Convolutional Neural Network Approach Research Articles

Classification of tea leaf disease using convolutional neural network approach

Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.

BRAIN TUMOR DETECTION USING MACHINE LEARNING

A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre- trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence of brain tumors. Keywords—Brain tumor; machine learning; convolutional neural network; MRI images

The powerful lens galaxy cluster PLCK G287.0+32.9 (θE ∼ 43″)

Aims. We present a new high-precision strong-lensing model of PLCK G287.0+32.9, a massive lens galaxy cluster at z = 0.383, with the aim of obtaining an accurate estimation of its effective Einstein radius and total mass distribution. We also present a spectroscopic catalog containing accurate redshift measurements for close to 500 objects up to redshift z = 6, including multiply lensed sources and cluster member galaxies. Methods. We exploited high-quality spectroscopic data from the Multi Unit Spectroscopic Explorer (MUSE), covering a central 3 arcmin2 region of the cluster. We supplemented the spectroscopic catalog by including redshift measurements from VIsible MultiObject Spectrograph (VIMOS) and DEep Imaging Multi-Object Spectrograph (DEIMOS). We identified 129 spectroscopic cluster member galaxies with redshift values of 0.360 ≤ z ≤ 0.405, and mF160W ≤ 21. We complemented this galaxy cluster member sample with 24 photometric members identified with a convolutional neural network (CNN) approach. We also identified 114 multiple images from 28 background sources, of which 84 images from 16 sources are new and the remaining ones have already been identified in previous works. From these, we extracted “golden sample” of 47 secure multiple images and used them, together with the selected cluster member, to build and optimize several strong-lensing models with the software lenstool. Results. The best-fitting lens model shows a root mean square (RMS) separation value between the predicted and observed positions of the multiple images of 0.″75. Using its predictive power, we found three new multiple images and we confirm the configuration of three systems of multiple images that were not used for the optimization of the model. For a source at a redshift of zs = 2, we found a cluster with an Einstein radius of θE = 43.4″ ± 0.1″. This value is in agreement with previous estimates and corresponds to a total mass enclosed in the critical curve of ME = 3.33−0.07+0.02 × 1014 M⊙. Conclusions. The combined application of ancillary Hubble Space Telescope (HST) imaging, VIMOS and DEIMOS data, and the new MUSE spectroscopic observations allowed us to build a new lens model of the galaxy cluster PLCK G287.0+32.9, with an improvement in terms of reconstructing the observed positions of the multiple images of a factor of 2.5 with respect to previous models. The derived total mass distribution confirms this cluster to be a very prominent gravitational lens, with an effective Einstein radius of θE ∼ 43″. We were also able to construct an extensive spectroscopic catalog containing 490 objects, of which 153 are bright cluster members with mF160W ≤ 21, and 114 are multiple images.

Demonstration of event position reconstruction based on diffusion in the NEXT-white detector

Noble element time projection chambers are a leading technology for rare event detection in physics, such as for dark matter and neutrinoless double beta decay searches. Time projection chambers typically assign event position in the drift direction using the relative timing of prompt scintillation and delayed charge collection signals, allowing for reconstruction of an absolute position in the drift direction. In this paper, alternate methods for assigning event drift distance via quantification of electron diffusion in a pure high pressure xenon gas time projection chamber are explored. Data from the NEXT-White detector demonstrate the ability to achieve good position assignment accuracy for both high- and low-energy events. Using point-like energy deposits from 83m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{83\ extrm{m}}$$\\end{document}Kr calibration electron captures (E∼45\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E\\sim 45$$\\end{document} keV), the position of origin of low-energy events is determined to 2 cm precision with bias <1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 1~$$\\end{document}mm. A convolutional neural network approach is then used to quantify diffusion for longer tracks (E≥1.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E\\ge ~1.5$$\\end{document} MeV), from radiogenic electrons, yielding a precision of 3 cm on the event barycenter. The precision achieved with these methods indicates the feasibility energy calibrations of better than 1% FWHM at Qββ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{\\beta \\beta }$$\\end{document} in pure xenon, as well as the potential for event fiducialization in large future detectors using an alternate method that does not rely on primary scintillation.

Classification of mango disease using ensemble convolutional neural network

Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87 % training classification accuracy, 99.72 % validation accuracy, and 99.21 % testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.

