Search results for: multimedia learning tools

Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

Procedia PDF Downloads 246

Authors: Eija Raatikainen

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The aim of the study is to examine psychological safety in the context of change in working life and continuous learning in social- and educational organizations. The participants in the study are social workers and vocational teachers working as employees and supervisors in the capital region of Finland (public and private sectors). Research data has been collected during 2022-2023 using the qualitative method called empathy-based stories (MEBS). Research participants were asked to write short stories about situations related to their work and work community. As researchers, we created and varied the framework narratives (MEBS) in line with the aim of the study and theoretical background. The data were analyzed with content analysis. According to the results, the barriers and prerequisites for psychological safety at work could be located in four different working culture dimensions. The work culture dimensions were named as follows: 1) a work culture focusing on interaction and emotional culture between colleagues, 2) communal work culture, 3) a work culture that enables learning, and 4) a work culture focused on structures and operating models. All these have detailed elements of barriers and prerequisites of psychological safety at work. The results derived from the enlivening methods can be utilized when working with the work community and have discussed psychological safety at work. Also, the method itself (MEBS) can prevent open discussion and reflection on psychological safety at work because of the sensitivity of the topic. Method aloud to imagine, not just talk and share your experiences directly. Additionally, the results of the study can offer one tool or framework while developing phycological safety at work.

Keywords: psychological safety, empathy, empathy-based stories, working life

Procedia PDF Downloads 58

Authors: Maria V. Mishatkina

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The modelling of dialogical environment is an essential feature of modern education. The dialogue of cultures is a foundation and an important prerequisite for a formation of a human’s main moral qualities such as an ability to understand another person, which is manifested in such values as tolerance, respect, mutual assistance and mercy. A formation of a modern expert occurs in an educational environment that is significantly different from what we had several years ago. Nowadays university education has qualitatively new characteristics. They may be observed in Peoples’ Friendship University of Russia (RUDN University), a top Russian higher education institution which unites representatives of more than 150 countries. The content of its educational strategies is not an adapted cultural experience but material between science and innovation. Besides, RUDN University’s profiles and specialization are not equal to the professional structures. People study not a profession in a strict sense but a basic scientific foundation of an activity in different socio-cultural areas (science, business and education). RUDN University also provides a considerable unit of professional education components. They are foreign languages skills, economic, political, ethnic, communication and computer culture, theory of information and basic management skills. Moreover, there is a rich social life (festive multicultural events, theme parties, journeys) and prospects concerning the inclusive approach to education (for example, a special course ‘Social Pedagogy: Issues of Tolerance’). In our research, we use such methods as analysis of modern and contemporary scientific literature, opinion poll (involving students, teachers and research workers) and comparative data analysis. We came to the conclusion that knowledge transfer of RUDN student in the activity happens through making goals, problems, issues, tasks and situations which simulate future innovative ambiguous environment that potentially prepares him/her to dialogical way of life. However, all these factors may not take effect if there is no ‘personal inspiration’ of students by communicative and dialogic values, their participation in a system of meanings and tools of learning activity that is represented by cooperation within the framework of scientific and pedagogical schools dialogue. We also found out that dominating strategies of ensuring the quality of education are those that put students in the position of the subject of their own education. Today these strategies and approaches should involve such approaches and methods as task, contextual, modelling, specialized, game-imitating and dialogical approaches, the method of practical situations, etc. Therefore, University in the modern sense is not only an educational institution, but also a generator of innovation, cooperation among nations and cultural progress. RUDN University has been performing exactly this mission for many decades.

Keywords: dialogical developing situation, dialogue of cultures, readiness for dialogue, university graduate

Procedia PDF Downloads 205

Authors: Taha Ahmed Özbey, Sedat Nazlıbilek, Alparslan Çağrı Yapıcı

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Combline cavity filters have gained significant attention in recent years due to their exceptional narrowband characteristics, high unloaded Q, remarkable out-of-band rejection, and versatile post-manufacturing tuning capabilities. These filters play a vital role in various wireless communication systems, radar applications, and other advanced technologies where stringent frequency selectivity and superior performance are required. This paper represents combined cavity filter design and implementation by coupling matrix synthesis. Limited filter length, 50 dB out-of-band rejection, and agile design were aimed. To do so, CAD tools and intuitive methods were used.

