Search results for: perceived image

Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Tamer Koburtay, Radi Haloub

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Person-Organization fit theory concerns how people flourish in a workplace that is congruence with their values and other traits. This paper seeks to highlight the theoretical relevance that workplace spirituality may add to the existing theory development of the P-O fit. In specific, it aims to empirically test the emerged framework that encompasses how workplace and self-spirituality match may enhance the perceived P-O fit, and how such a fit can enhance both employees’ ethical behaviors (i.e., humanism and honesty) and spiritual leadership behaviors. Drawing on a survey of the private and public sectors in Jordan, the results reveal that increasing the match in workplace and employees’ spirituality positively enhances the perceived P-O fit. Further, ethical and spiritual behaviors were found to be positively linked with a higher P-O fit. The importance of this paper is by generating a concept (i.e., P-O spirituality fit) beyond the already vast literature on P-O fit.

Keywords: ethical behavior, leadership, P-O fit, spirituality, leadership

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Authors: Ludivine Gueho, Sylvain Fleury, Eric Jamet

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Driver distraction is known to be a major contributing factor of car accidents. Since many years, constructors have been designing embedded technologies to face this problem and reduce distraction. Being able to predict user acceptance would further be helpful in the development process to build appropriate systems. The present research aims at modelling the acceptability of a specific system, an innovative personalized and contextualized embedded radio, through an online survey of 202 people in France that assessed the psychological variables determining intentions to use the system. The questionnaire instantiated the dimensions of the extended version of the UTAUT acceptability model. Because of the specific features of the system assessed, we added 4 dimensions: perceived security, anxiety, trust and privacy concerns. Results showed that hedonic motivation, i.e., the fun or pleasure derived from using a technology, and performance expectancy, i.e., the degree to which individuals believe that the characteristics of the system meet their needs, are the most important dimensions in determining behavioral intentions about the innovative radio. To a lesser extent, social influence, i.e., the degree to which individuals think they can use the system while respecting their social group’s norms and while giving a positive image of themselves, had an effect on behavioral intentions. Moreover, trust, that is, the positive belief about the perceived reliability of, dependability of, and confidence in a person, object or process, had a significant effect, mediated by performance expectancy. In an applicative way, the present research reveals that, to be accepted, in-car embedded new technology has to address individual needs, for instance by facilitating the driving activity or by providing useful information. If it shows hedonic qualities by being entertaining, pretty or comfortable, this may improve the intentions to use it. Therefore, it is clearly important to include reflection about user experience in the design process. Finally, the users have to be reassured on the system’s reliability. For example, improving the transparency of the system by providing information about the system functioning, could improve trust. These results bring some highlights on determinant of acceptance of an in-vehicle technology and are useful for constructors to design acceptable systems.

Keywords: acceptability, innovative embedded radio, structural equation, user-centric evaluation, UTAUT

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Authors: Matt Fullmer, Carol Wilkinson, Keven Prusak, Dennis Eggett, Todd Pennington

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Obesity and chronic health issues are linked to physical inactivity. Physical education (PE) programs in school can play a major role in combating these health-related issues. This study focused on supporting competence through keeping a leisure-time physical activity (LTPA) record as part of PE. Keeping a daily LTPA record may be an effective intervention helping students feel more competent toward exercise, and thus, self-determined (within the context of self-determination theory) to exercise. Little is known about the relationship between daily LTPA records and perceived competence, motivational profiles, and LTPA levels of students enrolled in PE. This study examined the relationship between keeping a daily, online LTPA record and adolescent (a) motivational profiles toward LTPA, (b) perceived competence toward LTPA, and (c) LTPA behaviors. Secondary students (N = 124) in physical education classes completed a baseline questionnaire which consisted of the Behavioural Regulation in Exercise Questionnaire–2, the Perceived Competence Scale, and the Godin Leisure-Time Exercise Questionnaire. For three weeks, the students were assigned to keep the Presidential Active Lifestyle Award Challenge (PALAC) as their online LTPA record. They completed the questionnaire after each week. A 2 (gender) x 4 (trials) repeated measures ANCOVA examined the relationships between recording compliance and motivation, perceived competence, and physical activity. Results showed that recording compliance was not a significant predictor of perceived competence to participate in LTPA. Examining motivational factors, a significant interaction between recording compliance and introjected regulation was found. The more students recorded the less motivated they were by guilt or obligation to exercise in their leisure-time. Also, a significant interaction was found between recording compliance and intrinsic regulation, indicating that the more students recorded the more intrinsically motivated they were to exercise in their leisure-time. Lastly, there was a significant interaction between recording compliance and LTPA. As students kept the LTPA record, girls’ LTPA levels significantly decreased and boys’ LTPA levels significantly increased. The key findings are that, as implemented in this study: a) the lack of PALAC compliance suggests that daily LTPA records may NOT be the most effective intervention for this population, b) keeping a daily LTPA record did NOT help students feel more competent to exercise in their leisure-time, c) a daily LTPA record may help students move towards being more self-determined in their feelings towards LTPA, and d) the outcome of keeping a LTPA record on LTPA behavior is statistically significant, although actual differences may not be practically important.

