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Spectral and texture features play important roles in plantation leaf area index (LAI) estimation, and their combination may enhance LAI inversion accuracy. Furthermore, research on the impact of different machine learning (ML) models on their hyperparameter combinations and splitting ratios remains challenging. In our study, experiments based on spectral and textural features of GF-6 WFV data were conducted on Eucalyptus grandis plantation forests in Huangmian Town, Guangxi, China. ML methods such as multiple stepwise regression (MSR), random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR) were mainly utilized to perform model hyper-parameter tuning and split-ratio analysis in order to estimate the LAI. The results of the study showed that spectral and gray level co-occurrence matrix (GLCM) texture features were very sensitive to changes in Eucalyptus grandis LAI. The accuracy of combining the two was 10% higher than when they were not combined. Furthermore, it was found that the nonlinear methods (RF, BPNN, and SVR) outperformed the linear method (MSR), with the average 2 Rmax of the nonlinear model being 26% higher than that of the linear model, and the RMSE value being 29% lower than that of the linear model. In addition, by analyzing different combinations of features, model hyperparameter fine-tuning, and splitting ratios in the nonlinear model, it was found that the splitting ratios of different combinations of model hyperparameters have a great impact on the accuracy of the model. A total of 12 out of 21 data sets showed high accuracy and stability at a split ratio of 8.5:1.5 (ratio of 0.85), with the best-performing RF model differing from the lowest by 91% for 2 max R and 39% for 2 Rstd. Combining spectral and texture features provides highly accurate inversion data. Model hyper-parameter fine-tuning and segmental scale tuning can facilitate the application of inversion data to fully utilize the optimal performance of the ML model. [ABSTRACT FROM AUTHOR]

The timely detection and identification of crop diseases is a crucial aspect of the agricultural sector. It contributes significantly to the by and large productivity of the plant. One of the most crucial factors that we need to consider while determining a plant's susceptibility to a particular disease is the visual characteristics of the affected plant. The increasing popularity of automation and availability of efficient techniques for disease identification has led to the development of novel methods and engraved impactful technologies in field of automated disease detection. The traditional methods have not been able to provide the researchers with the most accurate results. The proposed model in this work can identify the rice crop disease without relying on subjective data and have many advantages over traditional approaches as evident from the results derived. It has the potential to improve the efficiency of the process and aid in early detection. Machine learning method presents real-time automated decision support systems and can help improve crop or plant growth productivity and quality. This work aims to introduce a new and enhanced method as Neuro-GA, which is a combination of both the artificial neural network (ANN) and the genetic algorithm (GA). It has been claimed that it is more powerful and accurate than the traditional methods. The pioneer and nascent stages of this analysis includes preprocessing of the data was carried out. The features were then extracted using Gray-level co-occurrence matrix (GLCM) and subsequently the finally extracted features were cascaded to the Neuro-GA classifier. The digital image processing (DIP) techniques used in this study for rendering visual images along with Neuro-GA classifier resulted in skyrocket accuracy level of 90% and above. The technique validated in this study has allowed the automated monitoring of various aspects of crop production and farming and an omnipotent promising efficiency hence this approach can be magnanimously effective in monitoring agricultural production and thereby plummeting waste allied with crop damage. [ABSTRACT FROM AUTHOR]

Lung cancer is relying as one of the significant and leading cause for the deaths which are based on cancer. So, an effective diagnosis is a crucial step to save the patients who are all dying due to lung cancer. Moreover, the diagnosis must be performed based on the severity of lung cancer and the severity can be addressed with the help of an optimal classification approach. So, this research introduced an Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing to perform in‐depth classification of the disease affected partitions of the image and results in better classification results. The attention layer with dispersed dynamic routing evaluates the digit capsule from feature vector in a constant manner. As the first stage, data acquisitioned from datasets such as Lung Nodule Analysis‐16 (LUNA‐16), The Cancer Imaging Archive (TCIA) dataset and Lung Image Database Consortium and Image Database Resource Initiative (LIDC‐IDRI). After acquisitioning data, pre‐processing is done to enhance the resolution of the image using Generative Adversarial Network. The pre‐processed output is given as output for extraction of features that takes place using GLCM and VGG‐16 which extracts the low level features and high level features respectively. Finally, categorization of lung cancer is performed using Attention based Capsule Network (A‐Caps Net) with dispersed dynamic routing which categorize the lung cancer as benign and malignant. The results obtained through experimental analysis exhibits that proposed approach attained better accuracy of 99.57%, 99.91% and 99.29% for LUNA‐16, LIDC‐IDRI and TCIA dataset respectively. The classification accuracy achieved by the proposed approach for LUNA‐16 dataset is 99.57% which is comparably higher than DBN, 3D CNN, Squeeze Nodule Net and 3D‐DCNN with multi‐layered filter with accuracies of 99.16%, 97.17% and 94.1% respectively. [ABSTRACT FROM AUTHOR]

