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Pepper bell leaf disease detection and classification using optimized convolutional neural network

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期82卷 12065-12080页

作者： Mustafa, Hassan Umer, Muhammad Hafeez, Umair Hameed, Ahmad Sohaib, Ahmed Ullah, Saleem Madni, Hamza Ahmad Khwaja Fareed Univ Engn & Informat Technol Dept Comp Engn Rahim Yar Khan Pakistan Islamia Univ Bahawalpur Dept Comp Sci Bahawalpur Pakistan Khwaja Fareed Univ Engn & Informat Technol Dept Comp Sci Rahim Yar Khan Pakistan

Agriculture production plays a significant role in the country's economy. Diseases are quite natural and common among plants. Identification of diseases in plants is necessary for averting losses in the yield of agricultural products. Manual monitoring of plants requires expertise, immense effort, and excessive time. Automatic detection will not only help in reducing time and effort but will also help in detecting disease at an early stage, as soon as it will start appearing on plant leaves. Recently, image processing in agriculture has attained a surge of interest by researchers. This study presents a five-layered CNN model for automatic detection of plant disease utilizing leaf images. In order to better train a CNN model, 20,000 augmented images are generated. Experimental results demonstrate that proposed optimized-CNN model can predict pepper bell plant leaf as healthy or bacterial with 99.99% accuracy. Robust results make the proposed optimized-CNN model a preliminary warning tool that can be applied as a disease identification system in a real cultivation environment.

关键词： Leaf disease Image classification Deep learning optimized convolutional neural network

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optimized Local Weber and Gradient Pattern-based medical image retrieval and optimized convolutional neural network-based classification

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期70卷 102971-102971页

作者： Mahesh, Dhupam Bhanu Murty, Gorti Satyanarayana Lakshmi, D. Rajya JNTUK CSE Kakinada India Aditya Inst Technol & Management CSE Tekkali India JNTUK UCEN CSE Kakinada India

This paper presents a novel image retrieval and classification model for medical images so-called as ContentBased Medical Image Retrieval. For the image retrieval, the main contribution of this paper is to develop a pattern descriptor termed as optimized Local Weber and Gradient Pattern. A hybrid algorithm with the integration of two well-known meta-heuristic algorithms like Barnacles Mating Optimization and Jaya Algorithm, namely Jaya-based Barnacle Mating Optimization, is used for improving the optimized Local Weber and Gradient Pattern-based image retrieval. Once the optimized Local Weber and Gradient Pattern features are determined for training and testing images, the retrieval of images is performed by the logarithmic similarity computation. Moreover, an improved deep learning model called optimized convolutional neural network is employed for performing the image classification. As an improvement, the activation function of convolutional neural network with sigmoid, tanh, Relu, and RRelu and the maximum epoch are optimized by the same Jayabased Barnacle Mating Optimization. Finally, extensive experiments are carried out over publicly available datasets, demonstrating that the proposed retrieval and classification models are better than the existing models.

关键词： Content-based image retrieval Medical data optimized Local Weber and Gradient Pattern Jaya-based barnacle mating optimization Logarithmic similarity computation optimized convolutional neural network

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Automated rain fall prediction enabled by optimized convolutional neural network-based feature formation with adaptive long short-term memory framework

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第11期34卷 e6868-e6868页

作者： Ananthajothi, K. Karthick, T. Amanullah, M. Misrimal Navajee Munoth Jain Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Data Sci & Business Syst Kattankulathur Tamil Nadu India Aalim Muhammed Salegh Coll Engn Dept Informat Technol Chennai Tamil Nadu India

The main concept of this article is to plan for the intelligent rainfall prediction using the combination of deep learning models. The dataset is gathered from the standard publically available dataset concerning the Tamil Nadu state. The collected data is given to the feature extraction, in which few features such as;"minimum value, maximum value, mean, median, standard deviation, kurtosis, entropy, skewness, variance, and zero cross" are extracted. Additionally, the extracted features are applied to the optimal feature formation, in which optimized convolutional neural network (O-CNN) is employed for the final feature formation. Here, the activation function, count of pooling layer, and count of hidden neurons are tuned with the intention of minimizing the correlation between the selected features. Once the optimal features are selected with less correlation, adaptive long short-term memory (A-LSTM) is adopted for the prediction model. Here, the enhancement is concentrated on minimizing the function concerning the error through the optimization of the hidden neurons of A-LSTM. The improvement of both the deep learning models O-CNN and A-LSTM is performed by the improved sun flower optimization (I-SFO). The research results reveal superior performance to existing techniques that offer novel thinking in rainfall prediction area with optimal rate of prediction.

