Search results for: deformable multimodal image registration

Authors: Aşina Gülerarslan, M. Faik Özdengül

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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: H. Apaza, L. Chévez, H. Loro

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Plastic and microplastic pollution in human food chain is a big problem for human health that requires more elaborated techniques that can identify their presences in different kinds of food. Hyperspectral imaging technique is an optical technique than can detect the presence of different elements in an image and can be used to detect plastics and microplastics in a scene. To do this statistical techniques are required that need to be evaluated and compared in order to find the more efficient ones. In this work, two problems related to the presence of plastics are addressed, the first is to detect and identify pieces of plastic immersed in almond seeds, and the second problem is to detect and quantify microplastic in almond flour. To do this we make use of the analysis hyperspectral images taken in the range of 900 to 1700 nm using 4 unmixing techniques of hyperspectral imaging which are: least squares unmixing (LSU), non-negatively constrained least squares unmixing (NCLSU), fully constrained least squares unmixing (FCLSU), and scaled constrained least squares unmixing (SCLSU). NCLSU, FCLSU, SCLSU techniques manage to find the region where the plastic is found and also manage to quantify the amount of microplastic contained in the almond flour. The SCLSU technique estimated a 13.03% abundance of microplastics and 86.97% of almond flour compared to 16.66% of microplastics and 83.33% abundance of almond flour prepared for the experiment. Results show the feasibility of applying near-infrared hyperspectral image analysis for the detection of plastic contaminants in food.

Keywords: food, plastic, microplastic, NIR hyperspectral imaging, unmixing

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Authors: Sidi M. A. Ghaly, Mohammad O. Khan, Mohammed Shalaby, Khaled A. Al-Snaie

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Electrical capacitance tomography (ECT) is a valuable, non-invasive technique used to monitor multiphase flow processes, especially within industrial pipelines. This study focuses on the design, testing, and performance comparison of ECT sensors configured with 8, 12, and 16 electrodes, aiming to evaluate their effectiveness in imaging accuracy, resolution, and sensitivity. Each sensor configuration was designed to capture the spatial permittivity distribution within a pipeline cross-section, enabling visualization of phase distribution and flow characteristics such as oil and water interactions. The sensor designs were implemented and tested in closed pipes to assess their response to varying flow regimes. Capacitance data collected from each electrode configuration were reconstructed into cross-sectional images, enabling a comparison of image resolution, noise levels, and computational demands. Results indicate that the 16-electrode configuration yields higher image resolution and sensitivity to phase boundaries compared to the 8- and 12-electrode setups, making it more suitable for complex flow visualization. However, the 8 and 12-electrode sensors demonstrated advantages in processing speed and lower computational requirements. This comparative analysis provides critical insights into optimizing ECT sensor design based on specific industrial requirements, from high-resolution imaging to real-time monitoring needs.

Keywords: capacitance tomography, modeling, simulation, electrode, permittivity, fluid dynamics, imaging sensitivity measurement

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Authors: J. Janani, S. Gomathi

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Despite the new economy is embodied with the ample size of talent pool, the corporate world is facing the hardship in the mismatch of talent demand supply. Therefore to combat with this fallout crisis, here depicts the relevance of Employer Branding. Employer branding is gaining its popularity in Large sized companies especially IT companies but less employer branding awareness among IT SMEs (Small and Medium size Enterprises). There are N range of analysis has been dole out on employer branding from different perspectives and in different industries. The hidden factor behind the employer branding namely the post employment dissonance was not given a lot of importance into the research picture. The present study examines the employer branding as the employer image and the organizational identity. It focuses on the two fold dimensional branding initiatives namely job offer attributes and organizational attractiveness. The study will depict the dissonance level and their variations among the foresaid initiatives from the former employees and the post-employment dissonance from the present employees in IT SMEs and it will also examine the employer perception from the prospective employees towards the stated branding initiatives. The demographic factors such as generational factors (gen X and gen Y) and the career stages are majorly focused in the study. The study will promote the IT SMEs to strengthen their employer branding effectively and efficiently through implementing varied strategies and this will help them to enhance the talent pool at their best. This will eventually result in talent attraction and talent retention.

