Search results for: learning society

Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

Procedia PDF Downloads 43

Authors: Joy Chadwick

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This Study of Teaching and Learning (SoTL) research focused on the experiences of teacher candidates involved in an inclusive education methods course within a four-year direct entry Bachelor of Education program. The placement of this course within the final fourteen-week practicum semester is designed to facilitate deeper theory-practice connections between effective inclusive pedagogical knowledge and the real life of classroom teaching. The course focuses on supporting teacher candidates to understand that effective instruction within an inclusive classroom context must be intentional, responsive, and relational. Diversity is situated not as exceptional but rather as expected. This interpretive qualitative study involved the analysis of twenty-nine teacher candidate reflective journals and six individual teacher candidate semi-structured interviews. The journal entries were completed at the start of the semester and at the end of the semester with the intent of having teacher candidates reflect on their beliefs of what it means to be an effective inclusive educator and how the course and practicum experiences impacted their understanding and approaches to teaching in inclusive classrooms. The semi-structured interviews provided further depth and context to the journal data. The journals and interview transcripts were coded and themed using NVivo software. The findings suggest that instructional frameworks such as universal design for learning (UDL), differentiated instruction (DI), response to intervention (RTI), social emotional learning (SEL), and self-regulation supported teacher candidate’s abilities to meet the needs of their students more effectively. Course content that focused on specific exceptionalities also supported teacher candidates to be proactive rather than reactive when responding to student learning challenges. Teacher candidates also articulated the importance of reframing their perspective about students in challenging moments and that seeing the individual worth of each child was integral to their approach to teaching. A persisting question for teacher educators exists as to what pedagogical knowledge and understanding is most relevant in supporting future teachers to be effective at planning for and embracing the diversity of student needs within classrooms today. This research directs us to consider the critical importance of addressing personal attributes and mindsets of teacher candidates regarding children as well as considering instructional frameworks when designing coursework. Further, the alignment of an inclusive education course during a teaching practicum allows for an iterative approach to learning. The practical application of course concepts while teaching in a practicum allows for a deeper understanding of instructional frameworks, thus enhancing the confidence of teacher candidates. Research findings have implications for teacher education programs as connected to inclusive education methods courses, practicum experiences, and overall teacher education program design.

Keywords: inclusion, inclusive education, pre-service teacher education, practicum experiences, teacher education

Procedia PDF Downloads 53

Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead

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Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: classification, falls, health risk factors, machine learning, older adults

Procedia PDF Downloads 132

Authors: Nataliya G. Olkhovik

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Nowadays in the system of Russian higher medical education there have been undertaken initiatives that resulted in focusing on the resources of e-learning in teaching foreign languages. Obviously, the face-to-face communication in foreign languages bears much more advantages in terms of effectiveness in comparison with the potential of e-learning. Thus, we’ve faced the necessity of strengthening the capacity of e-learning via integration of active methods into the process of teaching foreign languages, such as project activity of students. Successful project activity of students should involve the following components: monitoring, control, methods of organizing the student’s activity in foreign languages, stimulating their interest in the chosen project, approaches to self-assessment and methods of raising their self-esteem. The contemporary methodology assumes the project as a specific method, which activates potential of a student’s cognitive function, emotional reaction, ability to work in the team, commitment, skills of cooperation and, consequently, their readiness to verbalize ideas, thoughts and attitudes. Verbal activity in the foreign language is a complex conception that consolidates both cognitive (involving speech) capacity and individual traits and attitudes such as initiative, empathy, devotion, responsibility etc. Once we organize the project activity by the means of e-learning within the ‘Foreign language’ discipline we have to take into consideration all mentioned above characteristics and work out an effective way to implement it into the teaching practice to boost its educational potential. We have integrated into the e-platform Moodle the module of project activity consisting of the following blocks of tasks that lead students to research, cooperate, strive to leadership, chase the goal and finally verbalize their intentions. Firstly, we introduce the project through activating self-activity of students by the tasks of the phase ‘Preparation of the project’: choose the topic and justify it; find out the problematic situation and its components; set the goals; create your team, choose the leader, distribute the roles in your team; make a written report on grounding the validity of your choices. Secondly, in the ‘Planning the project’ phase we ask students to represent the analysis of the problem in terms of reasons, ways and methods of solution and define the structure of their project (here students may choose oral or written presentation by drawing up the claim in the e-platform about their wish, whereas the teacher decides what form of presentation to prefer). Thirdly, the students have to design the visual aids, speech samples (functional phrases, introductory words, keywords, synonyms, opposites, attributive constructions) and then after checking, discussing and correcting with a teacher via the means of Moodle present it in front of the audience. And finally, we introduce the phase of self-reflection that aims to awake the inner desire of students to improve their verbal activity in a foreign language. As a result, by implementing the project activity into the e-platform and project activity, we try to widen the frameworks of a traditional lesson of foreign languages through tapping the potential of personal traits and attitudes of students.

