Corrigendum: Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data by Xiaofei Sun, Weiwei Guo, Jing Shen

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Ki-Cook: clustering multimodal cooking representations through knowledge-infused learning by Revathy Venkataramanan, Swati Padhee, Saini Rohan Rao, Ronak Kaoshik, Anirudh Sundara Rajan, Amit Sheth

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Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection by Monica F. Danilevicz, Philipp E. Bayer, Farid Boussaid, Mohammed Bennamoun, David Edwards

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VL-Few: Vision Language Alignment for Multimodal Few-Shot Meta Learning by Han Ma, Baoyu Fan, Benjamin K. Ng, Chan-Tong Lam

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Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data by Xiaofei Sun, Weiwei Guo, Jing Shen

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CAMM: Cross-Attention Multimodal Classification of Disaster-Related Tweets by Anuradha Khattar, S. M. K. Quadri

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A co-learning method to utilize optical images and photogrammetric point clouds for building extraction by Yuxing Xie, Jiaojiao Tian, Xiao Xiang Zhu

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Attention-Based Multimodal Deep Learning on Vision-Language Data: Models, Datasets, Tasks, Evaluation Metrics and Applications by Priyankar Bose, Pratip Rana, Preetam Ghosh

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Multi-Output Learning Based on Multimodal GCN and Co-Attention for Image Aesthetics and Emotion Analysis by Haotian Miao, Yifei Zhang, Daling Wang, Shi Feng

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Operating between Cultures and Langugages: Multilingual Films in Foreign Language Classes by Gabriele Blell

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Advancing NATO’s quality assurance education by implementing the ‘learn-watch-ask’ training model by Radu Emilian BĂLĂNESCU

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Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis by Biró Attila, Cuesta-Vargas Antonio Ignacio, Szilágyi László

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Deep learning application for the classification of Alzheimer’s disease using 18F-flortaucipir (AV-1451) tau positron emission tomography by Sang Won Park, Na Young Yeo, Yeshin Kim, Gihwan Byeon, Jae-Won Jang

“…The AUC for AD classification from CU was 0.964 and 0.947 in 2D CNN-LSTM and multimodal learning. The AUC of 3D CNN showed 0.947, and 0.976 in multimodal learning. …”
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Assessing learning preferences of dental students using visual, auditory, reading-writing, and kinesthetic questionnaire by Darshana Bennadi, K R Kashinath, J V Bharateesh, Nandita Kshetrimayum

“…Results: Majority 75.8% of the students preferred multimodal learning style. Multimodal learning was common among clinical students. …”
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Which mode of learning do dental students prefer? A cross-sectional study in Chennai, Tamil Nadu by Kazi Safika, R Sivashankari, R Ganesh

“…Conclusion: As the majority of the dental students preferred multimodal learning style, a variety of teaching methods should be included in the curriculum according to the students′ preferences.…”
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Multimodal convolutional neural networks for sperm motility and concentration predictions by Goh, Voon Hueh

“…The purpose of this research study is to adopt computer vision deep learning techniques and multimodal learning approach in sperm parameters prediction using video-based and image-based input. …”
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STUDENT LEARNING STYLE OF MATHEMATICS EDUCATION BASED ON GENDER by Farman Farman

“…Male students have a preference for multimodal learning style in bimodal (AR and AK) as 5 (41.66%) of 12 students. …”
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Learning Multimodal Representations by Symmetrically Transferring Local Structures by Bin Dong, Songlei Jian, Kai Lu

“…Multimodal representations play an important role in multimodal learning tasks, including cross-modal retrieval and intra-modal clustering. …”
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The implementation of the cognitive theory of multimedia learning in the design and evaluation of an AI educational video assistant utilizing large language models by Rana AlShaikh, Norah Al-Malki, Maida Almasre

“…The integration of Artificial Intelligence (AI) holds immense potential for revolutionizing education; especially, in contexts where multimodal learning experiences are designed. This paper investigated the potential benefits of Generative Artificial Intelligence (AI) in education, concentrating on the design and evaluation of an AI Educational Video Assistant tailored for multimodal learning experiences. …”
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Does learning style preferences influence academic performance among dental students in Isfahan, Iran? by Najmeh Akhlaghi, Hosein Mirkazemi, Mehdi Jafarzade, Narjes Akhlaghi

“…Approximately half of the students (51.5%) had multimodal learning preferences. Among the unimodal group (48.5%), the most common mode was aural (24.0%), followed by kinesthetic (15.5%), reading-writing (8.0%), and visual (1.0%). …”
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