Visual Context Research Articles

Visual search for hazardous items: using virtual reality (VR) in laypersons to test wearable displays for firefighters

In virtual reality (VR), we assessed how untrained participants searched for fire sources with the digital twin of a novel augmented reality (AR) device: a firefighter’s helmet equipped with a heat sensor and an integrated display indicating the heat distribution in its field of view. This was compared to the digital twin of a current state-of-the-art device, a handheld thermal imaging camera. The study had three aims: (i) compare the novel device to the current standard, (ii) demonstrate the usefulness of VR for developing AR devices, (iii) investigate visual search in a complex, realistic task free of visual context. Users detected fire sources faster with the thermal camera than with the helmet display. Responses in target-present trials were faster than in target-absent trials for both devices. Fire localization after detection was numerically faster and more accurate, in particular in the horizontal plane, for the helmet display than for the thermal camera. Search was strongly biased to start on the left-hand side of each room, reminiscent of pseudoneglect in scene viewing. Our study exemplifies how VR can be used to study vision in realistic settings, to foster the development of AR devices, and to obtain results relevant to basic science and applications alike.

Exacerbation of paranoia-like thoughts following exposure to common misophonia trigger sounds

Paranoia-like thoughts refer to heightened suspicions and unfounded beliefs about being watched or persecuted by others. Recent research has found a significant correlation between misophonia symptoms, a form of decreased sound tolerance, and paranoia-like thoughts, both of which are linked to heightened negative emotions in clinical and non-clinical populations. Notably, it has been observed that misophonia may also be associated with the tendency to attribute hostile intent to those producing triggering sounds, a feature consistent with paranoid ideation (i.e., perceptions of intentional harm). However, existing research is based on correlational data, limiting causal inference. Therefore, an online study involving a non-clinical sample (N = 487) employed an experimental approach to examine the relationship between misophonia symptoms, negative emotional response, and paranoia-like thoughts. Participants were randomly assigned to one of four task conditions, each related to exposure to different stimulus types: orofacial human-produced sounds, non-human sounds, sounds without visual context, or visuals devoid of sound. The results of mixed model ANOVA and mediation analysis revealed that exposure to common misophonia trigger sounds with a human-related visual context raised, but not significantly, the levels of paranoia-like thoughts. However, it did lead to a significant increase in negative emotions, which, in turn, proved to be a significant mediator of an increase in paranoia-like thoughts. Conversely, exposure to non-human sounds or to only audio/visual stimuli either decreased both negative emotions and paranoia-like thoughts or showed no significant change. This emphasized the role of context and the involvement of negative emotional response to human-made sounds in amplifying paranoia-like thoughts. Importantly, this effect was observed in individuals who do not meet the provisional diagnostic criteria for misophonia, suggesting that symptoms of misophonia may extend beyond clinical diagnoses, with milder manifestations potentially being present within the general population.

Design and Art Tools Formation of Visual Context in Modern Anthropogenic Environment

The purpose of the article is to establish the design and artistic means of forming the cultural-artistic context of modern anthropogenic space. The research methodology involved the use of synthesis and analysis methods with the involvement of interdisciplinary connections (architecture, art history, cultural studies, psychology, design), which made it possible to develop the available theoretical and practical experience on the subject of the article; the method of theoretical generalisation was used to formulate conclusions regarding the outlined problem. The scientific novelty of the article lies in the fact that it actualises the issue of the role of the visual and plastic language of design in the formation of the communicative and cultural content of the anthropogenic environment. Conclusions. With the change of socio-cultural, political and economic conditions, people's requirements for their environment also change. Design and artistic techniques make it possible to dynamically adjust the visual context of the space, to modernise it. The visual plastic design language of the modern anthropogenic environment is presented in artistically decorated stationary and moving objects, their parts, which add character and individuality to it, cause certain emotions in people who interact with such a space. The presence of murals, graffiti, street stencils, sticker art, aerosol art, posters, photo wallpapers, knitted graffiti, mosaics, LED art, media facades, sculptures, video mapping contributes to the formation of a new visual context of the environment, 3D images. Unsystematic, chaotic formation of the visual context of the space can disrupt its artistic integrity. It is worth considering the location of the object, its role in the general structure of the environment, architectural, historical or ethnic context.

