Image Dimensions Research Articles

Deep Learning-Based Dzongkha Handwritten Digit Classification

In computer vision applications, pattern recognition is one of the important fields in artificial intelligence. With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. The purpose of conducting the research is to create the first-ever Dzongkha handwritten digit dataset and develop a model to classify the digit. In the study, the 3 layer set of CONV → ReLU → POOL, followed by a fully connected layer, dropout layer, and softmax function were used to train the digit. In the dataset, each class (0-9) contains 1500 images which are split into train, validation, and test sets: 70:20:10. The model was trained on three different image dimensions: 28 by 28, 32 by 32, and 64 by 64. Compared to image dimensions 28 by 28 and 32 by 32, 64 by 64 gave the highest train, validation, and test accuracy of 98.66%, 98.9%, and 99.13% respectively. In the future, the sample of digits needs to be increased and use the transfer learning concept to train the model.

Surfaces properties correlation with optical parameters of thickness dependent self-affine nanostructured SnS thin films: A study based on scaling law

Fractal concepts are employed to investigate the surface properties of tin sulfide (SnS) thin films, which are deposited on Fluorine-doped tin oxide (FTO)- coated glass substrates by the thermal evaporation (TE) technique. The surface topography of each deposited film is captured by AFM. The fractal dimensions of the AFM image were extracted through the Higuchi algorithm, and it is found that the thinner sample surface exhibits the maximum fractal dimension. The autocorrelation function and height-height correlation function are used to investigate the self-affinity behavior of surfaces. Dynamic surface roughening is characterized by different scaling exponents, such as the roughness exponent, the growth exponent, the dynamic scaling exponent, and the steepening exponent. It was observed that the values of average roughness, interface width, lateral correlation length, and mean square local slope are strongly influenced by film thickness. The self-affine fractal nature of thin film is estimated by the roughness exponent; however, island- or mound-type growth with quick surface roughening behavior is predicted by the growth exponent and the dynamic scaling exponent. Further, we have tried to correlate the optical properties such as transmission, reflection, and refractive index with the fractal dimension of SnS thin film. The increase in optical reflection with decreasing roughness indicates that absorber surfaces of the best crystalline films have the lowest reflectivity. The present results suggest that such surfaces, having a maximum fractal dimension and minimum optical reflectance, can be used as photon absorber layers for advanced solar cell devices.

The Role of Country Image on Consumers' Willingness to Pay for Imported Beefsteak in China.

In recent years, the world has experienced conflict. When political conflicts affect consumers' emotions and alter their perceptions of a country's image, it can influence their preferences. This study deconstructs the notion of a country image into multiple dimensions and examines their impact on consumers' willingness to pay for imported beef from Australia, Brazil, and the United States. Using a rank-ordered probit model and data from a survey of 935 respondents, results show that consumers' perceptions of a country's friendliness, economy, environment, and quality all have a positive and statistically significant effect on their willingness to pay for beefsteak imported from that country. Among these dimensions of the country image, the quality is the most important, followed by the economy, friendliness, and the environment. This study also finds heterogeneity in consumer perception of friendliness towards the United States, Australia, and Brazil. This study provides valuable insights for assessing the real losses resulting from a deteriorating international environment and suggests policies to enhance competitiveness in the food market.

Understanding the Crucial Role of Brand Image in the Students’ Higher Education Institution Choice: A Review of the Literature of Higher Education Branding

This article provides an extensive literature review on the topic of branding in Higher Education, along with an exploration of the numerous aspects that influence students’ choice of a Higher Education Institution (HEI). Within the competitive higher education landscape, branding has evolved into a vital part of HEI identity, image, and competitiveness, with a considerable impact on student decision-making. This review investigates the main concepts associated with branding and brand image in the educational context, beginning with the general branding concept and progressing to branding applied in this particular field, while taking into account the applicability and challenges of its transposition, before delving into the various perspectives of brand image, notably, the rational and emotional dimensions. By bringing together the existing body of knowledge concerning branding in higher education institutions, this article provides a better understanding of the impact of branding on the educational sector while also offering some insight into the appealing nature of the brand image throughout the students’ decision to enroll in a HEI.

