Pattern Matrix Research Articles

Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.

Machine Learning based Early Stage Identification of Liver Tumor using Ultrasound Images

Liver cancer is one of the most malignant diseases and its diagnosis requires more computational time. It can be minimized by applying a Machine learning algorithm for the diagnosis of cancer. The existing machine learning technique uses only the color-based methods to classify images which are not efficient. So, it is proposed to use texture-based classification for diagnosis. The input image is resized and pre-processed by Gaussian filters. The features are extracted by applying Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP in the preprocessed image. The Local Binary Pattern (LBP) is an efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The extracted features are classified by multi-support vector machine (Multi SVM) and K-Nearest Neighbor (K-NN) algorithms. The Advantage of combining SVM with KNN is that SVM measures a large number of values whereas KNN accurately measures point values. The results obtained from the proposed techniques achieved high precision, accuracy, sensitivity and specificity than the existing method.

Characterization of Breath Sensor at Different Frequencies in Outdoor Condition

This paper investigates a breath sensor device that is designed to detect the moisture in human bulk matrix in outdoor conditions. The human bulk matrix is rich in moisture and carbon dioxide apart from the 872 volatile organic compounds emanating from the human breath. Human breathing effort is a respiration process that involves inhalation and exhalation modes. The human bulk matrix is a product of the latter. Most research in human breath analysis is concentrating on both human bulk matrix and breathing pattern. The aim of this study is to characterize the fabricated breath sensor at different input wave frequencies in the outdoor environment. In this study, an outdoor experiment was carried out using the breath sensor device that is connected to the input waveform from the frequency generator and the output reading is captured using the oscilloscope. A single exhaled breath originated from the human subject is required to activate the breath sensor. This method is easier and simpler, and the output wave result is generated by an oscilloscope in real-time. The result indicates that the investigated breath sensor is suitable for clinical and healthcare monitoring. Early studies indicate that the breath sensor can diagnose diseases related to breathing problems such as sleep apnoea, asthma, and strokes.

Missing data recovery of wind speed in wind farms: A spatial-temporal tensor decomposition approach

Missing data recovery plays a critical role in improving the data quality of wind speed in wind farms, and numerous methods have been proposed to address this issue. However, most of them suffer from the inability to fully use the information of known data, and thus, poor performance of recovery is usually achieved. In this paper, we propose a missing data recovery method based on spatial-temporal tensor decomposition. The proposed method rearranges the whole data based on discrete wavelet transform to construct a four-dimensional tensor of “site × week × scale × hour” for representing the spatial and temporal correlation of wind speed. A completeness tensor is estimated to impute missing data based on Tucker decomposition and the nonlinear conjugate gradient algorithm. The proposed method not only inherits the advantages of imputation methods based on the matrix pattern but also well mines the spatial and temporal inherent correlation of wind speed. Wind speed data of a wind farm are used to verify the effectiveness of the proposed method. The results show that the proposed method recovers missing data with much smaller mean absolute error and root mean square error and requires less effort for recovering missing data of fragmented or continuously, compared with the traditional methods.

P‐7.5: Analysis and research on the improvement of OPC compensation aperture ratio

In this paper, we aim to improve the opening rate of color film substrate by proximity exposure mechanism, and carry out OPC (Optical Proximity Correction) at the corners of the pattern on the photo mask version, and DOE (design of experiments) to verify the optimal solution under the existing equipment and process conditions. The final result is that the color film black matrix pattern is closer to the design, and the substrate opening rate is increased by 1‐4%, which increases the product transmission rate and enhances the competitiveness of the product.

An exact characterization of saturation for permutation matrices

A 0-1 matrix \(M\) contains a 0-1 matrix pattern \(P\) if we can obtain \(P\) from \(M\) by deleting rows and/or columns and turning arbitrary 1-entries into 0s. The saturation function \(\mathrm{sat}(P,n)\) for a 0-1 matrix pattern \(P\) indicates the minimum number of 1s in an \(n \times n\) 0-1 matrix that does not contain \(P\), but changing any 0-entry into a 1-entry creates an occurrence of \(P\). Fulek and Keszegh recently showed that each pattern has a saturation function either in \(\mathcal{O}(1)\) or in \(\Theta(n)\). We fully classify the saturation functions of permutation matrices.Mathematics Subject Classifications: 05D99Keywords: Forbidden submatrices, saturation