Keywords: cavity, band pass filter, cavity combline filter, coupling matrix synthesis

Procedia PDF Downloads 62

Authors: Osman Ali, Jeanne Kriek

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Introduction: The current instructional approach entrenched in memorizing does not assist conceptual understanding in science. Instructional approaches that encourage research, investigation, and experimentation, which depict how scientists work, should be encouraged. One such teaching strategy is problem-based learning (PBL). PBL has many advantages; enhanced self-directed learning and improved problem-solving and critical thinking skills. However, despite many advantages, PBL has challenges. Research confirmed is time-consuming and difficult to formulate ill-structured questions. Professional development interventions are needed for in-service educators to adopt the PBL strategy. The purposively selected educators had to implement PBL in their classrooms after the intervention to develop their practice and then reflect on the implementation. They had to indicate their achievements and challenges. This study differs from previous studies as the rural educators were subjected to implementing PBL in their classrooms and reflected on their experiences, beliefs, and attitudes regarding PBL. Theoretical Framework: The study reinforced Vygotskian sociocultural theory. According to Vygotsky, the development of a child's cognitive is sustained by the interaction between the child and more able peers in his immediate environment. The theory suggests that social interactions in small groups create an opportunity for learners to form concepts and skills on their own better than working individually. PBL emphasized learning in small groups. Research Methodology: An exploratory case study was employed. The reason is that the study was not necessarily for specific conclusive evidence. Non-probability purposive sampling was adopted to choose eight schools from 89 rural public schools. In each school, two educators were approached, teaching physical sciences in grades 10 and 11 (N = 16). The research instruments were questionnaires, interviews, and lesson observation protocol. Two open-ended questionnaires were developed before and after intervention and analyzed thematically. Three themes were identified. The semi-structured interviews and responses were coded and transcribed into three themes. Subsequently, the Reform Teaching Observation Protocol (RTOP) was adopted for lesson observation and was analyzed using five constructs. Results: Evidence from analyzing the questionnaires before and after the intervention shows that participants knew better what was required to develop an ill-structured problem during the implementation. Furthermore, indications from the interviews are that participants had positive views about the PBL strategy. They stated that they only act as facilitators, and learners’ problem-solving and critical thinking skills are enhanced. They suggested a change in curriculum to adopt the PBL strategy. However, most participants may not continue to apply the PBL strategy stating that it is time-consuming and difficult to complete the Annual Teaching Plan (ATP). They complained about materials and equipment and learners' readiness to work. Evidence from RTOP shows that after the intervention, participants learn to encourage exploration and use learners' questions and comments to determine the direction and focus of classroom discussions.

Keywords: problem-solving, self-directed, critical thinking, intervention

Procedia PDF Downloads 102

Authors: Sara Shuger Fox, Oscar Reynaga

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This session explains the process and lessons learned as Central College (USA) faculty and staff developed undergraduate research opportunities within the model of a short-term faculty-led study abroad program in Costa Rica. The program in Costa Rica increases access to research opportunities across the disciplines and was developed by faculty from English, Biology, and Exercise Science. Session attendees will benefit from learning how faculty and staff navigated the program proposal process at a small liberal arts college and, in particular, how the program was built to be inclusive of departments with lower enrollment, like those currently seen in the humanities. Vital to this last point, presenters will explain how they negotiated issues of research supervision and disciplinary authority in such a way that the program is open to students from multiple disciplines without forcing the program budget to absorb costs for multiple faculty supervisors traveling and living in-country. Additionally, session attendees will learn how scouting laid the groundwork for mutually beneficial relationships between the program and the communities with which it collaborates. Presenters will explain how they built a coalition of students, faculty advisors, study abroad staff and local research hosts to support the development of research questions that are of value not just to the students, but to the community in which the research will take place. This program also incorporates principles of fair-trade learning by intentionally reporting research findings to local community members, as well as encouraging students to proactively share their research as a way to connect with local people.

