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28 articles for “Attention Capture”
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Research ArticleOpen accessIn the abstractAspect Based Sentiment Analysis Using Self-Attention Based LSTM Model with Word EmbeddingVimalkumar B. Vaghela, ZishanHaider Y. Noorani, Kalpesh Patel et al.Sentiment analysis is in advance more attention for research due to incremental convention in affairs of state, online marketing and social networking. Users afford their opinions for exacting object as reviews. Analysts categorize these reviews into prejudiced information. Consequently sentiment pulling out is the development of take out human awareness from amorphous text reviews. Extracted sentiment will assist to recognize … …Journal of Computer Science, Vol. 20, Issue 10 · 29 Jul 2024 · 6,521 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultimodal Face Expression Recognition Using Parametric Exponential Linear Unit-Long Short-Term MemoryKampa Ratna Babu, Akula Suneetha, Kampa Kanthi KumarMultimodal facial expression recognition combines information from multiple modalities of audio and video to achieve the required accuracy and robustness. By integrating different data sources, multimodal systems capture different aspects of human expression. However, accurately recognizing facial expressions across audio and video modalities causes challenges due to variations in expression representation. In this research, Parametric Exponential Linear Unit-Long Short-Term Memory … …Journal of Computer Science, Vol. 20, Issue 10 · 17 Aug 2024 · 4,620 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer DetectionAanchal Vij, Kuldeep Singh Kaswan, Anand NayyarEarly detection of lung cancer remains challenging due to high intra-class variation and inter-class similarity in Computed Tomography (CT) images. In this paper, we propose a hybrid deep learning model that combines convolutional, attention-based, and transformer-guided representations to address these challenges in multi-class lung cancer classification. For deep feature extraction, we use the EfficientNetV2-S architecture augmented with a Convolutional Block … …Journal of Computer Science, Vol. 22, Issue 6 · 3 Jun 2026 · 1,537 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing DataSawsan Morkos GharghoryClassification of multivariate time series has got massive attention in the last decade. The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. To include both of the effective features and the embedded relationships in the multivariate time series, a new hybrid model which incorporates both Convolutional Neural Network (CNN) and … …Journal of Computer Science, Vol. 17, Issue 9 · 29 Sep 2021 · 6,693 accessesView PDFCite
Research ArticleOpen accessIn the abstractUncertainty Assessment for the Buckling Analysis of Glass Columns with Random ParametersMohammad Momeni, Chiara BedonLoad-bearing elements composed of glass, as known, are often susceptible to buckling collapse mechanisms. This intrinsic characteristic (and thus potential limitation for design) typically derives from the use of relatively small thicknesses to cover large spans and surfaces, thus resulting in a multitude of columns, beams, or plates that are characterized by high slenderness. In the literature, accordingly, several and … …International Journal of Structural Glass and Advanced Materials Research, Vol. 4, Issue 1 · 22 Dec 2020 · 6,303 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing IndustryMicheal Omotayo Alabi, Ken Nixon, Ionel BotefAdditive Manufacturing (AM) which is also known as 3D printing technology; is recognized as a new paradigm for manufacturing industry. Additive manufacturing is rapidly expanding across different sectors such as healthcare, electronics, automotive, science and engineering, education, dental, etc. Machine Learning and Big Data are both emerging technologies which are becoming popular and gaining more attention from the industries and … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 3 · 14 Jul 2018 · 10,287 accessesView PDFCite
EditorialOpen accessIn the abstractWearable Technology: A New Era of Gadgetry InnovationHaider KhaleelToday, wearable technology comes in a wide variety of forms, from wrist and arm bands to eyewear, footwear and smart textiles. The number of wearable electronic devices available in today’s electronics market including smart watches, activity trackers, helmets and smart clothing seems to grow substantially. In fact, this technology is recognized as one of the hottest trends and one of … …American Journal of Engineering and Applied Sciences, Vol. 7, Issue 4 · 18 Apr 2015 · 7,481 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnimal Health Prediction Using Hybrid CNN Based BiLSTM Classification Model: A Deep Learning ApproachJ. Rathi, A. SumathiAccurate prediction of animal diseases is vital in veterinary medicine, as it can substantially enhance animal health outcomes and mitigate economic losses, making it a critical task that warrants attention and innovative solutions. This paper proposes a novel approach to animal condition classification, leveraging an Auto-encoder-based feature selection process and an Improved Hybrid Convolutional Neural Networks (CNN) with Bidirectional (Bi-LSTM) … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,488 accessesView PDFCite
Research ArticleOpen accessIn the abstractNeurofeedback Therapy Meets Transformers: Rewiring Sleep Disorders Through AI-Driven EEG ModulationAmala Ann KA, Vaidhehi VSleep disorders such as insomnia, sleep Apnea, and hypersomnia significantly impair neurophysiological functioning, yet conventional treatments like Cognitive Behavioral Therapy for Insomnia (CBT-I) remain resource-intensive and difficult to personalize. This study introduces a novel AI-powered neurofeedback simulation framework designed to detect dysregulated EEG frequency band activity across sleep stages and simulate targeted interventions. A Transformer-based model serves as the core … …Journal of Computer Science, Vol. 22, Issue 2 · 3 Mar 2026 · 1,680 accessesView PDFCite
Research ArticleOpen accessIn the abstractThe Grammaticalization of the Spanish Complement-taking Verb without a ComplementizerJiyoung YoonThis study examines authentic data samples taken from the Corpus de Referencia del Español Actual (CREA) in order to uncover any semantic trends that can be commonly observed in verbs taking a sentential complement without the complementizer que in Spanish. In doing so, special attention is given to the grammaticalization process that can account for epistemic fragments in which the … …Journal of Social Sciences, Vol. 11, Issue 3 · 12 Sep 2015 · 8,776 accessesView PDFCite