Search results for: online database

Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

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Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

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Authors: Argyro Maria Skourmalla

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The present paper draws on the example of the University of Luxembourg as a multilingual and international setting. The University of Luxembourg, which is located between France, Germany, and Belgium, has adopted a new multilingualism policy in 2020, establishing English, French, German, and Luxembourgish as the official languages of the Institution. With around 7.000 students, more than half of which are international students, the University is a meeting point for languages and cultures. This paper includes data from an online survey that with undergraduate students from different disciplines at the University of Luxembourg. Students shared their personal experience and opinions regarding language use in this higher education context, as well as practices they use in learning in this multilingual context. Findings show the role of technology in assisting students in different aspects of learning this multilingual context. At the same time, more needs to be done to avoid an exclusively monolingual paradigm in higher education. Findings also show that some languages remain ‘unseen’ in this context. Overall, even though linguistic diversity in this University is seen as an asset, a lot needs to be done towards the recognition of staff and students’ linguistic repertoires for inclusion and education equity.

Keywords: higher education, learning, linguistic diversity, multilingual practices

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Authors: Yogendra Singh, Mayur Pisode

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Many times it is observed that in multi-storeyed buildings the dynamic properties in the two directions are similar due to which there may be a coupling between the two orthogonal modes of the building. This is particularly observed in bi-symmetric buildings (buildings with structural properties and periods approximately equal in the two directions). There is a swapping of vibrational energy between the modes in the two orthogonal directions. To avoid this coupling the draft revision of IS:1893 proposes a minimum separation of more than 15% between the frequencies of the fundamental modes in the two directions. This study explores the seismic behaviour of bi-symmetrical buildings under uniaxial and bi-axial ground motions. For this purpose, three different types of 8 storey buildings symmetric in plan are modelled. The first building has square columns, resulting in identical periods in the two directions. The second building, with rectangular columns, has a difference of 20% in periods in orthogonal directions, and the third building has half of the rectangular columns aligned in one direction and other half aligned in the other direction. The numerical analysis of the seismic response of these three buildings is performed by using a set of 22 ground motions from PEER NGA database and scaled as per FEMA P695 guidelines to represent the same level of intensity corresponding to the Design Basis Earthquake. The results are analyzed in terms of the displacement-time response of the buildings at roof level and corresponding maximum inter-storey drift ratios.

Keywords: bi-symmetric buildings, design code, dynamic coupling, multi-storey buildings, seismic response

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Authors: Alpesh Adeshara, Rajendrasinh Jadeja, Praghnesh Bhatt

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Implementation of advanced technologies requires sophisticated instruments that deal with the operation, control, restoration and protection of rapidly growing power system network under normal and abnormal conditions. Presently, the applications of Phasor Measurement Unit (PMU) are widely found in real time operation, monitoring, controlling and analysis of power system network as it eliminates the various limitations of Supervisory Control and Data Acquisition System (SCADA) conventionally used in power system. The use of PMU data is very rapidly increasing its importance for online and offline analysis. Wide Area Measurement System (WAMS) is developed as new technology by use of multiple PMUs in power system. The present paper proposes a model of MATLAB based PMU using Discrete Fourier Transform (DFT) algorithm and evaluation of its operation under different contingencies. In this paper, PMU based two bus system having WAMS network is presented as a case study.

Keywords: GPS global positioning system, PMU phasor measurement system, WAMS wide area monitoring system, DFT, PDC

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Authors: Alisha Khanal, Gokhan Saygili

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It is commonly observed that aftershocks follow the mainshock. Aftershocks continue over a period of time with a decreasing frequency and typically there is not sufficient time for repair and retrofit between a mainshock–aftershock sequence. Usually, aftershocks are smaller in magnitude; however, aftershock ground motion characteristics such as the intensity and duration can be greater than the mainshock due to the changes in the earthquake mechanism and location with respect to the site. The seismic performance of slopes is typically evaluated based on the sliding displacement predicted to occur along a critical sliding surface. Various empirical models are available that predict sliding displacement as a function of seismic loading parameters, ground motion parameters, and site parameters but these models do not include the aftershocks. The seismic risks associated with the post-mainshock slopes ('damaged slopes') subjected to aftershocks is significant. This paper extends the empirical sliding displacement models for flexible slopes subjected to earthquake mainshock-aftershock sequences (a multi hazard approach). A dataset was developed using 144 pairs of as-recorded mainshock-aftershock sequences using the Pacific Earthquake Engineering Research Center (PEER) database. The results reveal that the combination of mainshock and aftershock increases the seismic demand on slopes relative to the mainshock alone; thus, seismic risks are underestimated if aftershocks are neglected.

