Article
Computer Science, Information Systems
Mohammed Salahat, Liaqat Ali, Taher M. Ghazal, Haitham M. Alzoubi
Summary: Knowing each other is crucial in a collaborative environment. Questionnaires can be used to assess personality traits for various applications, such as forensic departments, job interviews, and mental health diagnoses.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Artificial Intelligence
Ram Krishn Mishra, Siddhaling Urolagin, J. Angel Arul Jothi, Pramod Gaur
Summary: Image processing is a technique used to apply various operations to images to improve them or extract information, with facial recognition being a prominent application. This study examines the accuracy of categorizing human facial expressions using deep learning and transfer learning methods, proposing a deep hybrid learning approach that combines multiple deep learning models.
IMAGE AND VISION COMPUTING
(2022)
Article
Computer Science, Information Systems
Sk Mahmudul Hassan, Arnab Kumar Maji, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska
Summary: The study focuses on using deep convolutional neural network (CNN) models to identify and diagnose diseases in plants from their leaves. By achieving higher disease classification accuracy rates compared to traditional approaches, the implemented models show promise in efficient disease identification with less training time.
Article
Plant Sciences
Siyu Quan, Jiajia Wang, Zhenhong Jia, Mengge Yang, Qiqi Xu
Summary: The rapid development of image processing technology and computing power has led to deep learning becoming one of the main methods for plant disease identification. A novel lightweight convolutional neural network is proposed to address the issues of computational complexity and deployment. Skip connections and optimized feature fusion weight parameters are introduced to achieve higher classification accuracy. The model is pre-trained on plant classification tasks instead of using ImageNet, which enhances performance and robustness. Experimental results show that the proposed model outperforms existing plant disease diagnosis models in terms of accuracy, parameter count, and complexity.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Agronomy
S. K. Mahmudul Hassan, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska, Arnab Kumar Maji
Summary: Various plant diseases pose threats to agriculture, and automated disease identification is beneficial for timely control. Two methods were proposed to identify plant diseases, with the shallow VGG with Xgboost model showing superior performance in accuracy.
Article
Computer Science, Information Systems
Faiqa Adnan, Mazhar Javed Awan, Amena Mahmoud, Haitham Nobanee, Awais Yasin, Azlan Mohd Zain
Summary: Plant diseases can have a significant impact on agricultural productivity if not dealt with promptly. This paper proposes an efficient multi-class plant disease classification approach using pre-trained deep CNNs. The study finds that the EfficientNetB3-AADL model outperforms other models and conventional methods, achieving a remarkable accuracy of 98.71%. The research highlights the potential of this model in offering accurate, real-time disease diagnostics in agricultural systems.
Article
Chemistry, Multidisciplinary
Musarat Karim, Malik Muhammad Saad Missen, Muhammad Umer, Saima Sadiq, Abdullah Mohamed, Imran Ashraf
Summary: Citation frequency is widely used to measure the impact of research, but qualitative aspects should also be considered for fair evaluation. This study utilizes deep learning models and word embedding for in-text sentiment analysis and finds that the combination of deep learning CNN and fastText word embedding produces the best results.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Software Engineering
Arniel Labrada, Benjamin Bustos, Ivan Sipiran
Summary: Significant advances have been made in tasks like 3D model retrieval, classification, and segmentation. However, traditional 3D representations have limitations in cognitive processes due to their high redundancy and complexity. To address this, we propose a deep learning architecture that utilizes image views to represent 3D models, achieving high effectiveness in similarity assessment and improving training and inference times compared to state-of-the-art techniques.
Article
Computer Science, Information Systems
P. P. Fathimathul Rajeena, S. U. Aswathy, Mohamed A. Moustafa, Mona A. S. Ali
Summary: Farmers are not aware of the various corn diseases that affect agriculture, resulting in increasing crop failures due to lack of effective treatment or identification methods. Common corn diseases such as rust, blight, and northern leaf grey spot are prevalent. Accurate detection of diseases is not possible by visual observation alone, leading to improper pesticide use and potential harm to human health. Therefore, ensuring food security depends on accurate and automatic disease detection, which can be achieved by applying modern digital technologies.