Keywords: active methods, e-learning, improving verbal activity in foreign languages, personal traits and attitudes

Procedia PDF Downloads 94

Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

Procedia PDF Downloads 319

Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

Procedia PDF Downloads 94

Authors: Vesa Salminen, Esa Santakallio, Heikki Ruohomaa

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Introduction: The city of Riihimäki has decided robotics on well-being, service and industry as the main focus area on their ecosystem strategy. Robotics is going to be an important part of the everyday life of citizens and present in the working day of the average citizen and employee in the future. For that reason, also education system and education programs on all levels of education from diaper age to doctorate have been directed to fulfill this ecosystem strategy. Goal: The objective of this activity has been to develop education continuity from diaper age to doctorate. The main target of the development activity is to create a unique robotics study entity that enables ongoing robotics studies from preprimary education to university. The aim is also to attract students internationally and supply a skilled workforce to the private sector, capable of the challenges of the future. Methodology: Education instances (high school, second grade, Universities on all levels) in a large area of Tavastia Province have gradually directed their education programs to support this goal. On the other hand, applied research projects have been created to make proof of concept- phases on areal real environment field labs to test technology opportunities and digitalization to change business processes by applying robotic solutions. Customer-oriented applied research projects offer for students in robotics education learning environments to learn new knowledge and content. That is also a learning environment for education programs to adapt and co-evolution. New content and problem-based learning are used in future education modules. Major findings: Joint robotics education entity is being developed in cooperation with the city of Riihimäki (primary education), Syria Education (secondary education) and HAMK (bachelor and master education). The education modules have been developed to enable smooth transitioning from one institute to another. This article is introduced a case study of the change of education of wellbeing education because of digitalization and robotics. Riihimäki's Elderly citizen's service house, Riihikoti, has been working as a field lab for proof-of-concept phases on testing technology opportunities. According to successful case studies also education programs on various levels of education have been changing. Riihikoti has been developed as a physical learning environment for home care and robotics, investigating and developing a variety of digital devices and service opportunities and experimenting and learn the use of equipment. The environment enables the co-development of digital service capabilities in the authentic environment for all interested groups in transdisciplinary cooperation.

Keywords: ecosystem strategy, digitalization and robotics, education continuity, learning environment, transdisciplinary co-operation

