Article
Computer Science, Artificial Intelligence
R. Bhuvaneswari, S. Ganesh Vaidyanathan
Summary: This study proposes a method using a Mixture of Ensemble Classifiers to classify and grade diabetic retinopathy images, achieving accuracies of 95.8% and 96.2% respectively. Automatic classification of diabetic retinopathy using convolutional neural networks and hierarchical features is challenging, but effective in this research.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Kazuki Endo, Masayuki Tanaka, Masatoshi Okutomi
Summary: A convolutional neural network is proposed in this paper that can automatically infer ensemble weights in classifying degraded images of various levels of degradation. It also reveals how the image quality of training data for a classification network affects the classification performance of degraded images.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
K. Shankar, Eswaran Perumal
Summary: The COVID-19 pandemic is escalating rapidly with limited access to rapid test kits, prompting researchers to explore new methods using AI techniques and radiological imaging for more accurate disease diagnosis and classification. The proposed FM-HCF-DLF model demonstrated superior performance in experimental validation, with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2%, and kappa value of 93.5%.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Hongmin Gao, Yiyan Zhang, Zhonghao Chen, Chenming Li
Summary: A novel multiscale dual-branch feature fusion and attention network was proposed to address feature extraction singleness and rough feature fusion process in existing methods, achieving improved classification performance by extracting spatial-spectral features at a granular level and utilizing a dual-branch feature fusion interactive module. Additionally, the introduction of a shuffle attention mechanism further enhanced the adaptive weighting of spatial and spectral features, leading to improved classification performance over state-of-the-art methods on benchmark datasets.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Jing Liu, Zhe Yang, Yi Liu, Caihong Mu
Summary: A pixel frequency spectrum feature is introduced to CNNs to improve the classification accuracy of HRSIs through deep fusion feature extraction. Experimental results demonstrate better recognition results with more discriminant information using multi-branch CNNs.
Article
Computer Science, Information Systems
Meiling Feng, Jingyi Wang, Kai Wen, Jing Sun
Summary: The article introduces a new intelligent classification model for diabetic retinopathy by combining convolutional neural networks and graph neural networks, which successfully improves the accuracy of diabetic retinopathy diagnosis.
Article
Computer Science, Information Systems
Saeed Iqbal, Adnan N. Qureshi, Ghulam Mustafa
Summary: Skin cancer (melanoma), with its high aggressiveness and increased prevalence due to ultraviolet radiation, requires timely detection and management. This study proposes a hybrid approach using convolutional neural networks (CNN) and local binary patterns (LBP) to improve classification accuracy. The experimental results show promising performance in distinguishing different types of skin cancers, providing a valuable tool for research and clinical settings.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Engineering, Electrical & Electronic
Vineeta Das, Samarendra Dandapat, Prabin Kumar Bora
Summary: The proposed Deep Multi-scale Fusion Convolutional Neural Network (DMF-CNN) effectively encodes multi-scaled disease characteristics by using multiple CNNs with different receptive fields and fusing them, resulting in reliable classification for retinal diseases. The method achieves state-of-the-art performance on publicly available OCT databases and offers an impressive overall accuracy for diagnostic purposes.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Interdisciplinary Applications
Hyungu Kang, Seokho Kang
Summary: This study combines two different approaches to build base classifiers and uses a stacking ensemble to improve the accuracy of wafer map pattern classification.
COMPUTERS IN INDUSTRY
(2021)
Article
Computer Science, Artificial Intelligence
Javier Huertas-Tato, Alejandro Martin, Julian Fierrez, David Camacho
Summary: This paper proposes an ensemble method for accurate image classification, which combines automatically detected features and statistical indicators to achieve better performance. Testing on various datasets shows that including additional indicators and using an ensemble classification approach can improve performance.
INFORMATION FUSION
(2022)
Article
Environmental Sciences
Marjan Stoimchev, Dragi Kocev, Saso Dzeroski
Summary: Images are now being generated at an unprecedented rate, and remote sensing images have attracted considerable research attention in image classification. Recently, the task of assigning multiple semantic categories to an image, known as multi-label classification, has become increasingly complex. This work explores different strategies for model training using pre-trained convolutional neural network architectures and traditional tree ensemble methods for multi-label classification, and conducts extensive experimental analysis on publicly available remote sensing image datasets.
Article
Computer Science, Artificial Intelligence
Nicole D. Cilia, Tiziana D'Alessandro, Claudio De Stefano, Francesco Fontanella
Summary: Early diagnosis of neurodegenerative diseases is crucial for effective treatment, with handwriting being one of the first skills affected. Researchers proposed a method using color images and convolutional neural networks to extract features and improve the diagnosis. They also expanded the database by adding more complex drawing samples and conducted experiments comparing the results with standard feature methods.
MACHINE VISION AND APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Clopas Kwenda, Mandlenkosi Gwetu, Jean Vincent Fonou-Dombeu
Summary: In this study, a hybrid ontological bagging algorithm and an ensemble technique of convolutional neural network (CNN) models have been developed to improve forest image classification accuracy. The experimental results show that our proposed model outperformed other baseline classifiers in terms of accuracy and root-mean-square error.
