Article
Computer Science, Artificial Intelligence
Muhammad Imran Sharif, Muhammad Attique Khan, Musaed Alhussein, Khursheed Aurangzeb, Mudassar Raza
Summary: This article proposes a new automated deep learning method for the classification of multiclass brain tumors, using deep transfer learning and feature selection techniques to improve accuracy.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Cardiac & Cardiovascular Systems
Christian S. Hansen, Daniel G. K. Rasmussen, Tine W. Hansen, Signe Holm Nielsen, Simone Theilade, Morten A. Karsdal, Federica Genovese, Peter Rossing
Summary: This study identified previously undescribed associations between markers of collagen turnover and the risk of cardiovascular autonomic neuropathy and distal symmetrical polyneuropathy in patients with type 1 diabetes.
CARDIOVASCULAR DIABETOLOGY
(2023)
Article
Mathematical & Computational Biology
Miao Wang, Fuyi Li, Hao Wu, Quanzhong Liu, Shuqin Li
Summary: In this study, we proposed a novel two-layer predictor, PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning, and demonstrated its superiority in promoter prediction compared to existing methods.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Kunfeng Wang, Yadong Wang, Shuqin Zhang, Yonglin Tian, Dazi Li
Summary: This article proposes a self-learning multi-scale object detection network, named SLMS-SSD, which balances the semantic information and spatial information to effectively improve the accuracy of object detection, especially for small object detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Jie Shi, Zhengyu Li, Hong Zhao
Summary: In this paper, a hierarchical feature selection method is proposed to improve classification difficulty by maximizing inter-class independence and minimizing intra-class redundancy using structure and feature relations. The method utilizes the hierarchy in the class space as structural information to improve performance and transforms the feature correlations into a mathematical representation to minimize redundancy.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xi-Ao Ma, Hao Xu, Chunhua Ju
Summary: This paper proposes a class-specific feature selection method based on information theory. A class-specific feature evaluation criterion called CSMDCCMR is developed, and a feature selection algorithm is designed to select a suitable feature subset for each class. Experimental results demonstrate the superiority of the proposed method compared to other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Inzamam Mashood Nasir, Asima Bibi, Jamal Hussain Shah, Muhammad Attique Khan, Muhammad Sharif, Khalid Iqbal, Yunyoung Nam, Seifedine Kadry
Summary: In this study, a combination of artificial intelligence and deep learning techniques with contour features was used to classify fruits and their diseases, achieving an accuracy of up to 99.6%. This method is applicable to 5G technology, cloud computing, and the Internet of Things.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Artificial Intelligence
Aiqing Fang, Xinbo Zhao, Jiaqi Yang, Yanning Zhang, Xiang Zheng
Summary: This paper proposes a cross-modal image fusion method that combines illuminance factors and attention mechanisms, studying and simulating human visual characteristics to achieve the final fusion map selection based on multi-scale decomposition and nonlinear fusion. Experimental results demonstrate the superiority of our fusion method under complex illumination environments and validate the effectiveness of our simulation of human visual characteristics.
PATTERN RECOGNITION
(2021)
Article
Construction & Building Technology
Zeynep Duygu Tekler, Adrian Chong
Summary: This study performs occupancy prediction based on a minimum sensing strategy using a comprehensive set of sensor data and a proposed feature selection algorithm. The findings highlight the crucial features for predicting occupancy across all space types as indoor CO2 levels and Wi-Fi connected devices.
BUILDING AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Qian Liu, Zebin Wu, Xiuping Jia, Yang Xu, Zhihui Wei
Summary: A class feature fused fully convolutional network (CFF-FCN) is proposed, which incorporates a local feature extraction block and a class feature fusion block to utilize local and global information. Experimental results demonstrate the superiority of the network over other deep learning methods, especially in cases with a small number of training samples.