Keywords: Costa Rica, research, sustainability, transdisciplinary

Procedia PDF Downloads 1049

Authors: Rawan K. Al-Duaij, Salem A. Al-Salem

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Projects, the unique and temporary process of achieving a set of requirements have always been challenging; Planning the schedule and budget, managing the resources and risks are mostly driven by a similar past experience or the technical consultations of experts in the matter. With that complexity of Projects in Scope, Time, and execution environment, Adjustment Orders are tools to reflect changes to the original project parameters after Contract signature. Adjustment Orders are the official/legal amendments to the terms and conditions of a live Contract. Reasons for issuing Adjustment Orders arise from changes in Contract scope, technical requirement and specification resulting in scope addition, deletion, or alteration. It can be as well a combination of most of these parameters resulting in an increase or decrease in time and/or cost. Most business leaders (handling projects in the interest of the owner) refrain from using Adjustment Orders considering their main objectives of staying within budget and on schedule. Success in managing the changes results in uninterrupted execution and agreed project costs as well as schedule. Nevertheless, this is not always practically achievable. In this paper, a detailed study through utilizing Industrial Engineering & Systems Management tools such as Six Sigma, Data Analysis, and Quality Control were implemented on the organization’s five years records of the issued Adjustment Orders in order to investigate their prevalence, and time and cost impact. The analysis outcome revealed and helped to identify and categorize the predominant causations with the highest impacts, which were considered most in recommending the corrective measures to reach the objective of minimizing the Adjustment Orders impacts. Data analysis demonstrated no specific trend in the AO frequency in past five years; however, time impact is more than the cost impact. Although Adjustment Orders might never be avoidable; this analysis offers’ some insight to the procedural gaps, and where it is highly impacting the organization. Possible solutions are concluded such as improving project handling team’s coordination and communication, utilizing a blanket service contract, and modifying the projects gate system procedures to minimize the possibility of having similar struggles in future. Projects in the Oil and Gas sector are always evolving and demand a certain amount of flexibility to sustain the goals of the field. As it will be demonstrated, the uncertainty of project parameters, in adequate project definition, operational constraints and stringent procedures are main factors resulting in the need for Adjustment Orders and accordingly the recommendation will be to address that challenge.

Keywords: adjustment orders, data analysis, oil and gas sector, systems management

Procedia PDF Downloads 148

Authors: Yahya Almurtadha, Mukhtar Ghaleb, Ahmed M. Shamsan Saleh

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The world witnessed a fierce attack from the Covid-19 virus, which affected public life socially, economically, healthily and psychologically. The world's governments tried to confront the pandemic by imposing a number of precautionary measures such as general closure, curfews and social distancing. Scientists have also made strenuous efforts to develop an effective vaccine to train the immune system to develop antibodies to combat the virus, thus reducing its symptoms and limiting its spread. Artificial intelligence, along with researchers and medical authorities, has accelerated the vaccine development process through big data processing and simulation. On the other hand, one of the most important negatives of the impact of Covid 19 was the state of anxiety and fear due to the blowout of rumors through social media, which prompted governments to try to reassure the public with the available means. This study aims to proposed using Sentiment Analysis (AKA Opinion Mining) and deep learning as efficient artificial intelligence techniques to work on retrieving the tweets of the public from Twitter and then analyze it automatically to extract their opinions, expression and feelings, negatively or positively, about the symptoms they may feel after vaccination. Sentiment analysis is characterized by its ability to access what the public post in social media within a record time and at a lower cost than traditional means such as questionnaires and interviews, not to mention the accuracy of the information as it comes from what the public expresses voluntarily.

Keywords: deep learning, opinion mining, natural language processing, sentiment analysis

Procedia PDF Downloads 159

Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 117

Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

Procedia PDF Downloads 88

Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb

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Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.

