Search results for: physical-chemical features

Authors: Kiknadze T, Tevdorashvili G, Muzashvili T, Gachechiladze M, Burkadze G

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Uterine leiomyomas decrease the quality of life by causing significant morbidity among women of reproductive age. Histologically various types of leiomyoma's can be differentiated. We have analysed th histopathological, proliferation, apoptotic, and hormonal profile in different types of leiomyomas. Study included altogether140 cases distributed into the following groups: group I-normal myometrium (20cases), group II-classic leiomyoma (69 cases), group III-cellular leiomyoma (15 cases), group IV-bizarre cell/atypical leiomyoma (22cases), group V-smooth muscle tumors of uncertain malignancy potential (STUMP) (8 cases) and group VI-leiomyosarcoma (6 cases). Together with classic histopathological features such as nuclear atypia, cellularity, presence of mitoses, vasculature and necrosis, immunohistochemical phenotype using antibodies against Ki67,Cas3, ER, and PR were analysed. The results of our study showed that leiomyomas are charterised with variable histopathological and immunohistocthemical phenotype. Histopathological parameters mainly correlate with the degree of malignancy except for two bizarre/atypical leiomyoma and STUMP, where two distinct subgroups could be identified. In bizarre/ atipycal leiomyoma, 31% of cases are characterized with the features of classic leiomyoma, whilst the rest of the cases reveal more atipycal phenotype. In STUMP 37.5 % of cases are characterized with the features of atipycal leiomyomas. The result of the immunohistochemical study also reveald that half of bizarre/atipycal leiomyomas are characterized with the low proliferation index, high apoptotic index, and high ER and PR index, whilst another half is characterized with high proliferation index, low apoptotic index, and low ER and PR index. Similarly, part of the STUMP cases are characterized with low proliferation index, high Er, and PR index and whilst part of the cases are characterized whith high proliferation index, low apoptotic index and low ER and PR index. The results of the histopathological and immunohistochemical study indicate that these two entities represent the heterogenous group of diseases, which might be the explanation of their different prognosis. Presented histopathological and immunohistochemical features should be considered in the diagnosis of myometrial smooth muscle tumors.

Keywords: proliferation, apoptosis, bizarre cell, leiomyosarcoma., leiomyoma

Procedia PDF Downloads 108

Authors: Geetika Barman, B. S. Daya Sagar

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In this article, we proposed a pixel-wise classification of hyperspectral images using a mathematical morphology operator-based distance metric called “dilation distance” and “erosion distance”. This method involves measuring the spatial distance between the spectral features of a hyperspectral image across the bands. The key concept of the proposed approach is that the “dilation distance” is the maximum distance a pixel can be moved without changing its classification, whereas the “erosion distance” is the maximum distance that a pixel can be moved before changing its classification. The spectral signature of the hyperspectral image carries unique class information and shape for each class. This article demonstrates how easily the dilation and erosion distance can measure spatial distance compared to other approaches. This property is used to calculate the spatial distance between hyperspectral image feature vectors across the bands. The dissimilarity matrix is then constructed using both measures extracted from the feature spaces. The measured distance metric is used to distinguish between the spectral features of various classes and precisely distinguish between each class. This is illustrated using both toy data and real datasets. Furthermore, we investigated the role of flat vs. non-flat structuring elements in capturing the spatial features of each class in the hyperspectral image. In order to validate, we compared the proposed approach to other existing methods and demonstrated empirically that mathematical operator-based distance metric classification provided competitive results and outperformed some of them.

Keywords: dilation distance, erosion distance, hyperspectral image classification, mathematical morphology

Procedia PDF Downloads 86

Authors: Jyothirmayee Pola

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Ideally, a test-driven development (TDD) or agile or any other process should be able to define and implement performance, scalability, and reliability (PSR) of the product with a higher quality of service (QOS) and should have the ability to fix any PSR issues with lesser cost before it hits the production. Most PSR test strategies for new product introduction (NPI) include assumptions about production load requirements but never accurate. NPE (New product Enhancement) include assumptions for new features that are being developed whilst workload distribution for older features can be derived by analyzing production transactions. This paper talks about how to shift left PSR towards design phase of release management process to get better QOS w.r.t PSR for any product under development. It also explains the ROI for future customer onboarding both for Service Oriented Architectures (SOA) and Microservices architectures and how to define PSR requirements.

