A hybrid fusion framework to multi-modal bio metric identification

Younis, Mohammed Chachan; Abuhammad, Huthaifa

doi:10.1007/s11042-021-10818-y

Cited by 10 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence (AI) plays an essential role in solving different types of complex real-world problems including autonomous vehicles (Malik et al 2021), bio-metrics (Younis and Abuhammad 2021), healthcare (Albahli et al 2019) (Jin et al 2020;Albahli et al 2021), industrial applications (Gheisari et al 2021;Gao et al 2020a), renewable energy (Gao et al 2020b, c), and optimization applications (Rauf et al 2020). Deep learning algorithms, especially CNN (Meraj et al 2019), have been used in different computer vision-based algorithms (Younis 2021), including transportation patterns of drivers.…”

mentioning

confidence: 99%

Deep CNN-based autonomous system for safety measures in logistics transportation

et al. 2021

View full text Add to dashboard Cite

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

mentioning

confidence: 99%

Deep CNN-based autonomous system for safety measures in logistics transportation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The core idea is to combine information from different modalities to obtain more comprehensive and accurate information. Common multimodal feature fusion ways include early/late/hybrid fusion strategies [20].…”

Section:  mentioning

confidence: 99%

The Performance of a Temporal Multi-Modal Sentiment Analysis Model Based on Multitask Learning in Social Networks

He,

2024

ijacsa

View full text Add to dashboard Cite

When conducting sentiment analysis on social networks, facing the challenge of temporal and multi-modal data, it is necessary to enable the model to deeply mine and combine information from various modalities. Therefore, this study constructs an emotion analysis model based on multitask learning. This model utilizes a comprehensive framework of convolutional networks, bidirectional gated recurrent units, and multi head selfattention mechanisms to represent single modal temporal features in an innovative way, and adopts a cross modal feature fusion strategy. The experiment showed that the model accomplished 0.83 average precision and a 0.83 F1-value, respectively. In contrast with multi-scale attention (0.69, 0.70), aspect-based sentiment analysis (0.78, 0.74), and long short-term memory network (0.71, 0.78) models, this model demonstrated higher robustness and classification accuracy. Especially in terms of parallel computing efficiency, the acceleration ratio of the model reached 1.61, which is the highest among all compared models, highlighting the potential for time savings in large data volumes. This study has shown good performance in sentiment analysis in social networks, providing a novel perspective for solving complex sentiment classification problems.

show abstract

“…This large and complex data collection, with its diverse types and forms, is commonly called "big data". Big data is characterized by its size and complexity to the extent that it becomes difficult to manage using conventional database management tools or data processing applications [11,12]. Consequently, big data presents several Figure 1: KDD Process Steps challenges related to data capture, creation, storage, search, participation, transportation, analysis, and visualization [13].…”

Section: Research Background and Related Workmentioning

confidence: 99%

Improved model for traffic management system using KDD and big data: case study Jordan traffic

Alzyoud

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

View full text Add to dashboard Cite

This paper addresses the challenge of the increasing number of traffic accidents resulting from population growth and increased vehicle usage, leading to significant economic and environmental impacts. To address this issue, this paper proposes an improved model for traffic accident management that employs Knowledge Discovery in Databases (KDD) and extensive data analysis techniques to extract the main factors contributing to car accidents. A case study of traffic accidents in Jordan is conducted, utilizing actual data from the Department of Statistics and the Public Security Directorate Records between 2016 to 2020 to identify the primary factors contributing to road accidents and their effects on various types of injuries. The study identifies 11 factors that contribute to car accidents, with driver error being the primary factor for increasing the number of accidents and injuries. Additionally, minor car accidents are found to cause more injuries based on the analysis of the proposed approach. The findings provide a solid foundation for developing an accurate and precise scheme to reduce or eliminate the number of road accidents, which is essential for transitioning to smart cities. Using big data and KDD techniques could significantly improve current traffic accident management practices, informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. This paper presents a promising solution for addressing traffic accident management, contributing to safer and sustainable urban environments.

show abstract

A hybrid fusion framework to multi-modal bio metric identification

Cited by 10 publications

References 57 publications

Deep CNN-based autonomous system for safety measures in logistics transportation

Deep CNN-based autonomous system for safety measures in logistics transportation

The Performance of a Temporal Multi-Modal Sentiment Analysis Model Based on Multitask Learning in Social Networks

Improved model for traffic management system using KDD and big data: case study Jordan traffic

Contact Info

Product

Resources

About