Yao Hu,1,* Jiao Ai2,* 1Department of Obstetrics and Gynecology, Jingzhou Hospital Affiliated to Yangtze University,Jingzhou Central Hospital, Jingzhou, Hubei, People’s Republic of China; 2Department of Urology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou Central Hospital, Jingzhou, Hubei, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yao Hu, Email huyao3784609@163.comObjective: To develop an early warning system that enables accurate parametrial invasion (PMI) risk prediction in cervical cancer patients with early-stage.Methods: We retrospectively collected 218 early-stage cervical cancer patients who were treated in Jingzhou Central Hospital from January 31, 2015, to January 31, 2023, and diagnosed with early stage cervical cancer by pathology. The prediction model training is achieved by randomly dividing 70% of the training queue population, with the remaining 30% used as the testing queue. Then, a prediction model based on machine learning algorithms (including random forest, generalized linear regression, decision tree, support vector machine, and artificial neural network) is constructed to predict the risk of PMI occurrence. Ultimately, the analysis of receiver operating characteristic curve (ROC) and decision curve analysis (DCA) is used to evaluate the predictive ability of various prediction models.Results: We finally included radiomics-based candidate variables that can be used for PMI model. Multivariate logistic regression analysis showed that energy, correlation, sum entropy (SUE), entropy, mean sum (MES), variance of differences (DIV), and inverse difference (IND) were independent risk factors for PMI occurrence. The predictive performance AUC of five types of machine learning ranges from 0.747 to 0.895 in the training set and can also reach a high accuracy of 0.905 in the testing set, indicating that the predictive model has ideal robustness.Conclusion: Our ML-based model incorporating GLCM parameters can predict PMI in cervical cancer patients with stage IB1 to IIA2, particularly the RFM, which could contribute to distinguishing PMI before surgery, especially in assisting decision-making on surgical scope.Keywords: cervical cancer, machine learning, parametrial invasion, gray level co-occurrence matrix, prediction model

Blindness refers to a condition in which an individual experiences limitations in their visual ability. Individuals with visual impairments require specific assistance to facilitate their movement from one location to another. The need for this assistance arises due to various obstacles scattered throughout their environment. One of the most significant challenges is navigating descending stairs. To address this issue, a descending stairs detection system based on digital image processing has been developed. Through this approach, the mobility of individuals with visual impairments can be enhanced. The descending stairs detection system is designed using the Gray Level Co-occurrence Matrix (GLCM) method to extract distinctive features of descending stairs and the surrounding floor surfaces. Seven GLCM features are incorporated into the development of this system, allowing it to differentiate between descending stairs and floors using the Extra Tree Classifier classification method. Through a series of tests, the system's accuracy is measured at 84%, demonstrating its adequate ability to identify descending stairs. Additionally, the average computation time for detecting descending stairs and floors is recorded at 0.121 (s), indicating the efficient performance of this system in supporting the mobility of individuals in need.

Detecting descending stairs and floors is a crucial aspect of implementing autonomous systems in smart wheelchairs. When the obstacle detection system used in wheelchairs fails to accurately identify descending stairs, it can lead to severe consequences for users, including injuries or, in the worst-case scenario, fatal accidents. Therefore, there is a pressing need for an algorithm that not only exhibits high accuracy in detecting obstacles on descending stairs but also operates with minimal computational delay to ensure an immediate response in wheelchair braking. In this research, We utilize the GLCM technique to extract texture characteristics. Out of these methods, the Decision Tree exhibits the highest accuracy, reaching 94%, with a remarkably fast computational time of 0.01299 s. These promising results were achieved by utilizing the GLCM method with a distance of 2 and an angle of 45°. The accuracy obtained has increased by 2.5% compared to the previous research. Such a level of accuracy, coupled with fast computational performance, enables smart wheelchairs to effectively assist users in identifying obstacles while descending stairs.

This research designs a touchless fingerprint identification biometric system. This research problem stems from the user's need to change the conventional system to touchless in the hope of minimizing direct contact that can be scanned by someone who is not interested. This research aims to implement palm images in validating identity using GLCM (Gray-Level Co-occurrence Matrix) features with the K-Nearest Neighbours (K-NN) classification method in MATLAB. The identification process is divided into several stages: image acquisition, pre-processing, feature extraction with GLCM, and database matching using KNN classification. System testing uses the 10-fold cross validation method with 100 image samples (90 training images and 10 test images) that are tested in turn to calculate the average accuracy and analyze system performance. Furthermore, the test used GLCM angles (0o, 45o, 90o dan 135o) and K-NN with k values of 1, 5 and 7. The results showed the highest accuracy of 72% using an angle of 0° in GLCM and k=1 and the lowest at an angle of 90o and k=7 in K-NN. The advantage of this design is the recognition of the identity of the fingerprint owner in real time.

