Search results for: content based image retrieval (CBIR)

Authors: Richardson Ciguene, Bertrand Marron, Nicolas Habert

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Today, more and more consumers are purchasing their products and services online. At the same time, the penetration rate of very small and medium-sized businesses on marketplaces continues to increase, which has the direct impact of intensifying competition between sellers. Thus, only the best-optimized deals are ranked well by algorithms and are visible to consumers. However, it is almost impossible to know all the Brand Content rules and criteria established by marketplaces, which is essential to optimizing their product sheets, especially since these rules change constantly. In this paper, we propose to detail this question of Brand Content optimization by taking into account the case of Amazon in order to capture the scientific dimension behind such a subject. In a second step, we will present the genesis of our research project, DEEPERFECT, which aims to set up original methods and effective tools in order to help sellers present on marketplaces in the optimization of their branded content.

Keywords: e-commerce, scoring, marketplace, Amazon, brand content, product sheets

Procedia PDF Downloads 105

Authors: Al-Molhem, Y.

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This work aimed to study the influence of a geotextile material under sprinkler irrigation on the availability of soil moisture content and salinity of 40 cm top soil profile. Field experiment was carried out to measure soil moisture content, soil salinity and water application efficiency under sprinkler irrigation system. The results indicated that, the mats placed at 20 cm depth leads to increasing of the availability of soil moisture content in the root zone. The results further showed increases in water application efficiency because of using the geotextile material. In addition, soil salinity in the root zone decreased because of increasing soil moisture content.

Keywords: geotextile, moisture content, sprinkler irrigation

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Authors: Olatunji Sabitu Alimi

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This study investigated the extent to which administrators’ information storage, retrieval and processing capacities influence decisions on staff promotion in the Teaching Service Commissions (TESCOMs) in The South-West, Nigeria. One research question and two research hypotheses were formulated and tested respectively at 0.05 level of significance. The study used the descriptive research of the survey type. One hundred (100) staff on salary grade level 09 constituted the sample. Multi- stage, stratified and simple random sampling techniques were used to select 100 staff from the TESCOMs in The South-West, Nigeria. Two questionnaires titled Administrators’ Information Storage, Retrieval and Processing Capacities (AISRPC), and Staff Promotion Effectiveness (SPE) were used for data collection. The inventory was validated and subjected to test-re-test and reliability coefficient of r = 0.79 was obtained. The data were collected and analyzed using Pearson Product Moment Correlation coefficient and simple percentage. The study found that Administrators at TESCOM stored their information in files, hard copies, soft copies, open registry and departmentally in varying degrees while they also processed information manually and through electronics for decision making. In addition, there is a significant relationship between administrators’ information storage and retrieval capacities in the TESCOMs in South – West, Nigeria, (r cal = 0.598 > r table = 0.195). Furthermore, administrators’ information processing capacity and staff promotion effectiveness were found to be significantly related (r cal = 0.209 > r table = 0.195 at 0.05 level of significance). The study recommended that training, seminars, workshops should be organized for administrators on information management, while educational organizations should provide Information Management Technology (ICT) equipment for the administrators in the TESCOMs. The staff of TESCOM should be promoted having satisfied the promotion criteria such as spending required number of years on a grade level, a clean record of service and vacancy.

Keywords: information processing capacity, staff promotion effectiveness, teaching service commission, Nigeria