Breast cancer prediction: a CNN approach

Detecting breast cancer promptly holds utmost importance in ensuring effective treatment, as it is a matter of great concern in the global health context. The primary objective of this research endeavor is to enhance the process of breast cancer identification through the utilization of a Convolutional Neural Network (CNN). The purpose of this study is to mitigate potential errors in human interpretation of mammograms by comparing this approach to conventional machine-learning techniques. In our present investigation on breast imaging, we have leveraged the well-established mammographic dataset, CBIS-DDSM. This dataset effectively categorizes the images into three distinct classes: normal, benign, or malignant. This compilation encompasses a grand sum of 10,239 images. A myriad of approaches were employed to arrange the content, including the manipulation of the image dimensions to a size of 256x256 pixels. A CNN architecture that was specifically crafted was educated through the fusion of backpropagation and angle plunge techniques. Numerous measures, such as sensitivity, specificity, F1 score, and accuracy, were deftly utilized to thoroughly assess the model's effectiveness. It is truly awe-inspiring to witness the outstanding exhibition of performance showcased by the CNN model, as evidenced by the extraordinary values attained for sensitivity, specificity, F1 score, total precision, and accuracy, all of which are undeniably remarkable. These evaluations undeniably serve as irrefutable evidence that the model possesses exceptional diagnostic capabilities, surpassing even the most advanced techniques currently in use. In truth, the model's performance is so exceptional that it has firmly established itself as a pioneering force in the field, leaving other techniques astounded and trailing far behind, in utter admiration of its immense potential and resounding success. This research elucidates the ability of Convolutional Neural Networks (CNNs) to mechanize and enhance the identification of breast cancer in mammographic images. The findings bring to light a captivating realm for forthcoming exploration, potentially fostering advancements in screen layout and the integration of more easily navigable diagnostic functionalities.

Perfekcjonizm a obraz ciała w grupie młodych kobiet i mężczyzn

Perfectionism is understood as a personality trait manifested by excessive striving for perfection, high standards and personal criticism. Criticism may concern self-evaluation, behavior, but also one’s body image. The purpose of the study was to investigate how perfectionism is associated with body perception. A question was posed to verify which dimensions of perfectionism have the greatest potential to explain the assessment of representatives of both genders. A total of 213 people were tested, including 108 women (M = 21.76; SD = 2.35). The study used the Big Three Perfectionism Scale (BTPS) and Body Esteem Scale (SOC-BES), of which the former was used to research 10 dimensions of perfectionism, and the latter to analyze three dimensions of self-image for women and three dimensions for men. The results of the regression analysis showed that some dimensions of perfectionism significantly explain individual dimensions of body image for both genders. For women, the following were significant: Self-oriented perfectionism, Self-criticism, Doubts about actions, and Hypercriticism. For men, the following were significant: Self-oriented perfectionism, Concern over mistakes, and Doubts about actions. All regression models explained 17% to 28% of the variance in body image assessment, indicating their associations with perfectionism dimensions.

HyperColorization: propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images.

Hyperspectral cameras face challenging spatial-spectral resolution trade-offs and are more affected by shot noise than RGB photos taken over the same total exposure time. Here, we present a colorization algorithm to reconstruct hyperspectral images from a grayscale guide image and spatially sparse spectral clues. We demonstrate that our algorithm generalizes to varying spectral dimensions for hyperspectral images, and show that colorizing in a low-rank space reduces compute time and the impact of shot noise. To enhance robustness, we incorporate guided sampling, edge-aware filtering, and dimensionality estimation techniques. Our method surpasses previous algorithms in various performance metrics, including SSIM, PSNR, GFC, and EMD, which we analyze as metrics for characterizing hyperspectral image quality. Collectively, these findings provide a promising avenue for overcoming the time-space-wavelength resolution trade-off by reconstructing a dense hyperspectral image from samples obtained by whisk or push broom scanners, as well as hybrid spatial-spectral computational imaging systems.

