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Article
Green & Sustainable Science & Technology
Normaisharah Mamat et al.
Summary: An accurate image retrieval technique is needed due to the increasing number of images, and it is important to implement fast, simple, and automatic image annotation techniques. This study focuses on the agriculture field and uses automatic image annotation to classify the ripeness of oil palm fruit and recognize different types of fruits. The proposed approach improves fruit classification methods and enhances production for farmers. Simple and effective models using deep learning with You Only Look Once (YOLO) versions are developed and trained with a dataset of 100 oil palm fruit images and 400 images of various fruits in RGB format. The performance and accuracy of the models in annotating 3500 fruit images are evaluated, achieving excellent results with an mAP of 98.7% for oil palm fruit and 99.5% for different types of fruit.
Article
Green & Sustainable Science & Technology
Tao He et al.
Summary: A forest tree species dataset (FTSD) based on Sentinel-2 images was built, containing 8,815 images of nine kinds of forest tree species in Qingyuan County. PCA and NDVI were combined to improve the classification accuracy, and ResNet outperformed other deep learning algorithms in remote sensing classification. It was found that a 50-layer ResNet performed better than 34 and 101 layers in the FTSD.
Article
Green & Sustainable Science & Technology
Yonis Gulzar
Summary: Due to the rapid emergence and evolution of AI applications, the utilization of smart imaging devices has significantly increased. Researchers have used deep learning models, such as CNN, for image classification without the need for handcrafted features. In this study, a dataset of 26,149 images of 40 different types of fruits was used to develop TL-MobileNetV2, a modified version of MobileNetV2, using transfer learning and a customized head. TL-MobileNetV2 achieved an accuracy of 99%, outperforming other models, and demonstrated the effectiveness of transfer learning and dropout technique in improving results.
Article
Green & Sustainable Science & Technology
Sonam Aggarwal et al.
Summary: Predicting subcellular protein localization has become popular due to its utility in understanding disease mechanisms and developing drugs. Automated microscopic imaging technology has led to increased interest in using bio-images for protein localization. The Human Protein Atlas project aims to map the human proteome, but fewer techniques exist for predicting protein localization, particularly multi-label classification. Deep learning offers potential for automatic labeling, and this research proposes an ensemble technique to improve existing convolutional neural networks and pretrained models for more reliable and robust classification.
Article
Chemistry, Multidisciplinary
Shuqiu Tan et al.
Summary: This paper proposes a CASVM model that introduces coordinate attention and support vector machines in image feature extraction to improve the performance and robustness of the model. Experimental results show that CASVM achieves higher accuracy in image classification compared to traditional methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Erhui Xi
Summary: This paper proposes a hybrid model based on convolutional neural network (CNN) and random forest (RF) for image classification and recognition. Experiments show that the hybrid model achieves higher classification accuracy and improved generalization ability compared to RF.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Chemistry, Multidisciplinary
Shreya Biswas et al.
Summary: The novel SARS-CoV-2 virus has had a significant impact on the world, with over 3.9 million deaths reported as of June 2021. While vaccinations have begun, challenges remain in developing countries. Utilizing computer-assisted identification systems and CT imaging can aid in the accurate diagnosis of COVID-19.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Monika Bansal et al.
Summary: Research has shown that the performance of image classification can be improved by combining deep learning and traditional handcrafted feature extraction methods, and a combined approach using deep learning features and traditional handcrafted features can achieve better classification results.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Proceedings Paper
Automation & Control Systems
Jalila Filali et al.
Summary: The study compared the performance of Bag-of-Visual Words (BoVW) model and HMAX model for image classification on large datasets. Features obtained from SIFT descriptors using BoVW model were compared to HMAX features, with results showing that HMAX model outperformed BoVW model in classification accuracy.
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021)
(2021)
Review
Computer Science, Theory & Methods
Laith Alzubaidi et al.
Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.
JOURNAL OF BIG DATA
(2021)
Article
Environmental Sciences
Mohd Anul Haq et al.
Summary: The application of deep learning algorithms in UAV-based remote sensing image classification shows high accuracy and potential in forest feature recognition and coverage assessment. The combination of UAV remote sensing and deep learning techniques plays an important role in planning and managing forest areas.
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Jalila Filali et al.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2020)
Proceedings Paper
Computer Science, Software Engineering
Jalila Filali et al.
PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Jie Lei et al.
2017 14TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Francois Chollet
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Psychology
CA Collin et al.
PERCEPTION & PSYCHOPHYSICS
(2005)