Keywords: component PSR, performance engineering, performance tuning, reliability, return on investment, scalability, system PSR

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Authors: Recep Duzsoz, Elif Bilgir, Derya Yildirim, Ozlem Gormez

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Radicular cysts develop in the root apex of tooth that is devitalized. Cysts are pathologic lesions with an epithelial lining encapsulated by connective tissue. Radicular cysts originate from epithelial remnants of the periodontal ligament in the root apex as a result of inflammation. They are most commonly observed in the maxillary anterior region, among men and in the third decade of life. Radiographically, they are seen as ovoid radiolucent lesions surrounded by a thin radioopaque margin. In this case, series was carried out in 15 radicular cysts of the jaws diagnosed in individuals. The cysts were evaluated age, sex, and localization. 12 of the cysts were localized in the maxillae, 3 of them were localised in the mandible. The female/male ratio of the lesions was 1/2. In conclusion, we evaluated age, localization and sex distribution of radicular cysts in this study. The knowledge of the features of the jaw cysts is a basic aspect to achieve diagnosis, complications and proper treatment.

Keywords: radicular cyst, jaws, CBCT, treatment

Procedia PDF Downloads 287

Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Mairo Musa Galadima, Phoebe Mshelia

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In Nigeria, the national policy of education stipulates that the kindergarten primary schools and the legislature are to use the three popular Nigerian Languages namely: Hausa, Igbo and Yoruba. However, the English language seems to be preferred and this calls for this paper. Attempts were made to draw out the challenges faced by learners in understanding English speech sounds and using them to communicate effectively in English; using 5(five) selected secondary school in Bauchi. It was discover that challenges abound in the wrong use of stress and intonation, transfer of phonetic features from their first language. Others are inadequate qualified teachers and relevant materials including text-books. It is recommended that teachers of English should lay more emphasis on the teaching of supra-segmental features and should be encouraged to go for further studies, seminars and refresher courses.

Keywords: kindergarten, stress, phonetic and intonation, Nigeria

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Authors: Jovana Dj. Jovanovic, Svetlana M. Stevovic

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The highest priority of so called, projected passive houses is to meet the appropriate energy demand. Every single material and layer which is injected into a dwelling has a certain energy quantity stored. The passive houses include optimized insulation levels with minimal thermal bridges, minimum of air leakage through the building, utilization of passive solar and internal gains, and good circulation of air which leans on mechanical ventilation system. The focus of this paper is on passive house features, benefits and targets, their feasibility and energy demands which are set up during each project. Numerous passive house-standards outline the very significant role of zero-energy dwellings towards the modern label of sustainable development. It is clear that the performance of both built and existing housing stock must be addressed if the population across the world sets out the energy objectives. This scientific article examines passive house features since the many passive house cases are launched.

Keywords: benefits, energy demands, passive houses, sustainable development

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Authors: Rida Nasir, Waseem Iqbal

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Due to the constant revolution in technology and the increase in anti-forensic techniques used by attackers to remove their traces, professionals often struggle to choose the best tool to be used in digital forensic investigations. This paper compares two of the most well-known and widely used licensed commercial tools, i.e., Encase & FTK. The comparison was drawn on various parameters and features to provide an authentic evaluation of licensed versions of these well-known commercial tools against various real-world scenarios. In order to discover the popularity of these tools within the digital forensic community, a survey was conducted publicly to determine the preferred choice. The dataset used is the Computer Forensics Reference Dataset (CFReDS). A total of 70 features were selected from various categories. Upon comparison, both FTK and EnCase produce remarkable results. However, each tool has some limitations, and none of the tools is declared best. The comparison drawn is completely unbiased, based on factual data.

Keywords: digital forensics, commercial tools, investigation, forensic evaluation

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Authors: Sruti Akula

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Assessment criteria are mostly used for assessing skills like writing and speaking. However, they could be used as a pedagogic tool to develop writing skills. A study was conducted with higher secondary learners (Class XII Kendriya Vidyalaya) to investigate the effectiveness of assessment criteria to develop argumentative essay writing. In order to raise awareness about the features of argumentative essay, assessment criteria were shared with the learners. Along with that, self-evaluation checklists were given to the learners to guide them through the writing process. During the study learners wrote multiple drafts with the help of assessment criteria, self-evaluation checklists and teacher feedback at different stages of their writing. It was observed that learners became more aware of the features of argumentative essay which in turn improved their argumentative essay writing. In addition the self evaluation checklists imporved their ability to reflect on their work there by increasing learner autonomy in the class. Hence, it can be claimed that both assessment criteria and self evaluation checklists are effective pedagogic tools to develop argumentative essay writing. Thus, teachers can be trained to create and use tools like assessment criteria and self-evaluation checklists to develop learners’ writing skills in an effective way. The presentation would discuss the approach adopted in the study to teach argumentative essay writing along with the rationale. The tools used in the study would be shared and the data collected in the form of written scripts, self-evaluation checklists and student interviews will be analyzed to validate the claims. Finally, the practical implication of the study like the ways of using assessment criteria and checklists to raise learner awareness and autonomy, using such tools to keep the learners informed about the task requirements and genre features, and the like will be put forward.