Keywords: behavioural regulation in exercise questionnaire–2, Godin leisure–time exercise questionnaire, online physical activity log, perceived competence scale, self-determination theory

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Authors: J. Erina, I. Erins

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The goals of the present research are to estimate Six Sigma implementation in Latvian commercial banks and to identify the perceived benefits of its implementation. To achieve the goals, the authors used a sequential explanatory method. To obtain empirical data, the authors have developed the questionnaire and adapted it for the employees of Latvian commercial banks. The questions are related to Six Sigma implementation and its perceived benefits. The questionnaire mainly consists of closed questions, the evaluation of which is based on 5 point Likert scale. The obtained empirical data has shown that of the two hypotheses put forward in the present research Hypothesis 1 has to be rejected, while Hypothesis 2 has been partially confirmed. The authors have also faced some research limitations related to the fact that the participants in the questionnaire belong to different rank of the organization hierarchy.

Keywords: six sigma, quality, commercial banking sector, latvian

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Authors: Misha Alexander, S. B. Waykar

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Visual Cryptography is a special unbreakable encryption technique that transforms the secret image into random noisy pixels. These shares are transmitted over the network and because of its noisy texture it attracts the hackers. To address this issue a Natural Visual Secret Sharing Scheme (NVSS) was introduced that uses natural shares either in digital or printed form to generate the noisy secret share. This scheme greatly reduces the transmission risk but causes distortion in the retrieved secret image through variation in settings and properties of digital devices used to capture the natural image during encryption / decryption phase. This paper proposes a new NVSS scheme that extracts the secret key from randomly selected unaltered multiple natural images. To further improve the security of the shares data hiding techniques such as Steganography and Alpha channel watermarking are proposed.

Keywords: decryption, encryption, natural visual secret sharing, natural images, noisy share, pixel swapping

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Authors: Abraham Terán Salcedo, Didier Samayoa Ochoa

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This work presents a numerical implementation of a method for estimating the box-counting dimension of self-avoiding curves on a planar space, fractal objects captured on digital images; this method is named fractioning estimator. Classical methods of digital image processing, such as noise filtering, contrast manipulation, and thresholding, among others, are used in order to obtain binary images that are suitable for performing the necessary computations of the fractioning estimator. A user interface is developed for performing the image processing operations and testing the fractioning estimator on different captured images of real-life fractal objects. To analyze the results, the estimations obtained through the fractioning estimator are compared to the results obtained through other methods that are already implemented on different available software for computing and estimating the box-counting dimension.

