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Purpose The purpose of this paper is to explore public perceptions and key issues related to vasectomy in South Korea. In particular, this paper seeks to analyze how vasectomy is discussed on question-and-answer (Q&A) social platforms including NAVER Jisik iN (knowledge exchange service between NAVER users) and to identify topics that capture public interest. Methods In this study, based on data collected on NAVER Jisik iN from July 2019 to July 2024 with the keyword ‘vasectomy,’ this paper performs a co-occurrence matrix analysis on the Q&A data to identify key terms related to the topic. By applying connection centrality and community detection techniques, this paper visualizes and examines the network graph to uncover relationships between words and identify the primary topics of discussion. Results The co-occurrence matrix analysis reveals that keywords such as ‘surgery,’ ‘vasectomy,’ and ‘contraception’ play a central role in discussions about vasectomies. Both the Q&A data highlight the vasectomy procedure and contraceptive effectiveness as primary topics of discussion. The visualization graphs intuitively display the centrality and strength of connections between these keywords, as well as the interrelatedness of topics through distinct community groups. Conclusions The analysis shows that the questions primarily focused on the surgical procedure, while the answers were more concerned with contraceptive effectiveness and physiological aspects. These results suggest that the public’s interest in vasectomy is closely linked to its effectiveness, potential side effects, and related social issues.

Facial paralysis refers to the abnormal behavior of facial muscles caused by a disorder of the facial nerve, mainly manifested as facial asymmetry. In recent years, deep learning has found extensive applications in facial paralysis detection research. However, most existing methods are constrained to assessing the severity of facial paralysis, thereby concealing crucial symptoms within black-box models. Compared to the severity of facial paralysis, the symptoms of facial paralysis are of greater significance to both physicians and patients. To address this issue, this paper proposes a facial paralysis symptom detection model based on facial action units (AUs). To enhance the accuracy of AU intensity prediction, a novel Difference Ensemble Method (DEM) is introduced. This method leverages differential information between frames within the same video to improve the accuracy of predictions for the current frame. Building upon the predicted AU intensity sequences for keyframes in a video, an interpretable model for detecting facial paralysis symptoms is designed. This model employs an active means to describe the asymmetry in facial muscle strength and utilizes co-occurrence matrices to detect synkinesis. It is noteworthy that DEM is exclusively trained on a dataset of normal faces but exhibits excellent performance when transferred to a facial paralysis dataset. Additionally, DEM exhibits higher accuracy in predicting AU intensity compared to existing methods. The F1 scores for detecting facial muscle function in the eyebrow, eye, and mouth regions with our proposed model are 80.0%, 79.23%, and 90.91%, respectively. To demonstrate the model’s performance, a synkinesis detection experiment is conducted, further validating its applicability in facial paralysis detection.

Abstract Temporal data series of stable Artificial Lights At Night (ALAN) obtained from sources such as DMSP/OLS and VIIRS/DNB provide valuable insights into the dynamics of urban expansion. This study introduces a novel methodology for characterizing urban boundaries, which combines textural analysis utilizing the Co-occurrence matrix and urban surface delineation employing the Wombling contour detection algorithm. Applying this method to the city of Korhogo in northern Côte d'Ivoire, the findings reveal an irregular and gradual evolution of urban surfaces between 1992 and 2012, with a rate of change of 35 km2. However, starting from 2012, a rapid urbanization process is observed, continuing until 2020, characterized by an evolution rate of approximately 45 km2. Considering the significant urban expansion witnessed in the city of Korhogo, it is imperative to implement appropriate urban management strategies and measures for ecosystem protection.