Keywords: big data, social networks, sentiment analysis, twitter

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Authors: Jinan Fiaidhi, Hashmath Shaik

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Breaking the barrier for practicing evidence-based medicine relies on effective methods for rapidly identifying relevant evidence from the body of biomedical literature. An important challenge confronted by medical practitioners is the long time needed to browse, filter, summarize and compile information from different medical resources. Deep learning can help in solving this based on automatic question answering (Q&A) and transformers. However, Q&A and transformer technologies are not trained to answer clinical queries that can be used for evidence-based practice, nor can they respond to structured clinical questioning protocols like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that are based on transformer models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our transformer methods are reaching an acceptable state-of-the-art performance based on two staged bootstrapping processes involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question. Moreover, we are also reporting experimentations to empower our bootstrapping techniques with patch attention to the most important keywords in the clinical case and the PICO questions. Our bootstrapped patched with attention is showing relevancy of the evidence collected based on entropy metrics.

Keywords: automatic question answering, PICO questions, evidence-based medicine, generative models, LLM transformers

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Authors: Luay Jum'a

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Many challenges are facing third-party logistics (3PL) providers in the domestic and global markets which create a volatile decision making environment. All these challenges such as managing changes in consumer behaviour, demanding expectations from customers and time compressions have turned into complex problems for 3PL providers. Since the movement towards increased outsourcing outpaces movement towards insourcing, the need to achieve a competitive advantage over competitors in 3PL market increases. This trend continues to grow over the years and as a result, areas of strengths and improvements are highlighted through the analysis of the LSQ factors that lead to B2B customers’ satisfaction which become a priority for 3PL companies. Consequently, 3PL companies are increasingly focusing on the most important issues from the perspective of their customers and relying more on this value of information in making their managerial decisions. Therefore, this study is concerned with providing guidance for improving logistics service quality (LSQ) levels in the context of 3PL industry in Jordan. The study focused on the most important factors in LSQ and used a managerial tool that guides 3PL companies in making LSQ improvements based on a quadrant analysis of two main dimensions: LSQ declared importance and LSQ inferred importance. Although, a considerable amount of research has been conducted to investigate the relationship between logistics service quality (LSQ) and customer satisfaction, there remains a lack of developing managerial tools to aid in the process of LSQ improvement decision-making. Moreover, the main advantage for the companies to use 3PL service providers as a trend is due to the realised percentage of cost reduction on the total cost of logistics operations and the incremental improvement in customer service. In this regard, having a managerial tool that help 3PL service providers in managing the LSQ factors portfolio effectively and efficiently would be a great investment for service providers. One way of suggesting LSQ improvement actions for 3PL service providers is via the adoption of analysis tools that perform attribute categorisation such as Importance–Performance matrix. In mind of the above, it can be stated that the use of quadrant analysis will provide a valuable opportunity for 3PL service providers to identify improvement opportunities as customer service attributes or factors importance are identified in two different techniques that complete each other. Moreover, the data were collected through conducting a survey and 293 questionnaires were returned from business-to-business (B2B) customers of 3PL companies in Jordan. The results showed that the LSQ factors vary in their importance and 3PL companies should focus on some LSQ factors more than other factors. Moreover, ordering procedures, timeliness/responsiveness LSQ factors considered being crucial in 3PL businesses and therefore they need to have more focus and development by 3PL service providers in the Jordanian market.

Keywords: logistics service quality, managerial decisions, quadrant analysis, third party logistics service provider