关键词： adaptive long-short term memory automated rainfall prediction improved sun flower optimization optimized convolutional neural network

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An optimized convolutional neural network for Multi-Spectral Change Detection Technique

Journal of Uncertain Systems

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Journal of Uncertain Systems 2022年第3期15卷

作者： Ashok Kumar, L. Ebenezar Jebarani, M.R. School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai 600119 India

With the proliferation of multi-sensor remote sensing images, extraction of information has progressively concerned attention in recent years. It leads to great significant improvement in remote sensing areas for change detection techniques. Due to the advent of multi-spectral images in the change detection model, a change detection model named optimized convolutional neural network (CNN) framework is designed for monitoring land cover changes. This can assist to note the changes happening in the different periods. The proposed model is designed by integrating the normalization function and detection model by applying the deep learning (DL) algorithm. The actual experiment is carried out by taking the parameters accuracy and Kappa coefficient to show the effectiveness of the proposed model. The proposed experiment out performed well when compared to the existing mainstream techniques in multi-sensor images change detection. When compared with the existing DL techniques, the proposed design attained accuracy of 98.92% by possessing less loss function. © 2022 World Scientific Publishing Company.

关键词： Kappa coefficient loss function Multi-spectral optimized convolutional neural network remote sensing

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Secure prediction and assessment of sports injuries using deep learning based convolutional neural network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第3期12卷 3399-3410页

作者： Song, Hesheng Xiu-ying Han Montenegro-Marin, Carlos Enrique Krishnamoorthy, Sujatha Guizhou Normal Univ Dept Phys Educ Guiyang 550001 Guizhou Peoples R China Shandong Univ Financial & Econ Dept Phys Educ Jinan 250014 Shandong Peoples R China Univ Distrital Francisco Jose Caldas Fac Ingn Bogota Colombia Wenzhou Kean Univ Wenzhou Peoples R China

In recent days, many information and image evidence in the field of medicine is being designed and developed by the advancement of computer technology. Presently, sport medical data is an essential department for medical sector and it is responsible for assuring sports safety based on the recovery level after an injury due to sports activity. The approach to reliably interpretation and valuable data information using a vast number of medical sports data and events has become an important research path in the collection and analysis of medical data. This paper discusses the extraction, study, and lack of training and accuracy of complex algorithms for critical sporting medical data. This paper involves with an optimized convolutional neural network (OCNN) based on deep-learning model to ensure successful detection and risk assessments of sport-medicine diseases and adopts the Self-Adjustment Resizing algorithm (SAR) augmented by the self-coding method of the convolution (SCM). CNN model helps to evaluate sports medicine in multi-dimensional results and suggested OCNN classification constitutes two convolutional layers, two pool layers, a fully connected layer, and a SoftMax structure that can be used for the classification of sport-related medical data. The CNN facilitates multi-dimensional sports medicine data analysis and to conclude a cloud-based loop model to create an advanced medical data network for sports medicine. Experiments illustrate that this approach offers technical support and guide to deploying a specific cloud-based fusion system.

关键词： optimized convolutional neural network Self-Adjustment Resizing algorithm Self-coding method Cloud-based loop model

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Integrating three smart predictive models using a power-law committee machine for the prediction of compressive strength in masonry made of clay bricks and cement mortar

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STRUCTURES 2023年 55卷 951-964页

作者： Gholami, Mahsa Ranjbargol, Mahdi Yousefzadeh, Reza Ghorbani, Zhaleh Bu Ali Sina Univ Dept Civil Engn Hamadan Hamadan Iran Univ Sci & Culture Dept Civil Engn Tehran Iran Amirkabir Univ Technol Dept Petr Engn Tehran Iran Iranian Offshore Oil Co Reservoir Div Tehran Iran

Due to the importance of the compressive strength of masonry (CSM) prisms made of clay bricks and cement mortar in the design of masonry structures, it is crucial to develop a reliable method for accurate prediction. This paper presents an innovative approach for estimating CSM by integrating an optimized convolutional neural network (OCNN), extreme learning machine (ELM), and decision tree (DT) using a power-law committee machine (PLCM). The strategy consists of two steps: firstly, three regression approaches (OCNN, ELM, and DT) are developed to model CSM, and secondly, the results of these methods are combined through a PLCM. The convolutional neural network is enhanced using the cuckoo search (CS) algorithm to determine its weights and bias, thereby improving its ability to model CSM accurately. The CS algorithm is also employed in the PLCM structure to extract the optimal contribution of each individual model in the overall prediction. To statistically evaluate the effectiveness and efficiency of the developed models and compare them with previously established empirical correlations for estimating CSM values, various visualization methods and statistical parameters are utilized. The evaluation demonstrates that OCNN, ELM, and DT can estimate CSM with remarkable accuracy and further confirms that PLCM outperforms its individual elements as well as empirical methods. This study provides evidence of the significant improvement in the performance of PLCM in modeling CSM.