Procedia PDF Downloads 510

Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

Procedia PDF Downloads 128

Authors: Radwa Mabrook

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Virtual Reality (VR) content creation is a complex and an expensive process, which requires multi-disciplinary teams of content creators. Grant schemes from technology companies help media organisations to explore the VR potential in journalism and factual storytelling. Media organisations try to do as much as they can in-house, but they may outsource due to time constraints and skill availability. Journalists, game developers, sound designers and creative artists work together and bring in new cultures of work. This study explores the collaborative experimental nature of VR content creation, through tracing every actor involved in the process and examining their perceptions of the VR work. The study builds on Actor Network Theory (ANT), which decomposes phenomena into their basic elements and traces the interrelations among them. Therefore, the researcher conducted 22 semi-structured interviews with VR content creators between November 2017 and April 2018. Purposive and snowball sampling techniques allowed the researcher to recruit fact-based VR content creators from production studios and media organisations, as well as freelancers. Interviews lasted up to three hours, and they were a mix of Skype calls and in-person interviews. Participants consented for their interviews to be recorded, and for their names to be revealed in the study. The researcher coded interviews’ transcripts in Nvivo software, looking for key themes that correspond with the research questions. The study revealed that VR content creators must be adaptive to change, open to learn and comfortable with mistakes. The VR content creation process is very iterative because VR has no established work flow or visual grammar. Multi-disciplinary VR team members often speak different languages making it hard to communicate. However, adaptive content creators perceive VR work as a fun experience and an opportunity to learn. The traditional sense of competition and the strive for information exclusivity are now replaced by a strong drive for knowledge sharing. VR content creators are open to share their methods of work and their experiences. They target to build a collaborative network that aims to harness VR technology for journalism and factual storytelling. Indeed, VR is instilling collaborative and experimental cultures in journalism.

Keywords: collaborative culture, content creation, experimental culture, virtual reality

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Authors: Subir Gupta, Subhas Ganguly

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In this paper, we demonstrate the new area of application of image processing in metallurgical images to develop the more opportunity for structure-property correlation based approaches of alloy design. The present exercise focuses on the development of image processing tools suitable for phrase segmentation, grain boundary detection and recognition of micro-constituents in SEM micrographs of ferrite and pearlite steels. A comprehensive data of micrographs have been experimentally developed encompassing the variation of ferrite and pearlite volume fractions and taking images at different magnification (500X, 1000X, 15000X, 2000X, 3000X and 5000X) under scanning electron microscope. The variation in the volume fraction has been achieved using four different plain carbon steel containing 0.1, 0.22, 0.35 and 0.48 wt% C heat treated under annealing and normalizing treatments. The obtained data pool of micrographs arbitrarily divided into two parts to developing training and testing sets of micrographs. The statistical recognition features for ferrite and pearlite constituents have been developed by learning from training set of micrographs. The obtained features for microstructure pattern recognition are applied to test set of micrographs. The analysis of the result shows that the developed strategy can successfully detect the micro constitutes across the wide range of magnification and variation of volume fractions of the constituents in the structure with an accuracy of about +/- 5%.

Keywords: SEM micrograph, metallurgical image processing, ferrite pearlite steel, microstructure

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Authors: Gao Qin, Hu Liwei, Dong Xiangzhou, Zhu Qifa, Cheng Tingming, Zhao Limei, Yang Mengmeng, Zhai Zhen, Dai Huaxin, Liang Taibo, Zhang Shixiang, Xue Chaoqun

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The functional strain T976 for starch degradation was isolated from Nicotiana tabacum L. Yunyan 97 tobacco leaves, the ratio of starch hydrolysis transparent circle diameter to colony diameter of the strain was 4.14, 16S rDNA sequencing identified these strains as Enterobacter ludwigii. Then Enterobacter ludwigii T976 was fermented and spaying Yunyan 97 plant in vigorous growing stage. The results of once spraying fermentation broth of Enterobacter ludwigii T976 showed that starch content of upper leaves decreased slightly, from 3.77% to 3.1%, the reducing sugar content increased from 4.39% to 5.53%, and the total sugar content increased from 5.82% to 7.39%. The chemical content was also checked after three time spraying. The starch content of middle leaves decreased from 5.63% to 3.74%, while the content of total sugar and reducing sugar decreased slightly. And the starch content of upper leaves decreased from 7.62% to 4.78%, the total sugar and reducing sugar decreased slightly, and starch content of middle leaf decreased from 6.27% to 3.62%, the total sugar and reducing sugar did not change much, and other chemical components were in a suitable range.