Keywords: box-counting, digital image processing, fractal dimension, numerical method

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Authors: Sanja Obradovic, Vladimir Stojanovic

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The purpose of this paper is to examine the attitudes of residents toward sustainable tourism development in the Bačko Podunavlje Biosphere Reserve (BPBR) in northwestern Serbia. BPBR is a part of 'the European Amazon', world's first five-country Transboundary UNESCO Biosphere Reserve 'Mura-Drava-Danube'. Sustainable tourism development requires the engagement of local residents. Within the initial stage of tourism development, it is important to address residents' attitudes from the early beginning, thus further involve the local community through all phases of development, which in return will largely influence overall success. Data were collected through in-person (face-to-face) questionnaire. The research also addresses the quality of the sustainable tourism attitude scale (SUS-TAS), perceived as an instrument to measure local communities' attitudes towards sustainable tourism development. SUS-TAS has seven variables, which are named as environmental sustainability, perceived social cost, long-term planning, perceived economic benefit, community center economy, ensuring visitor satisfaction, and maximizing community participation. Data were analyzed using SPSS. Findings indicate that residents have a positive attitude toward the development of sustainable tourism in the BPBR. They also recognized the importance of environmental sustainability and preservation for future generations. The study shows that BPBR has a very good community to support sustainable tourism activities in each area considered.

Keywords: biosphere reserve, local resident's attitude, sustainable tourism attitude scale, SUS-TAS, sustainable tourism

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Authors: Jacques Wels

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Japan has one of the highest average retirement ages within the OCDE and is paving the way to raise the retirement age to 70. However, the Japanese labour market is facing two main issues that can have detrimental effects on health: non-standard employment forms are widespread among the ageing workforce, and poor working conditions can contribute to explain poor health in late career. To assess such a relationship, the study uses data from JSTAR. Using mediation analysis, it particularly looks at the association between job dissatisfaction, employment status, self-perceived health (SPH), and use of health care services. Results show that work quality and employment status are associated with SPH. Contract work has a particularly negative impact and therefore contributes to explain the use of health care services but is not significantly associated with lower job satisfaction levels. SPH is a good predictor of the use of health care services.

Keywords: self-reported health, occupational health, employment, older workers, mediation

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Authors: Ankur Dixit, Hiroaki Wagatsuma

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The crack detection on a concrete bridge is an important and constant task in civil engineering. Chronically, humans are checking the bridge for inspection of cracks to maintain the quality and reliability of bridge. But this process is very long and costly. To overcome such limitations, we have used a drone with a digital camera, which took some images of bridge deck and these images are processed by morphological component analysis (MCA). MCA technique is a very strong application of sparse coding and it explores the possibility of separation of images. In this paper, MCA has been used to decompose the image into coarse and fine components with the effectiveness of two dictionaries namely anisotropic diffusion and wavelet transform. An anisotropic diffusion is an adaptive smoothing process used to adjust diffusion coefficient by finding gray level and gradient as features. These cracks in image are enhanced by subtracting the diffused coarse image into the original image and the results are treated by Sobel edge detector and binary filtering to exhibit the cracks in a fine way. Our results demonstrated that proposed MCA framework using anisotropic diffusion followed by Sobel operator and binary filtering may contribute to an automation of crack detection even in open field sever conditions such as bridge decks.

Keywords: anisotropic diffusion, coarse component, fine component, MCA, Sobel edge detector and wavelet transform

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Authors: Ching-Yi Huang, Pi-Fen Cheng, Hui-Zhu Chen, Shi Ting Huang, Heng-Hua Wang

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This is a case of oral cancer after extensive resection and modified right lateral neck lymph node dissection followed by reconstruction with a skin flap. The nursing period lasted From September 25 to October 3, 2017, through observation, interview, physical assessment, and medical record review, the author identified the following nursing problems: acute pain, impaired oral mucous membrane, and body image change. During the nursing period, the author provided individual and overall nursing care and established mutual trust through the use of empathy. Author listened and eased the patient's physical indisposition, such as wound pain, we use medications and acupuncture massage to relieve pain. However, for oral mucosa change caused by surgery, provide continuous and complete oral care and oral exercise training to improve oral mucosal healing and restore swallowing function. In the body-image changes, guided him to express his feeling after the body-image change, and enhanced support and from the family, and encouraged him to attend head and neck cancer survivor alliance which allowed the patient to accept the altered body image and reaffirm self-worth. Hopefully, through sharing this nursing experience will help to the nursing care quality of nursing care for oral cancer patients after extensive resection and modified right lateral neck lymph node dissection followed by reconstruction with a skin flap.