Procedia PDF Downloads 165

Authors: Iman Jabbar

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Within the scope of professionalism and in order to compete with the modern world, teachers, are expected to develop their teaching skills and activities in addition to their professional knowledge. At the college level, the teacher should be able to face classroom challenges through his engagement with the learning situation to understand the students and their needs. In our field of TESOL, the role of the English teacher is no longer restricted to teaching English texts, but rather he should endeavor to enhance the students’ skills such as communication and critical analysis. Within the literature of professionalism, there are certain strategies and tools that an English teacher should adopt to develop his competence and performance. Reflective practice, which is an exploratory process, is one of these strategies. Another strategy contributing to classroom development is motivation. It is crucial in students’ learning as it affects the quality of learning English in the classroom in addition to determining success or failure as well as language achievement. This is a qualitative study grounded on interpretive perspectives of teachers and students regarding the process of professional development. This study aims at (a) understanding how teachers at the college level conceptualize reflective practice and motivation inside EFL classroom, and (b) exploring the methods and strategies that they implement to practice reflection and motivation. This study and is based on two questions: 1. How do EFL teachers perceive and view reflection and motivation in relation to their teaching and professional development? 2. How can reflective practice and motivation be developed into practical strategies and actions in EFL teachers’ professional context? The study is organized into two parts, theoretical and practical. The theoretical part reviews the literature on the concept of reflective practice and motivation in relation to professional development through providing certain definitions, theoretical models, and strategies. The practical part draws on the theoretical one, however; it is the core of the study since it deals with two issues. It involves the research design, methodology, and methods of data collection, sampling, and data analysis. It ends up with an overall discussion of findings and the researcher's reflections on the investigated topic. In terms of significance, the study is intended to contribute to the field of TESOL at the academic level through the selection of the topic and investigating it from theoretical and practical perspectives. Professional development is the path that leads to enhancing the quality of teaching English as a foreign or second language in a way that suits the modern trends of globalization and advanced technology.

Keywords: professional development, motivation, reflection, learning

Procedia PDF Downloads 432

Authors: C.Ardil

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Open Science translates the understanding and application of various theories and practices in open science philosophy, systems, paradigms and epistemology. Open Science originates with the premise that universal scientific knowledge is a product of a collective scholarly and social collaboration involving all stakeholders and knowledge belongs to the global society. Scientific outputs generated by public research are a public good that should be available to all at no cost and without barriers or restrictions. Open Science has the potential to increase the quality, impact and benefits of science and to accelerate advancement of knowledge by making it more reliable, more efficient and accurate, better understandable by society and responsive to societal challenges, and has the potential to enable growth and innovation through reuse of scientific results by all stakeholders at all levels of society, and ultimately contribute to growth and competitiveness of global society. Open Science is a global movement to improve accessibility to and reusability of research practices and outputs. In its broadest definition, it encompasses open access to publications, open research data and methods, open source, open educational resources, open evaluation, and citizen science. The implementation of open science provides an excellent opportunity to renegotiate the social roles and responsibilities of publicly funded research and to rethink the science system as a whole. Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods. Open Science represents a novel systematic approach to the scientific process, shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process, based on cooperative work and diffusing scholarly knowledge with no barriers and restrictions. Open Science refers to efforts to make the primary outputs of publicly funded research results (publications and the research data) publicly accessible in digital format with no limitations. Open Science is about extending the principles of openness to the whole research cycle, fostering, sharing and collaboration as early as possible, thus entailing a systemic change to the way science and research is done. Open Science is the ongoing transition in how open research is carried out, disseminated, deployed, and transformed to make scholarly research more open, global, collaborative, creative and closer to society. Open Science involves various movements aiming to remove the barriers for sharing any kind of output, resources, methods or tools, at any stage of the research process. Open Science embraces open access to publications, research data, source software, collaboration, peer review, notebooks, educational resources, monographs, citizen science, or research crowdfunding. The recognition and adoption of open science practices, including open science policies that increase open access to scientific literature and encourage data and code sharing, is increasing in the open science philosophy. Revolutionary open science policies are motivated by ethical, moral or utilitarian arguments, such as the right to access digital research literature for open source research or science data accumulation, research indicators, transparency in the field of academic practice, and reproducibility. Open science philosophy is adopted primarily to demonstrate the benefits of open science practices. Researchers use open science applications for their own advantage in order to get more offers, increase citations, attract media attention, potential collaborators, career opportunities, donations and funding opportunities. In open science philosophy, open data findings are evidence that open science practices provide significant benefits to researchers in scientific research creation, collaboration, communication, and evaluation according to more traditional closed science practices. Open science considers concerns such as the rigor of peer review, common research facts such as financing and career development, and the sacrifice of author rights. Therefore, researchers are recommended to implement open science research within the framework of existing academic evaluation and incentives. As a result, open science research issues are addressed in the areas of publishing, financing, collaboration, resource management and sharing, career development, discussion of open science questions and conclusions.