EVALUASI CITRA RUMAH SAKIT DI RUMAH SAKIT PKU MUHAMMADIYAH BANTUL

Background: DIY province has 70 hospitals, Bantul district has 10 hospitals that serve 859.968 inhabitants with 793 beds. Although the amount of hospital or beds was not enough, it was important for hospital to keep their image, it’s necessary to maintain or expand their market confidence.
 Method: It’s Quantitative-Qualitative study, cross-sectional survey and descriptive analytic. The quantitative used for determining images position, It’s used questionnaire to 118 respondents with purposive sampling methode. The qualitative used for supporting quantitative result and became reasons for improvements of dimensional image. Data Analysis used the differential semantic, four instruments and theory of marketing strategic.
 Result and discussion: There were 23 dimensions of hospital image, all of them show good images, doctors are skilled and experienced (98%.), skilled nursing (92%), professionalism (97%), neat and clean officer’s clothes (92%); organized and comfortable rooms (81%), building and rooms cleanliness (86%); complete facilities (85%), comfortable and hygine facilities (81%); complete medical equipments (88%), complete pharmaceuticals (95%); uncomplicated procedures (93%), officer’s timeliness (76%); sufficient consultation time (83%), attention and sympathy officers (92%), willingness to respond complaints (97%), hospitality (95%); appropriate price (95%), ease of insurance used (88%); strategic location and affordable (97%), inpatient room easily obtained (59%), ease to see the doctor (83%), information access (98%), understandly boards (76%). Image improvement priorities: timeliness officer, ease to see the doctor, adequacy of consultation time, board reconstruction, inpatient room additions, comfortable and hygine facilities, organized and comfortable rooms.
 Conclusions: All of hospital images were good. There were 7 dimension still need to be improved.

Evidence of Validity, Invariance, and Reliability of the Body Image Dimension in the Body Investment Scale: A Study in Spanish University Students

Background: This study focused on the widely used Body Image subscale, a dimension of the Body Investment Scale developed by Orbach and Mikulincer in 1998. Specifically, we explored its psychometric properties and potential use for health promotion research among young Spanish university students. Method: A sample of 793 participants (75.28% female) aged 18–26 years (M = 20.68; SD = 2.13) completed the questionnaire and related variables. Results: A unidimensional structure was confirmed with a good fit, demonstrating gender- and age-invariance, along with robust internal consistency. The scale exhibited a significant association with self-esteem, life satisfaction, a sense of coherence, and psychological distress. Conclusions: The Body Image subscale can be considered unidimensional. The obtained factor solution provides a reliable, valid, and invariant measure across gender and age for assessing body feelings in Spanish university students. Therefore, the instrument can effectively investigate the relationship between body image and health-related behaviors. Additionally, it can serve as a valuable tool in designing effective health interventions for university students to prevent mental health conditions, such as eating disorders or suicidal behaviors.

Relationship between quality of life, emotional symptomology and perceived emotional intelligence in a sample of burn victims

The main objective of this study is to analyse the association between Quality of Life (QOL), Emotional Symptomology and perceived Emotional Intelligence (EI) in burn patients. Additionally, it is intended determine the predictor models of QOL, and confirm the mediating effect of emotional symptomology between QOL and perceived EI. This is a transversal study developed in the Hospital da Prelada, Porto, Portugal, with a sample of 92 patients that were hospitalized in the Burn Unit and the Reconstructive Plastic Surgery Service. The assessment protocol consisted of a sociodemographic and clinical data sheet. To assess the perception of QOL of the burn patient it was used the Burn Specific Health Scale – Revised (BSHS-R), the emotional symptomology was measured by the Brief Symptom Inventory (BSI) and Trait Met-Mood Scale-24 (TMMS) was used to assess Emotional Intelligence (EI). The cross-sectional and correctional data were analysed through descriptive statistics, correlations, regressions and simple mediations. The results obtained suggest significant associations between QOL, perceived EI and Emotional Symptomology in burn patients. The results of the predictor models of the QOL domains encompass the Positive Symptom Distress Index (PSDI of Emotional Symptomology), where the total variance is explained mainly by the models of QOL Affect and Body Image 46% and Treatment 31%. The mediating effect of the PSDI in the relationship between QOL in the Affect and Body Image dimension and the Mood Repairs (MR) was also tested, having proved to have a total mediation (the Mood Repairs loses its contribution in the QOL model when the PSDI variable is introduced). This study underscores the importance of perceived Emotional Intelligence and its association with the burn impact in the different dimensions of QOL of the patients. The intention of this study is to alert health professionals for patient support in the search for strategies that aim for positive adaptation which promotes QOL and emotional adjustment of burn patients to their new condition.