Article
Computer Science, Artificial Intelligence
Syed Fawad Hussain, Fatima Shahzadi, Badre Munir
Summary: Feature selection is an important step in preprocessing high-dimensional data for machine learning. Existing techniques often select features based on their maximum dependency with the category and minimum redundancy with already selected features, which can lead to biased classification towards specific classes. In this paper, we propose a novel approach based on information theory that selects features in a class-wise fashion and utilizes a constrained search to enhance the feature selection. Experimental results demonstrate the effectiveness of our method in improving accuracy and reducing time complexity compared to other state-of-the-art algorithms.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Zhenyu Wang, Chenchen Wang, Jinmao Wei, Jian Liu
Summary: In this paper, a two-stage method is proposed to learn dynamic similarity matrix P and feature weighting matrix W for feature selection, aiming to capture reliable class correlation and select target features. Experiments on data with noisy features and various types of public data demonstrate the attractiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ruohong Huan, Ziwei Zhan, Luoqi Ge, Kaikai Chi, Peng Chen, Ronghua Liang
Summary: A hybrid CNN and BLSTM network is proposed for human complex activity recognition with multi-feature fusion. Experimental results show that the method outperforms traditional machine learning algorithms and state-of-the-art deep learning algorithms in complex activity recognition.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Agriculture, Multidisciplinary
Shizhuang Weng, Kaixuan Han, Zhaojie Chu, Gongqin Zhu, Cunchuan Liu, Zede Zhu, Zixi Zhang, Ling Zheng, Linsheng Huang
Summary: This study proposed a method for identifying the degree of FHB infection in wheat kernels using HSI and deep learning networks, achieving optimal classification accuracy with RACNN and RIs at different wavelengths. The method can efficiently extract distinctive features of different kernel classes and enable rapid, accurate and massive analysis of FHB infection degree in wheat kernels.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Endocrinology & Metabolism
Marilia Trindade, Jessica Castro de Vasconcelos, Gabriel Ayub, Alex Treiger Grupenmacher, Delma Regina Gomes Huarachi, Marina Viturino, Maria Lucia Correa-Giannella, Yeelen Ballesteros Atala, Denise Engelbrecht Zantut-Wittmann, Maria Candida Parisi, Monica Alves
Summary: This study aimed to evaluate ocular disease and its associations with peripheral neuropathy (PN) or cardiac autonomic neuropathy (CAN) in type 2 diabetes (T2D) and Charcot arthropathy (CA) patients. The results showed significant differences in various ocular variables between T2D patients, controls, and patients with both T2D and CA, highlighting the potential impact of CA on ocular findings and the association of DED with PN and CAN.
FRONTIERS IN ENDOCRINOLOGY
(2021)
Article
Ophthalmology
Muhammad Hassan, Mohammad Ali Sadiq, Maria Soledad Ormaechea, Gunay Uludag, Muhammad Sohail Halim, Rubbia Afridi, Diana Do, Yasir Jamal Sepah, Quan Dong Nguyen
Summary: Intravenous tocilizumab showed efficacy in improving or maintaining stability in patients with non-infectious uveitis when assessed using a composite endpoint scoring system.
BRITISH JOURNAL OF OPHTHALMOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Md Golam Rabiul Alam, Abde Musavvir Khan, Myesha Farid Shejuty, Syed Ibna Zubayear, Md Nafis Shariar, Meteb Altaf, Mohammad Mehedi Hassan, Salman A. AlQahtani, Ahmed Alsanad
Summary: This paper proposes an automated Ejection Fraction estimation system from 2D echocardiography images using deep semantic segmentation neural networks. Two parallel pipelines of deep semantic segmentation neural network models have been proposed for efficient left ventricle segmentation, and three different neural networks, UNet, ResUNet, and Deep ResUNet, have been implemented. The most accurate model achieved high Dice scores for left ventricle segmentation in both systolic and diastolic states. The proposed system can remove the eyeball estimation practice and reduce inter-observer variability.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Muhammad Umair Hassan, Dongmei Niu, Mingxuan Zhang, Xiuyang Zhao
Summary: This research proposes a novel asymmetric learning-based generative adversarial network (AGAN) for image retrieval, integrating feature learning with hashing and introducing three loss functions that significantly improve retrieval performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Ship Peng Xu, Ke Wang, Md Rafiul Hassan, Mohammad Mehedi Hassan, Chien-Ming Chen
Summary: This article proposes and analyzes a group of adversarial backdoor attack methods on neural-architecture-search (NAS) enabled edge AI systems in industrial Internet of Things (IIoT) domain. The article discusses the vulnerabilities of NAS-enabled edge AI models and provides effective strategies for attacking and defending against backdoor attacks. It also presents experimental evidence to support the findings.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Shehzad Ashraf Chaudhry, Khalid Yahya, Sahil Garg, Georges Kaddoum, Mohammad Mehedi Hassan, Yousaf Bin Zikria
Summary: The communication between smart meters (SMs) and neighborhood area network (NAN) gateways is essential for managing energy consumption. Existing schemes lack security and/or efficiency, calling for a more efficient and secure authentication scheme for smart grid infrastructure. This article proposes a privacy-preserving and lightweight authentication scheme (LAS-SG) for smart grids using elliptic curve cryptography, which has been proven to be secure under the standard model. Real-time experiments demonstrate the efficiency of LAS-SG, completing authentication in 20.331 ms with only two messages and 192 B exchanged. Due to its efficiency and security, LAS-SG is more suitable for smart grid environments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Geetanjali Rathee, Sahil Garg, Georges Kaddoum, Bong Jun Choi, Mohammad Mehedi Hassan, Salman A. AlQahtani
Summary: This article proposes a secure, reliable, and trusted decision-making scheme for collaborative AIoT using multiattribute methods. It uses backpropagation and Bayesian's rule to ensure fast and accurate decisions, and agent-based modeling and population-based modeling trust schemes to compute the legitimacy of the communicating model.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Chemistry, Analytical
Diego Avellaneda, Diego Mendez, Giancarlo Fortino
Summary: Positioning systems are important in many different sectors, but traditional systems like GPS are not accurate or scalable for indoor positioning. Fingerprinting is an alternative solution that uses RF signals to recognize location characteristics. This project uses a machine learning approach to classify RSSI information from scanning stations. The implementation uses TinyML, a growing technological paradigm for ML on resource-constrained embedded devices. The deployed system achieves a classification accuracy of 88%, which can be increased to 94% with post-processing.
