Search results for: mobile e- learning

Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Siddharth Sarma, Ayush Majumdar, Nidhi Sabu, Mufaddal Jiruwaala, Shilpa Paygude

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Digital Elevation Models (DEMs) are digital representations of the Earth’s topography, which include information about the elevation, slope, aspect, and other terrain attributes. DEMs play a crucial role in various applications, including terrain analysis, urban planning, and environmental modeling. In this paper, TerraEnhance is proposed, a distinct approach for high-resolution DEM generation using Generative Adversarial Networks (GANs) combined with Real-ESRGANs. By learning from a dataset of low-resolution DEMs, the GANs are trained to upscale the data by 10 times, resulting in significantly enhanced DEMs with improved resolution and finer details. The integration of Real-ESRGANs further enhances visual quality, leading to more accurate representations of the terrain. A post-processing layer is introduced, employing high-pass filtering to refine the generated DEMs, preserving important details while reducing noise and artifacts. The results demonstrate that TerraEnhance outperforms existing methods, producing high-fidelity DEMs with intricate terrain features and exceptional accuracy. These advancements make TerraEnhance suitable for various applications, such as terrain analysis and precise environmental modeling.

Keywords: DEM, ESRGAN, image upscaling, super resolution, computer vision

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Huicong Jia, Donghua Pan

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As a key component of disaster reduction management, disaster emergency relief and reconstruction is an important process. Based on disaster system theory, this study analyzed the Jiangsu tornado from the formation mechanism of disasters, through to the economic losses, loss of life, and social infrastructure losses along the tornado disaster chain. The study then assessed the emergency relief and reconstruction efforts, based on an analytic hierarchy process method. The results were as follows: (1) An unstable weather system was the root cause of the tornado. The potentially hazardous local environment, acting in concert with the terrain and the river network, was able to gather energy from the unstable atmosphere. The wind belt passed through a densely populated district, with vulnerable infrastructure and other hazard-prone elements, which led to an accumulative disaster situation and the triggering of a catastrophe. (2) The tornado was accompanied by a hailstorm, which is an important triggering factor for a tornado catastrophe chain reaction. (3) The evaluation index (EI) of the emergency relief and reconstruction effect for the ‘‘6.23’’ tornado disaster in Yancheng was 91.5. Compared to other relief work in areas affected by disasters of the same magnitude, there was a more successful response than has previously been experienced. The results provide new insights for studies of disaster systems and the recovery measures in response to tornado catastrophe in China.

Keywords: China, disaster system, emergency relief, tornado catastrophe

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: José Antonio Donaire Benito, Konstantina Zerva

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Historically, cities have been places where people from many nations and cultures have met and settled together, while population flows and density have had a significant impact on urban dynamics. Cities' high density of social, cultural, business offerings, everyday services, and other amenities not intended for tourists draw not only tourists but a wide range of city users as well. With the coordination of city rhythms and the porosity of the community, city users order and frame their urban experience. From one side, recent literature focuses on the shift in urban tourist experience from 'having' a holiday through 'doing' activities to 'becoming' a local by experiencing a part of daily life. On the other hand, there is a debate on the 'touristification of everyday life', where middle and upper class urban dwellers display attitudes and behaviors that are virtually undistinguishable from those of visitors. With the advent of globalization and technological advances, modern society has undergone a radical transformation that has altered mobility patterns within it, blurring the boundaries between tourism and everyday life, work and leisure, and "hosts" and "guests". Additionally, the presence of other 'temporary city' users, such as commuters, digital nomads, second home owners, and migrants, contributes to a more complex transformation of tourist cities. Moving away from this traditional clear distinction between 'hosts' and 'guests', which represents a more static view of tourism, and moving towards a more liquid narrative of mobility, academics on tourism development are embracing the New Mobilities Paradigm. The latter moves beyond the static structures of the modern world and focuses on the ways in which social entities are made up of people, machines, information, and images in a moving system. In light of this fluid interdependence between tourists and guests, a question arises as to whether overtourism, which is considered as the underlying cause of citizens' perception of a lower urban quality of life, is a fair representation of perceived mobility excessiveness, place consumption disruptiveness, and residents displacement. As a representative example of an overtourism narrative, Barcelona was chosen as a study area for this purpose, focusing on the incoming city users to reflect in depth the variety of people who contribute to mobility flows beyond those residents already have. Several statistical data have been analyzed to determine the number of national and international visitors to Barcelona at some point during the day in 2019. Specifically, tracking data gathered from mobile phone users within the city are combined with tourist surveys, urban mobility data, zenithal data capture, and information about the city's attractions. The paper shows that tourists are only a small part of the different incoming city users that daily enter Barcelona; excursionists, commuters, and metropolitans also contribute to a high mobility flow. Based on the diversity of incoming city users and their place consumption, it seems that the city's urban experience is more likely to be impacted by over-mobility tan over-tourism.