关键词： Compressive strength of masonry optimized convolutional neural network Extreme learning machine Decision tree Power-law committee machine Cuckoo search

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Cancer miRNA biomarkers classification using a new representation algorithm and evolutionary deep learning

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SOFT COMPUTING 2021年第4期25卷 3113-3129页

作者： Bagheri Khoulenjani, Niousha Saniee Abadeh, Mohammad Sarbazi-Azad, Saeed Jaddi, Najmeh Sadat Tarbiat Modares Univ Fac Elect & Comp Engn POB 14115-194 Tehran Iran

The diagnosis of cancer is presently undergoing a change of paradigm for the diagnostic panel using molecular biomarkers. MicroRNA (miRNA) is one of the most important genomic datasets presenting the genome sequences. Since several studies have shown the relationship between miRNAs and cancers, data mining and machine learning methods can be incorporated to extract a large amount of knowledge from cancer genomic datasets. However, previous research works on the identification of cancers from miRNAs have made it possible to diagnose cancer, and the accuracy of some classes is not quite satisfactory. Therefore, this research is aimed at promoting a super-class (meta-label) approach and deep learning in a three-phase method to diagnose cancers from miRNAs. The steps in the first phase of the proposed method, named Representation learning, are partitioning data into super-classes, meta-data creation and super-classes classification. This phase helps data to be split into some subsets to improve classification accuracy. In other words, the first phase groups labels based on the separability of classes into a meta-label, and then a multi-label learner is built to predict these meta-labels. In the second phase, a feature selection to reduce the dimensions of the problem is applied to each super-class to help to focus the attention of an induction algorithm in those features that are more important to predict the target concept. In the third phase of the proposed method, an evolutionary deep neural network for the classification of labels in each super-class is performed. The last two phases are done separately for each subset in which five super-classes and subsequently five deep neural networks are trained. The experimental results reveal that the proposed method achieved more efficient results than 19 recent machine learning methods. Despite the fact that evaluating the dataset which consists of 29 types of cancers provides a more complicated situation for the convolutio

关键词： MicroRNA Meta-label Super-class Intelligent reasoning Feature selection optimized convolutional neural network

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An improved deep convolutionary neural network for bone marrow cancer detection using image processing

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Informatics in Medicine Unlocked 2023年 38卷

作者： Ramasamy, Manjula Devi Dhanaraj, Rajesh Kumar Pani, Subhendu Kumar Das, Rashmi Prava Movassagh, Ali Akbar Gheisari, Mehdi Liu, Yang Porkar, P. Banu, Sabitha Department of Computer Science and Engineering KPR Institute of Engineering and Technology India School of Computing Science and Engineering Galgotias University India Krupajal Engineering College Biju Patnaik University of Technology Odisha India Department of Computer Science and Engineering CV Raman Global University Bhubaneswar India Department of Biomedical Engineering School of Medicine Tehran University of medical sciences Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Young researchers and elite club Islamic Azad University Parand Iran

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an optimized convolutional neural network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models. © 2023

关键词： Bone marrow CAT Algorithm Leukaemia Multiple myeloma optimized convolutional neural network

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Local Binary Pattern Induced optimized CNN for Brain Tumor Diagnosis from MRI Data 10th

Local Binary Pattern Induced Optimized CNN for Brain Tumor D...

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10th Biennial International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Nath, Pranay Mondal, Surajit Ghosh, Lidia Maulana Abul Kalam Azad Univ Technol Kolkata West Bengal India Univ Engn & Management Inst Engn & Management Kolkata West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Advancements in medical science have led to new approaches for preventing, diagnosing, and treating brain tumors, studied by researchers across different fields. The accurate identification of tumors in MRI scans can assist in disease identification, treatment evaluation, and radiation-based therapies. Currently, humans manually perform this task, but research has explored the integration of computer processing in MRI analysis. While MRIs provide a comprehensive view of the brain to identify tumors, they lack accuracy in pinpointing their location and size. To address this, an improved version of Local Binary Pattern (LBP) encoded optimized convolutional neural network is proposed to improve diagnostic accuracy. LBP captures texture information in a local neighborhood of each pixel, providing additional features to learn from and enhancing the accuracy of texture-based image classification. The model is evaluated on the Figshare dataset through multiple experiments.

关键词： optimized convolutional neural network Local Binary Pattern Brain tumor Figshare dataset MRI analysis

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Reversible Data Hiding based on optimized CNN predictor and Prediction Error Expansion with Lower Surround Background Complexity

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： Luo, Yuling Qiu, Yiqi Lu, Baoshan Qin, Sheng Fu, Qiang Zhang, Shunsheng Huang, Yiting Su, Yang Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Wireless Broadband Commun & Signal Proc Ke Guilin 541004 Peoples R China Swansea Univ Dept Comp Sci Swansea SA2 8PP Wales

convolutional neural network-based Predictors (CNNP) have emerged as a viable solution for enhancing global optimization and prediction capabilities in the field of Reversible Data Hiding (RDH). However, they often encounter limitations in data embedding space due to scarcity of zero-valued points in predicted images. To address these challenges, in this paper, we propose an optimized CNNP (OCNNP) technique that increases the zero-valued points in predicted images, significantly enhancing the embedding capacity. Additionally, we introduce a novel Lower Surround Background Complexity (LSBC)-based Prediction Error Expansion (PEE) method, which refines the sorting of prediction errors for data embedding, thereby reducing image distortion and improving overall embedding performance. Experimental results demonstrate that our approach markedly improves the Peak Signal-to-Noise Ratio (PSNR) across various embedding capacities compared to existing methods and shows high robustness against various noise types, establishing its superiority in enhancing both the capacity and quality of RDH in complex image distributions.

关键词： Reversible data hiding optimized convolutional neural network Prediction error expansion Lower surround background complexity

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