Keywords: oral cancer, acute pain, impaired oral mucous membrane, body image change

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Authors: Yi-Ling Lin, Lu-Ming Tseng

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Customer sexual harassment is recognized as a serious problem in the personal selling industry. At a personal level, customer sexual harassment could have very negative impacts on the salespeople's physical and mental health. At the organizational level, customer sexual harassment is destructive in terms of organizational reputation. Therefore, this research takes Taiwan's life insurance salesperson as the research sample and explores the impacts of customer power and perceived behavioral control on the life insurance salespeople's whistleblowing intentions to report quid pro quo and hostile work environment types of customer sexual harassment. This study then investigates how personal factors (such as gender difference) may relate to the intentions. Questionnaires are often used as a data collection instrument in studies on workplace sexual harassment. This study collects data through questionnaire surveys, and the research sample of this research is the full-time life insurance salespeople in Taiwan. The hypotheses are examined by using PLS regression approach. The main results show that the types of customer sexual harassment, customer power, and gender are related to the whistleblowing intentions. To our best knowledge, this is the first empirical study to test the relationships among customer reward power, customer coercive power, perceived behavioral control, and the salespeople's whistleblowing intentions toward customer sexual harassment. The findings may provide some implications for the researchers and official authorities.

Keywords: customer sexual harassment, life insurance salespeople, perceived behavioral control, PLS regression

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Authors: Hamza A. Al-Sewadi, Akram N. A. Aldakari

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Copyright protection and ownership proof of digital multimedia are achieved nowadays by digital watermarking techniques. A text watermarking algorithm for protecting the property rights and ownership judgment of color images is proposed in this paper. Embedding is achieved by inserting texts elements randomly into the color image as noise. The YIQ image processing model is found to be faster than other image processing methods, and hence, it is adopted for the embedding process. An optional choice of encrypting the text watermark before embedding is also suggested (in case required by some applications), where, the text can is encrypted using any enciphering technique adding more difficulty to hackers. Experiments resulted in embedding speed improvement of more than double the speed of other considered systems (such as least significant bit method, and separate color code methods), and a fairly acceptable level of peak signal to noise ratio (PSNR) with low mean square error values for watermarking purposes.

Keywords: steganography, watermarking, time complexity measurements, private keys

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Authors: Sanjeev Kumar, Swati Choudhary

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Background: A variety of studies warn that sedentary lifestyles can contribute to many preventable causes of death. This study was taken to determine the effects of two types of aerobic training programs on erythrocytes, leukocytes, hemoglobin concentration (Hb), platelets and hematocrit of sedentary people (N=60) with age group 20 to 30 years. Methods: All the subjects were randomly divided into three groups i.e. two experiments groups (aerobic dance & cardio fitness) and control group. Each group having 10 male and 10 females. Experimental groups undergone 60 minutes of training 5 times a week for 12 weeks whereas the control group did not participate in any training program except their daily routine. The aerobic dance group was chosen to perform exercise like step –touch, side-to-side, V-step and hand and body movements, etc. The cardio fitness group was chosen to perform exercises with modern fitness equipment like treadmill, elliptical trainer, stationary bike and rowing machine. Rating of perceived exertion (RPE) scale developed by Gunner Borg was used to monitor the intensity of the workout. Aerobic programs were encompassed of low-impact (0- 4 week & perceived exertion from 6 to 12), moderate-impact (4-8 week and perceived exertion from 12 to 16) and high-impact (8- 12 week & perceived exertion from 16 to 20). Results: To test the effectiveness of training programs paired t-test was used and significant difference (p<0.05) was observed in erythrocytes, hemoglobin concentration, platelets, hematocrit but no significant effects of training was found in leukocytes (p>0.05). Paired t-test also showed that no effect of time was seen in the control group in all the cases (p>0.05). Further analysis of covariance was used to know which program was more effective and it was seen that F value was found significant in the case of erythrocytes, hemoglobin concentration, platelets, and hematocrit as their associated p-value (p<0.05) is lesser than 0.05. As F value was found significant for hematological parameters, fishers least significant difference test was used and results of post hoc mean comparison indicated that experimental groups (aerobic dance group and cardio fitness group) had significant difference with control group in erythrocytes, hemoglobin concentration, platelets and hematocrit and insignificant difference was found between aerobic dance group & cardio fitness group in all the cases. Thus, it may be concluded that in general, both the aerobic training programs had adequate effects on all the hematological parameters except leukocytes.