BUS‐CAD: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

AbstractBreast cancer has become the most prominent type of cancer in the world. Early detection of breast cancer plays an important role in optimal treatment planning to decrease mortality. Breast ultrasound is widely used in diagnosing breast masses. Applications of machine learning in ultrasound imaging‐based classification have shown promising potential for early and accurate detection of breast cancer. In this study, a new computer‐aided diagnosis system based on machine learning techniques for breast cancer classification is proposed. Feature space is extended by using hybrid feature representations that combine both global and local texture statistics. A two‐step feature selection process is implemented using Boruta all‐relevant feature selection algorithm and iterative correlation analysis. A grid‐search strategy is followed along with 20 times repeated 10‐fold randomly stratified cross‐validation to optimize machine learning algorithms. Fourteen classification models based on random forest (RF) and support vector machine trained using all combinations of global features and the features driven from gray‐level co‐occurrence matrix and local binary patterns are tested. The experiments showed that the RF classifier on the hybrid feature vector that combines all global and local features achieved the best classification performance with average accuracy and area under the curve of 97.81% and 99.80%, respectively. The results suggest that the proposed system efficiently improves the classification performance of breast lesions on ultrasound images and can assist clinical decision‐making.

Phase-specific signatures of wound fibroblasts and matrix patterns define cancer-associated fibroblast subtypes

Healing wounds and cancers present remarkable cellular and molecular parallels, but the specific roles of the healing phases are largely unknown. We developed a bioinformatics pipeline to identify genes and pathways that define distinct phases across the time-course of healing. Their comparison to cancer transcriptomes revealed that a resolution phase wound signature is associated with increased severity in skin cancer and enriches for extracellular matrix-related pathways. Comparisons of transcriptomes of early- and late-phase wound fibroblasts vs skin cancer-associated fibroblasts (CAFs) identified an "early wound" CAF subtype, which localizes to the inner tumor stroma and expresses collagen-related genes that are controlled by the RUNX2 transcription factor. A "late wound" CAF subtype localizes to the outer tumor stroma and expresses elastin-related genes. Matrix imaging of primary melanoma tissue microarrays validated these matrix signatures and identified collagen- vs elastin-rich niches within the tumor microenvironment, whose spatial organization predicts survival and recurrence. These results identify wound-regulated genes and matrix patterns with prognostic potential in skin cancer.

Study on land use and landscape pattern change in the Huaihe River Ecological and economic zone from 2000 to 2020

Exploring the relationship between land use change and landscape patterns can provide a basis for regional ecological management. In this paper, based on remote sensing images of the Huaihe River Ecological and Economic Zone for the years 2000, 2005, 2010, 2015 and 2020, the spatial and temporal evolution patterns of land use in the region were quantitatively described by using the methods of land use shift matrix and landscape pattern analysis. The relationship between land use change and landscape pattern was analyzed with the Grey Relation Analysis (GRA) model. The results show that: (1) the land use of the Huaihe River Ecological and Economic Zone has changed significantly in the past 20 years, with the conversion of arable land into construction and forest lands, in addition to the growth of water areas and a decline in the areas of arable land, grassland and unused land. (2) The landscape pattern fragmentation of each type of land in the study area from 2000 to 2020 fluctuated and decreased, and the landscape connectivity and landscape diversity increased significantly. (3) The GRA model shows that construction, arable and forest lands played the most significant role in the change of landscape pattern of the Huaihe River Ecological and Economic Zone. Countermeasures are proposed to better coordinate and optimize the relationship between spatial development and landscape pattern for the Huaihe River ecological and economic Zone.

Roughness detection method based on image multi-features

Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM

The primary goal of this study is to predict the presence of a brain tumor using MRI brain images. These images are first pre-processed to remove the boundary borders and the undesired regions. Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern method (LBP) approaches are mixed for extracting multiple local and global features. The best features are selected using the ANOVA statistical approach, which is based on the largest variance. Then, the selected features are applied to many state of arts classifiers as well as to Extreme Learning Machine (ELM) neural network model, where the weights are optimized via the regularization of RELM using a suitable ratio of Cross Validation (CV) for the images' classification into one of two classes, namely normal (benign) and abnormal (malignant). The proposed ELM algorithm was trained and tested with 800 images of BRATS 2015 datasets types, and the experimental results demonstrated that this approach has better performance on several evaluation criteria, including accuracy, stability, and speedup. It reaches to 98.87% accuracy with extremely low classification time. ELM can improve the classification performance by raising the accuracy more than 2% and reducing the number of processes needed by speeding up the algorithm by a factor of 10 for an average of 20 trials.

