Search Results - multi-modal data presentation

Developing Multi-Modal Communication Tools for Retrofit Guidance in Ageing Bushfire-Prone Communities by Shuang Wu, Mona Salam, Aso Hajirasouli, Iina Lohi, Alison Wain, Sara Wilkinson, Gregory M. Morrison, Saeed Banihashemi

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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…This prevents downtime and improves efficiency, contributing towards industry innovation. This paper presents a method for predicting equipment failures using multi-modal sensor data. …”
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A novel multi-modal rehabilitation monitoring over human motion intention recognition by Saleha Kamal, Saleha Kamal, Mohammed Alshehri, Yahya AlQahtani, Abdulmonem Alshahrani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…However, existing systems struggle with recognizing such subtle movements due to complex postural variations and environmental noise. This paper presents a novel multi-modal framework that integrates RGB and depth data to extract high-resolution spatial-temporal and anatomical features for accurate HMIR. …”
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Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users by George Alex Stelea, Livia Sangeorzan, Nicoleta Enache-David

“…The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. …”
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A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing by Chin-Yi Lin, Tzu-Liang (Bill) Tseng, Tsung-Han Tsai

“…To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modeling, short- and long-term drift detection, interpretability, and a drift-aware federated paradigm. …”
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Multi-Modal Dynamic Fusion for Defect Detection in Electronic Products: A Novel Approach Based on Energy and Deep Learning by Yulin Liu, Yang Gao

“…To address these shortcomings, this study introduces a dynamic multi-modal fusion framework that leverages data from sensors, visual imagery, and component attributes to enhance detection performance. …”
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A Multi-Modal Approach Using a Hybrid Vision Transformer and Temporal Fusion Transformer Model for Stock Price Movement Classification by Ibanga Kpereobong Friday, Sarada Prasanna Pati, Debahuti Mishra

“…Stock market price movement primarily focuses on accurately classifying buy and sell signals, which enables traders to maximize profits with well-timed market entry and exit trading positions. This study presents and implements a multi-modal deep learning approach to classifying stock price movement. …”
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DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images by Abdul Hanan, Mehak Khan, Nieves Fernandez-Anez, Reza Arghandeh

“…Traditional methods, such as manual field surveys, are labor-intensive and limited in scope. This study presents DeepBioFusion, a multi-modal deep learning framework that first estimates AGB for validation as ground truth generation by using LiDAR-derived tree heights and a Tree Species map, employing allometry equations to relate tree height to Diameter at Breast Height (DBH). …”
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Multimodal-Based Non-Contact High Intraocular Pressure Detection Method by Zibo Lan, Ying Hu, Shuang Yang, Jiayun Ren, He Zhang

“…To address these limitations, we present a multi-modal framework incorporating CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and the Kolmogorov–Arnold Network (KAN) for efficient fusion of heterogeneous data. …”
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Toward unsupervised building extraction from very high-resolution remote sensing images using SAM and CLIP by Chenxiao Zhang, Peng Yue

“…To address the misclassification of fragmented objects, we design a zoom-out strategy to restore broken segments. Next, we present a hybrid feature fusion network that combines CLIP patch tokens with task-specific features, achieving high data adaptability while maintaining text-related visual features. …”
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Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO by Mohanad Diab, Polychronis Kolokoussis, Maria Antonia Brovelli

“…Having more accessible interfaces and tools that allow people of little or no experience to intuitively interact with RS data of multiple formats is a potential provided by this integration. …”
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Estimating Perceptual Attributes of Haptic Textures Using Visuo-Tactile Data by Mudassir Ibrahim Awan, Seokhee Jeon

“…Accurate prediction of perceptual attributes of haptic textures is essential for advancing VR and AR applications and enhancing robotic interaction with physical surfaces. This paper presents a deep learning-based multi-modal framework, incorporating visual and tactile data, to predict perceptual texture ratings by leveraging multi-feature inputs. …”
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A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities by Jungpil Shin, Najmul Hassan, Abu Saleh Musa Miah, Satoshi Nishimura

“…This survey includes only peer-reviewed research papers published in English to ensure linguistic consistency and academic integrity. This paper presents a comprehensive survey of the latest advancements in HAR from 2014 to 2025, focusing on Machine Learning (ML) and Deep Learning (DL) approaches categorized by input data modalities. …”
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Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. …”
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A multimodal experimental dataset on agile software development team interactions by Diego Miranda, Carlos Escobedo, Dayana Palma, Rene Noel, Adrián Fernández, Cristian Cechinel, Jaime Godoy, Roberto Munoz

“…Studying collaborative dynamics in agile development teams requires multi- modal data that captures verbal and non-verbal communication. …”
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New tools for diagnosis of primary immunodeficiencies: from awareness to artificial intelligence by Pere Soler-Palacín, Pere Soler-Palacín, Pere Soler-Palacín, Pere Soler-Palacín, Jacques G. Rivière, Jacques G. Rivière, Jacques G. Rivière, Jacques G. Rivière, Siobhán O. Burns, Siobhán O. Burns, Nicholas L. Rider

“…In this review, we describe two projects designed to decrease the time to diagnosis in PI patients: 1) the expert-driven PIDCAP project conducted in Spain to promote early diagnosis in the primary care setting, and 2) a multi-modal data-driven approach using artificial intelligence and machine learning to identify individuals at high risk for PI. …”
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Tests of robustness of GMM speaker verification in VoIP telephony by Piotr Staroniewicz

“…As a reference database XM2VTS (Extended multi Modal Verification for Teleservices and Security appliations Data Base) containing English speech (strings of digits) was applied. …”
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Smart Aging: Harnessing Artificial Intelligence to Enhance Elderly Health Care and Independence by Rahul Garg

“…Future directions in this field include advancements in natural language processing, multi-modal data integration, and the development of explainable AI systems. …”
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Research status and prospect of UWB radar echo signal processing technology for mine rescue by Xuezhao ZHENG, Chaoyang SUN, Yuan HUANG, Wen DING, Yang MA, Qiang LI, Haowei MOU

“…The extraction and fusion of multi-modal life signals and the optimization of data processing process are realized by using deep learning related technologies. …”
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Anomaly Detection for GOOSE Spoofing Attacks in Digital Substations Using Deep Learning Models: A DNN and LSTM Approach by Trinal Fernando, Gowri Ramachandran, Mahinda Vilathgamuwa, Dhammika Jayalath

“…To achieve this, the proposed models are trained on a multi-modal dataset consisting of GOOSE message attributes and physical electrical system measurements, and achieve the ability to accurately detect anomalies. …”
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