Keywords: Open Science, Open Science Philosophy, Open Science Research, Open Science Data

Procedia PDF Downloads 117

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 143

Authors: Brady, Imelda Katherine, Da Cunha Fanego, Iria

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Today's university students are considered digital natives and the use of Information Technologies (ITs) forms a large part of their study and learning. In the context of language studies, applications that help with revisions of grammar or vocabulary are particularly useful, especially if they are open access. There are studies that show the effectiveness of this type of application in the learning of English as a foreign language and that using IT can help learners become more autonomous in foreign language acquisition, given that these applications can enhance awareness of the learning process; this means that learners are less dependent on the teacher for corrective feedback. We also propose that the exploitation of these technologies also enhances the work of the language instructor wishing to incorporate IT into his/her practice. In this context, the aim of this paper is to present the creation of a repository of tools that provide support in the writing and correction of texts in English and the assessment of their usefulness on behalf of university students enrolled in the English Studies Degree. The project seeks to encourage the development of autonomous learning through the acquisition of skills linked to the self-correction of written work in English. To comply with the above, our methodology follows five phases. First of all, a selection of the main open-access online applications available for the correction of written texts in English is made: AutoCrit, Hemingway, Grammarly, LanguageTool, OutWrite, PaperRater, ProWritingAid, Reverso, Slick Write, Spell Check Plus and Virtual Writing Tutor. Secondly, the functionalities of each of these tools (spelling, grammar, style correction, etc.) are analyzed. Thirdly, explanatory materials (texts and video tutorials) are prepared on each tool. Fourth, these materials are uploaded into a repository of our university in the form of an institutional blog, which is made available to students and the general public. Finally, a survey was designed to collect students’ feedback. The survey aimed to analyse the usefulness of the blog and the quality of the explanatory materials as well as the degree of usefulness that students assigned to each of the tools offered. In this paper, we present the results of the analysis of data received from 33 students in the 1st semester of the 21-22 academic year. One result we highlight in our paper is that the students have rated this resource very highly, in addition to offering very valuable information on the perceived usefulness of the applications provided for them to review. Our work, carried out within the framework of a teaching innovation project funded by our university, emphasizes that teachers need to design methodological strategies that help their students improve the quality of their productions written in English and, by extension, to improve their linguistic competence.

Keywords: academic blog, open access tools, online self-correction, written production in English, university learning

Procedia PDF Downloads 87

Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

Procedia PDF Downloads 112

Authors: L. K. Shitole

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Usually on the face of it, personality is regarded as the external appearance of an individual. In psychology, the personality is not viewed merely as self or external appears, but it adds much more. Human resources development encompasses the personality development of the students. The student’s development starts right from the childhood and gradually continues right up to the completion of education in professional courses. This paper attempts to find out the role of the educational institutions in shaping the personality of the students from the rural area. Schools and colleges have infrastructural limitations, obtaining good quality and devoted teaching staff poses problems and even outside the school environment there are no private classes which may take care of this deficiency. The researcher has used the standardized test namely “Vyaktitva Shodhika” developed by Gyan Prabodhini, Pune for the students in Daund Taluka. There are 68 objective types of questions in the said questionnaire. Totally a sample size of 4191 students was selected. The sample was quite representative. It is observed that by and large the response indicates that the educational institutions are taking sincere efforts in shaping the personality of the students. In the semi-urban area i.e. at educational institutions of all levels, the performance on this front is excellent and at rest of Daund Taluka there is scope for improvement. Educational institutions of all levels are showing excellent performance in ensuring availability of the requisite infrastructure conducive for the development of the personality of the students. In rest of Daund Taluka there is ample scope for improving the situation. As far as data relating to role of co-curricular activities and sports programs in mental and physical development at various educational institutions is concerned Daund educational institutions have repeated their performance in securing “A” category, while in the rural area of Daund Taluka, there is need to step up the efforts in this regard. In today’s world of knowledge industry, one cannot ignore the importance of education and thereby the personality growth of the students. Accordingly, the educational institutions should undertake consistent research and extension activities in the area of personality development.