Matrix-Patterned Micromarkers for Tracking Local Contractile Movements of Myotubes Cultured on Thin-Film Scaffold

Abstract To measure the distribution of local contractile movements in the layer of the myotubes, the movement of the matrix pattern of micromarkers placed on the back surface of the myotube-attached thin film has been tracked in vitro. A matrix pattern of micromarkers (diameter: 4 μm, height: 2 μm, interval: 30 μm) was fabricated on the back of a polydimethylsiloxane thin film (thickness: 6 μm) using photolithography. Mouse myoblasts were seeded on the front of the thin film and cultured for 10 days to differentiate into myotubes. Periodic electrical pulses (amplitude 2 V, pulse period 0.5 s, pulse width 1 ms) were applied between the electrodes of titanium wires immersed in the medium. Movements of both myotubes and markers were observed simultaneously with an optical microscope. Experimental results indicate that the motion distribution of each marker is related to the local periodic motion of the myotube layer. Thin films with matrix-patterned micromarkers fabricated on the back side allow in vitro analysis of the distribution of local repetitive contractile movements in the myotube layer.

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of several objects like hair, reflections, air bubbles, and oils on almost all images. Active contour is one of the suitable methods with some drawbacks for the segmentation of irregular shapes. An entropy and morphology-based automated mask selection is proposed for the active contour method. The proposed method can improve the overall segmentation along with the boundary of melanoma images. In this study, features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP). When four different moments pull out in six different color spaces like HSV, Lin RGB, YIQ, YCbCr, XYZ, and CIE L*a*b then global information from different colors channels have been combined. Therefore, hybrid fused texture features; such as local, color feature as global, shape features, and Artificial neural network (ANN) as classifiers have been proposed for the categorization of the malignant and non-malignant. Experimentations had been carried out on datasets Dermis, DermQuest, and PH2. The results of our advanced method showed superiority and contrast with the existing state-of-the-art techniques.

On-the-Job Training in Vocational College: Issue and Improvement Plan

This study was conducted to identify the issues and problems which arise from the perceptions of students, lecturers, supervisors and the industry towards the on-the-job training (OJT) program, which was implemented in Malaysian Vocational College (KV) for 6 months in selected industries. This study is also looking for potential suggestions for the improvement of OJT within Vocational College. This study was carried out using a qualitative research design through a multiple-embedded case study approach. The context of this study includes the challenges faced in the implementation of Vocational College OJT involving students (Case A), OJT supervisor lecturers (Case B) and organizational supervisors (Case C). The sample involved in this study was selected using the purposive sampling method. This study uses the thematic analysis method, using NVIVO12 software as well as content analysis using the matrix method and pattern matching for the cases involved. This study has identified several challenges and issues in Vocational College OJT involving the suitability of the training ground, OJT supervision, the evaluation process and the allowance which affects the implementation of OJT. An important suggestion for future best practise, is to come up with a database of local industries that offer OJT places. This is necessary to avoid the placement of students outside the skill area.

Magnetic Systems for Regenerative Medicine

Magnetic Systems for Regenerative Medicine

Parametric Models of Covariance Matrices for Repeated Measures: A Simulation Study on Fit, Error and Statistical Power in Mixed Linear Models