Keywords: employer image, organizational identity, post-employment dissonance, job offer attributes, organizational attractiveness, talent pool, career stages, generational factors, information technology, SMEs

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Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Zhuang Yiwen

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The paper examines the challenges facing the Singapore healthcare system related to the tracking and traceability of medical devices. One of the major challenges identified is the lack of a standard coding system for medical devices, which makes it difficult to track them effectively. The paper suggests the use of the Unique Device Identifier (UDI) as a single standard for medical devices to improve tracking and reduce errors. The paper also explores the use of barcoding and image recognition to identify and document medical devices in nursing practices. In nursing practices, the use of barcodes for identifying medical devices is common. However, the information contained in these barcodes is often inconsistent, making it challenging to identify which segment contains the model identifier. Moreover, the use of barcodes may be improved with the use of UDI, but many subsidized accessories may still lack barcodes. The paper suggests that the readiness for UDI and barcode standardization requires standardized information, fields, and logic in electronic medical record (EMR), operating theatre (OT), and billing systems, as well as barcode scanners that can read various formats and selectively parse barcode segments. Nursing workflow and data flow also need to be taken into account. The paper also explores the use of image recognition, specifically the Tesseract OCR engine, to identify and document implants in public hospitals due to limitations in barcode scanning. The study found that the solution requires an implant information database and checking output against the database. The solution also requires customization of the algorithm, cropping out objects affecting text recognition, and applying adjustments. The solution requires additional resources and costs for a mobile/hardware device, which may pose space constraints and require maintenance of sterile criteria. The integration with EMR is also necessary, and the solution require changes in the user's workflow. The paper suggests that the long-term use of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) as a supporting terminology to improve clinical documentation and data exchange in healthcare. SNOMED CT provides a standardized way of documenting and sharing clinical information with respect to procedure, patient and device documentation, which can facilitate interoperability and data exchange. In conclusion, the paper highlights the challenges facing the Singapore healthcare system related to the tracking and traceability of medical devices. The paper suggests the use of UDI and barcode standardization to improve tracking and reduce errors. It also explores the use of image recognition to identify and document medical devices in nursing practices. The paper emphasizes the importance of standardized information, fields, and logic in EMR, OT, and billing systems, as well as barcode scanners that can read various formats and selectively parse barcode segments. These recommendations could help the Singapore healthcare system to improve tracking and traceability of medical devices and ultimately enhance patient safety.

Keywords: medical device tracking, unique device identifier, barcoding and image recognition, systematized nomenclature of medicine clinical terms

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Authors: Divya Vats, Sanjay Mahajani

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The clarification of sugarcane juice plays a pivotal role in the production of non-centrifugal sugar (NCS), profoundly influencing the quality of the final NCS product. In this study, we have investigated the kinetics and mathematical modeling of the batch clarification process. The turbidity of the clarified cane juice (NTU) emerges as the determinant of the end product’s color. Moreover, this parameter underscores the significance of considering other variables as performance indicators for accessing the efficacy of the clarification process. Temperature-controlled experiments were meticulously conducted in a laboratory-scale batch mode. The primary objective was to discern the essential and optimized parameters crucial for augmenting the clarity of cane juice. Additionally, we explored the impact of pH and flocculant loading on the kinetics. Particle Image Velocimetry (PIV) is employed to comprehend the particle-particle and fluid-particle interaction. This technique facilitated a comprehensive understanding, paving the way for the subsequent multiphase computational fluid dynamics (CFD) simulations using the Eulerian-Lagrangian approach in the Ansys fluent. Impressively, these simulations accurately replicated comparable velocity profiles. The final mechanism of this study helps to make a mathematical model and presents a valuable framework for transitioning from the traditional batch process to a continuous process. The ultimate aim is to attain heightened productivity and unwavering consistency in product quality.

Keywords: non-centrifugal sugar, particle image velocimetry, computational fluid dynamics, mathematical modeling, turbidity

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Authors: Shatha Ghareeb, Rawaa Al-Jumeily, Thar Baker

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In Abu Dhabi, there are many different education curriculums where sector of private schools and quality assurance is supervising many private schools in Abu Dhabi for many nationalities. As there are many different education curriculums in Abu Dhabi to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. In fact, each curriculum has a different number of years, assessment techniques, reassessment rules, and exam boards. Currently, students that transfer curriculums are not being placed in the right year group due to different start and end dates of each academic year and their date of birth for each year group is different for each curriculum and as a result, we find students that are either younger or older for that year group which therefore creates gaps in their learning and performance. In addition, there is not a way of storing student data throughout their academic journey so that schools can track the student learning process. In this paper, we propose to develop a computational framework applicable in multicultural countries such as UAE in which multi-education systems are implemented. The ultimate goal is to use cloud and fog computing technology integrated with Artificial Intelligence techniques of Machine Learning to aid in a smooth transition when assigning students to their year groups, and provide leveling and differentiation information of students who relocate from a particular education curriculum to another, whilst also having the ability to store and access student data from anywhere throughout their academic journey.

