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Deep Learning Based Models for Paddy Disease Identification and classification: A Systematic Survey

IEEE ACCESS 2024年 12卷 100862-100891页

作者： Tasfe, Mahrin Nivrito, Akm Al Machot, Fadi Ullah, Mohib Ullah, Habib Norwegian Univ Life Sci NMBU Fac Sci & Technol REALTEK N-1433 As Norway Norwegian Univ Sci & Technol NTNU Dept Comp Sci IDI Intelligent Syst & Analyt ISA Res Grp N-2815 Gjovik Norway

Automated early detection and classification of paddy diseases help in applying treatment efficiently according to the detected diseases. Early detection also minimises the usage of chemical substances and pesticides and hinders the spread of the disease to healthy crops. On a broader scale, it aids in halting the global spread of diseases. Thus, it ultimately promotes healthier rice crops and increased yield. In this survey paper, we present a thorough exploration of deep learning (DL) models for the classification of paddy diseases. Our paper delves into the motivation behind this research study, reveals different paddy diseases and their associated symptoms, and unravels various deep-learning models employed for disease detection. We have also discussed strategies used by researchers for improving the performance of DL models, along with adaptations tailored for application-specific contexts. Additionally, we illustrate relevant research findings, explore datasets utilised in this domain, and analyse approaches for data augmentation. Through an exhaustive investigation, we emphasise existing research gaps, challenges, and open issues, concluding in a discussion on avenues for future exploration.

关键词： Plant diseases Lesions Deep learning Surveys Seeds (agriculture) Manuals Smart agriculture Farming classification algorithms Smart farming precision agriculture paddy disease detection paddy disease classification paddy disease segmentation deep learning

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classification of Autism Spectrum Disorder using Supervised Machine Learning algorithms: A Review

Classification of Autism Spectrum Disorder using Supervised ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P Yugander M Jagannath School of Electronics Engineering Vellore Institute of Technology Chennai India Department of ECE Kakatiya Institute of Technology and Science Warangal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects the cognitive, speech, and social communication of individuals. Globally, around 2% of the population suffers from ASD. The problems associated with ASD start in childhood and continue through adulthood. It can be identified using several methods, such as Autism Diagnostic Observation Schedule (ADOS), Machine learning, deep learning, and computer-aided design (CAD) tools. Over the past two decades, CAD tools have become more prevalent in diagnosing diseases. The CAD tools identify abnormalities in autistic individuals using machine learning algorithms and magnetic resonance images. This paper aims to present various ASD classification algorithms and abnormalities of ASD controls. Articles are searched in various databases such as IEEE Explore, Springer, ACM Digital Library, PubMed, and Science Direct. A total of 22 complete articles from 2010 to 2024 are included in the review. Various supervised ML algorithms like Logistic regression (LR), Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), and Support vector machine (SVM) are investigated. This study revealed numerous structural and functional abnormalities of ASD controls.

关键词： Support vector machines Autism Logistic regression Machine learning algorithms Design automation Reviews Nearest neighbor methods classification algorithms Decision trees Random forests

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Takagi-Sugeno-Kang Fuzzy Systems for High-Dimensional Multilabel classification

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第6期32卷 3790-3804页

作者： Bian, Ziwei Chang, Qin Wang, Jian Pedrycz, Witold Pal, Nikhil R. China Univ Petr East China Coll Sci Qingdao 266580 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Macau Univ Sci & Technol Macau Inst Syst Engn Taipa 999078 Macau Peoples R China Istinye Univ Res Ctr Performance & Prod Anal TR-34396 Istanbul Turkiye Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Multilabel classification (MLC) refers to associating each instance with multiple labels simultaneously. MLC has gained much importance due to its ability to better reflect the complexity of the real world classification problems. Fuzzy system (FS) has excellent nonlinear modeling capability and strong interpretability, which makes it a promising model for complex MLC problems. However, it is widely known that FS suffers from the "curse of dimensionality." Here, an adaptive membership function (MF) along with its generalized version is proposed to address high-dimensional problems. These MFs can effectively overcome "numeric underflow" in FS while preserving interpretability as much as possible. On this basis, a novel fuzzy rule based MLC framework called multilabel high-dimensional Takagi-Sugeno-Kang fuzzy system (ML-HDTSK FS) is proposed. This model can handle data with over ten thousand dimensionality. In addition, ML-HDTSK FS uses a decomposed label correlation learning strategy to efficiently capture both high and low levels of relationship between labels, and adopts a group L21 penalty to realize the learning of label-specific features. Combining these two new multilabel learning strategies and the novel adaptive MF, ML-HDTSK FS becomes a more powerful tool for various MLC problems. The effectiveness of ML-HDTSK FS is demonstrated on seventeen benchmark multilabel datasets, and its performance is compared with eleven MLC algorithms. The experimental results confirm the validity of the proposed ML-HDTSK FS, and demonstrate the superiority of it in dealing with MLC problems, especially for high dimensional ones.