Abstract Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

Objective: To achieve surface wear life prediction of abrasive blast rollers of grinding machines. Methods: The wear images of the grinding roller surface were acquired by the built image acquisition system, and the texture parameters such as second order moments, entropy value, contrast and correlation in the wear cycle of the grinding roller were obtained based on the grey scale co-generation matrix algorithm, and the obtained texture feature parameters were input into the constructed PSO-based LS-SVM algorithm model to finally predict the wear life of the blast roller. Results: The particle swarm algorithm could optimize the penalty factor and kernel parameters of LS-SVM well, and the PSO-LS-SVM algorithm was far superior to the LS-SVM algorithm model. The wear state of the blast roller surface of the mill could be accurately identified using the PSO-LS-SVM algorithm. Conclusion: The system can accurately predict the service life of the blast rollers.

ObjectiveIn recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases.MethodsTo address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health.Results and DiscussionsThe improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality.ConclusionsThe multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods.

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent. Article highlights: This article presents boosting and optimum feature selection based efficient machine learning technique for accurate prediction of crop yield Proposed methodology uses Gray Level Co-occurrence Matrix technique is used to select features. The AdaBoost Decision Tree, ANN, and KNN algorithms are used for classification. The performance of AdaBoost machine learning is more accurate for crop yield prediction. Adaboost has achieved 98 percent accuracy, 99 percent Recall rate. [ABSTRACT FROM AUTHOR]

Plant disease (PD) detection is a substantial problem that needs to be tackled to develop the economy and improve agricultural production. Using conventional methods to classify plant leaf diseases consumes more time, undergoes vanishing gradients problems, overfitting issues, etc. However, automatic PD detection using deep learning (DL) has attained great significance in detecting PD during the early stages. Therefore, this paper proposes a hybrid strategy based on optimized automatic DL for plant leaf disease classification (PLDC). Initially, the proposed model performs pre-processing using image resizing and Gaussian filtering. Then, the disease infected region is then segmented using the UNet technique to acquire the relevant region and enhance disease classification accuracy. During segmentation, the weight of the UNet model has been tuned by employing the hunter-prey optimization (Hunt-PO) algorithm. Next, feature extraction is accomplished by means of a gray level co-occurrence matrix (GLCM), scale-invariant feature transform (SIFT) and a Gabor filter to extract the crucial features for classification. Further, based on the extracted features, PLDC is performed using artificial driving-EfficientNet (AD-ENet). The proposed PLDC model is implemented in the python platform through the PlantVillage dataset and assessed the performance in terms of different evaluation measures. Moreover, a proposed model's performance is compared with existing classifiers. The maximum classification accuracy obtained by the proposed PLDC model is 99.91%, superior to the existing classifiers for leaf disease classification. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD), the most common type of dementia, is characterized by memory issues that worsen with time. When evaluating single modalities, there are specific efficient learning techniques that produce a poor identification rate. The clinical significance of Mild Cognitive Impairment (MCI) has clinical importance, which is crucial for identifying and categorizing AD patients. This study includes the utilization of 3D T1 weighted /Magnetic Resonance Imaging (3D T1WI MRI) images where the structural changes in the brain are identified for predicting early AD. The classification accuracy for AD, MCI, and Normal Control (NC) has also increased due to cognitive evaluations when comparing three one vs one classifications, i.e. AD vs. NC, MCI vs. NC, AD vs. MCI, and multi-class classification. The proposed approach's novelty is a binary and multi-class classification of structural MRI images using a Siamese 3D Convolutional Neural Network (Siamese 3D-CNN). The Stochastic Gradient Descent (SGD) has been employed to modify the proposed network's weights, and 5-Fold Cross-Validation (CV) has been used for each binary classification task. Our study used the neuropsychological data of 417 people from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the suggested methodology. Experimental results demonstrate that accuracy, sensitivity, and specificity for three binary classifications produce the maximum percentage, in contrast to other relevant approaches found in the literature. In addition, the multi-class classification accuracy of 99.8% was attained. [ABSTRACT FROM AUTHOR]

A significant challenge for Photovoltaic (PV) power systems is the accumulation of dust on solar panels, particularly prevalent in desert areas. Dust accumulation on solar panels cause a high degradation in the output power and thus, solar panels should be monitored and cleaned continuously to keep their efficiency high. Automating the inspection of solar panels can serve as a viable alternative of human inspection due to the impact of labor expenses and human difficulties on decision-making on such an environment. In this work, we are proposing a computer vision approach that is capable of inspecting solar panels and determines its condition in terms of dust accumulation. The proposed approach aims to prove the capability of dust detection on distinct panels by means of visible light imaging and computer vision techniques. It deploys both gray level co-occurrence matrix (GLCM) textural features and local binary patterns (LBP) of solar panels' images in addition to support vector machine (SVM) to build a classification model for this purpose. The proposed approach has been tested on images of solar panels that suffer from moderate and heavy accumulation of desert sands and dusts. The experimental findings successfully illustrated the effectiveness of the proposed feature description and the overall dust detection approach of solar panels with an accuracy of 94.3%. [ABSTRACT FROM AUTHOR]