Keywords: nicotiana tabacum, yunyan 97, leaf, starch, degradation, enterobacter ludwigii

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Authors: Boukhatem Mohamed Belkaid, Lahdi Mourad

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In this paper, we propose a new encryption system for security issues medical images. The hybrid encryption scheme is based on AES and RSA algorithms to validate the three security services are authentication, integrity, and confidentiality. Privacy is ensured by AES, authenticity is ensured by the RSA algorithm. Integrity is assured by the basic function of the correlation between adjacent pixels. Our system generates a unique password every new session of encryption, that will be used to encrypt each frame of the medical image basis to strengthen and ensure his safety. Several metrics have been used for various tests of our analysis. For the integrity test, we noticed the efficiencies of our system and how the imprint cryptographic changes at reception if a change affects the image in the transmission channel.

Keywords: AES, RSA, integrity, confidentiality, authentication, medical images, encryption, decryption, key, correlation

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Authors: Alaa El-Din Rezk

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In Egypt, management of crops still away from what is being used today by utilizing the advances of mechanical design capabilities, sensing and electronics technology. These technologies have been introduced in many places and recorm, for Straight Path, Curved Path, Sine Wave ded high accuracy in different field operations. So, an autonomous robotic platform based on machine vision has been developed and constructed to be implemented in Egyptian conditions as self-propelled mobile vehicle for carrying tools for inter/intra-row crop management based on different control modules. The experiments were carried out at plant protection research institute (PPRI) during 2014-2015 to optimize the accuracy of agricultural robotic platform control using machine vision in term of the autonomous navigation and performance of the robot’s guidance system. Results showed that the robotic platform' guidance system with machine vision was able to adequately distinguish the path and resisted image noise and did better than human operators for getting less lateral offset error. The average error of autonomous was 2.75, 19.33, 21.22, 34.18, and 16.69 mm. while the human operator was 32.70, 4.85, 7.85, 38.35 and 14.75 mm Path, Offset Discontinuity and Angle Discontinuity respectively.

Keywords: autonomous robotic, Hough transform, image processing, machine vision

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Authors: Julie Berame

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The study probed that out of eight (8) lessons in Science Six have been validated, lessons 1-3 got the descriptive rating of very satisfactory and lessons 4-8 got the descriptive rating of outstanding based on the content analysis of the prepared CBL lesson plans. The evaluation of the lesson plans focused on the three main features such as statements of the lesson objectives, lesson content, and organization and effectiveness. The study used developmental research procedure that contained three phases, namely: Development phase consists of determining the learning unit, lesson plans, creation of the table of specifications, exercises/quizzes, and revision of the materials; Evaluation phase consists of the development of experts’ assessment checklist, presentation of checklist to the adviser, comments and suggestions, and final validation of the materials; and try-out phase consists of identification of the subject, try-out of the materials using CBL strategy, administering science attitude questionnaire, and statistical analysis to obtain the data. The findings of the study revealed that the relevance and usability of CBL lessons 1 and 2 in terms of lesson objective, lesson content, and organization and effectiveness got the rating of very satisfactory (4.4) and lessons 3-8 got the rating of outstanding (4.7). The lessons 1-8 got the grand rating of outstanding (4.6). Additionally, results showed that CBL strategy helped foster positive attitude among students and achieved effectiveness in psychomotor learning objectives.

Keywords: development, validation, center-based learning, science

Procedia PDF Downloads 218

Authors: Shreesh Sinha, Alexander B. Barker, Megan Parkin, Emma Wilson, Rachael L. Murray

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Background and Objectives: Exposure to HFSS imagery is associated with the consumption of foods high in fat, sugar or salt (HFSS), and subsequently obesity, among young people. We report and compare the results of two content analyses, one of two popular terrestrial children's television channels in the UK and the other of a selection of children's programmes available on video-on-demand (VOD) streaming sites. Methods: Content analysis of three days' worth of programmes (including advertisements) on two popular children's television channels broadcast on UK television (CBeebies and Milkshake) as well as a sample of 40 highest-rated children's programmes available on the VOD platforms, Netflix and Amazon Prime, using 1-minute interval coding. Results: HFSS content was seen in 181 broadcasts (36%) and in 417 intervals (13%) on terrestrial television, 'Milkshake' had a significantly higher proportion of programmes/adverts which contained HFSS content than 'CBeebies'. In VOD platforms, HFSS content was seen in 82 episodes (72% of the total number of episodes), across 459 intervals (19% of the total number of intervals), with no significant difference in the proportion of programmes containing HFSS content between Netflix and Amazon Prime. Conclusions: This study demonstrates that HFSS content is common in both popular UK children's television channels and children's programmes on VOD services. Since previous research has shown that HFSS content in the media has an effect on HFSS consumption, children's television programmes broadcast either on TV or VOD services are likely to have an effect on HFSS consumption in children, and legislative opportunities to prevent this exposure are being missed.