An Effective Deep Learning Approach For Handwritten Ol-Chiki Character Recognition

In this paper, we propose a deep convolution neural network approach to recognize Ol-Chiki handwritten characters. Here we describe recognition of handwritten basic characters of Ol-Chiki script, used by more than 10 million tribal people in India mostly from Assam, Bengal, Bihar, Odisha and Jharkhand. There are 30 basic characters and 10 numeral digits in Ol-Chiki. We have used a dataset of 10000 handwritten isolated character samples written by 100 persons. Convolution Neural Network (CNN) architecture has been used for the recognition of handwritten isolated Ol-Chiki characters. Our system has been tested on Ol-Chiki isolated dataset and we have achieved 92% accuracy on almost on all Ol-Chiki characters.

An ergonomic evaluation using a deep learning approach for assessing postural risks in a virtual reality-based smart manufacturing context

This study proposes an integrated ergonomic evaluation designed to identify unsafe postures, whereby postural risks during industrial work are assessed in the context of virtual reality-based smart manufacturing. Unsafe postures were recognised by identifying the displacements of the centre of mass (COM) of body keypoints using a computer vision-based deep learning (DL) convolutional neural network approach. The risk levels for the identified unsafe postures were calculated using ergonomic risk assessment tools rapid upper limb assessment and rapid whole-body assessment. An analysis of variance was conducted to determine significant differences between the vertical and horizontal directions of postural movements associated with the most unsafe postures. The findings assess the ergonomic risk levels and identify the most unsafe postures during industrial work in smart manufacturing using DL method. The identified postural risks can help industry managers and researchers acquire a better understanding of unsafe postures.

Clusternets: a deep learning approach to probe clustering dark energy

ABSTRACT Machine learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a convolutional neural network (CNN) trained on matter density snapshots to distinguish clustering dark energy (DE) from the cosmological constant scenario and to detect the speed of sound (cs) associated with clustering DE. We compare the CNN results with those from a Random Forest (RF) algorithm trained on power spectra. Varying the DE equation of state parameter wDE within the range of −0.7 to −0.99 while keeping $c_s^2 = 1$, we find that the CNN approach results in a significant improvement in accuracy over the RF algorithm. The improvement in classification accuracy can be as high as 40 per cent depending on the physical scales involved. We also investigate the ML algorithms’ ability to detect the impact of the speed of sound by choosing $c_s^2$ from the set {1, 10−2, 10−4, 10−7} while maintaining a constant wDE for three different cases: wDE ∈ {−0.7, −0.8, −0.9}. Our results suggest that distinguishing between various values of $c_s^2$ and the case where $c_s^2=1$ is challenging, particularly at small scales and when wDE ≈ −1. However, as we consider larger scales, the accuracy of $c_s^2$ detection improves. Notably, the CNN algorithm consistently outperforms the RF algorithm, leading to an approximate 20 per cent enhancement in $c_s^2$ detection accuracy in some cases.

Currency Classification Using Deep Learning

Among the uses of machine learning is the recognition of facial expressions. Based on the features that are derived from an image, it assigns a facial expression to one of the classes of facial expressions. Convolutional Neural Network (CNN) is a classification technique that may also be used to identify patterns in an image. We used the CNN approach to identify facial expressions in our proposed study. To increase the precision of facial emotion recognition, the wavelet transform is used after CNN processing. Seven distinct facial expressions are included in the facial expression image dataset that was obtained from Kaggle. The findings of the face expression recognition experiment utilizing CNN and wavelet transform show that the accuracy is improved, and the output is audible. Index Terms: Facial and Convolutional Neural Nets

Big data in transportation: a systematic literature analysis and topic classification

AbstractThis paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies were evaluated from a dataset of 3532 collected papers, and bibliometric techniques were applied to capture the evolution of research interest over the years and identify the most influential studies. The proposed unsupervised classification model defined categories and classified the relevant articles based on their particular scientific interest using representative keywords from the title, abstract, and keywords (referred to as top words). The model’s performance was verified with an accuracy of 91% using Naïve Bayesian and Convolutional Neural Networks approach. The analysis identified eight research topics, with urban transport planning and smart city applications being the dominant categories. This paper contributes to the literature by proposing a methodology for literature analysis, identifying emerging scientific areas, and highlighting potential directions for future research.