[Objective] In recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases. [Methods] To address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health. [Results and Discussions] The improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality. [Conclusions] The multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods. [ABSTRACT FROM AUTHOR]

This study aimed to classify the maturity of strawberries and evaluate its impact on surface glossiness during storage using digital images. Five hundred images of strawberries were captured over 10 days using a digital camera. Color and texture features of the gray level co-occurrence matrix were extracted from each component of the HSV color space, which were used to classify maturity levels through linear discriminant analysis. The strawberry samples were classified with an overall accuracy of 82% in five classes. Furthermore, as an index of surface glossiness to reflect visual freshness, changes in the luminance distribution on the strawberry surface were evaluated. The later the harvest, the quicker the deterioration in the glossiness of strawberries. Strawberries that are ripe enough for harvesting but still relatively immature can be stored for a longer period than fully ripe strawberries while retaining better surface glossiness. [ABSTRACT FROM AUTHOR]

In view of the low detection rate of steel surface defect classification, a surface defect classification method based on texture multi-scale feature fusion is adopted. Gabor filter and gray level co-occurrence matrix are used to establish multi-scale feature vectors of texture images. At the same time, convolution operation is used to extract features of texture images, and hybrid dilated convolution module is introduced to increase receptive field. The two feature vectors are fused to obtain the enhanced fused texture feature vector. The fused features are input into Long Short-Term Memory(LSTM) network in sequence to build a classification model, and the classification results are evaluated using the confusion matrix. The results show that the classification accuracy of this method on the NEU(Northeastern University) data set is 97.5%. The LSTM network, BP(Back Propagation) neural network, SVM(Support Vector Machine), KNN(K-Nearest Neighbor), CART(Classification And Regression Tree) and other classification methods are set up for comparative experiments. The results show that LSTM classification method performs best in multi-scale, and F1 index is the highest. Ablation experiments are conducted with BP network, LSTM network, SVM, KNN, CART, CNN, AlexNet and other methods to verify the universality of this method. This method fully exploits the multi-scale feature information of the texture image, and has important significance for the research of the classification method of steel surface defects. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19 has been a devastating pandemic, causing serious and sometimes irreparable damages to body organs. The sooner the existence of this virus in the body is recognized, the more effective the treatments are. This early detection can break the transmission chain faster, reducing the burden of this disease on the society. Since there exist issues regarding the reliability of RT-PCR tests to diagnose COVID-19, examining chest radiographs, especially chest X-ray images, are recommended as well. In this paper, we propose a machine learning algorithm to automatically classify patients in the target groups of COVID-19, pneumonia, and normal, based on chest X-ray images. Our algorithm generates two complementary images from each raw image in the dataset, and only works on a 4-feature vector extracted from the gray level co-occurrence matrix of each image for the classification, based on the k-nearest neighbors algorithm. It can work robustly in the presence of limited data. The speed, simplicity, and the independence from large computational resources are of other advantages of the proposed algorithm. Despite the simplicity and speed, as the results show, the algorithm can compete tightly with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Skin Cancer is the most common form of the disease and is responsible for millions of deaths each year. Most of the relevant studies concentrate on algorithms that are based on machine learning, and few on deep learning as well. However, due to the several challenges in dermoscopic image acquisition, these algorithms are unable to deliver the highest possible level of accuracy and specificity. Therefore, this article implements skin cancer detection and classification (SCDC) system using multilevel feature extraction (MFE)-based artificial intelligence (AI) with unsupervised learning (USL), here after denoted as MFEUsLNet. Initially, the given skin images are preprocessed using bilateral filter, which removes the noise artifacts from the source images. Then, a well-known USL approach named K-means clustering (KMC) is used for segmentation of skin lesion, which can detect the affected skin lesion quite efficiently. Then, gray level co-occurrence matrix (GLCM), and redundant discrete wavelet transform (RDWT) are used for low level, texture and colour feature extraction. Finally, recurrent neural network (RNN) classifier is used to train with these multi-level features and classify the multiple types of skin cancer. The simulations proven that the proposed MFEUsLNet model is outperformed state-of-the-art SCDC approaches in terms of medical statistical quality metrics such as classification accuracy, specificity, precision, recall, F1-score, and sensitivity for ISIC-2020 dataset.