Keywords: city users, density, new mobilities paradigm, over-tourism.

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Authors: Ruwan Gunawardane

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This paper attempts to emphasize the benefits of conducting an intensive course in English for university entrants. In the Sri Lankan university context, an intensive course in English is usually conducted amidst various obstacles. In the 1970s and 1980s, undergraduates had intensive programmes in English for two to three months. Towards the end of the 1990s, a programme called General English Language Training (GELT) was conducted for the new students, and it was done outside universities before they entered their respective universities. Later it was not conducted, and that also resulted in students’ poor performance in English at university. However, having understood its importance, an eight week long intensive course in English was conducted for the new intake of the Faculty of Science, University of Ruhuna. As the findings show, the students heavily benefited from the programme. More importantly, they had the opportunity to refresh their knowledge of English gained at school and private institutions while gaining new knowledge. Another advantage was that they had plenty of time to enjoy learning English since the learners had adequate opportunities to carry out communicative tasks and the course was not exam-oriented, which reduced their fear of making mistakes in English considerably. The data was collected through an open-ended questionnaire given to 60 students, and their oral feedback was also taken into consideration. In addition, a focus group interview with 6 teachers was also conducted to get an idea about their experience and perception. The data were qualitatively analyzed. The findings suggest that an intensive programme in English undoubtedly lays a good foundation for the students’ academic career at university.

Keywords: intensive course, English, teachers, undergraduates, experience, perception

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Authors: Mahfoodh Saleh Al Sabbagh, Venkoba Rao

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There is plenty of research, both in academic and consultancy circles, about the importance and benefits of employee engagement and customer engagement and how it gives organization an opportunity to reduce variability and improve performance. Customer engagement is directly related to the engagement level of the organization's employees. It is therefore important to measure both. This research drawing from the work of Human Sigma by Fleming and Asplund, attempts to assess engagement level of customer and employees - the human systems of business - in an educational setup. Student is important to an educational institute and is a customer to be served efficiently and effectively. Considering student as customer and faculty as employees serving them, in–depth interviews were conducted to analyze the relationship between faculty and student engagement in two leading colleges in Oman, one from private sector and another from public sector. The study relied mainly on secondary data sources to understand the concept of engagement. However, the search of secondary sources was extensive to compensate the limited primary data. The results indicate that high faculty engagement is likely to lead to high student engagement. Engaged students were excited about learning, loved the feeling of they being cared as a person by their faculty and advocated the organization to other. The interaction truly represents an opportunity to build emotional connection to the organization. This study could be of interest to organizations interest in building and maintaining engagement with employees and customers.

Keywords: customer engagement, consumer psychology, strategy, educational institutes

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Authors: Abhijeet Ostawal, Parmjit Lall

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The run times for a large strategic model that we were managing had become too long leading to delays in project delivery, increased costs and loss in productivity. Software developers are continuously working towards developing more efficient tools by changing their algorithms and processes. The issue faced by our team was how do you apply the latest technologies on validated existing models which are based on much older versions of software that do not have the latest software capabilities. The multi-model transport model that we had could only be run in sequential assignment order. Recent upgrades to the software now allowed the assignment to be run in parallel, a concept called parallelization. Parallelization is a Python script working only within the latest version of the software. A full model transfer to the latest version was not possible due to time, budget and the potential changes in trip assignment. This article is to show the method to adapt and update the Python script in such a way that it can be used in older software versions by calling the latest version and then recalling the old version for assignment model without affecting the results. Through a process of trial-and-error run time savings of up to 30-40% have been achieved. Assignment results were maintained within the older version and through this learning process we’ve applied this methodology to other even older versions of the software resulting in huge time savings, more productivity and efficiency for both client and consultant.