Keywords: admissions, algorithms, cloud computing, differentiation, fog computing, levelling, machine learning

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Authors: J. Marie Hicks

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The art of the poetic conjoins meaning and symbolism with imagery and rhythm. Perhaps the reader might read this opening sentence as 'The art of the poetic combines meaning and symbolism with imagery and rhythm,' which holds a similar message, but is not quite the same. The reader understands that these factors are combined in this literary form, but to gain a sense of the conjoining of these factors, the reader is forced to consider that these aspects of poetry are not simply combined, but actually adjoin, abut, skirt, or touch in the poetic form. This alternative word choice is an example of substitutional inference. Poetry is, ostensibly, a literary form where language is used precisely or creatively to evoke specific images or emotions for the reader. Often, the reader can predict a coming rhyme or descriptive word choice in a poem, based on previous rhyming pattern or earlier imagery in the poem. However, there are instances when the poet uses an unexpected word choice to create multiple meanings and connections. In these cases, the reader is presented with an unusual phrase or image, requiring that they think about what that image is meant to suggest, and their mind also suggests the word they expected, creating a second, overlying image or meaning. This is what is meant by the term 'substitutional inference.' This is different than simply using a double entendre, a word or phrase that has two meanings, often one complementary and the other disparaging, or one that is innocuous and the other suggestive. In substitutional inference, the poet utilizes an unanticipated word that is either visually or phonetically similar to the expected word, provoking the reader to work to understand the poetic phrase as written, while unconsciously incorporating the meaning of the line as anticipated. In other words, by virtue of a word substitution, an inference of the logical word choice is imparted to the reader, while they are seeking to rationalize the word that was actually used. There is a substitutional inference of meaning created by the alternate word choice. For example, Louise Bogan, 4th Poet Laureate of the United States, used substitutional inference in the form of homonyms, malapropisms, and other unusual word choices in a number of her poems, lending depth and greater complexity, while actively engaging her readers intellectually with her poetry. Substitutional inference not only adds complexity to the potential interpretations of Bogan’s poetry, as well as the poetry of others, but provided a method for writers to infuse additional meanings into their work, thus expressing more information in a compact format. Additionally, this nuancing enriches the poetic experience for the reader, who can enjoy the poem superficially as written, or on a deeper level exploring gradations of meaning.

Keywords: poetic inference, poetic word play, substitutional inference, word substitution

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Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Subham Kharel, Sudha Ravindranath, A. Vidya, B. Chandrasekaran, K. Ganesha Raj, T. Shesadri

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Digitization of urban features is a tedious and time-consuming process when done manually. In addition to this problem, Indian cities have complex habitat patterns and convoluted clustering patterns, which make it even more difficult to map features. This paper makes an attempt to classify urban objects in the satellite image using object-oriented classification techniques in which various classes such as vegetation, water bodies, buildings, and shadows adjacent to the buildings were mapped semi-automatically. Building layer obtained as a result of object-oriented classification along with already available building layers was used. The main focus, however, lay in the extraction of high-rise buildings using spatial technology, digital image processing, and modeling, which would otherwise be a very difficult task to carry out manually. Results indicated a considerable rise in the total number of buildings in the city. High-rise buildings were successfully mapped using satellite imagery, spatial technology along with logical reasoning and mathematical considerations. The results clearly depict the ability of Remote Sensing and GIS to solve complex problems in urban scenarios like studying urban sprawl and identification of more complex features in an urban area like high-rise buildings and multi-dwelling units. Object-Oriented Technique has been proven to be effective and has yielded an overall efficiency of 80 percent in the classification of high-rise buildings.

Keywords: object oriented classification, shadow extraction, high-rise buildings, satellite imagery, spatial technology

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Authors: Kasidis Chomrat, Nopasit Chakpitak, Anukul Tamprasirt, Annop Thananchana

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Internet of Things devices or IoT and Edge Computing has become one of the biggest things happening in innovations and one of the most discussed of the potential to improve and disrupt traditional business and industry alike. With rises of new hang cliff challenges like COVID-19 pandemic that posed a danger to workforce and business process of the system. Along with drastically changing landscape in business that left ruined aftermath of global COVID-19 pandemic, looming with the threat of global energy crisis, global warming, more heating global politic that posed a threat to become new Cold War. How emerging technology like edge computing and usage of specialized design visual processing units will be great opportunities for business. The literature reviewed on how the internet of things and disruptive wave will affect business, which explains is how all these new events is an effect on the current business and how would the business need to be adapting to change in the market and world, and example test benchmarking for consumer marketed of newer devices like the internet of things devices equipped with new edge computing devices will be increase efficiency and reducing posing a risk from a current and looming crisis. Throughout the whole paper, we will explain the technologies that lead the present technologies and the current situation why these technologies will be innovations that change the traditional practice through brief introductions to the technologies such as cloud computing, edge computing, Internet of Things and how it will be leading into future.