Article
Biotechnology & Applied Microbiology
Guo-Sheng Han, Qi Li, Ying Li
Summary: This study proposes three new deep learning models for nucleosome positioning and finds a better performing integrated model. The models can effectively extract local features and base order features of DNA sequences, providing valuable practical applications.
Review
Agronomy
Jinzhu Lu, Lijuan Tan, Huanyu Jiang
Summary: This review article discusses the latest CNN networks relevant to plant leaf disease classification, summarizes the DL principles involved in plant disease classification, presents the main problems and corresponding solutions of CNN used for plant disease classification, and discusses the future development direction in plant disease classification.
Article
Engineering, Multidisciplinary
Zhongying Zhao, Hui Zhou, Liang Qi, Liang Chang, MengChu Zhou
Summary: Network embedding aims to map a network to a low-dimensional vector space while preserving its properties; attributed networks model relationships and attributes of real entities. The proposed inductive embedding model, using a convolutional neural network and semi-supervised learning mechanism, learns robust representations for partially-unseen attributed networks.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Agronomy
Gianni Fenu, Francesca Maridina Malloci
Summary: This study investigates the factors influencing the classification of plant diseases by analyzing the same plant and disease under different conditions. The experiments show that model performance significantly decreases when using representative datasets, and the features learned from the network may not always belong to the leaf lesion.
Article
Agronomy
K. C. Kamal, Zhendong Yin, Dasen Li, Zhilu Wu
Summary: The study combined segmentation, background subtraction, and convolutional neural networks to improve accuracy on datasets with images containing clean and cluttered backgrounds. Experimental results suggest that image sets with clean backgrounds tend to start training with higher accuracy and converge faster.
Article
Agriculture, Multidisciplinary
Dongfang Wang, Jun Wang, Wenrui Li, Ping Guan
Summary: This paper discusses the importance of detecting plant diseases and the application of convolutional neural networks in crop and disease recognition, proposing a new method that separates crop and disease identification and demonstrating its effectiveness. The results show high accuracy in crop and disease identification in controlled laboratory environments, and decent accuracy in real-world environments as well.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Hongfei Wang, Yanyan Shen, Shuqiang Wang, Tengfei Xiao, Liming Deng, Xiangyu Wang, Xinyan Zhao
Article
Computer Science, Artificial Intelligence
Liming Deng, Wenjing Shen, Hongfei Wang, Shuqiang Wang
Summary: This paper introduces a novel empirical model for predicting the remaining useful life of lithium-ion batteries by modeling both global and local degradation processes. The model outperforms state-of-the-art methods in capturing degradation and regeneration phenomena.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Rulan Wang, Zhuo Wang, Hongfei Wang, Yuxuan Pang, Tzong-Yi Lee
SCIENTIFIC REPORTS
(2020)
Article
Medicine, General & Internal
Hongfei Wang, Teng Zhang, Changmeng Zhang, Liangyu Shi, Samuel Yan-Lik Ng, Ho-Cheong Yan, Karen Ching-Man Yeung, Janus Siu-Him Wong, Kenneth Man-Chee Cheung, Graham Ka-Hon Shea
Summary: This study developed a machine learning-based prediction model to accurately predict the risk of progression in adolescent idiopathic scoliosis (AIS) curves. By integrating clinical data, X-rays, and hand X-rays, the model can predict the risk of curve progression at the first clinic visit.
Article
Medicine, General & Internal
Hongfei Wang, Teng Zhang, Kenneth Man-Chee Cheung, Graham Ka-Hon Shea
Summary: This study successfully utilized deep learning models and 3D reconstruction techniques to predict curve progression risk in AIS, providing a new approach for personalized treatment strategies and clinical decision-making.
Proceedings Paper
Computer Science, Artificial Intelligence
Shuqiang Wang, Wei Liang, Hongfei Wang, Zhuo Chen, Yiqian Lu
2019 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS AND TECHNOLOGIES (AIAAT 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Shuqiang Wang, Hongfei Wang, Yanyan Shen, Xiangyu Wang
2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
(2018)