关键词： Correlation Fuzzy systems Representation learning Firing Takagi-Sugeno model classification algorithms Adaptation models High-dimensional data label correlation lear ning label-specific feature learning multilabel classification (MLC) Takagi-Sugeno-Kang fuzzy systems

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A Novel Deep Learning Framework With Meta-Heuristic Feature Selection for Enhanced Remote Sensing Image classification

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IEEE ACCESS 2024年 12卷 91974-91998页

作者： Ahmed, Bilal Akram, Tallha Rameez Naqvi, Syed Alsuhaibani, Anas Altherwy, Youssef N. Masud, Usman COMSATS Univ Islamabad Dept Elect & Comp Engn Wah Campus Rawalpindi 47040 Pakistan Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Informat Syst Al Kharj 11942 Saudi Arabia Univ Kassel Dept Elect Commun Engn D-34127 Kassel Germany

We propose a novel deep learning architecture, called XcelNet17, for image classification in remote sensing. Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Additionally, we present BA-ABC, a new hybrid feature selection algorithm that inherits the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm. Together these contributions significantly enhance the performance and accuracy of remote sensing image classification tasks. The proposed framework is thoroughly trained and verified using five benchmark datasets typically used for remote sensing image classification, namely AID, RSSCN7, SIRI-WHU, UC Merced, and WHU RS-19. Our simulation results suggest that in terms of classification accuracy, XcelNet17 outperforms most of the well established networks including AlexNet, VGG16, VGG19, ResNet50, and DarkNet19 by obtaining accuracy values in the range of 94.6% and 99.9%. Furthermore, the proposed features selection method, when integrated with XcelNet17, yields much improved classification accuracy in comparison to various benchmarks including WOA, GWO, BA, ABC, and ACO algorithms. For example, an 8% superior performance on WHU-RS19 dataset has been observed. The attained results are further validated by an in-depth statistical analysis.

关键词： Image classification classification algorithms Feature extraction Accuracy Remote sensing Convolutional neural networks Benchmark testing Bees algorithm Artificial bee colony bat algorithm bio-inspired feature selection CNN architecture image classification remote sensing

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Feature Set to sEMG classification Obtained With Fisher Score

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IEEE ACCESS 2024年 12卷 13962-13970页

作者： Toledo-Perez, Diana C. Aviles, Marcos Gomez-Loenzo, Roberto A. Rodriguez-Resendiz, Juvenal Univ Autonoma Queretaro Fac Ingn Queretaro 76010 Mexico

The best way to represent EMG signals for classification is a topic that has been widely studied due to the need to improve precision when identifying the type of movement being performed. However, by increasing the number of features when forming a matrix that represents the signals, the processing time increases since it not only involves calculating the features that are extracted from the signal but also the time that the classifier takes to answer. The central purpose of this research is to develop and validate a methodology that uses the Fisher Score to select a set of features in the classification of sEMG signals. This selected set is descriptive enough to achieve high levels of accuracy in detecting EMG signal patterns across multiple subjects. The analysis shows that using a variant of MAV, SSC, WAMP, RMS, and the maximum value together with the Shannon entropy and zero crossings of the Wavelet transform has an accuracy greater than 99%. Finally, a group of features is proposed to classify EMG signals that yield an accuracy greater than 98% and do not require more than 15 ms of processing time.

关键词： classification algorithms Feature extraction Muscles Electromyography Time-domain analysis Electrodes Databases SVM Fisher score feature selection sEMG pattern recognition

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Multi-Label classification With High-Rank and High-Order Label Correlations

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 4076-4088页

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao Southeast Univ Chien Shiung Wu Coll Nanjing 210096 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Southeast Univ Key Lab New Generat Artificial Intelligence Techno Minist Educ Nanjing 210096 Peoples R China Caritas Inst Higher Educ Sch Comp & Informat Sci Hong Kong Peoples R China Southeast Univ Sch Comp Sci & Engn Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 210096 Peoples R China Shenzhen Univ Sch Math Sci Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen 518060 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China China Elect Technol Grp Res Inst 52 Beijing 100846 Peoples R China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically.

关键词： Correlation Matrix decomposition Task analysis Semantics classification algorithms Silicon Machine learning High-rank matrix approximation high-order label correlations multi-label classification

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Lesion classification of Coronary Artery CTA Images Based on CBAM and Transfer Learning

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Jin, Yongze Ye, Xin Feng, Nan Wang, Zirong Hei, Xinhong Liu, Junqi Mu, Lingxia Li, Yankai Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Hangzhou Normal Univ Affiliated Hosp Dept Med Imaging Radiol Hangzhou 310011 Zhejiang Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China

classification of coronary artery stenosis is essential in assisting physicians in diagnosing cardiovascular diseases. However, due to the complexity of medical diagnosis and the confidentiality of medical images, it is difficult to obtain many coronary artery stenosis image samples for scientific research in general. In addition, the degree, location, and morphology of coronary artery stenosis in different patients, as well as the noise in computed tomography angiography (CTA) images, make it challenging to extract typing features effectively. To address the above problems, first, a joint segmentation method is proposed based on maximum between-class variance and region growing to extract key regions from CTA images to facilitate further feature extraction. Then, a coronary artery stenosis classification model based on convolutional block attention module (CBAM) and transfer learning is constructed, which can effectively improve the model training effect under the insufficient samples. Finally, a dataset of CTA images from actual patients is applied for experimental verification. The experimental results show that the accuracy (Acc) of the proposed method is up to 98.99%, which is greatly improved compared with several machine learning algorithms and neural network methods. It can be concluded that the accuracy of coronary stenosis classification can be considerably improved, and a reasonable scientific basis is provided for clinical medical diagnosis.