Vocabulary Acquisition in Beginner L2 Learners

This project focuses on how different vocabulary teaching methods may develop efficient lexical-semantic networks in L2 learners. Vocabulary that frequently co-occurs in the environment becomes associated in the lexicon (e.g., food + sports) and it strengthens the representation of vocabulary. This experiment tests the effect of co-occurrence presentation and thematic list presentation on vocabulary processing. The goal is to inform vocabulary teaching methods. L2 learners completed a pre-test, four sessions of vocabulary training, and an immediate post-test. During training sessions, participants saw images on a computer and reviewed Spanish vocabulary, under one of two conditions: one word presented at a time in thematic lists (e.g., food), or words displayed in a visual scene or context (e.g., dinner party). During testing, participants completed a picture naming task to assess how different types of vocabulary exposure impacts L2 processing. Other individual differences such as working memory and proficiency were collected as well. The results from the picture naming task show that effectiveness of training in thematic list (TL) verse visual scene (VS) depended on initial proficiency and working memory. Participants with a higher initial proficiency and working memory improved more when trained with the VS condition, while those with lower proficiency and working memory had greater success when trained via the TL condition.

Visual context learning based on cross-modal knowledge for continuous sign language recognition

Visual context learning based on cross-modal knowledge for continuous sign language recognition

Memory modulation: Dominance of negative visual context over neutral verbal memory.

Neutral memories can be modulated via intentional memory control paradigms such as directed forgetting. In addition, previous studies have shown that neutral visual memories can be modulated indirectly, via remember and forget instructions towards competing verbal memories. Here we show that direct modulation of neutral verbal memory strength is impaired by negative visual context, and that negative visual context is resistant to indirect memory modulation. Participants were directly instructed to intentionally remember or forget newly encoded neutral verbal information. Importantly, this verbal information was interleaved with embedded negative visual context. Results showed that negative visual context eliminated the well-documented effect of direct instructions to intentionally remember verbal content. Furthermore, negative visual memory was highly persistent, overcoming its sensitivity to indirect modulation shown in previous studies. Finally, these memory effects persisted to the following day. These results demonstrate the dominance of negative visual context over neutral content, highlighting the challenges associated with memory modulation in psychopathologies involving maladaptive processing of negative visual memories.

A chromatic feature detector in the retina signals visual context changes.

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

How does visual context of urban open spaces drive soundscape pleasantness and eventfulness: a laboratory experiment using a higher order Ambisonics speaker system and immersive virtual reality

Perception of sound environments is influenced by the context we experience them in. A big portion of the contextual information comes from the visual domain, including our understanding of what a place is but also from its visual features. A laboratory study combining questionnaire responses and eye-tracking tools was designed to investigate if the soundscape outcomes and participants' behaviour inside the simulation can be explained by the perceptual outcomes defined by visual information. 360 degree videos and First Order Ambisonics audio recordings of 27 different urban open spaces taken from the International Soundscape Database were used as the stimuli delivered via a Virtual Reality Head-Mounted Display and a Higher Order Ambisonics speaker array, while a neutral grey environment without sounds being reproduced represented the baseline scenario. A questionnaire tool was deployed within the IVR simulation to collect participants' responses describing their perception of the reproduced environments corresponding to the circumplex model featured in the Method A of the ISO/TS 12913-2. The results revealed a good coverage of the two-dimensional perceptual circumplex space and significant differences between perceptual outcomes driven by sound and those driven by visual stimuli.

Prediction by Young Autistic Children from Visual and Spoken Input.

Recent theoretical accounts suggest that differences in the processing of probabilistic events underlie the core and associated traits of autism spectrum disorder (ASD). These theories hypothesize that autistic individuals are differentially impacted by disruptions in probabilistic input relative to neurotypical peers. According to this view, autistic individuals assign disproportionate weight to prediction errors such that novel input is overweighted relative to the aggregation of prior input; this is referred to as 'hyperplasticity' of learning. Prediction among autistic individuals has primarily been examined in nonverbal, visual contexts with older children and adults. The present study examined 32 autistic and 32 cognitively-matched neurotypical (NT) children's ability to generate predictions and adjust to changes in predictive relationships in auditory stimuli using two eye gaze tasks. In both studies, children were trained and tested on an auditory-visual cue which predicted the location of a reward stimulus. In Experiment 1 the cue was non-linguistic (instrumental sound) whereas in Experiment 2 the cue was linguistically-relevant (speaker gender). In both experiments, the cue-reward contingency was switched after the first block of trials, and predictive behavior was evaluated across a second block of trials. Results: Analyses of children's looking behavior revealed similar performance in both groups on the non-linguistic task (Exp. 1). In the linguistically-relevant task (Exp. 2), predictive looking was less disrupted by the contingency switch for autistic children than NT children. Results suggest that autistic children may demonstrate hyperplastic learning in linguistically-relevant contexts, relative to NT peers.