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

Procedia PDF Downloads 49

Authors: Khaled Elbehiery, Hussam Elbehiery

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The success of education is dependent on evolution and adaptation, while the traditional system has worked before, one type of education evolved with the digital age is virtual education that has influenced efficiency in today’s learning environments. Virtual learning has indeed proved its efficiency to overcome the drawbacks of the physical environment such as time, facilities, location, etc., but despite what it had accomplished, the educational system over all is not adequate for being a productive system yet. Earning a degree is not anymore enough to obtain a career job; it is simply missing the skills and creativity. There are always two sides of a coin; a college degree or a specialized certificate, each has its own merits, but having both can put you on a successful IT career path. For many of job-seeking individuals across world to have a clear meaningful goal for work and education and positively contribute the community, a productive correlation and cooperation among employers, universities alongside with the individual technical skills is a must for generations to come. Fortunately, the proposed research “Entrepreneur Universal Education System” is an evolution to meet the needs of both employers and students, in addition to gaining vital and real-world experience in the chosen fields is easier than ever. The new vision is to empower the education to improve organizations’ needs which means improving the world as its primary goal, adopting universal skills of effective thinking, effective action, effective relationships, preparing the students through real-world accomplishment and encouraging them to better serve their organization and their communities faster and more efficiently.

Keywords: virtual education, academic degree, certificates, internship, amazon web services, Microsoft Azure, Google Cloud Platform, hybrid models

Procedia PDF Downloads 83

Authors: Angela U. Makolo

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Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

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Authors: Masoumeh Mofarrah, Amir NahanMoghadam

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Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise, and improve EOR methods and their application. Recently, Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic, and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization infeasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

Procedia PDF Downloads 477

Authors: Antti Lanki, Justiina Halonen, Juuso Punnonen, Emmi Rantavuo

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Marine oil spill response operation requires extensive vessel maneuvering and navigation skills. At-sea oil containment and recovery include both single vessel and multi-vessel operations. Towing long oil containment booms that are several hundreds of meters in length, is a challenge in itself. Boom deployment and towing in multi-vessel configurations is an added challenge that requires precise coordination and control of the vessels. Efficient communication, as a prerequisite for shared situational awareness, is needed in order to execute the response task effectively. To gain and maintain adequate maritime skills, practical training is needed. Field exercises are the most effective way of learning, but especially the related vessel operations are resource-intensive and costly. Field exercises may also be affected by environmental limitations such as high sea-state or other adverse weather conditions. In Finland, the seasonal ice-coverage also limits the training period to summer seasons only. In addition, environmental sensitiveness of the sea area restricts the use of real oil or other target substances. This paper examines, whether maritime simulator training can offer a complementary method to overcome the training challenges related to field exercises. The objective is to assess the efficiency and the learning impact of simulator training, and the specific skills that can be trained most effectively in simulators. This paper provides an overview of learning results from two oil spill response pilot courses, in which maritime navigational bridge simulators were used to train the oil spill response authorities. The simulators were equipped with an oil spill functionality module. The courses were targeted at coastal Fire and Rescue Services responsible for near shore oil spill response in Finland. The competence levels of the participants were surveyed before and after the course in order to measure potential shifts in competencies due to the simulator training. In addition to the quantitative analysis, the efficiency of the simulator training is evaluated qualitatively through feedback from the participants. The results indicate that simulator training is a valid and effective method for developing marine oil spill response competencies that complement traditional field exercises. Simulator training provides a safe environment for assessing various oil containment and recovery tactics. One of the main benefits of the simulator training was found to be the immediate feedback the spill modelling software provides on the oil spill behaviour as a reaction to response measures.

Keywords: maritime training, oil spill response, simulation, vessel manoeuvring

Procedia PDF Downloads 153

Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Katerina Florou

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Two decades after coined the term "learner corpora" as collections of texts created by foreign or second language learners across various language contexts, and some years following suggestion to incorporate "focusing on form" within a Task-Based Learning framework, this study aims to explore how learner corpora, whether annotated with errors or not, can facilitate a focus on form in an educational setting. Argues that analyzing linguistic form serves the purpose of enabling students to delve into language and gain an understanding of different facets of the foreign language. This same objective is applicable when analyzing learner corpora marked with errors or in their raw state, but in this scenario, the emphasis lies on identifying incorrect forms. Teachers should aim to address errors or gaps in the students' second language knowledge while they engage in a task. Building on this recommendation, we compared the written output of two student groups: the first group (G1) employed the focusing on form phase by studying a specific aspect of the Italian language, namely the past participle, through examples from native speakers and grammar rules; the second group (G2) focused on form by scrutinizing their own errors and comparing them with analogous examples from a native speaker corpus. In order to test our hypothesis, we created four learner corpora. The initial two were generated during the task phase, with one representing each group of students, while the remaining two were produced as a follow-up activity at the end of the lesson. The results of the first comparison indicated that students' exposure to their own errors can enhance their grasp of a grammatical element. The study is in its second stage and more results are to be announced.