Keywords: personality, attitude, infrastructure, quality of education, learning environment, teacher’s contribution, family and society’s role

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Authors: Babatunde Joel Todowede

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It is a public knowledge that lack of quality and qualitative education breeds problems besetting Nigeria as a nation today. This paper entitled “Quality and Qualitative Education for all, panacea for insecurity and political unrest in Nigeria” seeks to explore how quality and qualitative education for all will tends to put an end to insecurity and political unrest in Nigeria as a Nation. It may be pertinent to note at this juncture that the development of any modern society or nation is primarily hinged on the functionality of its educational system. There is no developed nation in the world today, which does not owe its advancement to quality and qualitative education. In other words, Education is a vital instrument in the nation’s economic competitiveness, in its people, and in its communities. Hence, Education is not luxury to be cut in difficult economic times – it is an essential element of growth. In fact, education is the bedrock of any society that hopes to be numbered among the developed economies in the world. Nigeria, as a nation, has made continual efforts to assume its rightful place in education on the African continent, but has not been quite lucky. Interestingly however, Quality and Qualitative Education for all will come about if all stakeholders in the Education Sector perform their roles with skill and efficiency. Education is a very sensitive area, hence, needs to be passionate about education, and focused on building a future for the sector.” Quality and qualitative education instill significant core values in every student, which shape them into mature, caring and independent individuals. These values include commitment, collaboration, integrity, responsibility and respect. By imbibing these values in every aspect of their life, they are able to contribute their skills and talents while supporting each other in attaining their lifelong goals. This paper identified lack of proper education as the bane of insecurity and political unrest in the Country and urged the government to review the policy in a way that there will be quality and standard to check insurgency in the Country. More so, until the fallen standard of education in Nigeria is fixed to engage out of school children, the incessant attack on innocent Nigerians, particularly in the North East may get worse.

Keywords: quality and qualitative education, panacea, insecurity, political unrest

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

Procedia PDF Downloads 106

Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Bruna Azzari Puga, Richard Roe, Andre Pagani de Souza

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abstract outlines a proposed study to examine how and to what extent interactive, experiential, learner centered methodology develops learning of basic civic and democratic competencies among young children. It stems from the Literacy and Law course taught at Georgetown University Law Center in Washington, DC, since 1998. Law students, trained in best literacy practices and legal cases affecting literacy development, read “law related” children’s books and engage in interactive and extension activities with emerging readers. The law students write a monthly journal describing their experiences and a final paper: a conventional paper or a children’s book illuminating some aspect of literacy and law. This proposal is based on the recent adaptation of Literacy and Law to Brazil at Mackenzie Presbyterian University in São Paulo in three forms: first, a course similar to the US model, often conducted jointly online with Brazilian and US law students; second, a similar course that combines readings of children’s literature with activity based learning, with law students from a satellite Mackenzie campus, for young children from a vulnerable community near the city; and third, a course taught by law students at the main Mackenzie campus for 4th grade students at the Mackenzie elementary school, that is wholly activity and discourse based. The workings and outcomes of these courses are well documented by photographs, reports, lesson plans, and law student journals. The authors, faculty who teach the above courses at Mackenzie and Georgetown, observe that literacy, broadly defined as cognitive and expressive development through reading and discourse-based activities, can be influential in developing democratic civic skills, identifiable by explicit civic competencies. For example, children experience justice in the classroom through cooperation, creativity, diversity, fairness, systemic thinking, and appreciation for rules and their purposes. Moreover, the learning of civic skills as well as the literacy skills is enhanced through interactive, learner centered practices in which the learners experience literacy and civic development. This study will develop rubrics for individual and classroom teaching and supervision by examining 1) the children’s books and students diaries of participating law students and 2) the collection of photos and videos of classroom activities, and 3) faculty and supervisor observations and reports. These rubrics, and the lesson plans and activities which are employed to advance the higher levels of performance outcomes, will be useful in training and supervision and in further replication and promotion of this form of teaching and learning. Examples of outcomes include helping, cooperating and participating; appreciation of viewpoint diversity; knowledge and utilization of democratic processes, including due process, advocacy, individual and shared decision making, consensus building, and voting; establishing and valuing appropriate rules and a reasoned approach to conflict resolution. In conclusion, further development and replication of the learner centered literacy and law practices outlined here can lead to improved qualities of democratic teaching and learning supporting mutual respect, positivity, deep learning, and the common good – foundation qualities of a sustainable world.