In view of the low accuracy of objective automatic evaluation of fabric sewing flatness, an objective evaluation model of fabric sewing flatness based on feature parameters and deep belief network (DBN) is proposed. Firstly, image preprocessing including image grayscale processing, median filter denoising, and histogram equalization enhancement was performed on the image of the fabric sewing sample to extract the texture features of the fabric, so as to obtain a fabric feature image with higher quality and facilitate subsequent processing. Then the gray level co-occurrence matrix was constructed, and the four key feature parameters of energy, entropy, contrast and correlation of fabric image were extracted at 0°, 45°, 90° and 135°. On this basis, an automatic evaluation model of fabric sewing flatness based on DBN was constructed, and the model was trained using sewing images. Last it was verified by the extracted fabric sewing images. The results show that the evaluation accuracy of the model reaches 98.74%. Compared with the two methods of multiple linear regression model and BP network model, the proposed evaluation method can effectively evaluate the flatness of sewing objectively and provide a theoretical basis for the quality control of fabric garment appearance.

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).

Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on image segmentation and stem/calyx recognition to realize the detection and recognition of surface defects in apples. Preliminary defect segmentation results were obtained by eliminating the interference of light reflection inhomogeneity through adaptive bilateral filtering-based single-scale Retinex (SSR) luminance correction and using adaptive gamma correction to enhance the Retinex reflective layer, and later segmenting the Retinex reflective layer by using a region-growing algorithm. The texture features of apple surface defects under different image processing methods were analyzed based on the gray level co-occurrence matrix, and a support vector machine was introduced for binary classification to differentiate between stems/calyxes and scars. Deploying the proposed defect detection method into the embedded device OpenMV4H7Plus, the accuracy of stem/calyx recognition reached 93.7%, and the accuracy of scar detection reached 94.2%. It has conclusively been shown that the proposed defect detection method can effectively detect apple surface defects in the presence of uneven light reflectivity and stem/calyx interference. [ABSTRACT FROM AUTHOR]

Early detection of lung cancer in patients can decrease the mortality rate and helps in early diagnosis. An efficient lung cancer prediction system is proposed in this paper known as a long short-term memory (LSTM) based modified remora optimization algorithm (MROA) with multi-objective criteria. Although the MRO is proposed by several researchers, it was based on single-objective with traditional objective functions which tried to maximize the accuracy. This led to the biased classifier with high accuracy and sacrificed sensitivity which resulted in insufficiency of one class and prevalence of other class. To overcome this limitation, a Multi-objective MROA (MMROA) for hyperparameter tuning is proposed, where both accuracy and sensitivity are equally considered as a fitness function. Initially the histopathology images of Lung and Colon 25000 (LC25000) dataset is normalised using colour normalization and segmented with saliency driven edge dependent top-down level set (SDREL) method. The features are extracted using Grey Level Cooccurrence-Matrix (GLCM) and GoogleNet followed by the feature selection using enhanced grasshopper optimization algorithm (EGOA). The selected features are optimized (hyperparameter tuning) using MMROA and fed to LSTM classifier. The proposed model is compared with the existing models such as convolutional neural network (CNN) and enhanced grasshopper optimization algorithm based random forest (EGOARF) and obtained remarkable results with accuracy, precision, recall, and f-measure values of 99.02%, 99.17%, 99.03%, and 99.24% respectively. [ABSTRACT FROM AUTHOR]

The existence of machine learning has been exploited to solve difficulties in various fields, including the classification of leaf species in agriculture. Betel leaf is one of the plants that provide health advantages. The objective of using a machine learning approach is to classify the betel leaf species. This study involved several processes: image acquisition, region of interest (ROI) detection, pre-processing, feature extraction, and classification. The feature extraction used the combination features of color and texture. Furthermore, the classification applied four classifiers, including artificial neural network (ANN), K-nearest neighbors (KNN), Naive Bayes, and support vector machine (SVM). The evaluation in this study implemented cross-validation with a K-fold value of 5. The method performance produced the highest accuracy value of 100% using the color and texture features with the SVM classifier. [ABSTRACT FROM AUTHOR]