Keywords: Corpus interlanguage analysis, task based learning, Italian language as F1, learner corpora

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Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Miracle Ajah

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One of the biggest challenges facing Theological/Religious Education in Nigeria is access to quality learning materials. For instance at the Trinity (Union) Theological College, Umuahia, it was difficult for lecturers to access suitable and qualitative materials for instruction especially the ones that would suit the African context and stimulate a deep rooted interest among the students. Some textbooks written by foreign authors were readily available in the School Library, but were lacking in the College bookshops for students to own copies. Even when the College was able to order some of the books from abroad, it did not usher in the needed enthusiasm expected from the students because they were either very expensive or very difficult to understand during private studies. So it became necessary to develop contextual materials which were affordable and understandable, though with little success. The National Open University of Nigeria (NOUN)’s innovation in the development and sharing of learning resources through its Open Course ware is a welcome development and of great assistance to students. Apart from NOUN students who could easily access the materials, many others from various theological/religious institutes across the nation have benefited immensely. So, the thesis of this paper is that the promotion of open educational resources in theological/religious education in Nigeria would facilitate a better informed/equipped religious leadership, which would in turn impact its adherents for a healthier society and national development. Adopting a narrative and historical approach within the context of Nigeria’s educational system, the paper discusses: educational traditions in Nigeria; challenges facing theological/religious education in Nigeria; and benefits of open educational resources. The study goes further to making recommendations on how OER could positively influence theological/religious education in Nigeria. It is expected that theologians, religious educators, and ODL practitioners would find this work very useful.

Keywords: OER, theological education, religious education, Nigeria

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology.

Keywords: agile methodology, health analytics, unified process model, UML

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Authors: Nasser S. Shebka

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Fuzzy logic is used in complex adaptive systems where classical tools of representing knowledge are unproductive. Nevertheless, the incorporation of fuzzy logic, as it’s the case with all artificial intelligence tools, raised some inconsistencies and limitations in dealing with increased complexity systems and rules that apply to real-life situations and hinders the ability of the inference process of such systems, but it also faces some inconsistencies between inferences generated fuzzy rules of complex or imprecise knowledge-based systems. The use of fuzzy logic enhanced the capability of knowledge representation in such applications that requires fuzzy representation of truth values or similar multi-value constant parameters derived from multi-valued logic, which set the basis for the three t-norms and their based connectives which are actually continuous functions and any other continuous t-norm can be described as an ordinal sum of these three basic ones. However, some of the attempts to solve this dilemma were an alteration to fuzzy logic by means of non-monotonic logic, which is used to deal with the defeasible inference of expert systems reasoning, for example, to allow for inference retraction upon additional data. However, even the introduction of non-monotonic fuzzy reasoning faces a major issue of conflict resolution for which many principles were introduced, such as; the specificity principle and the weakest link principle. The aim of our work is to improve the logical representation and functional modelling of AI systems by presenting a method of resolving existing and potential rule conflicts by representing temporal modalities within defeasible inference rule-based systems. Our paper investigates the possibility of resolving fuzzy rules conflict in a non-monotonic fuzzy reasoning-based system by introducing temporal modalities and Kripke's general weak modal logic operators in order to expand its knowledge representation capabilities by means of flexibility in classifying newly generated rules, and hence, resolving potential conflicts between these fuzzy rules. We were able to address the aforementioned problem of our investigation by restructuring the inference process of the fuzzy rule-based system. This is achieved by using time-branching temporal logic in combination with restricted first-order logic quantifiers, as well as propositional logic to represent classical temporal modality operators. The resulting findings not only enhance the flexibility of complex rule-base systems inference process but contributes to the fundamental methods of building rule bases in such a manner that will allow for a wider range of applicable real-life situations derived from a quantitative and qualitative knowledge representational perspective.