Keywords: public health, junk food, children's TV, HFSS

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Authors: Julianne Cai, Weili Xue, Yibin Wu

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Social media retailing (SMR) platforms have become popular nowadays. It is characterized by a creative combination of content creation and product selling, which differs from traditional e-tailing (TE) with product selling alone. Motivated by real-world practices like social media platforms “TikTok” and douyin.com, we endeavor to study if the SMR model performs better than the TE model in a monopoly setting. By building a stylized economic model, we find that the SMR model does not always outperform the TE model. Specifically, when the SMR platform collects less commission from the seller than the TE platform, the seller, consumers, and social welfare all benefit more from the SMR model. In contrast, the platform benefits more from the SMR model if and only if the creator’s social influence is high enough or the cost of content creation is small enough. For the incentive structure of the content rewards in the SMR model, we found that a strong incentive mechanism (e.g., the quadratic form) is more powerful than a weak one (e.g., the linear form). The previous one will encourage the creator to choose a much higher quality level of content creation and meanwhile allowing the platform, consumers, and social welfare to become better off. Counterintuitively, providing more generous content rewards is not always helpful for the creator (seller), and it may reduce her profit. Our findings will guide the platform to effectively design incentive mechanisms to boost the content creation and retailing in the SMR model and help the influencers efficiently create content, engage their followers (fans), and price their products sold on the SMR platform.

Keywords: content creation, creator economy, incentive strategy, platform retailing

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

Procedia PDF Downloads 486

Authors: Haley Gershon

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The transition from using fossil fuel-based building material to formulating eco-friendly and biobased building materials plays a key role in sustainable building. The growing demand on a global level for biobased materials in the building and construction industries heightens the importance of carbon-14 testing, an analytical method used to determine the percentage of biobased content that comprises a material’s ingredients. This presentation will focus on the use of carbon-14 analysis within the building materials sector. Carbon-14, also known as radiocarbon, is a weakly radioactive isotope present in all living organisms. Any fossil material older than 50,000 years will not contain any carbon-14 content. The radiocarbon method is thus used to determine the amount of carbon-14 content present in a given sample. Carbon-14 testing is performed according to ASTM D6866, a standard test method developed specifically for biobased content determination of material in solid, liquid, or gaseous form, which requires radiocarbon dating. Samples are combusted and converted into a solid graphite form and then pressed onto a metal disc and mounted onto a wheel of an accelerator mass spectrometer (AMS) machine for the analysis. The AMS instrument is used in order to count the amount of carbon-14 present. By submitting samples for carbon-14 analysis, manufacturers of building materials can confirm the biobased content of ingredients used. Biobased testing through carbon-14 analysis reports results as percent biobased content, indicating the percentage of ingredients coming from biomass sourced carbon versus fossil carbon. The analysis is performed according to standardized methods such as ASTM D6866, ISO 16620, and EN 16640. Products 100% sourced from plants, animals, or microbiological material are therefore 100% biobased, while products sourced only from fossil fuel material are 0% biobased. Any result in between 0% and 100% biobased indicates that there is a mixture of both biomass-derived and fossil fuel-derived sources. Furthermore, biobased testing for building materials allows manufacturers to submit eligible material for certification and eco-label programs such as the United States Department of Agriculture (USDA) BioPreferred Program. This program includes a voluntary labeling initiative for biobased products, in which companies may apply to receive and display the USDA Certified Biobased Product label, stating third-party verification and displaying a product’s percentage of biobased content. The USDA program includes a specific category for Building Materials. In order to qualify for the biobased certification under this product category, examples of product criteria that must be met include minimum 62% biobased content for wall coverings, minimum 25% biobased content for lumber, and a minimum 91% biobased content for floor coverings (non-carpet). As a result, consumers can easily identify plant-based products in the marketplace.