Keywords: democracy, law, learner-centered, literacy

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Authors: Kwizera Mireille, Kharesh Ahmed Al-Khadher

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This paper explores the critical issue of girls' and women's right to education, exploring the challenges they face in accessing and benefiting from quality education. Gender disparities in education have persisted globally, hindering social progress and sustainable development. The fundamental importance of education in empowering individuals and promoting gender equality is acknowledged, making it imperative to address the disparities that hinder girls' and women's educational opportunities. The paper discusses various factors contributing to these disparities, including cultural norms(common in third-world countries), socio-economic constraints, and systemic biases. Drawing on a wide range of scholarly sources, empirical studies, and reports from international organizations, this paper highlights the broader societal benefits of educating girls and women, ranging from improved health outcomes to enhanced economic development and greater social and political participation. The paper further outlines strategies and initiatives aimed at overcoming these challenges. These include policy interventions, community-based programs, and international collaborations that work towards eliminating gender-based discrimination in educational settings. The paper emphasizes the significance of not only ensuring access but also fostering an inclusive and safe learning environment that encourages girls and women to thrive academically and personally. By analyzing successful case studies and best practices from around the world, the paper offers insights into effective approaches that can be adopted to enhance girls' and women's right to education globally. Furthermore, it emphasizes the importance of raising awareness of girl's and women's education. In conclusion, this paper underscores the urgency of prioritizing and protecting the educational rights of girls and women's right to education as a fundamental human right and catalyst for gender equality. It calls for a concerted effort from governments, NGOs, educational institutions, and society as a whole to create an equitable and empowering educational landscape that contributes to gender equality and sustainable development.

Keywords: empowerment, gender equality, inclusive education, right to education

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Authors: Lixin Tian, Wei Xue

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Many jobs in society go underground, such as mine mining, tunnel construction and subways, which are vital to the development of society. Once accidents occur in these places, the interruption of traditional wired communication is not conducive to the development of rescue work. In order to realize the positioning, early warning and command functions of underground personnel and improve rescue efficiency, it is necessary to develop and design an emergency ground communication system. It is easy to be subjected to narrowband interference when performing conventional underground communication. Spreading communication can be used for this problem. However, general spread spectrum methods such as direct spread communication are inefficient, so it is proposed to use parallel combined spread spectrum (PCSS) communication to improve efficiency. The PCSS communication not only has the anti-interference ability and the good concealment of the traditional spread spectrum system, but also has a relatively high frequency band utilization rate and a strong information transmission capability. So, this technology has been widely used in practice. This paper presents a PCSS communication model-multiple detection parallel combined spread spectrum (MDPCSS) communication system. In this paper, the principle of MDPCSS communication system is described, that is, the sequence at the transmitting end is processed in blocks and cyclically shifted to facilitate multiple detection at the receiving end. The block diagrams of the transmitter and receiver of the MDPCSS communication system are introduced. At the same time, the calculation formula of the system bit error rate (BER) is introduced, and the simulation and analysis of the BER of the system are completed. By comparing with the common parallel PCSS communication, we can draw a conclusion that it is indeed possible to reduce the BER and improve the system performance. Furthermore, the influence of different pseudo-code lengths selected on the system BER is simulated and analyzed, and the conclusion is that the larger the pseudo-code length is, the smaller the system error rate is.