APPLIED SCIENCES-BASEL
(2023)
Article
Geochemistry & Geophysics
Yu Liu, Zhengyang Zhao, Shanwen Zhang, Lei Huang
Summary: In this study, a novel multiregion scale-aware network is proposed to accurately extract buildings with varying scales and layouts in remote sensing images. The network utilizes a multiregion attention module to capture long-range context dependencies and a graph-based scale-aware structure to model and reason the interactions between different scale features. Extensive experiments demonstrate that the proposed method outperforms other state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Remote Sensing
Wangbin Li, Kaimin Sun, Hepeng Zhao, Wenzhuo Li, Jinjiang Wei, Song Gao
Summary: Building extraction from remote sensing imagery is a common task in surveying, mapping and geographic information systems. This study proposes a pyramid feature extraction method to address the challenges of automatic building extraction. The method constructs multi-scale representations of buildings and incorporates attention modules and feature alignment modules to improve the accuracy and integrity of the extraction results.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Engineering, Civil
Mohammad Mahdi Bejani, Mehdi Ghatee
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Computer Science, Information Systems
Nastaran Pardakhti, Hedieh Sajedi
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Hedieh Sajedi, Fatemeh Mohammadipanah, Ali Pashaei
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Environmental Sciences
Mohammad Ahmadlou, A'kif Al-Fugara, Abdel Rahman Al-Shabeeb, Aman Arora, Rida Al-Adamat, Quoc Bao Pham, Nadhir Al-Ansari, Nguyen Thi Thuy Linh, Hedieh Sajedi
Summary: Floods are one of the most destructive natural disasters globally, and researchers have been using data-driven techniques combined with remote sensing and geographic information systems for flood susceptibility mapping. A novel hybrid autoencoder-MLP model was proposed, which outperformed the traditional MLP model according to the AUROC criterion.
JOURNAL OF FLOOD RISK MANAGEMENT
(2021)
Review
Computer Science, Artificial Intelligence
Mohammad Mahdi Bejani, Mehdi Ghatee
Summary: This paper discusses the differences between shallow and deep neural networks in processing features, as well as the issue of overfitting. It provides a systematic review of overfitting control methods, categorizing them into passive, active, and semi-active subsets. Additionally, it highlights the adjustment of model complexity to data complexity, and the relationship between overfitting control, regularization, network compression, and network simplification.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Review
Biochemical Research Methods
Fatemeh Mohammadipanah, Hedieh Sajedi
Summary: The development of microbial databases based on blockchain technology is crucial for the realization of open science, enabling more effective data collection, management, and transparent data sharing. However, there are still challenges and constraints in practice that need further research and solutions.
BIOLOGICAL PROCEDURES ONLINE
(2021)
Article
Computer Science, Artificial Intelligence
Mohammad Mahdi Bejani, Mehdi Ghatee
Summary: The paper introduces a method of Least Auxiliary Loss-functions with Impact Growth Adaptation to address model selection and overfitting issues in Convolutional Neural Networks. Experimental results demonstrate its effectiveness across various datasets, particularly showing significant improvements in Visual Geometry Group networks.
Article
Health Policy & Services
Khadije Nadri, Leila Shahmoradi, Hedieh Sajedi, Ata Salehi
Summary: This study focused on identifying factors affecting self-care skills improvement in Cutaneous Leishmaniasis (CL) patients and designing a mobile-based app to enhance self-care, which can help control the disease and reduce complications.
HEALTH POLICY AND TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Shiva Rahimipour, Mehdi Ghatee, S. M. Hashemi, Ahmad Nickabadi
Summary: This paper aims to develop an activity-based travel demand model using cellular network data. The model incorporates probabilities and fuzzy theory to account for uncertainties in human behaviors and ambiguity in features affecting users' activities. Results show high accuracy in activity recognition and promising efficiency in converting activity plans to traffic volumes on transportation network links.
COMPUTER COMMUNICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlstrom
Summary: This paper focuses on driver identification in intelligent transportation systems and proposes a hybrid GAN-SGM approach for data augmentation, achieving excellent performance even with an increasing number of drivers.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Mohsen Ghassemi Parsa, Hadi Zare, Mehdi Ghatee
Summary: This paper introduces a novel unsupervised feature selection approach, which utilizes low-rank representation with dictionary learning and spectral analysis. By proposing a unified objective function and efficient algorithm, the method outperforms existing algorithms on various standard datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Yasaman Mashhadi Hashem Marandi, Hedieh Sajedi, Sepehr Pirasteh
Summary: This research introduces a method to transform shape into music and music into shape, which can also be applied in music cryptography. By defining mappings between musical notations and shapes, the conversion between shapes and music is achieved.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Nillofar Jazayeri, Hedieh Sajedi
Summary: This paper introduces the DNAVS algorithm for the general job-shop scheduling problem, utilizing parallelization in DNA computing. DNAVS reduces the time complexity of DNA computing by employing the VS algorithm and shows effectiveness on currently silicon-based computers.
EVOLUTIONARY INTELLIGENCE
(2021)