Impact of Athlete Brand Image Dimensions on the Young Consumers’ Psychological Attachment

Purpose: The study is designed to check the impact of human brand image dimensions (other than performance related) on young customers’ psychological attachment toward athlete brands. Design/Methodology/Approach: The study was conducted among young athlete fans randomly selected from Kerala, India. The data were collected from 200 sample respondents using a questionnaire. The data were analyzed using the structural equation model. Findings: The findings reveal that variables such as relationship effort and life story of the athletes contribute to the creation of psychological attachment among the fans. Practical Implications: The result of the present study is a value addition for the marketing of human brands, especially for athlete brands. The results will be helpful in determining what factors should be concentrated on and how much weightage should be given to each factor for making a human brand successful in the market. Originality/Value: The study examines the influence of human brand image dimensions (other than performance-related dimensions) on young customers’ psychological attachment toward athlete brands. As the majority of the sports aspirants are from the young age group, the study variables are tested among young customers of athlete brands. Individual players, managers, and coaches can use the tested variables for analyzing, testing, and managing human brands (athlete brands).

Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions

Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions

Distinguishing tea stalks of Wuyuan green tea using hyperspectral imaging analysis and Convolutional Neural Network

Wuyuan green tea is a famous agricultural product in China and a product protected by national geo-graphical indications. The processed green tea also needs to remove impurities, such as stones, tea stalks, etc. However, tea stalks cannot be classified from Wuyuan green tea using photoelectric sorting and 2D image recognition technology since they have similar colors. This paper adopts hyperspectral imaging technology to solve the problem of inaccurate sorting caused by their similar colors. Green tea containing tea stalks was imaged using a visible and near-infrared camera with a wavelength of 400nm-1000nm. What’s more, Principal Component Analysis (PCA) was adopted to reduce the dimension of the col-lected hyperspectral image. And the Convolutional Neural Network (CNN) was used constructively to identify tea stalks in hyperspectral image, the CNN can automatically learn the corresponding features, avoid the complex feature extraction process. The experimental results showed that the recognition accuracy for tea stalks reaches 98.53%. The method has a high recognition rate and can meet the actual production requirements. After field testing, the selection rate is as high as 97.05%.

3D versus 2D: Effects of the number of dimensions of product images on perceptions of product size

AbstractWhen shopping online, it is difficult to estimate size from visual cues in the form of product images. While package shape and color have been discussed as factors influencing perceptions of product size, this research focuses on the influence of visual cues, such as the number of dimensions of the product image (i.e., two‐dimensional image vs. three‐dimensional image) on perceptions of product size. This study involved three surveys whose results revealed that three‐dimensional product images are perceived as larger products than two‐dimensional product images. Moreover, three‐dimensional product images positively influenced perceived product weight, suggesting a downstream effect from perceived weight on willingness to pay (WTP, Study 3). The results of this study contribute to a better understanding of how consumers perceive product size from product images in online retailing. Specifically, the results of this study extend our understanding of the mechanisms behind product size perception by including the influential factor of product image dimensionality in size perception bias. In addition, these findings have practical implications. In recent years, food packaging has become smaller, but consumers may react negatively to smaller packages, even when the volume of the contents remains unchanged. For these practical concerns, it may be possible to mitigate the negative effects of downsizing by presenting product images in a three‐dimensional format.