The premise in experiments with repeated measures is that observations taken in the same experimental unit are correlated and that correlations decrease proportionally to the increase in the distance between measurements in time or space. Nevertheless, these experiments are often analyzed as if the correlations between the repeated measures were constant or using methods that only consider correlations different, which may impact on the rejection rate of the null hypothesis, and ultimately type I error rate and statistical power. In this context, this study investigated the application of mixed linear models with different assumptions about the covariance matrix in data sets from simulated experiments with repeated measures. 84 scenarios that varied in terms of the covariance matrix pattern (14 structures), number of repeated measurements (4 and 8) and sample size (4, 8 and 12) were evaluated. 10,000 data sets were simulated for each scenario based on a multivariate normal distribution and were subsequently analyzed using mixed linear models. Type I error rate and statistical power for the hypothesis test of the interaction between treatments and repeated measures were estimated as the proportion of p values less than or equal to 0.01 or 0.05 out of a total of 10,000 tests for each scenario. The models were also evaluated for their ability to fit the data using Bayesian Information Criteria (BIC). Thus, the frequency with which the covariance structures were chosen by the selection criteria was computed. Results indicate that the assumption chosen most frequently by the information criteria resulted from the specified covariance structure that corresponded to the empirical covariance structure of the analyzed data sets, particularly for those with larger number of repeated measures and sample sizes. Results also indicate that the use of covariance models that do not recognize heterogeneous correlations between repeated measures can inflate type I error or reduce it to very conservative levels, which may affect the conclusion of agricultural experiments. For a 5% significance level, type I error bias was greater than 2α, while for 1% significance level, bias was over 5α. In addition, the statistical power was reduced when the assumption about the covariance matrix of the data sets did not correspond to the empirical covariance structure, particularly for those data sets with a smaller sample size.

Jaringan Saraf Tiruan Pengenalan Pola Huruf Sistem Matriks dengan Algoritma Hebb Rule

The science of artificial intelligence is currently very developed, this can be seen from the development of patterns and characters of artificial intelligence. This stems from the need to maintain security and information related to data so that it can be adjusted with a confidentiality key in the form of pattern and character lock recognition. Pattern recognition consists of various kinds such as facial recognition patterns and fingerprint patterns. The problem in this study is the lack of understanding in maintaining data confidentiality so that the benefits of this research are able to maintain data confidentiality by using a matrix pattern. This study uses the Hebb rule method to obtain the final results. The matrix variable used is from x1 to x25 input and bias 1 with initial weight value 0, W and M patterns are initialized with character x worth 1 and character o worth -1 with bipolar output, U target 1 and S target -1, function f( net) is 1 if Y >= 0 and -1 if Y < 0. The final result after the search process is obtained the value W = -8 and M=-8which produces the same output as the target and can be recognized

Blue-Green Space Changes of Baiyangdian Wetland in Xiong’an New Area, China

As a regulator of ecological environment, Baiyangdian Wetland is in a pivotal position in constructing the blue-green space (BGS) of Xiong’an New Area in China. This study aims to reveal the spatiotemporal changes of the BGS in Baiyangdian Wetland from 2016 to 2021. It uses Google Earth Engine (GEE) to calculate NDVI and NDWI based on Sentinel-2 Satellite remote sensing data and extracts the blue-green space by a classification model driven by NDVI and NDWI. Moreover, the land-use transfer matrix and landscape pattern indices are applied for evaluating the BGS changes in the wetland. According to the results, vegetation in the wetland shows no obvious spatial transfer. From 2016 to 2020, the BGS proportion in the wetland showed a stable increase, with the blue space getting larger by 10.8%. The indicators of the Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Contagion, and Landscape Shape Index (LSI) of the wetland decreased, suggesting a better ecological environment since the establishment of Xiong’an New Area in 2017. Based on the results, the author makes the following conclusion: the construction of BGS in Baiyangdian Wetland results in a well-organized ecological environment. The study provides a reference for building Xiong’an New Area and monitoring BGS changes in other regions.

Sharing Knowledge Online Lintas Fungsi sebagai Strategi Komunikasi Organisasi saat Pandemi

This research is based on the phenomenon that the management of PT Astra Agro Lestari Tbk applies the concept of a matrix organization in terms of the roles and responsibilities of public relations. The matrix pattern makes coordination happen across functions. The implementation of communication programs in the form of media relations in all subsidiaries spread across nine provinces becomes increasingly difficult when the work from home work pattern is implemented. This study wants to find out how the online sharing session strategy is carried out by the Communications and Public Affairs Division in overcoming the lack of knowledge of the Community Development Division as the field implementer of the communication program. The research method used is descriptive qualitative. Collecting data in this study using observation and interview techniques. Research concludes that online coordination can be a solution, but its effectiveness needs to be combined with cultural elements and formal strengths.

Hybrid Techniques for Diagnosis with WSIs for Early Detection of Cervical Cancer Based on Fusion Features

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%.