Background: Medical images of cancer patients are usually evaluated qualitatively by clinical specialists which makes the accuracy of the diagnosis subjective and related to the skills of clinicians. Quantitative methods based on the textural feature analysis may be useful to facilitate such evaluations. This study aimed to analyze the gray level co-occurrence matrix (GLCM)-based texture features extracted from T1-axial magnetic resonance (MR) images of glioblastoma multiform (GBM) patients to determine the distinctive features specific to treatment response or disease progression. Methods: 20 GLCM-based texture features, in addition to mean, standard deviation, entropy, RMS, kurtosis, and skewness were extracted from step I MR images (obtained 72 h after surgery) and step II MR images (obtained three months later). Responded and not responded patients to treatment were classified manually based on the radiological evaluation of step II images. Extracted texture features from Step I and Step II images were analyzed to determine the distinctive features for each group of responsive or progressive diseases. MATLAB 2020 was applied to feature extraction. SPSS version 26 was used for the statistical analysis. P value < 0.05 was considered statistically significant. Results: Despite no statistically significant differences between Step I texture features for two considered groups, almost all step II extracted GLCM-based texture features in addition to entropy M and skewness were significantly different between responsive and progressive disease groups. Conclusions: GLCM-based texture features extracted from MR images of GBM patients can be used with automatic algorithms for the expeditious prediction or interpretation of response to the treatment quantitatively besides qualitative evaluations. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a malignant bone tumor that usually affects children and adolescents. Early detection of osteosarcoma tumors increases the likelihood of successful therapy. Manual identification of osteosarcoma requires highly skilled doctors. In this study, we attempt to create a model to automatically diagnose tumors into three categories; non-tumor, viable-tumor, and osteosarcoma tumor. The suggested methodology can help medical professionals identify tumors correctly and quickly. The proposed approach uses the gray level co-occurrence matrix (GLCM) to extract features for feature extraction and three different classifiers for tumor detection. The used classifier are XG-Boost, support vector machine (SVM), and K-nearest neighbors. Finally, ensemble voting is used by combining the predictions from these classifiers. The system achieves 91.8% accuracy. [ABSTRACT FROM AUTHOR]

ObjectivesTo determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine.MethodsIn a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation.ResultsOf the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78.ConclusionsThe radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. Small differences between symptoms of these diseases make it difficult to use the naked eye to obtain better results in detecting and distinguishing these diseases. The Artificial Neural Network outperformed other classifiers with an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. Generally, the models confused samples originally belonging to Helminthosporium with Curvularia. The extracted texture features show great potential to classify the different tomato leaf fungal diseases. The results of this study show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in the establishment of tomato leaf disease classification systems and can facilitate the implementation of preventive measures by farmers, resulting in enhanced yield quality and quantity.

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.

[Display omitted] Aboveground biomass (AGB) is an essential factor in rice ecological research. Optical variables (e.g. reflectivity and vegetation index (VI)) are widely adopted in monitoring AGB while spatial information (e.g. texture) is less introduced into research. Limited research is available regarding the impact on the monitoring accuracy caused by the utility of VI and texture in AGB estimation at different combinations of growth stages. To compare the improvements caused by the involvement of texture at different combinations of growth stages, a hyperspectral camera was mounted on an unmanned aerial vehicle (UAV) to obtain images of rice field during the early growth stages (including the tillering, jointing and booting stage) under five nitrogen levels over two years. Spectral and spatial information derived from images was utilized to compute VIs and textures at different combinations of growth stages. After analyzing their correlations with AGB, multiple stepwise regression (MSR) and multiple linear regression (MLR) techniques were employed to establish rice AGB models using vegetation index (VI), vegetation index combined with the corresponding-band texture (VI-CBT) and vegetation index combined with the full-band texture (VI-FBT). It was found that the models using VI and texture enhanced the capability of the conventional VI to estimate AGB at different combinations of growth stages. The combination of VI and FBT achieved the most accurate estimation, followed by the VI-CBT. Different combinations of growth stages had diverse responses to textures. Overall, the tillering stage had the maximum response to the involvement of textures, followed by the booting stage. Models using VI-CBT and VI-FBT significantly improved the AGB estimation at the booting and tillering stage, respectively. The monitoring accuracy of the jointing stage showed a slight response to the involvement of texture. At the multiple growth stages, the monitoring effect of the tillering-booting stages was significantly improved, followed by the jointing-booting stages either using the model based on VI-CBT or VI-FBT. Models using VI and texture tended to yield larger improvements on the error values at the extreme AGB levels. Near-infrared and red-edge bands were the sensitive bands to estimate rice AGB, and MEA, COR were favorable textures to monitor the AGB. This study quantified the model improvements of using VI and texture in rice AGB estimation at different combinations of growth stages, delivering guidance for timely and accurate rice management in the field. [ABSTRACT FROM AUTHOR]

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset. [ABSTRACT FROM AUTHOR]