Keywords: model run time, demand model, parallelisation, python scripting

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Authors: James Semmens, James Boyd, Anna Ferrante, Katrina Spilsbury, Sean Randall, Adrian Brown

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‘Big data’ is a relatively new concept that describes data so large and complex that it exceeds the storage or computing capacity of most systems to perform timely and accurate analyses. Health services generate large amounts of data from a wide variety of sources such as administrative records, electronic health records, health insurance claims, and even smart phone health applications. Health data is viewed in Australia and internationally as highly sensitive. Strict ethical requirements must be met for the use of health data to support health research. These requirements differ markedly from those imposed on data use from industry or other government sectors and may have the impact of reducing the capacity of health data to be incorporated into the real time demands of the Big Data environment. This ‘big data revolution’ is increasingly supported by national governments, who have invested significant funds into initiatives designed to develop and capitalize on big data and methods for data integration using record linkage. The benefits to health following research using linked administrative data are recognised internationally and by the Australian Government through the National Collaborative Research Infrastructure Strategy Roadmap, which outlined a multi-million dollar investment strategy to develop national record linkage capabilities. This led to the establishment of the Population Health Research Network (PHRN) to coordinate and champion this initiative. The purpose of the PHRN was to establish record linkage units in all Australian states, to support the implementation of secure data delivery and remote access laboratories for researchers, and to develop the Centre for Data Linkage for the linkage of national and cross-jurisdictional data. The Centre for Data Linkage has been established within Curtin University in Western Australia; it provides essential record linkage infrastructure necessary for large-scale, cross-jurisdictional linkage of health related data in Australia and uses a best practice ‘separation principle’ to support data privacy and security. Privacy preserving record linkage technology is also being developed to link records without the use of names to overcome important legal and privacy constraint. This paper will present the findings of the first ‘Proof of Concept’ project selected to demonstrate the effectiveness of increased record linkage capacity in supporting nationally significant health research. This project explored how cross-jurisdictional linkage can inform the nature and extent of cross-border hospital use and hospital-related deaths. The technical challenges associated with national record linkage, and the extent of cross-border population movements, were explored as part of this pioneering research project. Access to person-level data linked across jurisdictions identified geographical hot spots of cross border hospital use and hospital-related deaths in Australia. This has implications for planning of health service delivery and for longitudinal follow-up studies, particularly those involving mobile populations.

Keywords: data integration, data linkage, health planning, health services research

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

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Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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Authors: Waleed Alanzi

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The purpose of this qualitative study was to add to the existing literature and provide a more detailed understanding of the individual experiences and perceptions of 15 Deans at the University of Kuwait regarding their first year of planning, developing, and implementing crisis distance education (CDE) in response to the COVID-19 epidemic. An interpretative phenomenological approach was applied, using the thematic analysis of interview transcripts to describe the challenging journeys taken by each of the Deans from the first-person point of view. There was objective evidence, manifested by four primary themes (“Obstacles to the implementation of CDE”; “Planning for CDE”; “Training for CDE,” and “Future Directions”) to conclude that the faculty members, technical staff, administrative staff, and students generally helped each other to overcome the obstacles associated with planning and implementing CDE. The idea that CDE may turn homes into schools and parents into teachers was supported. The planning and implementation of CDE were inevitably associated with a certain amount of confusion, as well as disruptions in the daily routines of staff and students, as well as significant changes in their responsibilities. There were contradictory ideas about the future directions of distance education after the pandemic. Previous qualitative research on the implementation of CDE at higher education institutions in the Arab world has focused mainly on the experiences and perceptions of students; however, little is known about the experiences and perceptions of the students at the University of Kuwait during the COVID19 pandemic, providing a rationale and direction for future research.

Keywords: distance learning, qualitative research, COVID-19 epidemic, Kuwait university

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Authors: Christopher Leung, Tony Kim, Rebecca Lendzion, Scott Mackenzie

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Retroperitoneal abscesses are a rare but serious condition with often delayed diagnosis, non-specific symptoms, multiple causes and high morbidity/mortality. With the advent of more readily available cross-sectional imaging, retroperitoneal abscesses are treated earlier and better outcomes are achieved. Occasionally, a retroperitoneal abscess is present as a huge retroperitoneal abscess, as evident in this 53-year-old male. With a background of chronic renal disease and left partial nephrectomy, this gentleman presented with a one-month history of left flank pain without any other symptoms, including fevers or abdominal pain. CT abdomen and pelvis demonstrated a huge retroperitoneal abscess spanning from the diaphragm, abutting the spleen, down to the iliopsoas muscle and abutting the iliac vessels at the pelvic brim. This large retroperitoneal abscess required open drainage as well as drainage by interventional radiology. A long course of intravenous antibiotics and multiple drainages was required to drain the abscess. His blood culture and fluid culture grew Proteus species suggesting a urinary source, likely from his non-functioning kidney, which had a partial nephrectomy. Such a huge retroperitoneal abscess has rarely been described in the literature. The learning point here is that the basic principle of source control and antibiotics is paramount in treating retroperitoneal abscesses regardless of the size of the abscess.