Keywords: internet of things, edge computing, machine learning, pattern recognition, image classification

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Authors: K. Rana, H.A.Saeed, S. Zahir

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Inter Laminar Shear Stress (ILSS) is a main key parameter which quantify the properties of composite materials. These properties can ascertain the use of material for a specific purpose like aerospace, automotive etc. A modelling approach for determination of ILSS is presented in this paper. Geometric modelling of composite material is performed in TEXGEN software where reinforcement, cured matrix and their interfaces are modelled separately as per actual geometry. Mechanical properties of matrix and reinforcements are modelled separately which incorporated anisotropy in the real world composite material. ASTM D2344 is modelled in ANSYS for ILSS. In macroscopic analysis model approximates the anisotropy of the material and uses orthotropic properties by applying homogenization techniques. Shear Stress analysis in that case does not show the actual real world scenario and rather approximates it. In this paper actual geometry and properties of reinforcement and matrix are modelled to capture the actual stress state during the testing of samples as per ASTM standards. Testing of samples is also performed in order to validate the results. Fibre volume fraction of yarn is determined by image analysis of manufactured samples. Fibre volume fraction data is incorporated into the numerical model for correction of transversely isotropic properties of yarn. A comparison between experimental and simulated results is presented.

Keywords: ILSS, FEA, micromechanical, fibre volume fraction, image analysis

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Authors: Paweł Konieczka, Lech Raczyński, Wojciech Wiślicki, Oleksandr Fedoruk, Konrad Klimaszewski, Przemysław Kopka, Wojciech Krzemień, Roman Shopa, Jakub Baran, Aurélien Coussat, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Meysam Dadgar, Kamil Dulski, Aleksander Gajos, Beatrix C. Hiesmayr, Krzysztof Kacprzak, łukasz Kapłon, Grzegorz Korcyl, Tomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Dominik Panek, Szymon Parzych, Elena Pérez Del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa łucja Stępień, Faranak Tayefi, Paweł Moskal

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This paper develops the transformation of non-image data into 2-dimensional matrices, as a preparation stage for classification based on convolutional neural networks (CNNs). In positron emission tomography (PET) studies, CNN may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, much PET data still exists in non-image format and this fact opens a question on whether they can be used for training CNN. In this contribution, the main focus of this paper is the problem of processing vectors with a small number of features in comparison to the number of pixels in the output images. The proposed methodology was applied to the classification of PET coincidence events.

Keywords: convolutional neural network, kernel principal component analysis, medical imaging, positron emission tomography

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Authors: Zhao Gao, Eran Edirisinghe

Abstract:

The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Anahit Khananyan

Abstract:

The results of the long-term Jungian analysis with female clients from Eastern and Asian countries, which belong to collectivist cultures, are summarised in the article. The goal of the paper is to describe the cultural complex, which was found by the author in the analysis of women of collectivistic culture. It was named “the repression of Animus”. Generally, C.G.Jung himself and the Post-Jungians studied conditions caused by the possession by Animus. The conditions and cases of the repressed Animus, depending on the type of culture and cultural complexes, as we know, were not widely disseminated. C.G. Jung discovered and recognized the Animus as the second component of a pair of opposites of the psyche of women – femininity and Animus. In the way of individuation, an awareness of manifestations of Animus plays an important role: understanding the differences between negative and positive Animus as well as the Animus and the Shadow, then standing the tension of the presence of a pair of opposites - femininity and Animus, acceptance of the tension of them, finding the balance between them and reconciliation of this opposites. All of the above are steps towards the realization of the Animus, its release Animua, and the healing of the psyche. In the paper, the author will share her experience of analyzing the women of different collectivist cultures and her experience of recognizing the repressed Animus during the analysis. Also, she will describe some peculiarities of upbringing and cultural traditions, which reflected the cultural complex of repression of Animus. This complex is manifested in the traditions of girls' upbringing in accordance with which an image of a woman with overly developed femininity and an absence or underdeveloped Animus is idealized and encouraged as well as an evaluating attitude towards females who have to correspond to this image and fulfill the role prescribed in this way in the family and society.

Keywords: analysis, cultural complex, animus, manifestation, culture

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Authors: S. Gharbi, S. Labidi, M. Mars, M. Chelli, F. Ladeb

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The purpose of this study is to reduce radiation dose for chest CT examination by including Tube Current Modulation (TCM) to a standard CT protocol. A scan of an anthropomorphic male Alderson phantom was performed on a 128-slice scanner. The estimation of effective dose (ED) in both scans with and without mAs modulation was done via multiplication of Dose Length Product (DLP) to a conversion factor. Results were compared to those measured with a CT-Expo software. The size specific dose estimation (SSDE) values were obtained by multiplication of the volume CT dose index (CTDIvol) with a conversion size factor related to the phantom’s effective diameter. Objective assessment of image quality was performed with Signal to Noise Ratio (SNR) measurements in phantom. SPSS software was used for data analysis. Results showed including CARE Dose 4D; ED was lowered by 48.35% and 51.51% using DLP and CT-expo, respectively. In addition, ED ranges between 7.01 mSv and 6.6 mSv in case of standard protocol, while it ranges between 3.62 mSv and 3.2 mSv with TCM. Similar results are found for SSDE; dose was higher without TCM of 16.25 mGy and was lower by 48.8% including TCM. The SNR values calculated were significantly different (p=0.03<0.05). The highest one is measured on images acquired with TCM and reconstructed with Filtered back projection (FBP). In conclusion, this study proves the potential of TCM technique in SSDE and ED reduction and in conserving image quality with high diagnostic reference level for thoracic CT examinations.