Most of the existing static analysis-based detection methods adopt one or few types of typical static features for avoiding the problem of dimensionality and computational resource consumption. In order to further improve detecting accuracy with reasonable resource consumption, in this paper, a new Android malware detection model based on multiple features with feature selection method and feature vectorization method are proposed. Feature selection method for each type of features reduces the dimensionality of feature set. Weight-based feature vectorization method for API calls, intent and permission is designed to construct feature vector. Co-occurrence matrix-based vectorization method is proposed to vectorize opcode sequence. To demonstrate the effectiveness of our method, we conducted comprehensive experiments with a total of 30,000 samples. Experimental results show that our method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper a method to determine whether an image is forged (spliced) or not is presented. The proposed method is based on a classification model to determine the authenticity of a tested image. Image splicing causes many sharp edges (high frequencies) and discontinuities to appear in the spliced image. Capturing these high frequencies in the wavelet domain rather than in the spatial domain is investigated in this paper. Correlation between high-frequency sub-bands coefficients of Discrete Wavelet Transform (DWT) is also described using co-occurrence matrix. This matrix was an input feature vector to a classifier. The best accuracy of 92.79% and 94.56% on Casia v1.0 and Casia v2.0 datasets respectively was achieved. This performance is reached when Cb and Cr channels in YCbCr color space only are utilized to form this feature vector. The outcomes of the experiments showed that the proposed method achieved very good results in comparison with existing splicing detection approaches. [ABSTRACT FROM AUTHOR]

Studying images of a whole core (a sample of rock extracted from a well) is in demand in modern geophysics. The subject area determines the specifics of core image processing and the form of presentation of the results. A common way to represent well data is by depth-ordered measurement values. Core samples are also ordered by depth, and sample images are a collection of individual photographs or tomographic scans, often without data at some depths. A typical image of one core fragment contains a meter-long section of rock. In practice, it is often necessary to evaluate the characteristics of centimeter intervals. An approach to creating an ensemble of textural features of core images presented as depth-ordered profiles is proposed. The results can be used in conjunction with other geological and geophysical data. [ABSTRACT FROM AUTHOR]

Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations. Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrencematrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors. Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized. Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Geologists infer many issues associated with the formation of the Earth, depositional history and weathering processes based on rock assessment. Preparation of the thin sections of rocks and investigating these sections is one of the common methods in studying rocks. Since all further studies on the sections require the segmentation of existing minerals in thin sections, the accurate and automatic identification of minerals is very important. In this research, for the intelligent identification of minerals, digital images were prepared in ordinary and then polarized light of the minerals. The minerals were then classified by color features, and texture using support vector machine classification techniques. The result of applying the proposed algorithm on 75 thin sections consisting of 42 minerals show an accuracy of 99.25% in minerals segmentation using the polynomial-based support vector machine classification. Also, our proposed algorithm shows the best performance with other techniques by fivefold and tenfold cross-validation. The results indicate that the proposed method outperforms the state-of-the-art method in mineral thin section identification. In addition to the high accuracy, the proper speed of this method also shows the significant efficiency of the proposed method for the automatic identification of minerals. [ABSTRACT FROM AUTHOR]

Background. One of the most crucial aspects of someone’s life is health. Therefore, individuals should be conscious about keeping themselves healthy by regular monitoring their health, which can be done with the help of modern medical technologies. Wearable medical devices using wearable sensors are the popular names of emerging technologies in the modern healthcare domain. Aim. This work presents the results of a systematic investigation of extensive research that has occurred for the last two decades in these research streams to provide a comprehensive mapping and temporal distribution of wireless medical device research. Methods. This study presents a relationship between the bibliographic items, their quality, and the quantity representing the most effective research topics on wearable medical devices. The analysis is performed using two useful parameters, namely a bibliometric network and a co-occurrence matrix. Data collection, data standardization, data mapping, and result analysis are the steps involved in the bibliometric analysis technique. In this study, VOSviewer software for bibliometric analysis is applied to the Scopus database. Results. By analysing bibliometric indicators from the Scopus database and using VOSviewer, we represent their distribution in countries, institutions, top researchers, and top journals. Furthermore, we analyse the co-citation of cited authors and the co-occurrence of keywords. The outcomes of the clustering and keyword analysis indicate that the research domain primarily focuses on the Internet of Things, machine learning, wearable sensors, mobile health, electrocardiogram, etc. Conclusions. Statistical investigation in association with the visual exploration presented in this article provides more substantial information than either of them used separately. In the future, this article can illuminate researchers and practitioners to develop a different theory to look at the factors that influence predictability in the research domain of wearable medical devices.