Inexact augmented Lagrangian method-based full-waveform inversion with randomized singular value decomposition

Abstract The main advantage of full-waveform inversion (FWI) is the ability to obtain useful subsurface structure information, such as velocity and density, from complex seismic data. We have developed a novel inversion algorithm to improve the capability of FWI to achieve high-resolution imaging, even under complex conditions caused by random noise contamination, initial model dependence, or the selection of parameters to be estimated. Our algorithm considers an effective image processing and dimension reduction tool, randomized singular value decomposition-weighted truncated nuclear norm regularization, for embedding FWI to achieve high-resolution imaging results. This algorithm obtains a truncated matrix approximating the original matrix by reducing the rank of the velocity increment matrix, thus achieving the truncation of noisy data, with the truncation range controlled by weighted truncated nuclear norm regularization. Subsequently, we employ an inexact augmented Lagrangian method algorithm in the optimization to compress the solution space range, thus relaxing the dependence of FWI and randomized singular value decomposition-weighted truncated nuclear norm regularization on the initial model and accelerating the convergence rate of the objective function. We tested on one set of synthetic data, and the results show that, compared with traditional FWI, our method can more effectively suppress the impact of random noise, thus obtaining higher resolution and more accurate subsurface model information. This work indicates that the combination of randomized singular value decomposition-weighted truncated nuclear norm regularization and FWI is an effective imaging strategy that can help to solve the challenges faced by traditional FWI.

A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

Multi-secret visual sharing schemes are essential for secure and efficient sharing of sensitive visual information. However, existing schemes often overlook important considerations such as varied secret dimensions, contrast enhancement, and computational efficiency. This research addresses these challenges by proposing a comprehensive approach that addresses these limitations. Firstly, the scheme takes into account the varied dimensions of secret images encountered in real-world scenarios, allowing flexibility in sharing and reconstructing images of different sizes and aspect ratios. Secondly, the research integrates contrast enhancement techniques, such as blind super-resolution, to improve the visual quality and visibility of shared secret images affected by factors like noise, compression, or low lighting conditions. Lastly, to enhance the computational efficiency, the scheme leverages the power of Graphics Processing Units (GPUs) for parallel computing, enabling faster processing of large-scale image operations. The proposed scheme achieves outstanding results, with a high PSNR of 98.264, a strong NCC value of 0.965, an exceptionally low NAE value of 0.046, and an impressive SSIM value of 0.983. Furthermore, the GPU implementation provides a remarkable overall speedup of 112×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$112\ imes$$\\end{document} for 256×256\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$256 \ imes 256$$\\end{document} color images.

Modeling and analysis of spatial difference and evolution of agricultural parks based on topological segmentation and cluster analysis

When the traditional evolution model studies the regional distribution of agricultural parks, the relationship between regions is not clear enough, which leads to the lack of generality of regional distribution. In order to solve this problem, this study adopts the methods of topological division and cluster analysis, establishes the model of regional diversity and evolution of farms, and clusters the spatial information of agricultural parks. According to the feature factors, the images are classified, and the image dimension values are reduced. The data space is divided into cellular space by the method of network topology structure division, and the effective coefficient of each cell is calculated, and the spatial structure and characteristics of agricultural parks are extracted to reveal the similarities and differences between different parks. The experimental results show that the evolution model of agricultural parks constructed by topological division and clustering method shows obvious clustering characteristics in space, and the relationships among the factors are good. It is proved that the model can describe the spatial differences and evolution trends of agricultural parks more accurately, so as to provide more targeted suggestions for the planning, management and sustainable development of agricultural parks.