Keywords: cyclic shift, multiple detection, parallel combined spread spectrum, PN code

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Authors: Duangkamol Thitivesa

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This paper focuses on the use of project work as a pretext for applying the conventions of writing, or the correctness of mechanics, usage, and sentence formation, in a content-based class in a Rajabhat University. Its aim was to explore to what extent the student teachers’ academic achievement of the basic writing features against the 70% attainment target after the use of project is. The organization of work around an agreed theme in which the students reproduce language provided by texts and instructors is expected to enhance students’ correct writing conventions. The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of achievement test and student writing works. The scores in the summative achievement test were analyzed by mean score, standard deviation, and percentage. It was found that the student teachers do more achieve of practicing mechanics and usage, and less in sentence formation. The students benefited from the exposure to texts during conducting the project; however, their automaticity of how and when to form phrases and clauses into simple/complex sentences had room for improvement.

Keywords: project-based learning, project work, writing conventions, academic achievement

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Authors: Yan Zhang

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With the development of commercial movies, more and more instructors are drawn to adapt film pedagogy to teach history and culture. By challenging traditional standards of classroom culture, instruction through film represents an intersection of modernity and adaptability which is no longer optional but essential to maintaining educational accessibility. First, this presentation describes special features of the film that can be used in the classroom and help students acquire intercultural communicative competence (ICC) and achieve the learning goal. Second, the author brings forward the 5 A STAIRCASE model (Acknowledge-Adjust-Acculturate-Act-Assess) to explore how students acquire international communicative competence. Third, this article presents the intersections between new digital environments and classroom practice, such as how films can contribute to combining classical and contemporary Chinese cultures seamlessly and how film pedagogy can be an effective way to get students to engage in deeper critical thinking by exposing them to visuals, music, language, and styling which do not exist in traditional learning formats. Last, the student’s final video project will be exemplified at the end, demonstrating how to engage students in the analysis and experience of history and culture.

Keywords: intercultural education, curriculum, media, history

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Ayşegül Çabuk, Mine Işıksal

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Mathematics is a sense-making activity so that it requires meaningful learning. Hence based on this idea, meaningful mathematical connections are necessary to learn mathematics. At that point, the major question has become that which educational methods can provide opportunities to provide mathematical connections and to understand mathematics. The amalgam of reasoning and proof can be the one of the methods that creates opportunities to learn mathematics in a meaningful way. However, even if reasoning and proof should be included from prekindergarten to grade 12, studies in literature generally include secondary school students and pre-service mathematics teachers. With the light of the idea that the amalgam of reasoning and proof has significant effect on middle school students' mathematical learning, this study aims to investigate middle grade students' tendencies while reasoning the correctness of statements related to numbers. The sample included 272 middle grade students, specifically 69 of them were sixth grade students (25.4%), 101 of them were seventh grade students (37.1%) and 102 of them were eighth grade students (37.5%). Data was gathered through an achievement test including 2 essay types of problems about algebra. The answers of two items were analyzed both quantitatively and qualitatively in terms of students' solutions strategies while reasoning the correctness of the statements. Similar on the findings in the literature, most of the students, in all grade levels, used numerical examples to judge the statements. Moreover the results also showed that the majority of these students appear to believe that providing one or more selected examples is sufficient to show the correctness of the statement. Hence based on the findings of the study, even students in earlier ages have proving and reasoning abilities their reasoning's generally based on the empirical evidences. Therefore, it is suggested that examples and example-based reasoning can be a fundamental role on to generate systematical reasoning and proof insight in earlier ages.

Keywords: reasoning, mathematics learning, middle grade students

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Authors: Fatih Frank Alparslan