Keywords: seismic slope stability, mainshock, aftershock, landslide, earthquake, flexible slopes

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Authors: Wallace Wong, Julia Toews, Kirsten Graham, Teija Yli-Renko, Dorcas Chau, Judith Lai, Momoka Nabatame

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Many transgender individuals report recognizing their non-cisgender identity in early childhood. However, numerous transgender adolescents choose to delay coming out to their family or friends until after puberty. This study explored factors influencing this decision by analyzing data from over 100 cases of transgender adolescents who disclosed their identity post-puberty. Using a qualitative thematic analysis approach, we identified key factors affecting this choice, including fear of rejection, lack of affirming resources, internalized transphobia, and safety concerns. The study also revealed the positive impact of increased transgender visibility and online support communities in facilitating later coming-out experiences. Our findings highlight the intersection of personal, social, and environmental factors contributing to disclosure timing for transgender adolescents. These insights can inform supportive interventions and policies to assist transgender youth throughout their coming out process, regardless of when it occurs. This research contributes to a deeper understanding of the challenges faced by transgender adolescents and the support they need during this critical period.

Keywords: transgender, gender identity, coming out, adolescents

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

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Authors: Maria Spanoudaki, Pauldy C. J. Otermans, Dev Aditya

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Relationships form an integral part of our everyday wellbeing. In this study, the focus is on Inner Wellbeing which can be described as an individuals' thoughts and feelings about what they can do and be. Relationships can come in many forms and can be divided into Social Connections (thoughts and feelings about the social network people can establish and rely on), and Close Relationships (thoughts and feeling about the emotional support people can receive from significant others or their close, intimate circle). The purpose of this study is to compare the Social Connections and Close Relationship dimensions of Inner Wellbeing during the COVID-19 pandemic between the UK and India. 392 participants in the UK and 205 participants India completed an online questionnaire using the Inner Wellbeing scale. Factor analyses showed that the construct of Inner Wellbeing can be described as one factor for the UK sample whereas it can be described as two factors (one focusing on positive items and one focusing on negative items) for the Indian sample. Results showed that Social Connections were significantly during COVID-19 in the UK compared to India, whereas there is no significant difference for Close Relationships. The implications on relationships and wellbeing are discussed in detail.

Keywords: social networks, relationship maintenance, relationship satisfaction, COVID-19

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Authors: Nur A. Özdemir, Sude Erkin, Hatice K. Güney, Cemre S. Atılgan, Enes Huylu, Hüseyin Y. Altıntaş, Aysemin Top, Özak Durmuş

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Carbon emissions produced during a product’s life cycle, from extraction of raw materials up to waste disposal and market consumption activities are the major contributors to global warming. In the light of the science-based targets (SBT) leading the way to a zero-carbon economy for sustainable growth of the companies, carbon footprint reporting of the purchased goods has become critical for identifying hotspots and best practices for emission reduction opportunities. In line with Ford Otosan's corporate sustainability strategy, research was conducted to evaluate the carbon footprint of purchased products in accordance with Scope 3 of the Greenhouse Gas Protocol (GHG). The purpose of this paper is to develop a systematic and transparent methodology to calculate carbon footprint of the products produced by automotive OEMs (Original Equipment Manufacturers) within the context of automobile supply chain management. To begin with, primary material data were collected through IMDS (International Material Database System) corresponds to company’s three distinct types of vehicles including Light Commercial Vehicle (Courier), Medium Commercial Vehicle (Transit and Transit Custom), Heavy Commercial Vehicle (F-MAX). Obtained material data was classified as metals, plastics, liquids, electronics, and others to get insights about the overall material distribution of produced vehicles and matched to the SimaPro Ecoinvent 3 database which is one of the most extent versions for modelling material data related to the product life cycle. Product life cycle analysis was calculated within the framework of ISO 14040 – 14044 standards by addressing the requirements and procedures. A comprehensive literature review and cooperation with suppliers were undertaken to identify the production methods of parts used in vehicles and to find out the amount of scrap generated during part production. Cumulative weight and material information with related production process belonging the components were listed by multiplying with current sales figures. The results of the study show a key modelling on carbon footprint of products and processes based on a scientific approach to drive sustainable growth by setting straightforward, science-based emission reduction targets. Hence, this study targets to identify the hotspots and correspondingly provide broad ideas about our understanding of how to integrate carbon footprint estimates into our company's supply chain management by defining convenient actions in line with climate science. According to emission values arising from the production phase including raw material extraction and material processing for Ford OTOSAN vehicles subjected in this study, GHG emissions from the production of metals used for HCV, MCV and LCV account for more than half of the carbon footprint of the vehicle's production. Correspondingly, aluminum and steel have the largest share among all material types and achieving carbon neutrality in the steel and aluminum industry is of great significance to the world, which will also present an immense impact on the automobile industry. Strategic product sustainability plan which includes the use of secondary materials, conversion to green energy and low-energy process design is required to reduce emissions of steel, aluminum, and plastics due to the projected increase in total volume by 2030.