A Survey of Robot Intelligence with Large Language Models

Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks. These abilities of LLMs are promising in robotics. In general, traditional supervised learning-based robot intelligence systems have a significant lack of adaptability to dynamically changing environments. However, LLMs help a robot intelligence system to improve its generalization ability in dynamic and complex real-world environments. Indeed, findings from ongoing robotics studies indicate that LLMs can significantly improve robots’ behavior planning and execution capabilities. Additionally, vision-language models (VLMs), trained on extensive visual and linguistic data for the vision question answering (VQA) problem, excel at integrating computer vision with natural language processing. VLMs can comprehend visual contexts and execute actions through natural language. They also provide descriptions of scenes in natural language. Several studies have explored the enhancement of robot intelligence using multimodal data, including object recognition and description by VLMs, along with the execution of language-driven commands integrated with visual information. This review paper thoroughly investigates how foundation models such as LLMs and VLMs have been employed to boost robot intelligence. For clarity, the research areas are categorized into five topics: reward design in reinforcement learning, low-level control, high-level planning, manipulation, and scene understanding. This review also summarizes studies that show how foundation models, such as the Eureka model for automating reward function design in reinforcement learning, RT-2 for integrating visual data, language, and robot actions in vision-language-action models, and AutoRT for generating feasible tasks and executing robot behavior policies via LLMs, have improved robot intelligence.

Comparative analysis of art therapy in education: China vs. foreign countries using CiteSpace

Comparative research on art therapy in education is limited, both in China and internationally. This study examines the potential of art therapy to transform art education, focusing on improving students' physical and mental health and aesthetic abilities, both in China and other countries. This study provides a comprehensive comparative analysis of art therapy in education within a visual context. Using bibliometric analysis of publications from the CNKI and WOS databases over the past 20 years, and the CiteSpace visualization tool, research hotspots and frontiers in art therapy within education were identified. Clustering research topics were explored, and the influence of art therapy on current art education was discussed. The study also predicts the future development trend of art therapy in both Chinese and international art education. Bibliometric analysis of CNKI and WOS databases, coupled with the CiteSpace visualization tool, facilitated the identification of key nodes in the keyword network, highlighting research hotspots and frontiers. The development of art therapy has shown an upward trend over the past two decades, particularly in the last ten years. Comparative analysis revealed ongoing progress of art therapy in both China and other countries, with untapped potential in China. Future trends indicate a complex upward trajectory, emphasizing the integration of localized art therapy methods and educational approaches. This comparative research paper explores art therapy in education, focusing on its potential to improve physical and mental health, as well as aesthetic abilities, in students from China and other countries. Using bibliometric analysis and the CiteSpace visualization tool, research hotspots and frontiers in art therapy within education were identified. The findings reveal a growing trend in art therapy over the past two decades, particularly in China. The study highlights untapped potential and predicts a complex upward trajectory, emphasizing the integration of localized art therapy methods and educational approaches. These insights contribute to the advancement of art education practices in China and internationally, underscoring the need for further exploration and integration of art therapy methods.

Reduced on-line speech gesture integration during multimodal language processing in adults with moderate-severe traumatic brain injury: Evidence from eye-tracking

BackgroundLanguage is multimodal and situated in rich visual contexts. Language is also incremental, unfolding moment-to-moment in real time, yet few studies have examined how spoken language interacts with gesture and visual context during multimodal language processing. Gesture is a rich communication cue that is integrally related to speech and often depicts concrete referents from the visual world. Using eye-tracking in an adapted visual world paradigm, we examined how participants with and without moderate-severe traumatic brain injury (TBI) use gesture to resolve temporary referential ambiguity. MethodsParticipants viewed a screen with four objects and one video. The speaker in the video produced sentences (e.g., “The girl will eat the very good sandwich”), paired with either a meaningful gesture (e.g., sandwich-holding gesture) or a meaningless grooming movement (e.g., arm scratch) at the verb “will eat.” We measured participants’ gaze to the target object (e.g., sandwich), a semantic competitor (e.g., apple), and two unrelated distractors (e.g., piano, guitar) during the critical window between movement onset in the gesture modality and onset of the spoken referent in speech. ResultsBoth participants with and without TBI were more likely to fixate the target when the speaker produced a gesture compared to a grooming movement; however, relative to non-injured participants, the effect was significantly attenuated in the TBI group. DiscussionWe demonstrated evidence of reduced speech-gesture integration in participants with TBI relative to non-injured peers. This study advances our understanding of the communicative abilities of adults with TBI and could lead to a more mechanistic account of the communication difficulties adults with TBI experience in rich communication contexts that require the processing and integration of multiple co-occurring cues. This work has the potential to increase the ecological validity of language assessment and provide insights into the cognitive and neural mechanisms that support multimodal language processing.