In view of the low accuracy of objective automatic evaluation of fabric sewing flatness, an objective evaluation model of fabric sewing flatness based on feature parameters and deep belief network(DBN) is proposed. Firstly, image preprocessing including image grayscale processing, median filter denoising, and histogram equalization enhancement was performed on the image of the fabric sewing sample to extract the texture features of the fabric, so as to obtain a fabric feature image with higher quality and facilitate subsequent processing. Then the gray level co-occurrence matrix was constructed, and the four key feature parameters of energy, entropy, contrast and correlation of fabric image were extracted at 0°, 45°, 90° and 135°. On this basis, an automatic evaluation model of fabric sewing flatness based on DBN was constructed, and the model was trained using sewing images. Last it was verified by the extracted fabric sewing images. The results show that the evaluation accuracy of the model reaches 98.74%. Compared with the two methods of multiple linear regression model and BP network model, the proposed evaluation method can effectively evaluate the flatness of sewing objectively and provide a theoretical basis for the quality control of fabric garment appearance. [ABSTRACT FROM AUTHOR]

Image Processing has assisted researchers in a variety of ways, especially in the areas of security and medicine fields. Identifying blood types in emergencies or far-off places and regions where experts have not been available is a present-day challenge. Therefore, we have developed an automatic system that will detect the blood group and notify an alert system using GSM and various image processing methods. Prior to any treatment or operation, it is necessary to determine the blood type for a transfusion of blood, even in an emergency. Currently, technicians manually conduct these tests, which can cause human mistakes. Different systems have been created to automate these tests; none have been successful in completing the analysis in time for emergencies. This project intends to create an automated system to do these tests quickly, adjusting to urgent circumstances. Initially, the slide test is performed to collect the blood images. Furthermore, various image processing methods have been performed for processing images using the PI camera. Subsequently, an alert with the patient's blood group is then generated and sent to the concerned patient or the hospital to immediately consult the patient. Unit testing and load testing were performed on 950 images at a time which yielded 97% accuracy. [ABSTRACT FROM AUTHOR]

This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods. [ABSTRACT FROM AUTHOR]

Self-Attention based Generative Adversarial Capsule Network optimized with Atomic orbital search algorithm based Sentiment Analysis is proposed in this manuscript for Online Product Recommendation (SFA-AGCN-AOSA-SA-OPR). Here, Collaborative filtering (CF) and product-product (P-P) similarity method is utilized for designing the new recommendation system. CF is employed for predicting the best shops and P-P similarity method is employed to predict the better product. Initially, the datas are gathered via Amazon Product recommendation dataset. After that, the datas are given to pre-processing. During pre-processing, Markov chain random field (MCRF) co-simulation is used to remove the unwanted content and filtering relevant text. The preprocessing output is fed to feature extraction. The features, like manufacturing date, Manufacturing price, discounts, offers, quality ratings, and suggestions or reviews are extracted using Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. Finally, Self-Attention based Generative Adversarial Capsule Network (SFA-AGCN) categorizes the product recommendation as excellent, good, very good, bad, very bad. Atomic orbital search algorithm optimizes the SFA-AGCN weight parameters. The performance metrics, like accuracy, precision, sensitivity, recall, F-measure, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) is examined. The efficiency of the proposed method provides higher mean absolute percentage error 98.23%, 88.34%, 90.35% and 78.96% and lower Mean squared error 92.15%, 90.25%, 89.64% and 92.48% compared to the existing methods, such as sentiment analysis of online product reviews using DLMNN and future prediction of online product using IANFIS (DLMNN-IANFIS-SA-OPR), intelligent sentiment analysis approach using edge computing based deep learning technique (DCNN-SA-OPR), sentiment analysis for online product reviews in Chinese depending on sentiment lexicon and deep learning (CNN-BiGRU-SA-OPR) and sentiment analysis on product reviews depending on weighted word embedding and deep neural networks (CNN-LSTM-SA-OPR) respectively. [ABSTRACT FROM AUTHOR]

The expansion evolution law of internal fractures of coal under external load is of great significance to coalbed methane production and to control coal and gas outburst disasters. The coal body is in a three-dimensional (3D) stress state under the action of original in-situ stress. It is necessary to study the fracture evolution law of a loaded coal-containing gas under triaxial compression. The industrial computed tomographic (CT) scanning test of a loaded coal-containing gas under triaxial loading was carried out using the industrial micro-CT scanning system for the loaded coal-containing gas. The CT images and stress–strain curves of coal samples were obtained at each deformation stage. The 3D digital reconstruction of CT scanning data was carried out using image analysis software. Next, 3D visualization and quantitative characterization of coal sample internal fractures were realized. Based on the gray level co-occurrence matrix (GLCM) theory, the fracture dynamic expansion characteristics and laws of the loaded coal-containing gas were analyzed. The results show that the existence of gas pressure weakens the mechanical properties of the loaded coal-containing gas and accelerates the expansion of cracks. The two-dimensional fractures of the loaded coal-containing gas first close and then expand, and then expand rapidly after the peak, forming a connected two-dimensional fracture network. The 3D fracture volume and fracture density first show a decreasing and then an increasing trend, which can be divided into three stages: fracture compaction and closure, new fracture initiation and expansion, and main fracture accelerated expansion and penetration. In the GLCM analysis, the contrast first decreases and then increases, the energy and homogeneity first increase and then decrease, and the correlation presents a monotonic decreasing trend. The analysis results accurately describe the overall development law of the internal cracks of the loaded coal-containing gas changing with stress increase.