Keywords: automotive, carbon footprint, IMDS, scope 3, SimaPro, sustainability

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: A. Tahar Djebbar

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Modern technologies have penetrated all spheres of human life, education being one of them. This paper focuses the attention on the integration of technology-based education in the Algerian classroom in teaching foreign languages. It sheds light on a specific area of ICT application: ICT in English learning and teaching. Some Algerian teachers or tutors of English face many challenges among which the lack of teaching materials which are indispensable for transmitting knowledge to learners. Thus, they find themselves compelled to use online e-books or download them in PDF form to support their lessons. Teachers even download such teaching materials like pictures, videos, audios, podcasts, and flash cards from the internet and store them in their Flash USBs to shape up the teaching-learning conditions. They use computers, data shows, and the internet so as to facilitate the teaching–learning process in the classroom. Hence, technology has become a must in the Algerian classroom especially in teaching English which has become a very important language in a national and an international level. This study aims at showing that Algerian tutors/teachers who take up the challenge of getting involved in the technology-enhanced language learning and teaching in the Algerian schools and universities face many obstacles.

Keywords: computer, communication, English, internet, learners, language acquisition, teaching, technology

Procedia PDF Downloads 644

Authors: Shanhua Hu

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Social media as a critical propagandizing form of film, video games, and digital products has received substantial research attention, but there exists several critical barriers such as: (1) few studies exploring the internal and external connections of a product as part of the multimodal context that gives rise to readability and commercial return; (2) the lack of study of multimodal analysis in product’s official account of game publishers and its impact on users’ behaviors including purchase intention, social media engagement, and playing time; (3) no standardized ecologically-valid, game type-varying data can be used to study the complexity of official account’s postings within a time period. This proposed research helps to tackle these limitations in order to develop a model of readability study that is more ecologically valid, robust, and thorough. To accomplish this objective, this paper provides a more diverse dataset comprising different visual elements and messages collected from the official Twitter accounts of the Top 20 best-selling games of 2021. Video game companies target potential users through social media, a popular approach is to set up an official account to maintain exposure. Typically, major game publishers would create an official account on Twitter months before the game's release date to update on the game's development, announce collaborations, and reveal spoilers. Analyses of tweets from those official Twitter accounts would assist publishers and marketers in identifying how to efficiently and precisely deploy advertising to increase game sales. The purpose of this research is to determine how official game accounts use Twitter to attract new customers, specifically which types of messages are most effective at increasing sales. The dataset includes the number of days until the actual release date on Twitter posts, the readability of the post (Flesch Reading Ease Score, FRES), the number of emojis used, the number of hashtags, the number of followers of the mentioned users, the categorization of the posts (i.e., spoilers, collaborations, promotions), and the number of video views. The timeline of Twitter postings from official accounts will be compared to the history of pre-orders and sales figures to determine the potential impact of social media posts. This study aims to determine how the above-mentioned characteristics of official accounts' Twitter postings influence the sales of the game and to examine the possible causes of this influence. The outcome will provide researchers with a list of potential aspects that could influence people's judgments in limited-word posts. With the increased average online time, users would adapt more quickly than before in online information exchange and readings, such as the word to use sentence length, and the use of emojis or hashtags. The study on the promotion of official game accounts will not only enable publishers to create more effective promotion techniques in the future but also provide ideas for future research on the influence of social media posts with a limited number of words on consumers' purchasing decisions. Future research can focus on more specific linguistic aspects, such as precise word choice in advertising.