The Existence of Phraseological Units of Journalistic Deception in the Russian Pre-Revolutionary Press

Fake (false) news today has a global scale. Unfortunately, this fact confi rms the relevance of this study. The word fake is constantly found in modern media, but earlier phraseological units of deception were more common. The article examines the genesis of phraseological units of journalistic deception in the domestic pre-revolutionary press. The author reviewed about 40 periodicals of the Russian Empire, dictionaries, fi ction and ego-documents. The purpose of the study is to form a picture of hostility towards journalistic deception, phraseologically fi xed at the verbal and visual level of Russian periodicals of the 19th – early 20th centuries. Among the many European and domestic studies of phraseological units of journalistic lies, scientists paid almost no attention to the visual context. This article partially fi lls this gap. Illustrated publications of the 1860s–1880s were especially carefully reviewed to identify early visual images associated with phraseological units of journalistic lies. The research is based on the principles of historicism and systematicity; the methodology of the history of concepts and the history of images was used. During a continuous and selective review of Russian pre-revolutionary periodicals, 38 verbal examples of the use of phraseological units of journalistic deception and 69 visual examples were identifi ed. The most common are the word pouf and the phraseological unit newspaper hoax, much less often magazine hoax, literary hoax, political hoax, etc. The word pouf in the context of deception appeared in Russian journalism from the late 1830s and began to be widely used in the 1840s. The phraseological unit newspaper hoax entered the Russian language in the late 1850s, since the 1860s it had become understandable to most readers and was actively used by the domestic press throughout the pre-revolutionary period. Among the most desirable prospects for this research should be the identifi cation of phraseological units of journalistic deception in Russian periodicals of the early twentieth century. In this case, special attention should be paid to the poorly studied visual area. Keywords: Russian pre-revolutionary journalism, phraseology, journalistic deception, fake, pouf, newspaper hoax

Deep Learning Approach for Wildland Fire Recognition Using RGB and Thermal Infrared Aerial Image

Wildfires cause severe consequences, including property loss, threats to human life, damage to natural resources, biodiversity, and economic impacts. Consequently, numerous wildland fire detection systems were developed over the years to identify fires at an early stage and prevent their damage to both the environment and human lives. Recently, deep learning methods were employed for recognizing wildfires, showing interesting results. However, numerous challenges are still present, including background complexity and small wildfire and smoke areas. To address these challenging limitations, two deep learning models, namely CT-Fire and DC-Fire, were adopted to recognize wildfires using both visible and infrared aerial images. Infrared images detect temperature gradients, showing areas of high heat and indicating active flames. RGB images provide the visual context to identify smoke and forest fires. Using both visible and infrared images provides a diversified data for learning deep learning models. The diverse characteristics of wildfires and smoke enable these models to learn a complete visual representation of wildland fires and smoke scenarios. Testing results showed that CT-Fire and DC-Fire achieved higher performance compared to baseline wildfire recognition methods using a large dataset, which includes RGB and infrared aerial images. CT-Fire and DC-Fire also showed the reliability of deep learning models in identifying and recognizing patterns and features related to wildland smoke and fires and surpassing challenges, including background complexity, which can include vegetation, weather conditions, and diverse terrain, detecting small wildfire areas, and wildland fires and smoke variety in terms of size, intensity, and shape. CT-Fire and DC-Fire also reached faster processing speeds, enabling their use for early detection of smoke and forest fires in both night and day conditions.

Quantifying the mechanisms for the role of visual context on orientation judgments

Quantifying the mechanisms for the role of visual context on orientation judgments

Information Sources for Word Meaning in Children’s Picture Books: A Human Simulation Paradigm Study

ABSTRACT Picture book reading is widely regarded as an activity that promotes multiple aspects of children’s language acquisition, including their vocabulary development. Historically, researchers interested in what underlies the link between picture book reading and vocabulary development have investigated a suite of caregiver behaviors during picture book reading that might support learning. More recently, with the advent of modern text analytic tools, researchers have begun to look at the properties of the picture books themselves and have revealed many ways in which the language in picture book text may support learning. The current study adds to this growing literature but focuses not only on how the language in picture books might support word learning but also on how its visual content supports learning. In an adaptation of the Human Simulation Paradigm applied to commonly read picture books to English-learning children, the current study involved adults (N = 96). Results showed that a word’s linguistic and visual contexts in picture books are both valuable sources of information about that word’s meaning, and that their relative contributions may vary as a function of the book’s target audience and the word in question. Moreover, the combined visuo-linguistic context may be an especially valuable source of information for word learning. The implications of this work for the mechanisms by which picture books contribute to word learning are discussed.

