by employing publicly available data, as well as enhance them through Correlational Generative Adversarial Networks (CGANs) and the Synthetic Minority Oversampling Technique (SMOTE). The proposed method includes a preprocessing stage to denoise the data and identify anomalies, outliers, and dimensionality reduction. Then the data have undergone enhancement using the SMOTE and the proposed CGAN techniques. A unique encoder design has been proposed, which serves the purpose of exposing the hidden patterns among legitimate and fraudulent records. This research found anomalous deductions, income inconsistencies, recurrent transaction manipulations, and irregular filing practices that distinguish fraudulent from valid tax records. These patterns are identified by encoder-based feature extraction and synthetic data augmentation. Several machine learning classifiers, along with a voting ensemble technique, have been used both with and without data augmentation. Experimental results have shown that the proposed Soft-Voting technique outperformed the original without an ensemble method. Full article

The routine culling of male chicks in the laying hen industry raises significant ethical, animal welfare, and sustainability concerns. Current methods to determine chick embryo sex before hatching are costly, time-consuming, and invasive. This study aimed to develop a low-cost, non-invasive solution to predict chick embryo sex before hatching using the morphological features of eggs. A custom imaging apparatus was created using a smartphone and light box, enabling consistent image capture of chicken eggs. Egg length, width, area, eccentricity, and extent were measured, and machine learning models were trained to predict chick embryo sex. The wide neural network model achieved the highest accuracy of 88.9% with a mean accuracy of 81.5%. Comparison of the imaging apparatus to a high-cost industrial 3D scanner demonstrated comparable accuracy in capturing egg morphology. The findings suggest that this method can contribute to the prevention of up to 6.2 billion male chicks from being culled annually by destroying male embryos before they develop the capacity to feel pain. This approach offers a feasible, ethical, and scalable alternative to current practices, with potential for further improvements in accuracy and adaptability to different industry settings. Full article

The relationship between the cognitive and affective dimensions of understanding has remained unexplored due to the lack of reliable methods for measuring emotions and feelings during learning. Focusing on five phases of understanding—nascent understanding, misunderstanding, confusion, emergent understanding, and deep understanding—this study introduces an AI-driven solution to measure subjective understanding by analyzing physiological activity manifested in facial expressions. To investigate these phases, 103 participants remotely worked on 15 riddles while their facial expressions were video recorded. Action units (AUs) for each phase instance were measured using AFFDEX software. AU patterns associated with each phase were then identified through the application of six supervised machine learning algorithms. Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. These findings suggest that physiological activity can be leveraged to reliably measure understanding. Further, they advance a novel approach for measuring and fostering understanding in educational settings, as well as developing adaptive learning technologies and personalized educational interventions. Future studies should explore how physiological signatures of understanding phases both reflect and influence their associated cognitive processes, as well as the generalizability of this study’s findings across diverse populations and learning contexts (A suite of AI tools was employed in the development of this paper: (1) ChatGPT4o (for writing clarity and reference checking), (2) Grammarly (for grammar and editorial corrections), and (3) ResearchRabbit (reference management)). Full article

Background/Objectives: Tactile gnosis derives from the interplay between the hand’s tactile input and the memory systems of the brain. It is the prerequisite for complex hand functions. Impaired sensation leads to profound disability. Various invasive and non-invasive sensory substitution strategies for providing feedback from prostheses have been unsuccessful when translated to clinical practice, since they fail to match the feeling to genuine sensation of the somatosensory cortex. Methods: Herein, we describe a novel surgical technique for upper-limb-targeted sensory reinnervation (ulTSR) and report how single digital nerves selectively reinnervate the forearm skin and restore the spatial sensory capacity of single digits of the amputated hand in a case series of seven patients. We explore the interplay of the redirected residual digital nerves and the interpretation of sensory perception after reinnervation of the forearm skin in the somatosensory cortex by evaluating sensory nerve action potentials (SNAPs), somatosensory evoked potentials (SEPs), and amputation-associated pain qualities. Results: Digital nerves were rerouted and reliably reinnervated the forearm skin after hand amputation, leading to somatotopy and limb maps of the thumb and four individual fingers. SNAPs were obtained from the donor digital nerves after stimulating the recipient sensory nerves of the forearm. Matching SEPs were obtained after electrocutaneous stimulation of the reinnervated skin areas of the forearm where the thumb, index, and little fingers are perceived. Pain incidence was significantly reduced or even fully resolved. Conclusions: We propose that ulTSR can lead to higher acceptance of prosthetic hands and substantially reduce the incidence of phantom limb and neuroma pain. In addition, the spatial restoration of lost-hand sensing and the somatotopic reinnervation of the forearm skin may serve as a machine interface, allowing for genuine sensation and embodiment of the prosthetic hand without the need for complex neural coding adjustments. Full article

The purpose of this study is to provide an understanding of the experience of searching for tourism information through ChatGPT and discover the meaning of it. In order to achieve this purpose, data collection was conducted through in-depth interviews, and data analysis was conducted according to Giorgi. As a result, eight themes, 27 meaning units, and 226 meaningful statements were derived. First, the participants were found to partially trust tourism information searched through it and perceive it as a personal AI travel agent. In addition, they were found to be satisfied with it as their primary tool of searching for tourism information and perceived it as an AI travel mate in all processes of tourism. On the one hand, they were found to be disappointed that it still felt like a machine. Second, they were found to feel ambivalent about it and experience better moments with it than with humans. Moreover, they were found to feel enjoyment in the process of learning about it. This study discovered the meaning of experience in searching for tourism information through it, laid the foundation for follow-up research related to it, and presented the possibility of expanding the application of it in the tourism industry. Full article