Review
Chemistry, Analytical
Roohallah Alizadehsani, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H. M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny, Abbas Khosravi, U. Rajendra Acharya, Saeid Nahavandi, Giancarlo Fortino
Summary: Continuous advancements in technologies like the internet of things and big data analysis have enabled information sharing and smart decision-making using everyday devices. Swarm intelligence algorithms facilitate constructive interaction among individuals regardless of their intelligence level to address complex nonlinear problems. This paper examines the application of swarm intelligence algorithms in the internet of medical things, with a focus on wearable devices in healthcare. It reviews existing works on utilizing swarm intelligence in tackling IoMT problems such as disease prediction, data encryption, and resource allocation. The paper concludes with research perspectives and future trends.
Article
Chemistry, Analytical
Alaa Menshawi, Mohammad Mehedi Hassan, Nasser Allheeib, Giancarlo Fortino
Summary: A generic framework has been developed for heart problem diagnosis using a hybrid of machine learning and deep learning techniques. The framework utilizes a novel voting technique based on the prediction probabilities of multiple models to eliminate bias. Experimental results show that the framework outperforms single machine learning models, classical stacking techniques, and traditional voting techniques, achieving an accuracy of 95.6%.
Article
Mathematics
Senthil Kumar Jagatheesaperumal, Snegha Rajkumar, Joshinika Venkatesh Suresh, Abdu H. Gumaei, Noura Alhakbani, Md. Zia Uddin, Mohammad Mehedi Hassan
Summary: In order to promote a healthy lifestyle, individuals need to maintain a balanced diet and engage in customized workouts. A framework is presented in this study to assess an individual's health conditions, allowing people to conveniently evaluate their well-being without consulting a doctor. The framework includes a kit that measures various health indicators and requires minimal effort from nurses.
Article
Engineering, Multidisciplinary
Geetanjali Rathee, Sahil Garg, Georges Kaddoum, Mohammad Mehedi Hassan
Summary: Emotional aware intelligent healthcare mechanism is a smart technique used to overcome traditional communication issues in healthcare systems. This paper proposes a secure and transparent communication mechanism in intelligent emotional aware systems using AHP, blockchain system, and a mathematical model. The proposed mechanism outperforms existing models in terms of security and accuracy by approximately 89%.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Chenjing Tian, Haotong Cao, Sahil Garg, Georges Kaddoum, Mohammad Mehedi Hassan, Jun Xie
Summary: Improving the healthcare system is crucial for efficiency and cost reduction. The use of Internet of Medical Things (IoMT) has enabled various healthcare applications and services, including real-time monitoring and telemedicine. Network slicing technologies in 5G can be used to create customized communication networks for these use cases. This study proposes a solution for the virtual network embedding (VNE) problem in healthcare services using a two-sided matching theory-based virtual network embedding (MT-VNE) approach, which outperforms other baselines in terms of service acceptance and resource utilization.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Anika Tahsin, Palash Roy, Md. Abdur Razzaque, Md. Mamun-Or-Rashid, Mohammad Siraj, Salman A. AlQahtani, Md. Rafiul Hassan, Mohammad Mehedi Hassan
Summary: Energy Harvesting technology is crucial for green cellular networking by ensuring self-sustainability and eliminating environmental hazards. In a multi-operator cellular network, optimal energy cooperation among base stations (BSs) is challenging due to various factors such as cost, energy loss, future traffic load, and harvested energy. This work presents an optimal energy cooperation framework formulated as a multi-objective linear programming problem, considering the harvested energy and load of the BSs at future time slots.
Article
Computer Science, Information Systems
Syed Tauhidun Nabi, Md. Rashidul Islam, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Salman A. AlQahtani, Gianluca Aloi, Giancarlo Fortino
Summary: This research utilizes 6.2 million real network time series LTE data traffic and other associated parameters to build a traffic forecasting model using multivariate feature inputs and deep learning algorithms, which can forecast traffic at a granular eNodeB-level and provide eNodeB-wise forecasted PRB utilization.
Article
Health Care Sciences & Services
Md. Nazmul Islam, Md. Golam Rabiul Alam, Tasnim Sakib Apon, Md. Zia Uddin, Nasser Allheeib, Alaa Menshawi, Mohammad Mehedi Hassan
Summary: The coronavirus epidemic has spread worldwide causing significant health, financial, and emotional devastation. Therefore, developing a highly accurate AI-based auto-COVID detection system is crucial for healthcare services and the population.
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)