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The aviation industry is at a crucial turning point due to modern technologies, environmental concerns, and changing ways of transporting people and goods globally. The paper examines these challenges and opportunities comprehensively. It emphasizes the role of innovative management and advanced technology in shaping the future of air travel. This study begins with an overview of the current state of the aviation industry, identifying key areas where innovation and technology could be highly beneficial. It explores the latest advancements in airplane design, propulsion, and materials. These technological advancements are shown to enhance aircraft performance and environmental sustainability. The paper also discusses the use of artificial intelligence and machine learning in improving air traffic control, enhancing safety, and making flight operations more efficient. The management of these technologies is critically important. Therefore, the research delves into necessary changes in organization, culture, and operations to support innovation. It proposes a management approach that aligns with these modern technologies, underlining the importance of forward-thinking leaders who collaborate across disciplines and embrace innovative ideas. The paper addresses challenges in adopting these innovations, such as regulatory barriers, the need for industry-wide standards, and the impact of technological changes on jobs and society. It recommends that governments, aviation businesses, and educational institutions collaborate to address these challenges effectively, paving the way for a more innovative and eco-friendly aviation industry. In conclusion, the paper argues that the future of aviation relies on integrating new management practices with innovative technologies. It urges a collective effort to push beyond current capabilities, envisioning an aviation industry that is safer, more efficient, and environmentally responsible. By adopting a broad approach, this research contributes to the ongoing discussion about resolving the complex issues facing today's aviation sector, offering insights and guidance to prepare for future advancements.

Keywords: aviation innovation, technology integration, environmental sustainability, management strategies, multidisciplinary approach

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Authors: Samantha Rix

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The purpose of this research paper is to investigate the cognitive processing of learners who receive input but produce very little or no output, and who, when they do produce output, exhibit a similar language proficiency as do those learners who produced output more regularly in the language classroom. Previous studies have investigated the benefits of output (with somewhat differing results); therefore, the presentation will begin with an investigation of what may underlie gains in proficiency without output. Consequently, a pilot study was designed and conducted to gain insight into the cognitive processing of low-output language learners looking, for example, at quantity and quality of noticing. This will be carried out within the paradigm of action classroom research, observing and interviewing low-output language learners in an intensive English program at a small Midwest university. The results of the pilot study indicated that autonomy in language learning, specifically utilizing strategies such self-monitoring, self-talk, and thinking 'out-loud', were crucial in the development of language proficiency for academic-level performance. The presentation concludes with an examination of pedagogical implication for classroom use in order to aide students in their language development.

Keywords: cognitive processing, language learners, language proficiency, learning strategies

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Authors: A. Eiguren-Munitis, N. Berasategi, J. M. Correa

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Changes in family structures, immigration, economic crisis, among others, hinder the connection between different generations. This situation gives rise to a greater lack of social protection of the groups in vulnerable situations, such as the elderly and children. There is a growing need to search for shared spaces where different generations manage to break negative stereotypes and interact with each other. The school environment provides a favourable context in which the approach of different generations can be worked on. The intergenerational experiences that take place within the school context help to introduce the educational ideology for a lifetime. This induces bilateral learning, which encourages citizen participation. For this reason, the general objective of this research is to deepen the impact that intergenerational experiences have on participating students. The research is carried out based on mixed methods. The qualitative and quantitative evaluation included pre-test and post-test questionnaires (n=148) and group interviews (n=43). The results indicate that the intergenerational experiences influence different levels, on the one hand, help to promote school motivation and on the other hand, help to reduce negative stereotypes towards older people thus contributing to greater social cohesion.

Keywords: intergenerational learning, school, stereotypes, social cohesion

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Authors: Muhammad Ramzan

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STEM Education and Critical Thinking Skills are important 21st-century skills. STEM Education is necessary to promote secondary school students’ critical thinking skills. These skills are critical for teachers to respond to students. Pakistan is in the preliminary stages of integrating STEM Education in institutions like other developing countries. Unfortunately, most secondary school students in Pakistan are unaware of STEM Education and teachers are not applying critical thinking skills in classrooms. The study's objectives mainly deal with; to identify the importance of STEM Education in the teaching-learning process; to find out the factors affecting critical thinking skills that can develop interest in students in STEM Education and suggestions on how to improve critical thinking skills in students regarding STEM Education. This study was descriptive. The population of the study was secondary school students. Data was collected from 200 secondary school students through a questionnaire. The research results show that critical thinking skills develop interest in students towards STEM Education.

Keywords: STEM education, teachers, students, critical thinking skills, teaching and learning process

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