Metallurgical microstructure classification using CNN: A comprehensive study on heat treatment analysis for steel

Predicting metallurgical microstructures is challenging due to the intricate features that emerge during manufacturing. This research utilizes Convolutional Neural Networks (CNNs) to recognize microstructure images of mild steel alloys, representing diverse microstructures. The study focuses on forecasting steel microstructure properties, specifically identifying and predicting Pearlite (P) and Bainite (B) microstructures after steel heat treatment, using a CNN approach. This comprehensive workflow leads to robust microstructure classification, achieving a model training accuracy rate of 98.95 %. While its final validation accuracy of 98.8 %, highlighting its capability to generate precise predictions across both classes seamlessly. Valuable insights from confusion matrices highlight the model’s performance in distinguishing between “Bainite” and “Pearlite” microstructures. Additionally, the study underscores the significance of CNNs in metallurgical microstructure analysis, with insightful confusion matrices revealing the model’s capability to differentiate between microstructures. This is useful for innovative microstructure analysis in materials science, emphasizing the transformative potential of AI in enhancing our understanding of processing-microstructure relationships in industrial applications.

Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images

Breast cancer is a condition where the irregular growth of breast cells occurs uncontrollably, leading to the formation of tumors. It poses a significant threat to women’s lives globally, emphasizing the need for enhanced methods of detecting and categorizing the disease. In this work, we propose an Ensemble Deep Convolutional Neural Network (EDCNN) model that exhibits superior accuracy compared to several transfer learning models and the Vision Transformer model. Our EDCNN model integrates the strengths of the MobileNet and Xception models to improve its performance in breast cancer detection and classification. We employ various preprocessing techniques, including image resizing, data normalization, and data augmentation, to prepare the data for analysis. By following these measures, the formatting is optimized, and the model’s capacity to make generalizations is improved. We trained and evaluated our proposed EDCNN model using ultrasound images, a widely available modality for breast cancer imaging. The outcomes of our experiments illustrate that the EDCNN model attains an exceptional accuracy of 87.82% on Dataset 1 and 85.69% on Dataset 2, surpassing the performance of several well-known transfer learning models and the Vision Transformer model. Furthermore, an AUC value of 0.91 on Dataset 1 highlights the robustness and effectiveness of our proposed model. Moreover, we highlight the incorporation of the Grad-CAM Explainable Artificial Intelligence (XAI) technique to improve the interpretability and transparency of our proposed model. Additionally, we performed image segmentation using the U-Net segmentation technique on the input ultrasound images. This segmentation process allowed for the identification and isolation of specific regions of interest, facilitating a more comprehensive analysis of breast cancer characteristics. In conclusion, the study presents a creative approach to detecting and categorizing breast cancer, demonstrating the superior performance of the EDCNN model compared to well-established transfer learning models. Through advanced deep learning techniques and image segmentation, this study contributes to improving diagnosis and treatment outcomes in breast cancer.

Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.

Deep Neural Networks for Multimedia Forensics: Detection of Deepfake Content

Deep Neural Networks for Multimedia Forensics: Detection of Deepfake Content

A novel hybrid deep CNN model for breast cancer classification using Lipschitz-based image augmentation and recursive feature elimination

Breast cancer is the leading cause of death for women worldwide. Despite its difficulties, the histopathological diagnosis must be used because modern imaging techniques cannot differentiate between benign and malignant tumors. This article introduces a hybrid deep learning method with recursive feature elimination for categorizing histopathology images of breast lesions as benign or malignant, using standard datasets, to increase diagnostic accuracy. The proposed four-stage model includes several novel techniques. First, a modified Lipschitz-based image augmentation technique is utilized to ensure a proportionate representation of various sample classes. K-fold cross-validation is employed to mitigate the bias that emerges from the selection of training and testing datasets. Then, for efficient feature extraction, a hybrid convolutional neural network (CNN) approach is used by integrating three CNNs (PResNet-34, FE-VGG-16, and M-AlexNet). With the proposed universum twin support vector machine-based recursive feature elimination technique, the initial 1,000 features extracted by each deep model are further reduced to 100. Finally, the deep features are subsequently combined into a set of 300 features, which are then fed into machine learning classifiers such as decision tree, k-nearest neighborhood, linear discriminant analysis, linear regression, and support vector machine. The results demonstrate that the proposed hybrid CNN model outperforms state-of-the-art techniques in accurately classifying breast lesions as benign or malignant, with an accuracy of 99.99 %, sensitivity of 99.56 %, specificity of 99.53 %, precision of 99.55 %, and F1 score of 99.77 %.