Keywords: carbon-14 testing, biobased, biobased content, radiocarbon dating, accelerator mass spectrometry, AMS, materials

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Authors: Shamsundar Kulkarni

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Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances.

Keywords: video stabilization, point feature matching, salient points, image quality measurement

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Authors: Hyeongdo Jeong, Jong Kook Lee

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Granule morphology and microstructure were affected by slurry viscosity, chemical composition, particle size and spray drying process. In this study, we investigated granule morphology of zirconia powder with solid content on two-fluid spray drying. Zirconia granules after spray drying show sphere-like shapes with a diameter of 40-70 μm at low solid contents (30 or 40 wt%) and specific surface area of 5.1-5.6 m²/g. But a donut-like shape with a few cracks were observed on zirconia granules prepared from the slurry of high solid content (50 wt %), green compacts after cold isostatic pressing under the pressure of 200 MPa have the density of 2.1-2.2 g/cm³ and homogeneous fracture surface by complete destruction of granules. After the sintering at 1500 °C for 2 h, all specimens have relative density of 96.2-98.3 %. With increasing a solid content from 30 to 50 wt%, grain size increased from 0.3 to 0.6 μm, but relative density was inversely decreased from 98.3 to 96.2 %.

Keywords: zirconia, solid content, granulation, spray drying

Procedia PDF Downloads 199

Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Chin-Wen Chien

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Protocols are tools that help teachers inquire into the teaching and professional learning during the professional dialogue. This study focused on the integration of protocols into elementary school English teachers’ professional dialogue and discussed the influence of protocols on teachers’ teaching and learning. Based on the analysis of documents, observations, and interviews, this study concluded that with the introduction of protocols to elementary school English teachers, three major protocols were used during their professional dialogue. These protocols led the teachers to gain professional learning in content knowledge and pedagogical content knowledge. However, the facilitators’ lack of experience in using protocols led to interruptions during the professional dialogue. Suggestions for effective protocol-based professional dialogue are provided.

Keywords: protocols, professional learning, professional dialogue, classroom practice

Procedia PDF Downloads 366

Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: S. Bouhouche, R. Drai, J. Bast

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This paper is concerned with a method for uncertainty evaluation of steel sample content using X-Ray Fluorescence method. The considered method of analysis is a comparative technique based on the X-Ray Fluorescence; the calibration step assumes the adequate chemical composition of metallic analyzed sample. It is proposed in this work a new combined approach using the Kalman Filter and Markov Chain Monte Carlo (MCMC) for uncertainty estimation of steel content analysis. The Kalman filter algorithm is extended to the model identification of the chemical analysis process using the main factors affecting the analysis results; in this case, the estimated states are reduced to the model parameters. The MCMC is a stochastic method that computes the statistical properties of the considered states such as the probability distribution function (PDF) according to the initial state and the target distribution using Monte Carlo simulation algorithm. Conventional approach is based on the linear correlation, the uncertainty budget is established for steel Mn(wt%), Cr(wt%), Ni(wt%) and Mo(wt%) content respectively. A comparative study between the conventional procedure and the proposed method is given. This kind of approaches is applied for constructing an accurate computing procedure of uncertainty measurement.