Keywords: anthropomorphic phantom, computed tomography, CT-expo, radiation dose

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

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Authors: H. Nouri, S. Chavoshi Borujeni, S. Anderson, S. Beecham, P. Sutton

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The potential risk of salt leaching through wastewater irrigation is of concern for most local governments and city councils. Despite the necessity of salinity monitoring and management in urban greenery, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using near sensing and remote sensing approaches using EM38 surveys, and high-resolution multispectral image of WorldView3. Veale Gardens within the Adelaide Parklands was selected as the experimental site. The results of the near sensing investigation were validated by testing soil salinity samples in the laboratory. Over 30 band combinations forming salinity indices were tested using image processing techniques. The outcomes of the remote sensing and near sensing approaches were compared to examine whether remotely sensed salinity indicators could map and predict the spatial variation of soil salinity through a potential statistical model. Statistical analysis was undertaken using the Stata 13 statistical package on over 52,000 points. Several regression models were fitted to the data, and the mixed effect modelling was selected the most appropriate one as it takes to account the systematic observation-specific unobserved heterogeneity. Results showed that SAVI (Soil Adjusted Vegetation Index) was the only salinity index that could be considered as a predictor for soil salinity but further investigation is needed. However, near sensing was found as a rapid, practical and realistically accurate approach for salinity mapping of heterogeneous urban vegetation.

Keywords: WorldView3, remote sensing, EM38, near sensing, urban green spaces, green smart cities

Procedia PDF Downloads 162

Authors: Ratchadaporn Ungcharoen, Lily Ingsrisawang

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Capture-recapture methods are popular techniques for indirect estimation the size of wildlife populations and the completeness of cases in epidemiology and social sciences. The aim of this study was to estimate the completeness of pulmonary tuberculosis cases confirmed by two sources of hospital registrations and surveillance systems in 2013 in Nakhon Pathom province, Thailand. Several estimators of population size were considered: the Lincoln-Petersen estimator, the Chapman estimator, the Chao’s lower bound estimator, the Zelterman’s estimator, etc. We focus on the Chapman and Chao’s lower bound estimators for estimating the completeness of pulmonary tuberculosis from two sources. The retrieved pulmonary tuberculosis data from two sources were analyzed and bootstrapped for 30 samples, with 241 observations from source 1 and 305 observations from source 2 per sample, for additional exploration of the completeness of pulmonary tuberculosis. The results from the original data show that the Chapman’s estimator gave the estimation of a total 360 (95% CI: 349-371) pulmonary tuberculosis cases, resulting in 57% estimated completeness cases. But the Chao’s lower bound estimator estimated the total of 365 (95% CI: 354-376) pulmonary tuberculosis cases and its estimated completeness cases was 55.9%. For the results from bootstrap samples, the Chapman and the Chao’s lower bound estimators gave an estimated 347 (95% CI: 309-385) and 353 (95% CI: 315-390) pulmonary tuberculosis cases, respectively. If for two sources recoding systems are available, record-linkage and capture-recapture analysis can be useful for estimating the completeness of different registration system. Both Chapman and Chao’s lower bound estimator approaches produce very close estimates.

Keywords: capture-recapture, Chao, Chapman, pulmonary tuberculosis

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Authors: Ana L. M. Pavan, Guilherme Giacomini, Allan F. F. Alves, Marcela De Oliveira, Fernando A. B. Neto, Maria E. D. Rosa, Andre P. Trindade, Diana R. De Pina