Keywords: argumentative essay writing, assessment criteria, self evaluation checklists, pedagogic

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Authors: Mohammadsadegh Zanganehfar

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Since the introduction of fractal geometry in 1970, numerous efforts have been made by architects and researchers to transfer this area of mathematical knowledge in the discipline of architecture and postmodernist discourse. The discourse of complexity and architecture is one of the most significant ongoing discourses in the discipline of architecture from the '70s until today and has generated significant styles such as deconstructivism and parametrism in architecture. During these years, several projects were designed and presented by designers and architects using fractal geometry, but due to the lack of sufficient knowledge and appropriate comprehension of the features and characteristics of this nonlinear geometry, none of the fractal-based designs have been successful and satisfying. Fractal geometry as a geometric technology has a long presence in the history of architecture. The current research attempts to identify and discover the characteristics, features, potentials, and functionality of fractals despite their aesthetic aspect by examining case studies of pre-modern architecture in Asia and investigating the function of fractals.

Keywords: Asian architecture, fractal geometry, fractal technique, geometric properties

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 238

Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

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Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: fractal, micro-architecture analysis, multifractal, osteoporosis, SVM

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Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

Procedia PDF Downloads 48

Authors: H. Nejadriahi

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Traditional architecture is a valuable source of inspiration, which needs to be studied and integrated in the contemporary designs for achieving an identifiable contemporary architecture. Traditional architecture of Iran is among the distinguished examples of being contextually responsive, not only by considering the environmental conditions of a region, but also in terms of respecting the socio-cultural values of its context. In order to apply these valuable features to the current designs, they need to be adapted to today's condition, needs and desires. In this paper, the main features of the traditional architecture of Iran are explained to interrogate them in the formation of a contemporary house in Tehran, Iran. Also a table is provided to compare the utilization of the traditional design concepts in the traditional houses and the contemporary example of it. It is believed that such study would increase the awareness of contemporary designers by providing them some clues on maintaining the traditional values in the current design layouts particularly in the residential sector that would ultimately improve the quality of space in the contemporary architecture.

Keywords: contemporary housing design, Iran, Tehran, traditional architecture

Procedia PDF Downloads 469

Authors: Jaturong Som-ard

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The heavy rainfall from 3rd to 22th January 2017 had swamped much area of Ranot district in southern Thailand. Due to heavy rainfall, the district was flooded which had a lot of effects on economy and social loss. The major objective of this study is to detect flooding extent using Sentinel-1A data and identify a number of damaged buildings over there. The data were collected in two stages as pre-flooding and during flood event. Calibration, speckle filtering, geometric correction, and histogram thresholding were performed with the data, based on intensity spectral values to classify thematic maps. The maps were used to identify flooding extent using change detection, along with the buildings digitized and collected on JOSM desktop. The numbers of damaged buildings were counted within the flooding extent with respect to building data. The total flooded areas were observed as 181.45 sq.km. These areas were mostly occurred at Ban khao, Ranot, Takhria, and Phang Yang sub-districts, respectively. The Ban khao sub-district had more occurrence than the others because this area is located at lower altitude and close to Thale Noi and Thale Luang lakes than others. The numbers of damaged buildings were high in Khlong Daen (726 features), Tha Bon (645 features), and Ranot sub-district (604 features), respectively. The final flood extent map might be very useful for the plan, prevention and management of flood occurrence area. The map of building damage can be used for the quick response, recovery and mitigation to the affected areas for different concern organization.

Keywords: flooding extent, Sentinel-1A data, JOSM desktop, damaged buildings

Procedia PDF Downloads 192

Authors: Saleh Abu Hashanah, Abirami Radhakrishnan, Dessa David

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Sustainable practices in the supply chain have been an area of focus that require consideration of environmental, economic, and social sustainability practices. This paper aims to examine the use of blockchain as a disruptive technology to promote sustainable supply chains. Content analysis was used to analyze the uses of blockchain technology in sustainable supply chains. The results showed that blockchain technology features such as traceability, transparency, smart contracts, accountability, trust, immutability, anti-fraud, and decentralization promote sustainable supply chains. It is found that these features have impacted organizational efficiency in operations, transportation, and production, minimizing costs and reducing carbon emissions. In addition, blockchain technology has been found to elicit customer trust in the products.