Keywords: aerobic dance, cardio fitness, hematological variables, rating perceived exertion scale

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Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Syeda Shawana Mahasan, Muhammad Farooq Akhtar

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The objective of this study is to analyze the influence of the green marketing mix strategy and corporate social responsibility (CSR) on the performance of an organization, taking into account the mediating effect of corporate image. The impact of frugal innovation and corporate activism is being examined. The data was gathered from executives at various levels of management, including top, middle, and lower-level managers, from a total of 550 manufacturing enterprises of different sizes, ranging from small to medium to large. The collected replies are processed and analyzed using SMART PLS version 4.0.0.0. The application of PLS-SEM demonstrates that the green marketing mix strategy and corporate social responsibility have a significant impact on organizational performance. Therefore, it is imperative for organizations to effectively adopt environmentally sustainable and socially conscious methods within their operations. The results indicate that the corporate image has a key role in mediating the relationship between the green marketing mix strategy, corporate social responsibility, and organizational performance. This demonstrates the imperative for organizations to actively enhance their favorable reputation among stakeholders. The combination of frugal innovation and corporate activism enhances the connection between corporate image and organizational performance. The current study assists managers in recognizing the significance of these particular constructs in maintaining the long-term performance of the organization.

Keywords: green marketing mix strategy, CSR, corporate image, organizational performance, frugal innovation, corporate activism

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Authors: Kumar Kanudkuri, A. Sandhya

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Typically, in a MRI clinical setting, there are several protocols run, each indicated for a specific anatomy and disease condition. However, these protocols or parameters within them can change over time due to changes to the recommendations by the physician groups or updates in the software or by the availability of new technologies. Most of the time, the changes are performed by the MRI technologist to account for either time, coverage, physiological, or Specific Absorbtion Rate (SAR ) reasons. However, giving properly guidelines to MRI technologist is important so that they do not change the parameters that negatively impact the image quality. Typically a standard American College of Radiology (ACR) MRI phantom is used for Quality Control (QC) in order to guarantee that the primary objectives of MRI are met. The visual evaluation of quality depends on the operator/reviewer and might change amongst operators as well as for the same operator at various times. Therefore, overcoming these constraints is essential for a more impartial evaluation of quality. This makes quantitative estimation of image quality (IQ) metrics for MRI quality control is very important. So in order to solve this problem, we proposed that there is a need for a robust, open-source, and automated MRI image control tool. The Designed and developed an automatic analysis tool for measuring MRI image quality (IQ) metrics like Signal to Noise Ratio (SNR), Signal to Noise Ratio Uniformity (SNRU), Visual Information Fidelity (VIF), Feature Similarity (FSIM), Gray level co-occurrence matrix (GLCM), slice thickness accuracy, slice position accuracy, High contrast spatial resolution) provided good accuracy assessment. A standardized quality report has generated that incorporates metrics that impact diagnostic quality.

Keywords: ACR MRI phantom, MRI image quality metrics, SNRU, VIF, FSIM, GLCM, slice thickness accuracy, slice position accuracy

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: May A. Massoud, R. Harissi

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The fundamental principle and overall goal of an Environmental Management System is the concept of continual improvement. The implementation of such a system reveals a commitment to compliance and sustainable development. This research project aims at identifying and evaluating the prospects and challenges facing the adoption of ISO 14001 standard in the higher education system of Lebanon. It examines the corresponding barriers, drivers and incentives associated with the implementation of the standard. For this purpose, primary data were collected using quantitative method. The results revealed a significant lack of knowledge and sense of responsibility towards ISO 14001 standard and environmental accountability. Improving educational and social responsibility, improving environmental performance and enhancing institution image are the most noticeable drivers to adopt ISO 14001. The main perceived barriers for acquiring the standard are unclear benefits of ISO 14001, the lack of government support and the fact that the standard is not seen as a priority by top management. Lebanese Higher Education institutions are far likely to consider ISO 14001 before having proper accreditation programs or until ISO 14001 become widely-known in the Lebanese economic sectors.