Scenario endows a product with meanings. It has become the key to win the competition to design a product according to specific usage scene. Traditional scenario identification and product feature association methods have disadvantages such as subjectivity, high cost, coarse granularity, and limited scenario can be identified. In this regard, we propose a BERT-based scenario-feature identification model to effectively extract the information about users’ experience and usage scene from online reviews. First, the scenario-feature identification framework is proposed to depict the whole identification process. Then, the BERT-based scene-sentence recognition model is constructed. The Skip-gram and word vector similarity methods are used to construct the scene and feature lexicon. Finally, the triad is constructed through the analysis of scene-feature co-occurrence matrix, which realizes the association of scenario and product features. This proposed model is of great practical value for product developers to better understand customer’s requirements in specific scenarios. The experiments of scenario-feature identification from the reviews of Pacific Auto verifies the effectiveness of this method.

Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology.

Surface quality analysis of polished surface has been the subject of many classic studies in surface polishing technology and is a key indicator for evaluating the polishing path. However, as an important part of surface quality, surface texture features have been ignored in many related researches. In this paper, a new method to analyze the texture features of polishing based on co-occurrence matrix is proposed. It provides a new perspective of surface quality analysis focusing on surface texture features. It extends the previous approach termed the residual level co-occurrence matrix (RLCM) focusing on the distribution of surface polishing residues, leading to more targeted and stabilized evaluation results. This method can be used in the path planning stage to estimate the polishing quality of the planned path without physical processing, which can avoid resource waste in the physical world. Furthermore, in this method, the usage of images is avoided, which can ensure that the results are not affected by the light and image quality. Simulation experiments as well as empirical investigations were conducted to verify the feasibility of the method. The results of both consistently reveal that the proposed method is able to accurately describe the texture feature and correctly analyze the texture feature. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Purpose: The purpose of this paper is to explore public perceptions and key issues related to vasectomy in South Korea. In particular, this paper seeks to analyze how vasectomy is discussed on question-and-answer (Q&A) social platforms including NAVER Jisik iN (knowledge exchange service between NAVER users) and to identify topics that capture public interest., Methods: In this study, based on data collected on NAVER Jisik iN from July 2019 to July 2024 with the keyword 'vasectomy,' this paper performs a co-occurrence matrix analysis on the Q&A data to identify key terms related to the topic. By applying connection centrality and community detection techniques, this paper visualizes and examines the network graph to uncover relationships between words and identify the primary topics of discussion., Results: The co-occurrence matrix analysis reveals that keywords such as 'surgery,' 'vasectomy,' and 'contraception' play a central role in discussions about vasectomies. Both the Q&A data highlight the vasectomy procedure and contraceptive effectiveness as primary topics of discussion. The visualization graphs intuitively display the centrality and strength of connections between these keywords, as well as the interrelatedness of topics through distinct community groups., Conclusion: The analysis shows that the questions primarily focused on the surgical procedure, while the answers were more concerned with contraceptive effectiveness and physiological aspects. These results suggest that the public's interest in vasectomy is closely linked to its effectiveness, potential side effects, and related social issues.