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Mammography is a worldwide image modality used to diagnose breast cancer, even in asymptomatic women. Due to its large availability, mammograms can be used to measure breast density and to predict cancer development. Women with increased mammographic density have a four- to sixfold increase in their risk of developing breast cancer. Therefore, studies have been made to accurately quantify mammographic breast density. In clinical routine, radiologists perform image evaluations through BIRADS (Breast Imaging Reporting and Data System) assessment. However, this method has inter and intraindividual variability. An automatic objective method to measure breast density could relieve radiologist’s workload by providing a first aid opinion. However, pectoral muscle is a high density tissue, with similar characteristics of fibroglandular tissues. It is consequently hard to automatically quantify mammographic breast density. Therefore, a pre-processing is needed to segment the pectoral muscle which may erroneously be quantified as fibroglandular tissue. The aim of this work was to develop an automatic algorithm to segment and extract pectoral muscle in digital mammograms. The database consisted of thirty medio-lateral oblique incidence digital mammography from São Paulo Medical School. This study was developed with ethical approval from the authors’ institutions and national review panels under protocol number 3720-2010. An algorithm was developed, in Matlab® platform, for the pre-processing of images. The algorithm uses image processing tools to automatically segment and extract the pectoral muscle of mammograms. Firstly, it was applied thresholding technique to remove non-biological information from image. Then, the Hough transform is applied, to find the limit of the pectoral muscle, followed by active contour method. Seed of active contour is applied in the limit of pectoral muscle found by Hough transform. An experienced radiologist also manually performed the pectoral muscle segmentation. Both methods, manual and automatic, were compared using the Jaccard index and Bland-Altman statistics. The comparison between manual and the developed automatic method presented a Jaccard similarity coefficient greater than 90% for all analyzed images, showing the efficiency and accuracy of segmentation of the proposed method. The Bland-Altman statistics compared both methods in relation to area (mm²) of segmented pectoral muscle. The statistic showed data within the 95% confidence interval, enhancing the accuracy of segmentation compared to the manual method. Thus, the method proved to be accurate and robust, segmenting rapidly and freely from intra and inter-observer variability. It is concluded that the proposed method may be used reliably to segment pectoral muscle in digital mammography in clinical routine. The segmentation of the pectoral muscle is very important for further quantifications of fibroglandular tissue volume present in the breast.

Keywords: active contour, fibroglandular tissue, hough transform, pectoral muscle

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Authors: Lison Carlier

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The English language that is taught in school or used in media nowadays is defined as 'standard English,' although unstandardized Englishes, or 'parallel' Englishes, are practiced throughout the world. The existence of these 'parallel' Englishes has challenged standardization by imposing its own specific vocabulary or grammar. These non-standard languages tend to be regarded as inferior and, therefore, pose a problem regarding their translation. In the USA, 'slanguage', or slang, is a good example of a 'parallel' language. It consists of a particular set of vocabulary, used mostly in speech, and rarely in writing. Qualified as vulgar, often reduced to an urban language spoken by young people from lower classes, slanguage – or the language that is often first spoken between youths – is still the most common language used in the English-speaking world. Moreover, it appears that the prime meaning of 'informal' (as in an informal language) – a language that is spoken with persons the speaker knows – has been put aside and replaced in the general mind by the idea of vulgarity and non-appropriateness, when in fact informality is a sign of intimacy, not of vulgarity. When it comes to translating American slang, the main problem a translator encounters is the image and the cultural background usually associated with this 'parallel' language. Indeed, one will have, unwillingly, a predisposition to categorize a speaker of a 'parallel' language as being part of a particular group of people. The way one sees a speaker using it is paramount, and needs to be transposed into the target language. This paper will conduct an analysis of American slang – its use, perception and the image it gives of its speakers – and its translation into French, using the novel Is Everyone Hanging Out Without Me? (and other concerns) by way of example. In her autobiography/personal essay book, comedy writer, actress and author Mindy Kaling speaks with a very familiar English, including slang, which participates in the construction of her own voice and style, and enables a deeper connection with her readers.

Keywords: translation, English, slang, French

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Authors: Kayode A. Olaniyi, Olabanji F. Omotoye, Adeola A. Ogunleye

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The skew detection and correction form an important part of digital document analysis. This is because uncompensated skew can deteriorate document features and can complicate further document image processing steps. Efficient text document analysis and digitization can rarely be achieved when a document is skewed even at a small angle. Once the documents have been digitized through the scanning system and binarization also achieved, document skew correction is required before further image analysis. Research efforts have been put in this area with algorithms developed to eliminate document skew. Skew angle correction algorithms can be compared based on performance criteria. Most important performance criteria are accuracy of skew angle detection, range of skew angle for detection, speed of processing the image, computational complexity and consequently memory space used. The standard Hough Transform has successfully been implemented for text documentation skew angle estimation application. However, the standard Hough Transform algorithm level of accuracy depends largely on how much fine the step size for the angle used. This consequently consumes more time and memory space for increase accuracy and, especially where number of pixels is considerable large. Whenever the Hough transform is used, there is always a tradeoff between accuracy and speed. So a more efficient solution is needed that optimizes space as well as time. In this paper, an improved Hough transform (HT) technique that optimizes space as well as time to robustly detect document skew is presented. The modified algorithm of Hough Transform presents solution to the contradiction between the memory space, running time and accuracy. Our algorithm starts with the first step of angle estimation accurate up to zero decimal place using the standard Hough Transform algorithm achieving minimal running time and space but lacks relative accuracy. Then to increase accuracy, suppose estimated angle found using the basic Hough algorithm is x degree, we then run again basic algorithm from range between ±x degrees with accuracy of one decimal place. Same process is iterated till level of desired accuracy is achieved. The procedure of our skew estimation and correction algorithm of text images is implemented using MATLAB. The memory space estimation and process time are also tabulated with skew angle assumption of within 00 and 450. The simulation results which is demonstrated in Matlab show the high performance of our algorithms with less computational time and memory space used in detecting document skew for a variety of documents with different levels of complexity.