Keywords: Kalman filter, Markov chain Monte Carlo, x-ray fluorescence calibration and testing, steel content measurement, uncertainty measurement

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Aaminah Hassan

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Advertisements carry a great potential to influence our lives because they are crafted to meet particular ends. Stereotypical representation in advertisements is capable of forming unconscious attitudes among people towards any gender and their abilities. This study focuses on gender representation in Pakistani prime time advertisements. For this purpose, 13 advertisements were selected from three different categories of foods and beverages, cosmetics, cell phones and cellular networks from the prime time slots of one of the leading Pakistani entertainment channel, ‘Urdu 1’. Both quantitative and qualitative analyses are carried out for range of variables like gender, age, roles, activities, setting, appearance and voice overs. The results revealed that gender representation in advertisements is stereotypical. Moreover, in few instances, the portrayal of women is not only culturally inappropriate but is demeaning to the image of women as well. Their bodily charm is used to promote products. Comparing different entertainment channels for their prime time advertisements and broadening the scope of this research will yield greater implications for the researchers who want to carry out the similar research. It is hoped that the current study would help in the promotion of media literacy among the viewers and media authorities in Pakistan.

Keywords: Advertisements, Content Analysis, Gender, Prime time

Procedia PDF Downloads 196

Authors: Sohini Manna, Jong Woo Kim, Oleg Shpyrko, Eric E. Fullerton

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CVD techniques have emerged as a promising approach in the formation of a broad range of nanostructured materials. The realization of many practical applications will require efficient and economical synthesis techniques that preferably avoid the need for templates or costly single-crystal substrates and also afford process adaptability. Towards this end, we have developed a single-step route for the reduction-type synthesis of nanostructured Ni materials using a thermal CVD method. By tuning the CVD growth parameters, we can synthesize morphologically dissimilar nanostructures including single-crystal cubes and Au nanostructures which form atop untreated amorphous SiO2||Si substrates. An understanding of the new properties that emerge in these nanostructures materials and their relationship to function will lead to for a broad range of magnetostrictive devices as well as other catalysis, fuel cell, sensor, and battery applications based on high-surface-area transition-metal nanostructures. We use coherent X-ray diffraction imaging technique to obtain 3-D image and strain maps of individual nanocrystals. Coherent x-ray diffractive imaging (CXDI) is a technique that provides the overall shape of a nanostructure and the lattice distortion based on the combination of highly brilliant coherent x-ray sources and phase retrieval algorithm. We observe a fine interplay of reduction of surface energy vs internal stress, which plays an important role in the morphology of nano-crystals. The strain distribution is influenced by the metal-substrate interface and metal-air interface, which arise due to differences in their thermal expansion. We find the lattice strain at the surface of the octahedral gold nanocrystal agrees well with the predictions of the Young-Laplace equation quantitatively, but exhibits a discrepancy near the nanocrystal-substrate interface resulting from the interface. The strain in the bottom side of the Ni nanocube, which is contacted on the substrate surface is compressive. This is caused by dissimilar thermal expansion coefficients between Ni nanocube and Si substrate. Research at UCSD support by NSF DMR Award # 1411335.

Keywords: CVD, nanostructures, strain, CXRD

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Authors: Fatima Bellali, Mariem Kharroubi

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Fish scale wastes are one of the main sources of production of value-added products such as collagen. The main aim of this study is to investigate the optimization conditions of the sardine scale deproteinization using response surface methodology (RSM) on a pilot scale. In order to look for the optimal conditions, a Box–Behnken-based design of experiment (DOE) method was carried out. The model predicted values of product coal ash content were in good agreement with the experiment values (R2 = 0.9813). Finally, model-based optimization was carried out to identify the operating parameters (reaction time=4h and the solid-liquid ratio= 1/10) and to obtain the lowest collagen content.

Keywords: pilot scale, Plackett and Burman design, fish waste, deproteinization

Procedia PDF Downloads 135

Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

Abstract:

Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Zeineb Ben Salem, Wissem Frikha, Mounir Bouassida

Abstract:

Liquefaction has been responsible for tremendous amounts of damage in historical earthquakes around the world. The installation of stone columns is widely adopted to prevent liquefaction. Stone columns provide a drainage path, and due to their high permeability, allow for the quick dissipation of earthquake generated excess pore water pressure. Several excess pore pressure generation models in silty sand have been developed and calibrated based on the results of shaking table and centrifuge tests focusing on the effect of silt content on liquefaction resistance. In this paper, the generation and dissipation of excess pore pressure variation of reinforced silty sand by stone columns during liquefaction are analyzedwith different silt content based on test results. In addition, the installation effect of stone columns is investigated. This effect is described by a decrease in horizontal permeability within a disturbed zone around the column. Obtained results show that reduced soil permeability and a larger disturbed zone around the stone column increases the generation of excess pore pressure during the cyclic loading and decreases the dissipation rate after cyclic loading. On the other hand, beneficial effects of silt content were observed in the form of a decrease in excess pore water pressure.