Keywords: engagement, official account, promotion, twitter, video game

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Authors: Luis Pascasio

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In a time when murky debates on US immigration policy are polarizing a nation steeped in partisan and nativist politics, certain media texts are proposing to challenge the dominant ways in which immigrant discourses are shaped in political debates. The paper will examine how two media texts perform counter-hegemonic discourses against institutionalized concepts on citizenship. The article looks at Documented (2014), a documentary film, written and directed by Jose Antonio Vargas, a Pulitzer-winning journalist-turned-activist and a self-proclaimed undocumented immigrant; and DefineAmerican.com, an online media platform that articulates the convergence of multiple voices and discourses about post-industrial and post-semiotic citizenship. As sites of meaning production, the two media texts perform counter-narratives that inspire new forms of mediated social activism and postcolonial identities. The paper argues that a closer introspection of the media texts reveals emotional, thematic and ideological claims to an interrogation of a diasporic discourse on redefining the rules of inclusion and exclusion within the postmodern dialogic of citizenship.

Keywords: counter-narratives, documentary filmmaking, postmodern citizenship, diaspora media

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Authors: Kashvi Jain, Adam Burston

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Right-wing social movements are a dominant force in American politics, as evidenced by the January 6th Insurrection, the prevalence of extremist conspiracy theories, and a nationwide surge in hate crime. Despite an abundance of scholarship on contemporary right-wing extremism, there is little scholarship that explains their rise. This paper examines how the white power movement developed through tactical innovation and strategic use of increasingly powerful digital technologies. Using qualitative content analysis of archived digital bulletin boards and websites, we examine right-wing extremists’ digital communication during three consequential time periods of tactical and technological innovation: pre-internet (1980s), web 1.0 (1990s), and web 2.0 (2000s). Our analysis suggests that right-wing activists innovatively exploited the features and affordances of digital technologies and their knowledge of free speech rights to spread supremacist collective identity and ideology. Beyond our empirical contribution, we offer policy advice that school administrators can employ to limit hate.

Keywords: leaderless resistance, technological affordances, anti-defamation league, white power movement, tactical

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Authors: Wooyoung Jung, Ghang Lee

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This paper paper reports the worldwide status of building information modeling (BIM) adoption from the perspectives of the engagement level, the Hype Cycle model, the technology diffusion model, and BIM-uses. An online survey was distributed, and 156 experts from six continents responded. Overall, North America was the most advanced continent, followed by Oceania and Europe. Countries in Asia perceived their phase mainly as slope of enlightenment (mature) in the Hype Cycle model. In the technology diffusion model, the main BIM-users worldwide were “early majority” (third phase), but those in the Middle East/Africa and South America were “early adopters” (second phase). In addition, the more advanced the country, the more number of BIM services employed in general. In summary, North America, Europe, Oceania, and Asia were advancing rapidly toward the mature stage of BIM, whereas the Middle East/Africa and South America were still in the early phase. The simple indexes used in this study may be used to track the worldwide status of BIM adoption in long-term surveys.

Keywords: BIM adoption, BIM services, hype cycle model, technology diffusion model

Procedia PDF Downloads 557

Authors: Dennis L. Owuor, Okuthe P. Kogeda, Johnson I. Agbinya

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Mobile localization has attracted a great deal of attention recently due to the introduction of wireless networks. Although several localization algorithms and systems have been implemented and discussed in the literature, very few researchers have exploited the gap that exists between indoor localization, tracking, external storage of location information and outdoor localization for the purpose of digital forensics during and after a disaster. The contribution of this paper lies in the implementation of a robust system that is capable of locating, tracking mobile device users and store location information for both indoor and partially outdoor the cloud. The system can be used during disaster to track and locate mobile phone users. The developed system is a mobile application built based on Android, Hypertext Preprocessor (PHP), Cascading Style Sheets (CSS), JavaScript and MATLAB for the Android mobile users. Using Waterfall model of software development, we have implemented a three level system that is able to track, locate and store mobile device information in secure database (cloud) on almost a real time basis. The outcome of the study showed that the developed system is efficient with regard to the tracking and locating mobile devices. The system is also flexible, i.e. can be used in any building with fewer adjustments. Finally, the system is accurate for both indoor and outdoor in terms of locating and tracking mobile devices.