Background: Medical images of cancer patients are usually evaluated qualitatively by clinical specialists which makes the accuracy of the diagnosis subjective and related to the skills of clinicians. Quantitative methods based on the textural feature analysis may be useful to facilitate such evaluations. This study aimed to analyze the gray level co-occurrence matrix (GLCM)-based texture features extracted from T1-axial magnetic resonance (MR) images of glioblastoma multiform (GBM) patients to determine the distinctive features specific to treatment response or disease progression. Methods: 20 GLCM-based texture features, in addition to mean, standard deviation, entropy, RMS, kurtosis, and skewness were extracted from step I MR images (obtained 72 h after surgery) and step II MR images (obtained three months later). Responded and not responded patients to treatment were classified manually based on the radiological evaluation of step II images. Extracted texture features from Step I and Step II images were analyzed to determine the distinctive features for each group of responsive or progressive diseases. MATLAB 2020 was applied to feature extraction. SPSS version 26 was used for the statistical analysis. P value < 0.05 was considered statistically significant. Results: Despite no statistically significant differences between Step I texture features for two considered groups, almost all step II extracted GLCM-based texture features in addition to entropy M and skewness were significantly different between responsive and progressive disease groups. Conclusions: GLCM-based texture features extracted from MR images of GBM patients can be used with automatic algorithms for the expeditious prediction or interpretation of response to the treatment quantitatively besides qualitative evaluations.

Abstract Background Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information. Results In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins. Conclusions Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

Disability is a disruption or limitation of a person’s body functions in carrying out daily activities. A person with physical disabilities needs an assistive device such as a wheelchair. The latest wheelchair development is the smart wheelchair. Smart wheelchairs require a control system to detect obstacles quickly. This aims to provide safety, especially for users. One of the obstacles that are quite dangerous is descending stairs. Therefore the researchers propose a descending stairs detection system for smart wheelchairs. The proposed method in this study is the gray level co-occurrence matrix (GLCM) as the feature extraction algorithm, learning vector quantization (LVQ) as the classification algorithm, and sequential forward selection (SFS) for feature selection. Based on the simulation result, the SFS feature selection gets two selected GLCM features. The best accuracy is 94.5% with the selected features, namely contrast and dissimilarity. This result has an increase in accuracy when compared to using the GLCM 6 feature method with the LVQ classification that does not use feature selection, where the method gets 92.5% accuracy in off-time testing. Accuracy decreased to 78.21% when detecting floors and 89.06% when detecting descending stairs in real-time system testing.

Between 2015 and 2020, 7.8 million women were diagnosed with breast cancer. If the cancer is discovered early, it can be completely cured. Computer-aided detection and diagnosis systems are a helpful tool. We propose such a system: after pre-processing the mammography, the region of interest is identified using an unsupervised manner. Textural features are extracted from the Gray-Level Co-occurrence Matrix and used with the Linear Discriminant Analysis classifier, obtaining a diagnosis: benign or malignant. The proposed system is tested on the Mini-MIAS dataset, reaching an accuracy score of 95% and a precision and specificity of 100%. [ABSTRACT FROM AUTHOR]

• Gray level co-occurrence matrix and brightness measures can reliably quantify median nerve changes in CTS. • ROC curve analysis showed that image analysis measures had similar AUC values with cross sectional area in patients with CTS. • Future ultrasound machines should incorporate the appropriate software for the calculation of image analysis measures. To study the ability of image analysis measures to quantify echotexture changes of median nerve in order to provide a complementary diagnostic tool in CTS. Image analysis measures (gray level co-occurrence matrix (GLCM), brightness, hypoechoic area percentage using max entropy and mean threshold) were calculated in normalized images of 39 (19 younger and 20 older than 65y) healthy controls and 95 CTS patients (37 younger and 58 older than 65y). Image analysis measures were equivalent or superior (older patients) to subjective visual analysis. In younger patients, GLCM measures showed equivalent diagnostic accuracy with cross sectional area (CSA) (Area Under Curve (AUC for inverse different moment = 0.97). In older patients all image analysis measures showed similar diagnostic accuracy to CSA (AUC for brightness = 0.88). Moreover, they had abnormal values in many older patients with normal CSA values. Image analysis reliably quantifies median nerve echotexture alterations in CTS and offers similar diagnostic accuracy to CSA measurement. Image analysis may offer added value to existing measures in the evaluation of CTS, especially in older patients. Its clinical implementation would require incorporation of mathematically simple software code for online nerve image analysis in ultrasound machines. [ABSTRACT FROM AUTHOR]