Keywords: retroperitoneal abscess, retroperitoneal mass, sepsis, genitourinary infection

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Authors: Olivier Mills, Bernard McDonell, Laura A. S. MacDonald

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The increasing availability and quality of internet access throughout the developing world provides an opportunity to utilize online spaces to disseminate water, sanitation and hygiene (WASH) knowledge to practitioners. Since 2001, CAWST has provided in-person education, training and consulting services to thousands of WASH practitioners all over the world, supporting them to start, troubleshoot, improve and expand their WASH projects. As CAWST continues to grow, the organization faces challenges in meeting demand from clients and in providing consistent, timely technical support. In 2012, CAWST began utilizing online spaces to expand its reach by developing a series of resources websites and webinars. CAWST has developed a WASH Education and Training resources website, a Biosand Filter (BSF) Knowledge Base, a Household Water Treatment and Safe Storage Knowledge Base, a mobile app for offline users, a live chat support tool, a WASH e-library, and a series of webinar-style online training sessions to complement its in-person capacity development services. In order to determine the preliminary outcomes of providing these online services, CAWST has monitored and analyzed registration to the online spaces, downloads of the educational materials, and webinar attendance; as well as conducted user surveys. The purpose of this analysis was to find out who was using the online spaces, where users came from, and how the resources were being used. CAWST’s WASH Resources website has served over 5,800 registered users from 3,000 organizations in 183 countries. Additionally, the BSF Knowledge Base has served over 1000 registered users from 68 countries, and over 540 people from 73 countries have attended CAWST’s online training sessions. This indicates that the online spaces are effectively reaching a large numbers of users, from a range of countries. A 2016 survey of the Biosand Filter Knowledge Base showed that approximately 61% of users are practitioners, and 39% are either researchers or students. Of the respondents, 46% reported using the BSF Knowledge Base to initiate a BSF project and 43% reported using the information to train BSF technicians. Finally, 61% indicated they would like even greater support from CAWST’s Technical Advisors going forward. The analysis has provided an encouraging indication that CAWST’s online spaces are contributing to its objective of engaging and supporting WASH practitioners to start, improve and expand their initiatives. CAWST has learned several lessons during the development of these online spaces, in particular related to the resources needed to create and maintain the spaces, and respond to the demand created. CAWST plans to continue expanding its online spaces, improving user experience of the sites, and involving new contributors and content types. Through the use of online spaces, CAWST has been able to increase its global reach and impact without significantly increasing its human resources by connecting WASH practitioners with the information they most need, in a practical and accessible manner. This paper presents on CAWST’s use of online spaces through the CAWST-developed platforms discussed above and the analysis of the use of these platforms.

Keywords: education and training, knowledge sharing, online resources, water and sanitation

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Authors: Soofia Malik

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The purpose of this study is to develop an instrument to measure undergraduate students’ attitudes toward mathematics (MAT) and to assess the data collected from the instrument for validity and reliability. The instrument is developed using five subscales: anxiety, enjoyment, self-confidence, value, and technology. The technology dimension is added as the fifth subscale of attitude toward mathematics because of the recent trend of incorporating online homework in mathematics courses as well as due to heavy reliance of higher education on using online learning management systems, such as Blackboard and Moodle. The sample consists of 163 (M = 82, F = 81) undergraduates enrolled in College Algebra course in the summer 2017 semester at a university in the USA. The data is analyzed to answer the research question: if and how do undergraduate students’ attitudes toward mathematics load using Principal Components Analysis (PCA)? As a result of PCA, three subscales emerged namely: anxiety/self-confidence scale, enjoyment, and value scale. After deleting the last five items or the last two subscales from the initial MAT scale, the Cronbach’s alpha was recalculated using the scores from 20 items and was found to be α = .95. It is important to note that the reliability of the initial MAT form was α = .93. This means that employing the final MAT survey form would yield consistent results in repeated uses. The final MAT form is, therefore, more reliable as compared to the initial MAT form.