Keywords: indoor localization, digital forensics, fingerprinting, tracking and cloud

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Authors: Angela Nkiru Nwammuo

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This paper evaluates the influence of selected Nigerian gender-based Non-Governmental Organizations (NGOs) in embodying feminism within digital spaces.. The study will focus on notable organizations that make use of online platforms for sensitization, advocacy, education and mobilization of women and stakeholders against gender-based violence in tertiary institutions.. The strategies they adopt in carrying out these functions, and the nature of their influence will be analyzed. Through the adoption of in-depth interview as a tool for data collection, leaders of the selected NGOs will be studied. Also Gender Studies Directors in Nigerian tertiary institutions the NGOs included in their advocacy will be interviewed to ascertain if there are noticeable results. By investigating the use of technology by the NGOs in feminism activism, this study, will underscore the importance of digital spaces in amplifying women’s voices and in advancing feminist ideologies in Nigeria and Africa, as a whole. The findings will highlight how gender-based NGOs in Africa shape, and reshape perceptions of gender equality and women’s rights throughout the continent.

Keywords: embodied feminism, digital spaces, nigerian women, gender-based NGOs, feminism activism

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Authors: Hesham A. Khalek, Shafik S. Khoury, Remon F. Aziz, Mohamed A. Hakam

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Slipforming operation’s linearity is a source of planning complications, and operation is usually subjected to bottlenecks at any point, so careful planning is required in order to achieve success. On the other hand, Discrete-event simulation concepts can be applied to simulate and analyze construction operations and to efficiently support construction scheduling. Nevertheless, preparation of input data for construction simulation is very challenging, time-consuming and human prone-error source. Therefore, to enhance the benefits of using DES in construction scheduling, this study proposes an integrated module to establish a framework for automating the generation of time schedules and decision support for Slipform construction projects, particularly through the project feasibility study phase by using data exchange between project data stored in an Intermediate database, DES and Scheduling software. Using the stored information, proposed system creates construction tasks attribute [e.g. activities durations, material quantities and resources amount], then DES uses all the given information to create a proposal for the construction schedule automatically. This research is considered a demonstration of a flexible Slipform project modeling, rapid scenario-based planning and schedule generation approach that may be of interest to both practitioners and researchers.

Keywords: discrete-event simulation, modeling, construction planning, data exchange, scheduling generation, EZstrobe

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: Mohammad Enayet Hossain, Nur Farhah Mahadi

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The global COVID-19 pandemic has contaminated millions of people at a startling rate. The COVID-19 pandemic came out of mediocre and has since spread all over the world, causing to record 5 million deaths worldwide in just a few months. The economic crisis has triggered a global contraction, leading to an economic collapse expected over 2020-2021. This study examines whether Islamic social finance can effectively mitigate the dangers of humanitarian catastrophes. The study provides a multirange method for maximizing the advantage of Islamic social financings tools such as zakat and waqf. The information, documents, and data for this study are gathered using a qualitative method. The study employed ongoing research, literature review, news stories, reports, and trusted online sources. Eventually, this may add to knowledge by examining the role of Islamic social finance in the current Covid-19 crisis. The findings have consequences for governments and policymakers who want to solve the COVID-19 problem with Islamic social finance ideas and solutions, thereby enhancing people's social well-being and the global economy's development.

Keywords: covid-19 pandemic, Islamic social finance, zakat, waqf

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Authors: Tarek Abdel Azim Ahmed, Eman Sarhan Shaker

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This research aims to explore how well the extended model of internet commerce adoption (eMICA) model is often used to determine the extent of internet commerce adoption in the travel agencies sector in both Egypt and Kingdom of Saudi Arabia (KSA). The web content analysis method was used to analyze the level of adoption of Egyptian travel agencies and Saudi travel agencies according to data immensely available on their websites. Therefore, each site was categorized according to the phases and levels proposed. In order to achieve this, 120 websites were evaluated by the two authors over a three-month period, from August to October 2020, and then categorized according to the phases and levels of (eMICA). The results show that there are deficiencies in the application of the eMICA model by both KSA and Egyptian travel agencies, generally, updating their websites, the absence of quality certification, offering secure online payment, virtual tours, and videos using Flash animation. In general, the Egyptian companies slightly outperformed the KSA ones in applying eMICA model.