Keywords: ISO 14001, higher education institution, environmental management, system

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Authors: O. Elkana, O Heyman, S. Hamdan, M. Franko, J. Vatine

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Objective: To investigate whether a direct link exists between perceived pain (PP) and executive functions (EF), above and beyond the influence of depression symptoms, in the context of medical rehabilitation. Design: Cross-sectional study. Setting: Rehabilitation Hospital. Participants: 125 medical records of hospitalized patients were screened for matching to our inclusion criteria. Only 60 patients were found fit and were asked to participate. 19 decline to participate on personal basis. The 41 neurologically intact patients (mean age 46, SD 14.96) that participated in this study were in their sub-acute stage of recovery, with fluent Hebrew, with intact upper limb (to neutralize influence on psychomotor performances) and without an organic brain damage. Main Outcome Measures: EF were assessed using the Wisconsin Card Sorting Test (WCST) and the Stop-Signal Test (SST). PP was measured using 3 well-known pain questionnaires: Pain Disability Index (PDI), The Short-Form McGill Questionnaire (SF-MPQ) and the Pain Catastrophizing Scale (PCS). Perceived pain index (PPI) was calculated by the mean score composite from the 3 pain questionnaires. Depression symptoms were assessed using the Patient Health Questionnaire (PHQ-9). Results: The results indicate that irrespective of the presence of depression symptoms, PP is directly correlated with response inhibition (SST partial correlation: r=0.5; p=0.001) and mental flexibility (WSCT partial correlation: r=-0.37; p=0.021), suggesting decreased performance in EF as PP severity increases. High correlations were found between the 3 pain measurements: SF-MPQ with PDI (r=0.62, p<0.001), SF-MPQ with PCS (r=0.58, p<0.001) and PDI with PCS (r=0.38, p=0.016) and each questionnaire alone was also significantly associated with EF; thus, no specific questionnaires ‘pulled’ the results obtained by the general index (PPI). Conclusion: Examining the direct association between PP and EF, beyond the contribution of depression symptoms, provides further clinical evidence suggesting that EF and PP share underlying mediating neuronal mechanisms. Clinically, the importance of assessing patients' EF abilities as well as PP severity during rehabilitation is underscored.

Keywords: depression, executive functions, mental-flexibility, neuropsychology, pain perception, perceived pain, response inhibition

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Authors: Parisa Mansour

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The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

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Authors: Aminu Hamza

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This study examines the factors that play vital role in the adoption of mobile payment system among consumers in Nigeria. Technology Acceptance Model (TAM) was used with two additional variables to form the conceptual model. The study was conducted in three Universities in Kano state, Nigeria. Convenience sampling method was used with a total valid 202 respondents which involved the students of Bayero University Kano (BUK), Northwest University, and Kano University of Science and Technology (KUST) Wudil, Kano, Nigeria. Results of the regression analysis revealed that Perceived ease of use (PEOU) and Perceived usefulness (PU) have significant and positive correlation with the behavioral intention to adopt mobile payment system. The findings of this study would be useful to the policy makers Central Bank of Nigeria (CBN), mobile network operators and providers of the services.

Keywords: mobile payment system, Nigeria, technology adoption, technology acceptance model

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Authors: Mohammad Esmaeilpour

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One of the important open problems in the field of medical image processing is detection and quantification of small changes. In this poster, we try to investigate that, how the algebraic decomposition techniques can be used for semiautomatically detecting and quantifying subtle changes in Magnetic Resonance (MR) neuroimaging volumes. We mostly focus on the low-rank values of the matrices achieved from decomposing MR image pairs during a period of time. Besides, a skillful neuroradiologist will help the algorithm to distinguish between noises and small changes.