Convolutional Neural Networks Based Paddy Leaf Disease Detection

Abstract: This study proposes a novel approach for detecting paddy leaf diseases through the integration of Convolutional Neural Networks (CNNs). The process begins by converting input RGB images into HSV color space, where the saturation component is extracted. Subsequently, a binary image is generated, followed by background removal to enhance the accuracy of segmentation. The background-removed image is then converted back to the HSV color space for further processing. Kmeans segmentation is applied to segment the leaf regions effectively. The CNN architecture is employed for classification, categorizing the detected regions into various disease classes such as Healthy, Sheath Rot, Bacterial Leaf Blight, Brown Spot, and Leaf Blast. Additionally, a voice output feature is incorporated to provide audible feedback on the detected diseases. The study focuses on evaluating the accuracy of disease classification, aiming to demonstrate the effectiveness of the CNN approach in improving performance and reducing misclassifications compared to traditional methods. Overall, the proposed methodology offers a promising solution for accurate and efficient paddy leaf disease detection, with potential applications in agricultural management and crop protection

An effective ensemble approach for classification of chest X-ray images having symptoms of COVID: A precautionary measure for the COVID-19 subvariants

In recent years many people have died due to COVID-19 infection. The death rate increases due to the lack of proper diagnosis and treatment. It can be detected by RT-PCR test and from chest X-rays. Chest X-ray images can identify lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). Because many illnesses share similar patterns and diagnoses, radiologists and clinicians find it challenging to distinguish between them. This study uses convolutional neural networks (CNN) and ensemble models (VGG-19, ResNet50, vision transformer) to analyze chest X-ray images from internet sources to identify lung illness. The present research proposed an ensemble model comprised of VGG-19, ResNet50, and a vision transformer for the detection of COVID-19 and normal images with an accuracy of 92.46 percent and 94.52 % for the detection of COVID-19, normal and viral pneumonia respectively. Also, a comparative study has been conducted between CNN and ensemble approach for detecting COVID-19 and pneumonia disease from chest x-ray images. The proposed approach may also help in the early detection of other variants of COVID-19 such as JN.1, and HV.1 in the future.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Rapid pedestrian‐level wind field prediction for early‐stage design using Pareto‐optimized convolutional neural networks

AbstractTraditional computational fluid dynamics (CFD) methods used for wind field prediction can be time‐consuming, limiting architectural creativity in the early‐stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U‐Net architecture, to rapidly predict wind in simplified urban environments, representative of early‐stage design. The process of generating a wind field prediction at pedestrian level is reformulated from a 3D CFD simulation into a 2D image‐to‐image translation task, using the projected building heights as input. Testing on standard consumer hardware shows that our model can efficiently predict wind velocities in urban settings in less than 1 ms. Further tests on different configurations of the model, combined with a Pareto front analysis, helped identify the trade‐off between accuracy and computational efficiency. The fastest configuration is close to seven times faster, while having a relative loss, which is 1.8 times higher than the most accurate configuration. This CNN‐based approach provides a fast and efficient method for pedestrian wind comfort (PWC) analysis, potentially aiding in more efficient urban design processes.

Prediction and Classifications of Breast Cancer using Enhanced Convolutional Neural Network Approaches

In worldwide women mortality increases extremely every year due to breast cancer and diagnosis of the issue through prediction is very much imperative for healthy lifespan. Here precision of cancer extrapolation is an essential thing for survivability of patient with appropriate treatment. Deep learning algorithms have materialised as influential tool for predicting breast cancer in medical image processing, which leverages capabilities of artificial neural networks (ANN) that are intended to mimic an architecture and functionalities of human brain. Superior features of convolutional neural network (CNN) in deep learning for handling image-based data like, exploiting spatial information, hierarchical feature learning, parameter sharing and data augmentation are important parameters in medical image processing. In this paper CNN algorithm is incorporated for predicting breast cancer in earlier and malignant stage, the results are compared with other deep learning algorithms and our proposed algorithm is expected to give better performance in parameters like accuracy testing, image classifiers, gene sequence classifiers and malignancy detection.