Keywords: blockchain technology, sustainability, supply chains, economic sustainability, environmental sustainability, social sustainability

Procedia PDF Downloads 106

Authors: Liana Melissa E. de la Rosa, Jayson P. Ada

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This exploratory study was conducted to determine the sociology of the Facebook. Specifically, it aimed to know the socio-demographic profile of the respondents in terms of age, sex, year level and monthly allowance; find out the common usage of Facebook to the respondents; identify the features of Facebook that are commonly used by the respondents; understand the benefits and risks of using the Facebook; determine how frequent the respondents use the Facebook; and find out if there is a significant relationship between socio-demographic profile of the respondents and their Facebook usage. This study used the exploratory research design and correlational design employing research survey questionnaire as its main data gathering instrument. Students of the University of Eastern Philippines were selected as the respondents of this study through quota sampling. Ten (10) students were randomly selected from each college of the university. Based on the findings of this study, the following conclusion were drawn: The majority of the respondents are aged 18 and 21 old, female, are third year students, and have monthly allowance of P 2,000 above. On the respondents’ usage of Facebook, the majority of use the Facebook on a daily basis for one to two (1-2) hours everyday. And most users used Facebook by renting a computer in an internet cafe. On the use of Facebook, most users have created their profiles mainly to connect with people and gain new friends. The most commonly used features of Facebook, are: photos application, like button, wall, notification, friend, chat, network, groups and “like” pages status updates, messages and inbox and events. While the other Facebook features that are seldom used by the respondents are games, news feed, user name, video sharing and notes. And the least used Facebook features are questions, poke feature, credits and the market place. The respondents stated that the major benefit that the Facebook has given to its users is its ability to keep in touch with family members or friends while the main risk identified is that the users can become addicted to the Internet. On the tests of relationships between the respondents’ use of Facebook and the four (4) socio-demographic profile variables: age, sex, year level, and month allowance, were found to be not significantly related to the respondents’ use of the Facebook. While the variable found to be significantly related was gender.

Keywords: Facebook, sociology, social networking, exploratory study

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Authors: Majse Lind, Carla Sharp, Salome Vanwoerden

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Narrative identity is the dynamic and evolving story individuals create about their personal pasts, presents, and presumed futures. This storied sense of self develops in adolescence and is crucial for fostering a sense of self-unity and purpose in life. A growing body of work has shown that several characteristics of narrative identity are disturbed in adults suffering from borderline personality disorder (BPD). Very little research, however, has explored the stories told by adolescents with BPD features. Investigating narrative identity early in the lifespan and in relation to personality pathology is crucial; BPD is a developmental disorder with early signs appearing already in adolescence. In the current study, we examine narrative identity (focusing on themes of agency and communion) coded from self-defining memories derived from the child attachment interview in 174 inpatient adolescents (M = 15.12, SD = 1.52) at the time of admission. The adolescents’ social cognition was further assessed on the basis of their reactions to movie scenes (i.e., the MASC movie task). They also completed a trauma checklist and self-reported BPD features at three different time points (i.e., at admission, at discharge, and 6 months after admission). Preliminary results show that adolescents who told stories containing themes of agency and communion evinced better social cognition, and lower emotional abuse on the trauma checklist. In addition, adolescents who disclosed stories containing lower levels of agency and communion demonstrated more BPD symptoms at all three time points, even when controlling for the occurrence of traumatic life events. Surprisingly, social cognitive abilities were not significantly associated with BPD features. These preliminary results underscore the importance of narrative identity as an indicator, and potential cause, of incipient personality pathology. Thus, focusing on diminished themes of narrative-based agency and communion in early adolescence could be crucial in preventing the development of personality pathology over time.

Keywords: borderline personality disorder, inpatient adolescents, narrative identity, follow-ups

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

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Authors: Mehdi Brahimi, Kamal Medjaher, Noureddine Zerhouni, Mohammed Leouatni

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The Prognostics and Health Management is a system engineering discipline which provides solutions and models to the implantation of a predictive maintenance. The approach is based on extracting useful information from monitoring data to assess the “health” state of an industrial equipment or an asset. In this paper, we examine multiple extracted features from Pantograph-Catenary contact force in order to select the most relevant ones to achieve a diagnostics function. The feature extraction methodology is based on simulation data generated thanks to a Pantograph-Catenary simulation software called INPAC and measurement data. The feature extraction method is based on both statistical and signal processing analyses. The feature selection method is based on statistical criteria.