Keywords: e-commerce, internet marketing, eMICA, travel agencies, websites

Procedia PDF Downloads 137

Authors: Nan Deng, Zhenqiu Liu

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Thousands of patients with metastatic tumors were diagnosed with cancers of unknown primary sites each year. The inability to identify the primary cancer site may lead to inappropriate treatment and unexpected prognosis. Nowadays, a large amount of genomics and transcriptomics cancer data has been generated by next-generation sequencing (NGS) technologies, and The Cancer Genome Atlas (TCGA) database has accrued thousands of human cancer tumors and healthy controls, which provides an abundance of resource to differentiate cancer types. Meanwhile, deep convolutional neural networks (CNNs) have shown high accuracy on classification among a large number of image object categories. Here, we utilize 25 cancer primary tumors and 3 normal tissues from TCGA and convert their RNA-Seq gene expression profiling to color images; train, validate and test a CNN classifier directly from these images. The performance result shows that our CNN classifier can archive >80% test accuracy on most of the tumors and normal tissues. Since the gene expression pattern of distant metastases is similar to their primary tumors, the CNN classifier may provide a potential computational strategy on identifying the unknown primary origin of metastatic cancer in order to plan appropriate treatment for patients.

Keywords: bioinformatics, cancer, convolutional neural network, deep leaning, gene expression pattern

Procedia PDF Downloads 299

Authors: Valerie Porr, Sydney A. DeCaro

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TARA for borderline personality disorder (BPD), an education and advocacy organization, helps families to compassionately and effectively deal with troubling BPD behaviors. Our psychoeducational programs focus on understanding underlying neurobiological features of BPD and evidence-based methodology integrating dialectical behavior therapy (DBT) and mentalization based therapy (MBT,) clarifying the inherent misunderstanding of BPD behaviors and improving family communication. TARA4BPD conducts online surveys, workshops, and topical webinars. For over 25 years, we have collected data from BPD helpline callers. This data drew our attention to particular childhood idiosyncrasies that seem to characterize many of the children who later met the criteria for BPD. The idiosyncrasies we observed, heightened sensory sensitivity and hypervigilance, were included in Adolf Stern’s 1938 definition of “Borderline.” This aspect of BPD has not been prioritized by personality disorder researchers, presently focused on emotion processing and social cognition in BPD. Parents described sleep reversal problems in infants who, early on, seem to exhibit dysregulation in circadian rhythm. Families describe children as supersensitive to sensory sensations, such as specific sounds, heightened sense of smell, taste, textures of foods, and an inability to tolerate various fabrics textures (i.e., seams in socks). They also exhibit high sensitivity to particular words and voice tones. Many have alexithymia and dyslexia. These children are either hypo- or hypersensitive to sensory sensations, including pain. Many suffer from fibromyalgia. BPD reactions to pain have been studied (C. Schmahl) and confirm the existence of hyper and hypo-reactions to pain stimuli in people with BPD. To date, there is little or no data regarding what comprises a normative range of sensitivity in infants and children. Many parents reported that their children were tested or treated for sensory processing disorder (SPD), learning disorders, and ADHD. SPD is not included in the DSM and is treated by occupational therapists. The overwhelming anecdotal data from thousands of parents of children who later met criteria for BPD led TARA4BPD to develop a sensitivity survey to develop evidence of the possible role of early sensory perception problems as a pre-cursor to BPD, hopefully initiating new directions in BPD research. At present, the research community seems unaware of the role supersensory sensitivity might play as an early indicator of BPD. Parents' observations of childhood sensitivity obtained through family interviews and results of an extensive online survey on sensory responses across various ages of development will be presented. People with BPD suffer from a sense of isolation and otherness that often results in later interpersonal difficulties. Early identification of supersensitive children while brain circuits are developing might decrease the development of social interaction deficits such as rejection sensitivity, self-referential processes, and negative bias, hallmarks of BPD, ultimately minimizing the maladaptive methods of coping with distress that characterizes BPD. Family experiences are an untapped resource for BPD research. It is hoped that this data will give family observations the critical credibility to inform future treatment and research directions.