Keywords: college algebra, Cronbach's alpha reliability coefficient, Principal Components Analysis, PCA, technology in mathematics

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Authors: Rowshan Mamtaz, Shuvo Ahmed, Imran Noor, Sumaiya Rahman, Prithvi Shams, Fahmida Gulshan

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Last few decades have witnessed a phenomenal rise in the use of electrical and electronic equipment globally in our everyday life. As these items reach the end of their lifecycle, they turn into e-wastes and contribute to the waste stream. Bangladesh, in conformity with the global trend and due to its ongoing rapid growth, is also using electronics-based appliances and equipment at an increasing rate. This has caused a corresponding increase in the generation of e-wastes. Bangladesh is a developing country; its overall waste management system, is not yet efficient, nor is it environmentally sustainable. Most of its solid wastes are disposed of in a crude way at dumping sites. Addition of e-wastes, which often contain toxic heavy metals, into its waste stream has made the situation more difficult and challenging. Assessment of generation of e-wastes is an important step towards addressing the challenges posed by e-wastes, setting targets, and identifying the best practices for their management. Understanding and proper management of e-wastes is a stated item of the Sustainable Development Goals (SDG) campaign, and Bangladesh is committed to fulfilling it. A better understanding and availability of reliable baseline data on e-wastes will help in preventing illegal dumping, promote recycling, and create jobs in the recycling sectors and thus facilitate sustainable e-waste management. With this objective in mind, the present study has attempted to estimate the amount of e-wastes and its future generation trend in Bangladesh. To achieve this, sales data on eight selected electrical and electronic products (TV, Refrigerator, Fan, Mobile phone, Computer, IT equipment, CFL (Compact Fluorescent Lamp) bulbs, and Air Conditioner) have been collected from different sources. Primary and secondary data on the collection, recycling, and disposal of the e-wastes have also been gathered by questionnaire survey, field visits, interviews, and formal and informal meetings with the stakeholders. Material Flow Analysis (MFA) method has been applied, and mathematical models have been developed in the present study to estimate e-waste amounts and their future trends up to the year 2035 for the eight selected electrical and electronic equipment. End of life (EOL) method is adopted in the estimation. Model inputs are products’ annual sale/import data, past and future sales data, and average life span. From the model outputs, it is estimated that the generation of e-wastes in Bangladesh in 2018 is 0.40 million tons and by 2035 the amount will be 4.62 million tons with an average annual growth rate of 20%. Among the eight selected products, the number of e-wastes generated from seven products are increasing whereas only one product, CFL bulb, showed a decreasing trend of waste generation. The average growth rate of e-waste from TV sets is the highest (28%) while those from Fans and IT equipment are the lowest (11%). Field surveys conducted in the e-waste recycling sector also revealed that every year around 0.0133 million tons of e-wastes enter into the recycling business in Bangladesh which may increase in the near future.

Keywords: Bangladesh, end of life, e-waste, material flow analysis

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Authors: Qiao Mao

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There is a growing number of studies concerning STEM (Science, Technology, Engineering, and Mathematics) and STEAM (Science, Technology, Engineering, Arts, and Mathematics). However, the research is little on STEAM competitions from Arts' perspective. This study takes the annual PowerTech STEAM competition in Taiwan as an example. In this activity, students are asked to make wooden bionic mechanical beasts on the spot and participate in a model and speed competition. This study aims to explore how Arts influences STEM after it involves in the making of mechanical beasts. A case study method is adopted. Through expert sampling, five prize winners in the PowerTech Youth Science and Technology Creation Competition and their supervisors are taken as the research subjects. Relevant data which are collected, sorted out, analyzed and interpreted afterwards, derive from observations, interview and document analyses, etc. The results of the study show that in the PowerTech Youth Science and Technology Creation Competition, when Arts involves in STEM, (1) it has an impact on the athletic performance, balance, stability and symmetry of mechanical beasts; (2) students become more interested and more creative in making STEAM mechanical beasts, which can promote students' learning of STEM; (3) students encounter more difficulties and problems when making STEAM mechanical beasts, and need to have more systematic thinking and design thinking to solve problems.