Keywords: hough-transform, skew-detection, skew-angle, skew-correction, text-document

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Authors: G. Erkomaishvili, M. Kobalava, T. Lazariashvili, N. Damenia

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Current state of foreign direct investment (FDI) in Georgia is analyzed and evaluated in the paper, the existing legislative background for regulating investments and stimulating policies to attract investments are shown. It is noted that in developing countries encouragement of investment activity, support and implementation are of the most important tasks, implying a consistent investment policy, investor-friendly tax regime and the legal system, reducing administrative barriers and restrictions, fare competitive conditions and business development infrastructure. The work deals with the determining factor of FDIs and the main directions of stimulation, as well as prospective industries where new investments are needed. Contributing and hindering factors and stimulating measures are analyzed. As a result of the research, the direct and indirect factors attracting FDI have been identified. Facilitating factors to FDI inflow are as follows: simplicity of starting business, geopolitical location, low taxes, access to credit, ease of ownership registration, natural resources, low burden of regulations, low level of corruption and low crime rates. Hindering factors to FDI inflow are as follows: small market, lack of policy for attracting investments, low qualification of the workforce (despite the large number of unemployed people it is difficult to find workers with necessary special skills and qualifications), high interest rates, instability of national currency exchange rate, presence of conflict zones within the country and so forth.

Keywords: foreign direct investment, investor, investment attracting marketing policies, reinvestment

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Authors: Erfan Niazi, Marianne Fenech

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Red Blood Cells (RBCs) or erythrocytes tend to form chain-like aggregates under low shear rate called rouleaux. This is a reversible process and rouleaux disaggregate in high shear rates. Therefore, RBCs aggregation occurs in the microcirculation where low shear rates are present but does not occur under normal physiological conditions in large arteries. Numerical modeling of RBCs interactions is fundamental in analytical models of a blood flow in microcirculation. Population Balance Modeling (PBM) is particularly useful for studying problems where particles agglomerate and break in a two phase flow systems to find flow characteristics. In this method, the elementary particles lose their individual identity due to continuous destructions and recreations by break-up and agglomeration. The aim of this study is to find RBCs aggregation in a dynamic situation. Simplified PBM was used previously to find the aggregation rate on a static observation of the RBCs aggregation in a drop of blood under the microscope. To find aggregation rate in a dynamic situation we propose an experimental set up testing RBCs sedimentation. In this test, RBCs interact and aggregate to form rouleaux. In this configuration, disaggregation can be neglected due to low shear stress. A high-speed camera is used to acquire video-microscopic pictures of the process. The sizes of the aggregates and velocity of sedimentation are extracted using an image processing techniques. Based on the data collection from 5 healthy human blood samples, the aggregation rate was estimated as 2.7x10³(±0.3 x10³) 1/s.

Keywords: red blood cell, rouleaux, microfluidics, image processing, population balance modeling

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Authors: Pibool Waijittragum

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The purpose of this paper is to synthesize the research result of brand Identities of Thai Halal brands which related to the way of life for Thai Muslims. The results will be transforming to Thai Halal Brands packaging and label design. The expected benefit is an alternative of marketing strategy for brand building process for Halal products in Thailand. Four elements of marketing strategies which necessary for the brand identity creation is the research framework: consists of Attributes, Benefits, Values and Personality. The research methodology was applied using qualitative and quantitative; 19 marketing experts with dynamic roles in Thai consumer products were interviewed. In addition, a field survey of 122 Thai Muslims selected from 175 Muslim communities in Bangkok was studied. Data analysis will be according to 5 categories of Thai Halal product: 1) Meat 2) Vegetable and Fruits 3) Instant foods and Garnishing ingredient 4) Beverages, Desserts and Snacks 5) Hygienic daily products. The results will explain some suitable approach for brand Identities of Thai Halal brands as are: 1) Benefit approach as the characteristics of the product with its benefit. The brand identity created transform to the packaging design should be clear and display a fresh product 2) Value approach as the value of products that affect to consumers’ perception. The brand identity created transform to the packaging design should be simply look and using a trustful image 3) Personality approach as the reflection of consumers thought. The brand identity created transform to the packaging design should be sincere, enjoyable, merry, flamboyant look and using a humoristic image.

