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24 results for “Visual Attention”
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Research ArticleOpen accessPhrase in the titleA Non-Intrusive Experiment to Examine the Visual Attention Data on Exposure to a Brand Using Eye TrackingSurya Bahadur Thapa, Prasun Gahlot, Komal Suryavanshi et al.The objective of this study is to understand the influence of articulation on sports sponsorship posters of a sporting event. An eye tracking experiment was conducted to examine the visual attention data on exposure to the sponsor’s brand while viewing an advertisement poster. The respondents were asked to fill out a questionnaire based on what they could recall while viewing … …Journal of Computer Science, Vol. 19, Issue 3 · 28 Feb 2023 · 5,908 accessesView PDFCite
Review ArticleOpen accessPhrase in the titleThe Use of Eye Tracking in Assessing Visual AttentionAyiei AyieiIn aviation, procedural errors persist despite pilots having the correct knowledge and ability to perform particular flight tasks. These errors can be attributed to diagnostic errors, inaccurate situational assessment and poor hazard awareness. Eye tracking has emerged as a technology that can be used to monitor pilot performance and provide insights into attentional distribution and the underlying decisional processes. The … …Journal of Aircraft and Spacecraft Technology, Vol. 4, Issue 1 · 4 Sep 2020 · 7,424 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleVisual-Spatial Attention Abilities Among School Children Exposed to Low Environmental Lead LevelSana Maidoumi, Mariam Ouisselsat, Charif Radouan Ouaziz et al.Lead (Pb) is an intrusive toxic matter which harms the neuro-cognitive and behavioral development of children even with doses considered normal. The present paper aims at evaluating the visual-spatial attention function among 142 schoolchildren living in a mining area (cases) in comparison to 177 living in the Chouiter area (control group) in Marrakech. We used the Bell Dam Test (BDT) … …OnLine Journal of Biological Sciences, Vol. 22, Issue 4 · 12 Oct 2022 · 5,406 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEffects of Competing Lexical Distracters in a Visual Search TaskJeremy W. Grabbe, Tom Cioffi, Luke T. Zauner et al.Studies have shown that distracters that are relevant to a target can negatively affect visual search performance. Recent studies have looked at performance when targets are embedded within a distracter. Various factors have been explored such as automaticity, spatial location and timing. This study asked, &#34If distracters affected a different dimension would that produce a detriment to visual search performance&#34? … …Current Research in Psychology, Vol. 4, Issue 2 · 16 Jan 2014 · 7,373 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGender Differences in Cognitive AbilitesAhmed A. Teleb, Aida A. Al AwamlehThe purpose of this study was to examine whether gender differences in Visual Selective Attention, Working Memory, Anticipation Time, Auditory Reaction Time and Visual-Motor Coordination performance existed. Forty eight students at Assiut university (21 males and 27 females; between 19.0 and 23.58 years old) participated in this study. Results indicated that there was no statistically significant difference between males and … …Current Research in Psychology, Vol. 3, Issue 1 · 28 Dec 2012 · 9,244 accessesView PDFCite
Research ArticleOpen accessIn the abstractDual-Branch Shape Texture Learning With Pyramid Residual and Sobel Edge Layers for Rice Variety ClassificationM Pradeep, M SiddappaAutomated rice variety classification categorizes rice types based on features, such as grain texture, shape, color, and size. Traditional handcrafted feature-based models fail at rice classification due to lighting variations, background noise, and environmental conditions, leading to overlapping visual characteristics and frequent misclassification. To address these challenges, this research proposes a novel Dual-Branch Convolutional Neural Network with Pyramid Residual Units … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 627 accessesView PDFCite
Research ArticleOpen accessIn the abstractExplainable MedAttn-ResNet50 for Automated Kidney Tumor Classification from CT Images With Grad-CAM VisualizationD. Vetrithangam, Pramod Kumar, Shilpasree S. et al.Kidney tumor classification from CT imaging remains challenging due to CT-specific intensity variations, tumor heterogeneity, and limited clinically meaningful interpretability in existing deep learning models. To address these limitations, we propose an Explainable MedAttn-ResNet50 framework that integrates radiology-driven preprocessing, multi-scale feature extraction, and medical attention mechanisms within a unified architecture. The preprocessing pipeline applies radiology-inspired intensity normalization, contrast enhancement, and … …Journal of Computer Science, Vol. 22, Issue 8 · 13 Aug 2026 · 1,120 accessesView PDFCite
Research ArticleOpen accessIn the abstractMedFusion: A Unified Multimodal Framework for Visual Question Answering and Explainable Medical RecommendationSatyajit Mahapatra, Jibitesh Mishra, Kumar Janardan Patra et al.In clinical decision-making, the ability to ask visual questions about medical images and receive accurate, personalized, and interpretable recommendations can significantly enhance practitioner support systems. This paper presents MedFusion, a unified multimodal framework that integrates Visual Question Answering (VQA), personalized medical recommendation, and explainability within a single architecture. The proposed model employs co-attention–based visual–textual fusion augmented with retrieval-enhanced reasoning to … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,360 accessesView PDFCite
Research ArticleOpen accessIn the abstractRobust Adversarial Attack Detection via Generative Adversarial Network With Residual Multi-Layer Aggregation Based Contrastive Loss FunctionAmudha Gopalakrishnan, Nalini JosephAdversarial attacks in medical imaging refer to subtle modifications to images that mislead diagnostic systems, resulting in inaccurate diagnoses and assessments. These attacks exploit vulnerabilities in image processing, leading to misclassification or altered visual features that often go unnoticed. This raises serious concerns about the security and reliability of medical diagnosis, directly impacting clinical decision-making and patient safety. This research … …Journal of Computer Science, Vol. 22, Issue 1 · 10 Feb 2026 · 1,742 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Approaches for Early Detection of Skin Cancer Disease Detection Using Segmentation and Classification with Severity AnalysisPunam R. Patil, Ritu TandonThe utmost prevalent type of the disease, skin cancer claims millions of lives every year, poses a serious public health threat particularly melanoma, which is often fatal if not detected early. Early diagnosis is crucial, yet traditional methods frequently fall short due to image quality limitations and the complexity of visual differentiation. With an emphasis on severity analysis, this study … …Journal of Computer Science, Vol. 21, Issue 9 · 24 Nov 2025 · 2,051 accessesView PDFCite