Keywords: PowerTech, STEAM contest, mechanical beast, arts' role

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Authors: Kitda Praraththajariya

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The main purpose of this research was to study how to communicate the identity of the Mueang district, Samut Songkram province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows: 1. The identity of Amphur (District) Mueang, Samut Songkram province. This establishment was near the Mouth of Maekong River for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Hoy Lod (Razor Clam) and mangrove trees. Don Hoy Lod, is characterized by muddy beaches, is a coastal wetland for Ramsar Site. It is at 1099th ranging where the greatest number of Hoy Lod (Razor Clam) can be seen from March to May each year. 2. The communication of the identity of Amphur Mueang, Samut Songkram province which the researcher could find and design to present in English materials can be summed up in 4 items: 1) The history of Amphur Mueang, Samut Songkram province 2) Wat Phet Samut Worrawihan 3) The Learning source of Ecotourism: Don Hoy Lod and Mangrove forest 4) How to keep Amphur Mueang, Samut Songkram province for ecotourism.

Keywords: foreigner tourists, signified, semiotics, ecotourism

Procedia PDF Downloads 239

Authors: Jacob Wahl, Jane Zhang

Abstract:

This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

Procedia PDF Downloads 139

Authors: David Ross Cameron

Abstract:

In an attempt to alleviate the educational planning problem, double-shift schools have been created throughout various regions in Jordan, namely communities closer to the Syrian border, where a large portion of the refugee population settled, allowing Jordanians to attend the morning-shift and Syrians to attend the afternoon-shift. Subsequently, overcrowded classrooms have added a significant amount of stress on school facilities and teacher capacities. Established national policies and the implementation of inclusive educational practices have been jeopardized. In particular, teachers’ and student’s attitudes of the importance of inclusive education provisions in the classroom have deteriorated. To have a more comprehensive understanding of the current situation and possible plan for intervention, a focus study was carried out at a double-shift Jordanian/Syrian girls’ public school in Irbid, Jordan. Interviews and surveys of 29 students with physical, learning, emotional and behavioral disabilities, 33 students without any special needs and nine teachers were included with a mixed-method social research approach to highlight the current attitudes that students and teachers held and factors that contributed to shaping their inclinations and beliefs of inclusive education.

Keywords: capacity building, development, double-shift, Irbid, inclusive education, Jordan, pedagogy, planning, policy, refugee, special education, special needs, vulnerable population

Procedia PDF Downloads 255

Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

Procedia PDF Downloads 123

Authors: Dudu Ka Ruth Mkhize

Abstract:

Adolescents are at the front and centre of poor mathematics performance in schools. Literature has concluded in some countries that there is a permanent and perpetual mathematics crisis in schools of the persistent poor performance in mathematics by teens. There is no shortage of interventions and research to solve this problem. However, none has capitalised on the cognitive prowess of adolescents, which was revealed at the turn of the century by the introduction of neuroimaging technologies such as structural and functional magnetic resonance imaging (sMRI and fMRI). This research found that brain growth during adolescence results in enhanced cognitive abilities essential for mathematics learning. This paper is based on the four-year case study of rural high school adolescents who had a negative attitude towards mathematics and hence were failing mathematics. But through a ten-day intervention where teaching revolved around invoking their cognitive ability, their attitude and motivation for mathematics changed for the better. The paper concludes that despite educational psychology being part of teacher education as well as education systems, there are numerous overlooked gems of psychological theories which have the potential to enhance academic achievement for youth in schools. A recommendation is made to take cues from positive psychology, whose establishment was a rejection of the dominance of the disease model in psychology. Similarly, the general perspective of poor mathematics performance can take a u-turn towards the cognitive ability acquired by adolescents because of their developmental stage.

Keywords: adolescence, cognitive growth, mathematics performance

Procedia PDF Downloads 68

Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

Procedia PDF Downloads 140

Authors: D. Kekelidze, G. Tsotadze, G. Maisuradze, L. Akhalbedashvili, M. Chkhaidze