关键词： Arteries Image segmentation Transfer learning Medical diagnostic imaging classification algorithms Training Feature extraction Attention mechanism classification of stenosis coronary artery ResNet18 transfer learning

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Indian Traffic Sign Detection and classification Through a Unified Framework

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 14866-14875页

作者： Uikey, Rishabh Lone, Haroon R. Agarwal, Akshay IISER Bhopal Dept Elect & Comp Sci Engn Bhopal 462066 India IISER Bhopal Dept Data Sci & Engn Bhopal 462066 India

Traffic sign boards are vital in facilitating smart transportation systems. More than 90% of accidents happen due to drivers' inattentiveness over these boards. Hence, relaying traffic sign board information automatically to drivers becomes crucial to avoid such accidents. While numerous traffic sign detection and classification systems exist, it is important to note that these automated systems have not been adequately assessed within the context of Indian settings. The task of traffic sign detection and classification presents unique challenges in the Indian context due to the presence of multiple variations of signboards for a single action (e.g., left turn and right turn). This paper proposes a novel deep neural network architecture for detecting and classifying traffic signboards simultaneously. In the proposed architecture, several state-of-the-art convolutional neural networks such as AlexNet, VGG-19, ResNet-50, and EfficientNet v2 are used as a backbone to solve the task of bounding box regression and sign classification. Along with the proposed method, we collected the Indian traffic signs and information boards dataset. The collected dataset consists of 4257 raw images without any augmentation. We comprehensively evaluated the proposed algorithm on the collected dataset, resulting in the detection and classification accuracies of 85.5 % and 98.5 %, respectively. The proposed algorithm has also been evaluated on the existing traffic sign recognition dataset, and a comparison with state-of-the-art algorithms is done for both traffic sign detection and classification, demonstrating the effectiveness of the proposed multi-task network.

关键词： Cameras Image color analysis Feature extraction classification algorithms Videos Vehicles Shape Indian traffic sign boards object detection traffic sign classification

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Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases classification

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IEEE ACCESS 2024年 12卷 36622-36635页

作者： Altabaji, Wassem I. A. E. Umair, Muhammad Tan, Wooi-Haw Foo, Yee-Loo Ooi, Chee-Pun Multimedia Univ Fac Engn Cyberjaya 63100 Selangor Malaysia

Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

关键词： Plant diseases Detection algorithms Deep learning Convolutional neural networks Agriculture classification algorithms Crop yield Transfer learning Hyperparameter optimization convolutional neural networks transfer learning image classification

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A BCI System for Imagined Speech classification Based on Optimization Theory

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第4期70卷 6679-6690页

作者： Zheng, Xiao-Ben Ling, Bingo Wing-Kuen Guangdong Univ Technol Sch Informat Engn Guangzho 510006 Peoples R China

Electroencephalograms (EEGs) are used for establishing a connection between the human brain and the outside environment, so they are widely used in the brain computer interface (BCI). Nowadays, the imagined speech (IS) is a highly promising paradigm of the BCI. It can be used for controlling the external devices directly. However, the features for performing the IS are unknown. Hence, the numerous features are extracted. As a result, the dimension of the feature vectors is extremely large. To reduce the required computation, the clustering is required to be performed in the low dimensional space. Under this circumstance, the transform matrix affects both the dimensional reduction part and the clustering part. In fact, finding the transform matrix and the clustering centers under this scenario is challenging. To tackle this difficulty, this paper provides a modified joint principal component analysis (PCA) and k means algorithm for performing the IS. Here, the interclass separation among the feature vectors is also taken into an account of the problem formulation. In particular, the problem is formulated as a nonconvex constrained optimization problem. The total two norm reconstruction error of the feature vectors as well as the total two norm differences between the feature vectors and the clustering centers in the low dimensional space and the total two norm differences among the clustering centers are minimized subject to the orthogonality of the transform matrix. The numerical computer simulations are conducted based on the multi-class IS classification database. The obtained results show that our proposed method outperforms the various states of the art methods in terms of the clustering accuracy and the average required execution time. Overall, using the BCI system for performing the imagined speech classification plays an important role in the consumer electronics area.

关键词： Clustering algorithms Optimization Consumer electronics Transforms Principal component analysis Machine learning algorithms classification algorithms Vectors Electroencephalography Dimensionality reduction Semi-supervised imagined speech k means clustering in the low dimensional space principal component analysis linear discriminant analysis nonconvex constrained optimization problem

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