Categorically distinct subsets allow flexible memory selection in hybrid search.

In many everyday situations, we search our visual surroundings for any one of many memorized items held in memory, a process termed hybrid search. In some cases, only a portion of the memorized mental list is relevant within a specific visual context, thus, restricting memory search to the relevant subset would be desirable. Previous research had shown that participants largely fail to "partition" memory into several distinct subsets, on a trial-by-trial basis. However, given the known role of semantic content in long-term memory organization, we hypothesized that semantically defined subset categories might serve as a more powerful means for flexible memory selection in dynamic hybrid search situations. Experiment 1 revealed that, indeed, semantic characteristics (i.e., object category), but not perceptual features (e.g., arbitrary color), can provide a firm basis for flexible memory partitioning. Experiments 2 and 3 further showed that such memory partitioning is costless and is independent of the nature of the surrounding visual distractors (i.e., a categorically homogeneous or heterogeneous display). These findings demonstrate that confining one's memory search to a currently relevant subset of items is highly effective when the different memory subsets are defined by clear semantic categories. The results underscore the importance of conceptual information in the organization of activated long-term memory and in forming the basis for a flexible trial-by-trial memory selection. Our findings further highlight the relationship between visual search and memory search, and they may shed light on the processes contributing to a successful construction of bounded episodes in long-term memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A novel multi-stream hand-object interaction network for assembly action recognition

PurposeAssembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.Design/methodology/approachIn this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.FindingsThe method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.Originality/valueThe author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

Language conditioned multi-scale visual attention networks for visual grounding

Visual grounding (VG) is a task that requires to locate a specific region in an image according to a natural language expression. Existing efforts on the VG task are divided into two-stage, one-stage and Transformer-based methods, which have achieved high performance. However, most of the previous methods extract visual information at a single spatial scale and ignore visual information at other spatial scales, which makes these models unable to fully utilize the visual information. Moreover, the insufficient utilization of linguistic information, especially failure to capture global linguistic information, may lead to failure to fully understand language expressions, thus limiting the performance of these models. To better address the task, we propose a language conditioned multi-scale visual attention network (LMSVA) for visual grounding, which can sufficiently utilize visual and linguistic information to perform multimodal reasoning, thus improving performance of model. Specifically, we design a visual feature extractor containing a multi-scale layer to get the required multi-scale visual features by expanding the original backbone. Moreover, we exploit pooling the output of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to extract sentence-level linguistic features, which can enable the model to capture global linguistic information. Inspired by the Transformer architecture, we present the Visual Attention Layer guided by Language and Multi-Scale Visual Features (VALMS), which is able to better learn the visual context guided by multi-scale visual and linguistic features, and facilitates further multimodal reasoning. Extensive experiments on four large benchmark datasets, including ReferItGame, RefCOCO, RefCOCO+ and RefCOCOg, demonstrate that our proposed model achieves the state-of-the-art performance.

Elevating recommender systems: Cutting-edge transfer learning and embedding solutions

In today’s information age and connected economy, Recommender Systems (RS) plays a vital role in managing information overload and delivering personalized suggestions to users. This paper introduces a multistage model that leverages multimodal data embedding and deep transfer learning to accurately capture user preferences and item characteristics, resulting in highly tailored recommendations. A key innovation in this model is the incorporation of an image dataset in the second phase, which addresses cold-start problems for new items by providing additional visual context. Our approach excels in overcoming challenges related to data sparsity and cold-start issues, thereby providing users with realistic and relevant product recommendations. To validate the effectiveness of the proposed model, we conducted extensive evaluations using three diverse datasets: data from Brazilian e-commerce platforms, the MovieLens 1M dataset, and the Amazon Product Review dataset. These evaluations involved comprehensive comparisons with standard RS methods to assess performance improvements. The results indicate that our proposed model significantly outperforms traditional RS techniques in terms of accuracy and reliability. Our model provides more accurate and meaningful recommendations by effectively addressing issues such as cold-start and data scarcity. Specifically, the model achieved Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) scores of 0.5883 and 0.4012, respectively, which demonstrate its superior performance metrics across all datasets tested.