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Statement of Problem: The problem of solid wastes -dangerous sources of environmental pollution,is the urgent issue for Georgia as there are no waste-treatment and waste- incineration plants. Urban peripheral and rural areas, frequently along small rivers, are occupied by landfills without any permission. The study of the pollution of some environmental objects in the adjacent territories of solid waste landfill in Tbilisi carried out in 2020-2021, within the framework of project: “Ecological monitoring of the landfills surrounding areas and population health risk assessment”. Research objects: This research had goal to assess the ecological state of environmental objects (soil cover and surface water) in the territories, adjacent of solid waste landfill, on the base of changes heavy metals' (HM) concentration with distance from landfill. An open sanitary landfill for solid domestic waste in Tbilisi locates at suburb Lilo surrounded with densely populated villages. Content of following HM was determined in soil and river water samples: Pb, Cd, Cu, Zn, Ni, Co, Mn. Methodology: The HM content in samples was measured, using flame atomic absorption spectrophotometry (spectrophotometer of firm Perkin-Elmer AAnalyst 200) in accordance with ISO 11466 and GOST Р 53218-2008. Results and discussion: Data obtained confirmed migration of HM mainly in terms of the distance from the polygon that can be explained by their areal emissions and storage in open state, they could also get into the soil cover under the influence of wind and precipitation. Concentration of Pb, Cd, Cu, Zn always increases with approaching to landfill. High concentrations of Pb, Cd are characteristic of the soil covers of the adjacent territories around the landfill at a distance of 250, 500 meters.They create a dangerous zone, since they can later migrate into plants, enter in rivers and lakes. The higher concentrations, compared to the maximum permissible concentrations (MPC) for surface waters of Georgia, are observed for Pb, Cd. One of the reasons for the low concentration of HM in river water may be high turbidity – as is known, suspended particles are good natural sorbents that causes low concentration of dissolved forms. Concentration of Cu, Ni, Mn increases in winter, since in this season the rivers are switched to groundwater feeding. Conclusion: Soil covers of the areas adjacent to the landfill in Lilo are contaminated with HM. High concentrations in soils are characteristic of lead and cadmium. Elevated concentrations in comparison with the MPC for surface waters adopted in Georgia are also observed for Pb, Cd at checkpoints along and below (1000 m) of the landfill downstream. Data obtained confirm migration of HM to the adjacent territories of the landfill and to the Lochini River. Since the migration and toxicity of metals depends also on the presence of their mobile forms in water bodies, samples of bottom sediments should be taken too. Bottom sediments reflect a long-term picture of pollution, they accumulate HM and represent a constant source of secondary pollution of water bodies. The study of the physicochemical forms of metals is one of the priority areas for further research.

Keywords: landfill, pollution, heavy metals, migration

Procedia PDF Downloads 101

Authors: Gajendra Kumar Mourya, Dinesh Bhatia, Akash Handique, Sunita Warjri, Syed Achaab Amir

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Nowadays, Medical imaging has become an integral part of modern healthcare. Abdominal CT images are an invaluable mean for abdominal organ investigation and have been widely studied in the recent years. Diagnosis of liver pathologies is one of the major areas of current interests in the field of medical image processing and is still an open problem. To deeply study and diagnose the liver, segmentation of liver is done to identify which part of the liver is mostly affected. Manual segmentation of the liver in CT images is time-consuming and suffers from inter- and intra-observer differences. However, automatic or semi-automatic computer aided segmentation of the Liver is a challenging task due to inter-patient Liver shape and size variability. In this paper, we present a technique for automatic segmenting the liver from CT images using Random Forest Classifier. Random forests or random decision forests are an ensemble learning method for classification that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. After comparing with various other techniques, it was found that Random Forest Classifier provide a better segmentation results with respect to accuracy and speed. We have done the validation of our results using various techniques and it shows above 89% accuracy in all the cases.

Keywords: CT images, image validation, random forest, segmentation

Procedia PDF Downloads 313

Authors: Kathryn Norgang, Sarah Howrath, Auni Idi Muhire, Pacifique Umubyeyi

Abstract:

Description : A group of nurses address the shortage of trained staff to respond to critical patients at Rwanda Military Hospital (RMH) by developing a training program and a resuscitation response team. Members of the group who received the training when it first launched are now trainer of trainers; all components of the training program are organized and delivered by RMH staff-the clinical mentor only provides adjunct support. This two day training is held quarterly at RMH; basic life support and exposure to interventions for advanced care are included in the test and skills sign off. Seventy staff members have received the training this year alone. An increased number of admission/transfer to ICU due to successful resuscitation attempts is noted. Lessons learned: -Number of staff trained 2012-2014 (to be verified). -Staff who train together practice with greater collaboration during actual resuscitation events. -Staff more likely to initiate BLS if peer support is present-more staff trained equals more support. -More access to Advanced Cardiac Life Support training is necessary now that the cadre of BLS trained staff is growing. Conclusions: Increased access to training, peer support, and collaborative practice are effective strategies to strengthening resuscitation capacity within a hospital.

Keywords: resuscitation, basic life support, capacity building, resuscitation response teams, nurse trainer of trainers

Procedia PDF Downloads 304