Keywords: marketing strategies, brand identity, packaging and label design, Thai Halal products

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Authors: Tukundane Hillary

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This research paper analytically evaluates the project control practice levels used by the building construction companies within Kampala, Uganda. The research also assesses the outcome of project control practices on the productivity of the companies. The research was performed to ascertain the current control practices among 160 respondents from various construction companies registered with the Uganda Registration Services Bureau. This research used amalgamation from multiple literature to obtain the variables. The research adopts 34 standard control practices from four vital project control duties: planning, monitoring, analyzing, and reporting. These project control tasks were organized using mean response ratings grounded on their relevance to the construction companies. Results showed that evaluating performance with the use of curves (4.32), timely access to information and encouragement (4.55), report representation using quantitative tools 4.75, and cost value comparison application during analysis (4.76) were rated least among the control practices. On the other hand, the top project control practices included formulation of the project schedule (8.88), Project feasibility validation (8.86), Budgeting for each activity (8.84), Key project route definition (8.81), Team awareness of the budget (8.77), Setting realistic targets for projects (8.50) and Consultation from subcontractors (8.74). From the results obtained by the sample respondents specified, it can be concluded that planning is the most vital project control task practiced in the building construction industry in Uganda. In addition, this research ascertained a substantial relationship between project control practices and the performance of building construction companies. Accordingly, this research recommends that project control practices be effectively observed by both contracting and consulting companies to enhance their overall performance and governance.

Keywords: cost value, project control, cost control, time control, project performance, control practices

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Authors: B. Wattanakul, G. Ngamwongwan, S. Ngamkham

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Exposure to different learning experiences contributes to changing in learning style. Addressing students’ learning preference could help teachers provide different learning activities that encourage the student to learn effectively. Purpose: The purpose of this descriptive study was to describe learning styles of nursing students at Boromarajonani College of Nursing Chon Buri. Sample: The purposive sample was 463 nursing students who were enrolled in a nursing program at different academic levels. The 16-item VARK questionnaire with 4 multiple choices was administered at one time data collection. Choices have consisted with modalities of Visual, Aural, Read/write, and Kinesthetic measured by VARK. Results: Majority of learning preference of students at different levels was visual and read/write learning preference. Almost 67% of students have a multimodal preference, which is visual learning preference associated with read/write or kinesthetic preference. At different academic levels, multimodalities are greater than single preference. Over 30% of students have one dominant learning preference, including visual preference, read/write preference and kinesthetic preference. Analysis of Variance (ANOVA) with Bonferroni adjustment revealed a significant difference between students based on their academic level (p < 0.001). Learning style of the first-grade nursing students differed from the second-grade nursing students (p < 0.001). While learning style of nursing students in the second-grade has significantly varied from the 1st, 3rd, and 4th grade (p < 0.001), learning preference of the 3rd grade has significantly differed from the 4th grade of nursing students (p > 0.05). Conclusions: Nursing students have varied learning styles based on their different academic levels. Learning preference is not fixed attributes. This should help nursing teachers assess the types of changes in students’ learning preferences while developing teaching plans to optimize students’ learning environment and achieve the needs of the courses and help students develop learning preference to meet the need of the course.

Keywords: learning preference, VARK, learning style, nursing

Procedia PDF Downloads 359

Authors: Xinchen Zhu

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The rapid fame of the renowned female film star Tang Wei has made her a typical subject (or object) entangled with sensitive issues involving the official ideology, sexuality, and patriarchal values of contemporary China. In 2008, Tang Wei’s official ban has triggered the wave of debates concerning state power and censorship, actor’s rights, sexual ethics, and feminism in the public sphere. Her ban implies that Chinese film censorship acts as a key factor in reconstructing Chinese film stardom. Following the ban, as sensational media texts are re-interpreting the official discourses, the texts also functioned as a crucial vehicle in reconstructing Tang's female image. Therefore, the case study of Tang's film stardom allows us to further explore how female stardom has been entangled with the issues involving official ideology, female sexual ethics, and patriarchal values in contemporary China. This paper argues that Chinese female film stars shoulder the responsibility of film acting which would conform to the official male-dominated values. However, with the development of the Internet, the state no longer remains an absolute control over the new venues. The netizens’ discussion about her ban reshaped Tang’s image as a victim and scapegoat under the unfair oppression of the official authority. Additionally, this paper argues that similar to State’s discourse, netizens’ discourse did not reject patriarchal values, and in turn emphasized Tang Wei’s female chastity.

Keywords: film censorship, Chinese female film stardom, party-state’s power, national discourses, Tang Wei

Procedia PDF Downloads 170