Bhawna Goyal¹, Ayush Dogra², Dawa Chyophel Lepcha¹, Rajesh Singh³, Hemant Sharma⁴, Ahmed Alkhayyat⁵, Manob Jyoti Saikia^6,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746

Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal… More >

Kai Jiang, Bin Cao^*, Jing Fan

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2965-2984, 2024, DOI:10.32604/cmes.2023.046348

Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously. Specifically, we… More >

Lujuan Deng, Boyi Liu^*, Zuhe Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1157-1170, 2024, DOI:10.32604/cmc.2023.042150

Abstract Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data. Concatenating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method. This fusion method does not utilize the correlation information between modalities. To solve this problem, this paper proposes a model based on a multi-head attention mechanism. First, after preprocessing the original data. Then, the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence. Next, the input coding sequence is fed into the transformer model for further… More >

Ameerah Abdullah Alshahrani^*, Emad Sami Jaha, Nahed Alowidi

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3489-3509, 2023, DOI:10.32604/cmc.2023.044490

Abstract The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade, owing to the continuing advances in image processing and computer vision approaches. In multiple real-life applications, for example, social media, content-based face picture retrieval is a well-invested technique for large-scale databases, where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures. Humans widely employ faces for recognizing and identifying people. Thus, face recognition through formal or personal pictures is increasingly used in various real-life applications, such as helping crime investigators… More >

S. Kannan^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465

Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. With… More >

Eun Kyoung Han, Hae Kyoung Son^*

International Journal of Mental Health Promotion, Vol.25, No.11, pp. 1197-1208, 2023, DOI:10.32604/ijmhp.2023.041560

Abstract Dementia prevalence has soared due to population aging. In Mild Cognitive Impairment (MCI) as a pre-dementia stage, sleep disturbances have raised much interest as a factor in a bidirectional relationship with cognitive decline. Thus, this study developed the Sleep and Cognition Enhancement Multimodal Intervention (SCEMI) based on Lazarus’ multimodal approach and conducted a randomized controlled experiment to investigate the effects of the novel program on sleep and cognition in MCI elderly. The participants were 55 MCI elderly with sleep disturbances at two dementia care centers located in S-city, Gyeonggi-do, South Korea (n = 25 in the experimental group and n… More >

Xiaoliang Lei¹, Xiaosheng Yu^2,*, Hao Wu³, Chengdong Wu^2,*, Jingsi Zhang²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069

Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a… More >

Fida Mohammad^1,*, Mukhtaj Khan¹, Safdar Nawaz Khan Marwat², Naveed Jan³, Neelam Gohar⁴, Muhammad Bilal³, Amal Al-Rasheed⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202

Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep neural network (DNN) model, especially… More >

Feng Yang¹, Zhong Wu^2,*, Xiaoyan Teng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 719-738, 2024, DOI:10.32604/cmes.2023.028699

Abstract The low efficiency and high cost of fresh agricultural product terminal distribution directly restrict the operation of the entire supply network. To reduce costs and optimize the distribution network, we construct a mixed integer programming model that comprehensively considers to minimize fixed, transportation, fresh-keeping, time, carbon emissions, and performance incentive costs. We analyzed the performance of traditional rider distribution and robot distribution modes in detail. In addition, the uncertainty of the actual market demand poses a huge threat to the stability of the terminal distribution network. In order to resist uncertain interference, we further extend the model to a robust… More > Graphic Abstract