Given the increased risks of water scarcity and the presence of polluting agents in water resources, this paper aims at the development and presentation of a computational tool capable of assessing water quality based on digital processing techniques applied to satellite images. Initially, a database was created for Brazilian regions, consisting of hydrographic basins' satellite images associated with the Water Quality Index (WQI), according to the criteria established by the National Water Agency (ANA). Hitherto, the database consisted of 85 images, 61 were used in the training stage and 24 in the testing stage. In both stages, the images were subjected to thresholding using Otsu's Method, binarization, linear expansion on saturation, application of a Laplacian filter, extraction of characteristics by using co-occurrence matrices and classification by the Bayes Discriminant. Such techniques were also implemented on a computational platform in the MATLAB® environment, responsible for the interface between the system and users. The proposed system presented an approximate 70% success rate regarding the classification of WQIs, which can be improved as more information is made available to improve the databases. HIGHLIGHTS Application of satellite images and processing techniques to assess water quality.; Assessment of water quality in hydrographic basins without on-site measurements.; Application of Otsu's method, linear expansion by saturation and Laplacian filter.; Water features extraction and classification by co-occurrence matrices and Bayesian Linear Discriminant Analysis Classifier.;

Identifying possible research gaps is a main step in problem framing, however it is increasingly tedious and expensive considering the continuously growing amount of published material. This situation suggests the critical need for methodologies and tools that can assist researchers in their selection of future research topics. Related work mostly focuses on trend analysis and impact prediction but less on research gap identification. This paper presents our first approach in automated identification of feasible research gaps by using a double-threshold procedure to eliminate the research gaps that are currently difficult to study or offer little novelty. Gaps are then found by extracting subgraphs for the less-frequent co-occurrences and correlations of key terms describing domains. A case study applying the methodology for electronic design automation (EDA) domain is also discussed in the paper. [ABSTRACT FROM AUTHOR]

Complexity is one of the major attributes of the visual perception of texture. However, very little is known about how humans visually interpret texture complexity. A psychophysical experiment was conducted to visually quantify the seven texture attributes of a series of textile fabrics: complexity, color variation, randomness, strongness, regularity, repetitiveness, and homogeneity. It was found that the observers could discriminate between the textures with low and high complexity using some high-level visual cues such as randomness, color variation, strongness, etc. The results of principal component analysis (PCA) on the visual scores of the above attributes suggest that complexity and homogeneity could be essentially the underlying attributes of the same visual texture dimension, with complexity at the negative extreme and homogeneity at the positive extreme of this dimension. We chose to call this dimension visual texture complexity. Several texture measures including the first-order image statistics, co-occurrence matrix, local binary pattern, and Gabor features were computed for images of the textiles in sRGB, and four luminance-chrominance color spaces (i.e., HSV, YCbCr, Ohta's I1I2I3, and CIELAB). The relationships between the visually quantified texture complexity of the textiles and the corresponding texture measures of the images were investigated. Analyzing the relationships showed that simple standard deviation of the image luminance channel had a strong correlation with the corresponding visual ratings of texture complexity in all five color spaces. Standard deviation of the energy of the image after convolving with an appropriate Gabor filter and entropy of the co-occurrence matrix, both computed for the image luminance channel, also showed high correlations with the visual data. In this comparison, sRGB, YCbCr, and HSV always outperformed the I1I2I3 and CIELAB color spaces. The highest correlations between the visual data and the corresponding image texture features in the luminance-chrominance color spaces were always obtained for the luminance channel of the images, and one of the two chrominance channels always performed better than the other. This result indicates that the arrangement of the image texture elements that impacts the observer's perception of visual texture complexity cannot be represented properly by the chrominance channels. This must be carefully considered when choosing an image channel to quantify the visual texture complexity. Additionally, the good performance of the luminance channel in the five studied color spaces proves that variations in the luminance of the texture, or as one could call the luminance contrast, plays a crucial role in creating visual texture complexity. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika 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.)