Keywords: fuzzy rule-based systems, fuzzy tense inference, intelligent systems, temporal modalities

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Authors: Şule Yanık, Hasan Gürgür

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It is thought that it is important for disabled children to have the opportunity to benefit preschool education that smoothens transition process to formal education, and for the constitution of a precondition for their success. Within this context, it is important for the disabled in Turkey to be evaluated medically firstly and then educational-wise in order for them to benefit early inclusive education. Thus, disabled people are both diagnosed in hospitals and at Guidance and Research Centers (GRC) attached to Ministry of Education educational-wise. It is seen that standard evaluation tools are used and evaluations are done by special education teachers (SET) in order for educational diagnosis and evaluation (EDAE) to be realized. The literature emphasizes the importance of informal evaluation tools as well as formal ones. According to this, it is thought that another party, besides students in EDAE process and SETs, is family, because families are primary care takers for their children, and that the most correct and real information can be obtained via families beside results of educational evaluation processes (EEP). It is thought that obtaining opinions of families during EEP is important to be able to exhibit the present EDAE activities in Turkey, materialize any existing problems, and increase quality of the process. Within this context, the purpose of this study is to exhibit experiences regarding EDAE processes of 10 families having preschool children with hearing loss (CHL). The process of research is designed to be descriptive based on qualitative research paradigms. Data were collected via semi-structured interview questions, and the themes were obtained. As a result, it is seen that families, after they realize the hearing loss of their children, do not have any information regarding the subject, and that they consult to an ear-nose-throat doctor or an audiologist for support. It is seen that families go to hospitals for medical evaluation which is a pre-requisite for benefiting early education opportunities. However, during this process, as some families do not have any experience of having a CHL, it is seen that they are late for medical evaluation and hearing aids. Moreover, families stated that they were directed to GRC via audiologists for educational evaluation. Families stated that their children were evaluated regarding language, academic and psychological development in proportion with their ages in GRC after they were diagnosed medically. However, families stated that EEP realized in GRC was superficial, short and lacked detail. It is seen that many families were not included in EEP process, whereas some families stated that they were asked questions because their children are too small to answer. Regarding the benefits of EEP for themselves and their children, families stated that GRC had to give a report to them for benefiting the free support of Special Education and Rehabilitation Center, and that families had to be directed to inclusive education. As a result, it is seen that opinions of families regarding EDAE processes at GRC indicate inefficiency of the process as it is short and superficial, regardless being to the point.

Keywords: children with hearing loss, educational diagnosis and evaluation, guidance and research center, inclusion

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Authors: Abdulmjeid Aljerawi

Abstract:

This study aimed to investigate the students' attitudes towards the use of social networking in education. Due to the nature of the study, and on the basis of its problem, objectives, and questions, the researcher used the descriptive approach. An appropriate questionnaire was prepared and validity and reliability were ensured. The questionnaire was then applied to the study sample of 434 students from King Saud University.

Keywords: social networks, education, learning, students

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Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

Abstract:

Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

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Authors: Routsukol Sunalai

Abstract:

This research purpose is to study and compare the administration of Buddhist monks at northeastern Thailand. The samples used in the study are the priest in the Northeast by simple random sampling for 190 sampling. The tools used in this study is questioner were created in the 40 question items. The statistics used for data analysis were percentage, average, and standard deviation. The research found that the human resources management for the Buddhist monks as a whole is moderate. But it was found that the highest average is the policy followed by the management information. The Buddhist monks aged less than 25 years old with the overall difference was not significant. The priests who are less than 10 years in the monk experience and the priest has long held in the position for 10 years are not different in the significant level.

Keywords: employee job-related outcomes, ethical institutionalization, quality of work life, stock exchange of Thailand

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