Keywords: stone column, liquefaction, excess pore pressure, silt content, disturbed zone, reduced permeability

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Authors: Prabhakar C. Dhavale, Meenakshi M. Pawar

Abstract:

In this paper, everyday tons of data is embedded on digital media or distributed over the internet. The data is so distributed that it can easily be replicated without error, putting the rights of their owners at risk. Even when encrypted for distribution, data can easily be decrypted and copied. One way to discourage illegal duplication is to insert information known as watermark, into potentially valuable data in such a way that it is impossible to separate the watermark from the data. These challenges motivated researchers to carry out intense research in the field of watermarking. A watermark is a form, image or text that is impressed onto paper, which provides evidence of its authenticity. Digital watermarking is an extension of the same concept. There are two types of watermarks visible watermark and invisible watermark. In this project, we have concentrated on implementing watermark in image. The main consideration for any watermarking scheme is its robustness to various attacks

Keywords: watermarking, digital, DCT-DWT, security

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Authors: Aşina Gülerarslan, M. Faik Özdengül

Abstract:

The new economic, social and political world order has led to the emergence of a wide range of persuasion strategies and practices based on an ever expanding marketing axis that involves organizations, ideas and persons as well as products and services. It is seen that since the 1990's, a wide variety of competitive marketing ideas have been offered systematically to target audiences in the field of politics as in other fields. When the components of marketing are taken into consideration, all kinds of communication efforts involving “political leaders”, who are conceptualized as products in terms of political marketing, serve a process of social persuasion, which cannot be restricted to election periods only, and a manageable “image”. In this context, image, which is concerned with how the political product is perceived, involves not only the political discourses shared with the public but also all kinds of biographical information about the leader, the leader’s specific way of living and routines and his/her attitudes and behaviors in their private lives, and all these are regarded as components of the “product image”. While on the one hand the leader’s verbal or supra-verbal references serve the way the “spirit of the product” is perceived –just as in brand positioning- they also show their self-esteem levels, in other words how they perceive themselves on the other hand. Indeed, their self-esteem levels are evaluated in three fundamental categories in the “Functional Analysis”, namely parent, child and adult, and it is revealed that the words, tone of voice and body language a person uses makes it easy to understand at what self-esteem level that person is. In this context, words, tone of voice and body language, which provide important clues as to the “self” of the person, are also an indication of how political leaders evaluate both “themselves” and “the mass/audience” in the communication they establish with their audiences. When the matter is taken from the perspective of Turkey, the levels of self-esteem in the relationships that the political leaders establish with the masses are also important in revealing how our society is seen from the perspective of a specific leader. Since the leader is a part of the marketing strategy of a political party as a product, this evaluation is significant in terms of the forms of relationships between political institutions in our country with the society. In this study, the self-esteem level in the documentary entitled “Master’s Story”, where Recep Tayyip Erdoğan’s life history is told, is analyzed in the context of words, tone of voice and body language. Within the scope of the study, at what level of self-esteem Recep Tayyip Erdoğan was in the “Master’s Story”, a documentary broadcast on Beyaz TV, was investigated using the content analysis method. First, based on the Functional Analysis Literature, a transactional approach scale was created regarding parent, adult and child self-esteem levels. On the basis of this scale, the prime minister’s self-esteem level was determined in three basic groups, namely “tone of voice”, “the words he used” and “body language”. Descriptive analyses were made to the data within the framework of these criteria and at what self-esteem level the prime minister spoke throughout the documentary was revealed.

Keywords: political marketing, leader image, level of self-esteem, transactional approach

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

Abstract:

In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Jonathan Sohn

Abstract:

Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.

Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning

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