Keywords: alexithymia, dyslexia, hypersensitivity, sensory processing disorder

Procedia PDF Downloads 201

Authors: Jae-Hyung Jin, Kyung-Tae Lee, Yee-Seul So, Eunji Jeong, Yeonseon Lee, Dongpil Lee, Dong-Gi Lee, Yong-Sun Bahn

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Cryptococcus neoformans causes cryptococcal meningoencephalitis mainly in immunocompromised patients as well as immunocompetent people. But therapeutic options are limited to treat cryptococcosis. Some signaling pathways including cyclic AMP pathway, MAPK pathway, and calcineurin pathway play a central role in the regulation of the growth, differentiation, and virulence of C. neoformans. To understand signaling networks regulating the virulence of C. neoformans, we selected the 114 putative phosphatase genes, one of the major components of signaling networks, in the genome of C. neoformans. We identified putative phosphatases based on annotation in C. neoformans var. grubii genome database provided by the Broad Institute and National Center for Biotechnology Information (NCBI) and performed a BLAST search of phosphatases of Saccharomyces cerevisiae, Aspergillus nidulans, Candida albicans and Fusarium graminearum to Cryptococcus neoformans. We classified putative phosphatases into 14 groups based on InterPro phosphatase domain annotation. Here, we constructed 170 signature-tagged gene-deletion strains through homologous recombination methods for 91 putative phosphatases. We examined their phenotypic traits under 30 different in vitro conditions, including growth, differentiation, stress response, antifungal resistance and virulence-factor production.

Keywords: human fungal pathogen, phosphatase, deletion library, functional genomics

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Authors: Lalitchandra J. Shah, Katarzyna Bialek, Melinda L. Clarke, Jessica L. Jansson

Abstract:

Handwriting is essential to academic success; however, the current literature is limited in the identification of pre-handwriting skills. The purpose of this study was to identify the pre-handwriting skills, which occupational therapy practitioners deem important to handwriting success, as well as those which aid in intervention planning. The online survey instrument consisted of 33 questions that assessed various skills related to the development of handwriting, as well as captured demographic information. Both occupational therapists and occupational therapy assistants were included in the survey study. The survey found that the respondents were in agreement that purposeful scribbling, the ability of a child to copy (vertical/horizontal lines, circle, squares, and triangles), imitating an oblique cross, cognitive skills (attention, praxis, self-regulation, sequencing), grasp patterns, hand dominance, in hand manipulation skills (shift, translation, rotation), bilateral integration, stabilization of paper, crossing midline, and visual perception were important indicators of handwriting readiness. The results of the survey support existing research regarding the skills necessary for the successful development of handwriting in children.

Keywords: development, handwriting, occupational therapy, visual perceptual skills

Procedia PDF Downloads 350

Authors: Pooja Khanal, Huaming Zhang

Abstract:

Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

Procedia PDF Downloads 201

Authors: Jean Marie Tshimula, Atsushi Togashi

Abstract:

We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

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Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

Abstract:

The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: big data, COVID-19, health, indexing, NoSQL, sharding, scalability, well being

Procedia PDF Downloads 70

Authors: Anuradha Ranawakage, Chathurani Silva

Abstract:

With the growing expansion of global businesses, supply chains are increasingly exposed to numerous disruptions. Organizations adopt various strategies to mitigate the impact of these disruptions. Depending on the variations in the conditions and characteristics of supply chains, the adoption of resilience strategies may vary across industries. However, these differences are largely unexplored in the existing literature. Hence, this study aims to evaluate the adoption of three proactive strategies: proactive collaboration, digital connectivity, integrated SC risk management, and three reactive strategies: reactive collaboration, inventory and reserve capacity, and lifeline maintenance in the apparel and construction industries in Sri Lanka. An online questionnaire was used to collect data on the implementation of resilience strategies from a sample of 162 apparel and 185 construction companies operating in Sri Lanka. This research makes a significant contribution to the field of supply chain management by assessing the extent to which different resilience strategies are functioned within the apparel and construction industries in Sri Lanka, particularly in an era after a global pandemic that significantly disrupted supply chains all around the world.

Keywords: apparel, construction, proactive strategies, reactive strategies, supply chain resilience

Procedia PDF Downloads 56

Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

Abstract:

A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

Procedia PDF Downloads 125