Institusionalisasi Partai Politik: Studi Kasus Partai Demokrat di Era Kepemimpinan Agus Harimurti Yudhyono Periode 2020-2022

This research discusses the institutionalization of the Democratic Party in the leadership era of Agus Harimurti Yudhyono (AHY) for the 2020-2022 period. The research results show that the Democratic Party in the era of AHY's leadership for the 2020-2022 period has made various efforts to realize ideal party institutional strengthening. However, the identification of the Democratic Party as a Family Party with the placement of Susilo Bambang Yudhyono (SBY) as Chair of the Party High Council and Edi Baskoro Yudhyono (Ibas) as Chair of the Party Fraction in the structure of the Democratic Party's Central Leadership Council (DPP) for the 2020-2025 period is a challenge in itself. the process of strengthening the party's institutions. Nevertheless, the results of this research show that the Democratic Party in the era of AHY's leadership for the 2020-2022 period has a relatively strong level of institutionalization. This shows that, even though the Democratic Party is identified as the Family Party, the influence of the family party factor does not have a significant impact on the institutional weakening of the Democratic Party in the era of AHY's leadership for the 2020-2022 period. This research uses Randall and Svasand's theory of political party institutionalization through internal aspects viewed from the systemic dimension and external aspects viewed from the public image dimension. Meanwhile, to identify the Democratic Party as a Family Party, the theory and concept of the Family Party from Peter Mair and Case Mudde is used through the dimensions of the party's origins and ideology.

A Deep Learning Approach to Discover Router Firmware Vulnerabilities

An attack on Industrial IoT systems can cause severe damage to connected devices and their owners. Therefore, detecting router firmware vulnerabilities has become a critical issue. However, collecting a dataset of firmware samples is challenging as no open-source datasets are available online. A manual effort was required to verify the states of samples in both the Common Vulnerabilities and Exposures (CVE) and The National Vulnerability Database (NVD) databases as either vulnerable or benign. After verification, 1450 samples were collected. This paper investigates the effectiveness of using convolutional neural networks (CNNs) and computer vision techniques to analyze home router firmware. The collected firmware samples were read as an array of byte strings, divided into sub-arrays based on the image's dimensions, then layered on top of one another to produce the firmware images. The images were divided by manufacturer and used as inputs for various CNN models to test their accuracy. Three statistical filtering algorithms were used on each manufacturer's set to produce multiple versions of each set, totaling 24 datasets across four manufacturers, with six datasets per manufacturer (4 filtered images and two grayscale and RGB images). The image filter algorithms used include local binary pattern (LBP), histogram-oriented gradients (HOG), and Gabor filter used on the LBP and HOG sets. After testing all combinations of the filtered/normal datasets with the CNN training model, the HOG filter was the most accurate, with an average accuracy of 85.81% across all tests and models, with results as high as 97.94% when used with the appropriate CNN model.

DENG ENTROPY AND INFORMATION DIMENSION FOR COVID-19 AND COMMON PNEUMONIA CLASSIFICATION

Motivated by previous authors’ work, where Shannon entropy, box covering and information dimension were applied to quantify pulmonary lesions, this paper extends such a contribution in two fashions: (i) Following the approach to quantify pulmonary lesions with Deng entropy and Deng information dimension obtained through box covering method; (ii) exploiting the Shannon and Deng lesion quantification for pulmonary illnesses classification with a bidirectional Long Short Term Memory (bLSTM). The referred pulmonary illnesses are Common Pneumonia (CP) and COVID-19. Shannon entropy and information dimension are performed here and called the Shannon sequence. Then, Deng entropy and Deng information dimension are computed for chest Computed Tomography (CT) images to obtain and combine two data sequences to quantify the pulmonary lesions. The data sequence resulting from the data combination is called the Deng sequence. Both Shannon and Deng sequences are independently used as input for the bLSTM. CT lung scans of 531 healthy subjects, 497 confirmed COVID-19 diagnoses and 516 with CP were analyzed to obtain the Shannon and Deng sequences. The results demonstrate that Deng entropy and Deng information dimension of CT images can differentiate similar lung lesions between COVID-19 and CP. Besides, a statistical analysis shows that: (a) Classification by the bLSTM is better when using the Deng sequence than the Shannon sequence; (b) Deng sequences plus bLSTM significantly outperform DenseNet-201, GoogLeNet and MobileNet-v2 in classifying COVID-19, CP and Normal CT (healthy subjects) in time and accuracy. Hence, the Deng sequence and bLSTM are fast and accurate tools for helping in diagnosing CP and COVID-19.