Keywords: catenary/pantograph interaction, diagnostics, Prognostics and Health Management (PHM), quality of current collection

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Hanna Poikonen, Petri Toiviainen, Mari Tervaniemi

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Event-related potentials (ERPs) evoked by simple tones in the brain have been extensively studied. However, in reality the music surrounding us is spectrally and temporally complex and dynamic. Thus, the research using natural sounds is crucial in understanding the operation of the brain in its natural environment. Music is an excellent example of natural stimulation, which, in various forms, has always been an essential part of different cultures. In addition to sensory responses, music elicits vast cognitive and emotional processes in the brain. When compared to laymen, professional musicians have stronger ERP responses in processing individual musical features in simple tone sequences, such as changes in pitch, timbre and harmony. Here we show that the ERP responses evoked by rapid changes in individual musical features are more intense in musicians than in laymen, also while listening to long excerpts of the composition Carmen. Interestingly, for professional dancers, the amplitudes of the cognitive P300 response are weaker than for musicians but still stronger than for laymen. Also, the cognitive P300 latencies of musicians are significantly shorter whereas the latencies of laymen are significantly longer. In contrast, sensory N100 do not differ in amplitude or latency between musicians and laymen. These results, acquired from a novel ERP methodology for natural music, suggest that we can take the leap of studying the brain with long pieces of natural music also with the ERP method of electroencephalography (EEG), as has already been made with functional magnetic resonance (fMRI), as these two brain imaging devices complement each other.

Keywords: electroencephalography, expertise, musical features, real-life music

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Authors: Preecha Phongpeng

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The objectives of this research are to assess the prevalence of travel businesses websites in Thailand, investigate and evaluate the quality of travel business websites in Thailand. The sample size includes 323 websites from the population of 1,458 websites. The study covers 4 types of travel business websites including: 78 general travel agents, 30 online reservation travel agents, 205 hotels, 7 airlines, and 3 car-rental companies with nation-wide operation. The findings indicated that e-tourism in Thailand is at its growth stage, with only 13% of travel businesses having websites, 28% of them providing e-mail and the quality of travel business websites in Thailand was at the average level. Seven common problems were found in websites: lack of travel essential information, insufficient transportation information, lack of navigation tools, lack of link pages to other organizations, lack of safety features, unclear online booking functions, and lack of special features also as well.

Keywords: traveling business, website evaluation, e-commerce, e-tourism

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Mairo Musa Galadima, Phoebe Mshelia

Abstract:

In Nigeria, the national policy of education stipulates that the kindergarten-primary schools and the legislature are to use the three popular Nigerian Languages namely: Hausa, Igbo, and Yoruba. However, the English language seems to be preferred and this calls for this paper. Attempts were made to draw out the challenges faced by learners in understanding English speech sounds and using them to communicate effectively in English; using 5 (five) selected secondary school in Bauchi. It was discovered that challenges abound in the wrong use of stress and intonation, transfer of phonetic features from their first language. Others are inadequately qualified teachers and relevant materials including textbooks. It is recommended that teachers of English should lay more emphasis on the teaching of supra-segmental features and should be encouraged to go for further studies, seminars and refresher courses.

Keywords: stress and intonation, phonetic and challenges, teaching and learning English, secondary schools

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Authors: Ahmed Amin Mousa, Mona Yacoub

Abstract:

This study depends on tracking children style through what they have drawn after being introduced to 16 visual content through National Geographic Abu Dhabi Channel programs and the study of the changing features in their drawings before applying the visual act with them. The researchers used Goodenough-Harris Test to analyse children drawings and to extract the features which changed in their drawing before and after the visual content. The results showed a positive change especially in the shapes of animals and their properties. Children become more aware of animals’ shapes. The study sample was 220 kindergarten children divided into 130 girls and 90 boys at the Orman Experimental Language School in Dokki, Giza, Egypt. The study results showed an improvement in children drawing with 85% than they were before watching videos.

Keywords: National Geographic, children drawing, kindergarten, Goodenough-Harris Test

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Authors: B. S. Harish, M. B. Revanasiddappa

Abstract:

Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Chirawat Wattanapanich, Hong Wei

Abstract:

This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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

Abstract:

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: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

Abstract:

Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

Procedia PDF Downloads 231