We consider the problem of the semantic representation of words based on explicit basis word vectors. We focus on explicit semantic vectors in which each basis vector is equivalent to a unique word. In this paper, we propose a new approach to represent semantic vectors explicitly. The approach consists of three steps: First, we introduce two new factors called word similarity (WS) and the number of zeroes (NZ) for choosing informative context words. Second, we propose a dependency distance method by incorporating the syntactic relations of the words in the sentences. This means that the method uses not only semantic information but also syntactic one. Third, we propose two shifting methods based on WF and WS factors to eliminate the bias of the PPMI matrix. In this work, we use the ukWaC corpus to select context words and compute co-occurrence matrices. Then, we examine the results on MEN, RG-65, SimLex-999, and WordSim353 test sets. We report the performance in comparison with a Baseline in which, the word frequency has been used. The Baseline method uses a window to fix the influence of the neighboring words. Our approach improves the Spearman correlation coefficient for MEN, RG-65, SimLex-999, and WordSim353 datasets by 3.87%, 10.25%, 5.04%, and 5.24%, respectively. So, the experiment results show that the proposed context words selection, incorporating dependency distance, and using the WF-based shifting method significantly improve the performance. [ABSTRACT FROM AUTHOR]

Product-service system (PSS) is an effective solution for service-oriented manufacturing. In the life cycle of PSS, evaluation decision of PSS alternatives is of great significance for subsequent implementation. Supported by the big data of stakeholder comments, a PSS evaluation decision technique is explored. Based on the multi-stakeholder comments of PSS evaluation decision's influence factors, the index system considering the environmental effect is constructed through analyzing and summarizing the co-occurrence matrix and semantic network diagram of high-frequency words. To determine the index value of PSS alternative, the stakeholders' vague opinions expressed by trapezoidal fuzzy number are fused. At last, PSS alternatives are evaluated by Kullback-Leibler divergence (KLD) modified TOPSIS. The case of PSS evaluation decision for a printer company shows that the explored technique is effective.

This study aimed to investigate the potential application of image texture processing method on visible crumb structure of salty cake pogácsa, which was prepared with different baking times (5 and 7 min) and temperatures (200, 215, and 230°C). For this purpose, changes in gray level co‐occurrence matrix (GLCM) features including energy, contrast, correlation, homogeneity, and entropy were monitored and their relationship with the instrumental texture parameters (hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness) were assessed. The pore ratios were also extracted and visualized using image processing technique. Texture profile parameters indicated strong correlation (p <.01) with the image pattern parameters in different pogácsa groups. Gumminess showed strong correlation with contrast (0.503), correlation (−0.498), and homogeneity (0.401). Hardness also exhibited correlation with contrast (0.517), entropy (0.341), and correlation (−0.476). The pore ratio showed marked variation in crumb structure when different times and temperatures were used. Baking at 230°C for 7 min maximized the pore ratio (0.56). Penalty analysis revealed that oiliness, pore structure, and color of products were linked with baking time and temperature. Overall, the results suggested that the GLCM‐based technique had the potential to be used as a nondestructive method for rapid quality assessment of pogácsa. [ABSTRACT FROM AUTHOR]

Ductile fracture occurs with large plastic deformation prior to fracture and is primarily characterized by voids of varying sizes. In this work, a new approach based on machine learning is proposed for the characterization of ductile fractures. The method is based on the application of a pre-processing of the fractographies, followed by a processing that includes the generation of a mask through a mathematical morphology, a classification is made according to their size in micrometers to later make a histogram of the size of the cavity in relation respect to its frequency of repetition in each fractography. Besides, the histogram is then normalized to generate a Probability Density Function (PDF). Based on the PDF, a third-order statistical metric known as Fractography Skewness (FS) is used to evaluate fracture susceptibility and is related to the Degree of Fracture Susceptibility (DFS). If the FS is negative (indicating a left-skewed PDF), the fractography is characterized by large voids and is assigned a DFS of 1, indicating low susceptibility. Conversely, a positive FS (indicating a right-skewed PDF) indicates the presence of small voids, resulting in a DFS of 3, indicating high susceptibility. The proposed quantitative methodology demonstrates a higher accuracy than the most advanced current techniques based on deep learning and the subjectivity of the specialist. • A novel ductile fracture evaluation framework has been described. • New concepts: Fractography Skewness and the Degree of Fracture Susceptibility. • Machine learning based method for classifying ductile fractures. • Higher accuracy than current techniques based on the subjectivity of the specialist. [ABSTRACT FROM AUTHOR]