Keywords: magnetic resonance neuroimaging, subtle change detection and quantification, algebraic decomposition, basis functions

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Authors: Mohammad A. U. Khan, Tariq M. Khan, Yinan Kong

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Fingerprint image enhancement is one of the most important step in an automatic fingerprint identification recognition (AFIS) system which directly affects the overall efficiency of AFIS. The conventional fingerprint enhancement like Gabor and Anisotropic filters do fill the gaps in ridge lines but they fail to tackle scar lines. To deal with this problem we are proposing a method for enhancing the ridges and valleys with scar so that true minutia points can be extracted with accuracy. Our results have shown an improved performance in terms of enhancement.

Keywords: fingerprint image enhancement, removing noise, coherence, enhanced diffusion

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

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Authors: Mahwish Rabia, Najma Najam

Abstract:

The present study was conducted to identify emotional and behavioral disturbances among earthquake survived children and the perceived coping strategies of affected children. In the present study, a sample of 50 children (6-16 years) belonging to badly affected areas (earthquake) was selected from different camps in Islamabad. Child Behavioral Checklist (CBCL) and Rotter Incomplete Sentence Blank (RISB) interpretations were used to assess variety of emotional and behavioral patterns, and Child Coping Strategies Checklist (CCSC) was used to assess the perceived coping strategies of affected children. Results showed that some of the frequent emotional/behavioral reactions exhibited by children like withdrawal, anxiety\depression, aggression and attention seeking behavior. Whereas gender-based comparisons indicated that female children showed more internalizing behavioral patterns (withdrawn, somatic complaints) as compared to male children who exhibited more externalizing emotions (aggression, delinquent behavior).Coping strategies in which male children tried to adopt Positive Cognitive Restructuring and for distracting attention they used distraction strategies of coping. It is concluded that significant negative emotional and behavioral reactions are exhibited by the earthquake affected children. Male children adopt coping strategies more as compared to female children. The study identifies the negative emotional and behavioral reactions towards trauma, which can be helpful for identifying the problematic area for counseling and therapeutic interventions for these children.

Keywords: behavioural disturbances, emotional disturbances, coping strategies, earthquake, children

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

Abstract:

Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Thomas Louis, Irina Primac, Florent Morfoisse, Tania Durre, Silvia Blacher, Agnes Noel

Abstract:

In vitro and in vivo lymphangiogenesis assays are essential for the identification of potential lymphangiogenic agents and the screening of pharmacological inhibitors. In the present study, we analyse three biological models: in vitro lymphatic endothelial cell spheroids, in vivo ear sponge assay, and in vivo lymph node colonisation by tumour cells. These assays provide suitable 3D models to test pro- and anti-lymphangiogenic factors or drugs. 3D images were acquired by confocal laser scanning and light sheet fluorescence microscopy. Virtual scan microscopy followed by 3D reconstruction by image aligning methods was also used to obtain 3D images of whole large sponge and ganglion samples. 3D reconstruction, image segmentation, skeletonisation, and other image processing algorithms are described. Fixed and time-lapse imaging techniques are used to analyse lymphatic endothelial cell spheroids behaviour. The study of cell spatial distribution in spheroid models enables to detect interactions between cells and to identify invasion hierarchy and guidance patterns. Global measurements such as volume, length, and density of lymphatic vessels are measured in both in vivo models. Branching density and tortuosity evaluation are also proposed to determine structure complexity. Those properties combined with vessel spatial distribution are evaluated in order to determine lymphangiogenesis extent. Lymphatic endothelial cell invasion and lymphangiogenesis were evaluated under various experimental conditions. The comparison of these conditions enables to identify lymphangiogenic agents and to better comprehend their roles in the lymphangiogenesis process. The proposed methodology is validated by its application on the three presented models.

Keywords: 3D image segmentation, 3D image skeletonisation, cell invasion, confocal microscopy, ear sponges, light sheet microscopy, lymph nodes, lymphangiogenesis, spheroids

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

Abstract:

Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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