Search results for: student-centered teaching and learning
3798 The Use of Artificial Intelligence in Diagnosis of Mastitis in Cows
Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham
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In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis
Procedia PDF Downloads 553797 Synergism in the Inquiry Lab: An Analysis of Time Targets and Achievement
Authors: John M. Basey, Clinton D. Francis, Maxwell B. Joseph
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After gathering data from experimental procedures, inquiry-oriented-science labs often allow students the freedom to stay and complete the write up in class or leave lab early and complete the write up later. Teachers must decide whether to allow students this freedom to self-regulate this time. Student interviews have indicated four time-target strategies that may influence how students utilize this time: grade-target-A, grade-target-C, time-limited, and proficiency. The hypothesis tested was that variability in class composition relative to the four grade-target strategies has an impact on when students leave class, which in turn may influence their overall learning as exemplified by grades. Students were divided into the four indicated groups with a survey. Class composition and the GTA teaching the class had significant impacts on how long students stayed in class with class composition having the greatest impact. A factor analysis identified two factors. Factor 1 included classes with percentages of grade-target students opposite time-limited/proficiency students and explained 43% of the variance. Factor 2 included classes with percentages of grade-target-A/proficiency students opposite grade-target-C students and explained 33% of the variance. Students who stayed longer received significantly higher grades (P = 0.008) with no significant relationships between grade and Factor 1 or Factor 2 (P > 0.05). The time students stayed in class was significantly positively related to Factor 1 (P = 0.006) and significantly negatively related to Factor 2 (P = 0.008). These results support the hypothesis and indicate that teachers may want to know the composition of student-target strategies before deciding on how to have students allocate study time at the end of inquiry-oriented labs. According to these results, ideal classes for self-regulation have a high proportion of proficiency and time-limited students and a low proportion of grade-target students, or a high proportion of grade-target-A and proficiency students and a low proportion of grade-target-C students. Non-ideal classes for self-regulation were comprised of the inverse proportions.Keywords: grades, inquiry lab design, synergism in student motivation, class composition
Procedia PDF Downloads 1233796 Links and Blocks: the Role of Language in Samuel Beckett’s Selected Plays
Authors: Su-Lien Liao
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This article explores the language in the four plays of Samuel Beckett–Waiting for Godot, Endgame, Krapp’s Last Tape, and Footfalls. It considers the way in which Beckett uses language, especially through fragmentation utterances, repetitions, monologues, contradictions, and silence. It discusses the function of language in modern society, in the theater of the absurd, and in the plays. Paradoxically enough, his plays attempts to communicate the incommunicability of language.Keywords: language, Samuel Beckett, theater of the absurd, foreign language teaching
Procedia PDF Downloads 4363795 Applying Multiple Intelligences to Teach Buddhist Doctrines in a Classroom
Authors: Phalaunnnaphat Siriwongs
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The classroom of the 21st century is an ever changing forum for new and innovative thoughts and ideas. With increasing technology and opportunity, students have rapid access to information that only decades ago would have taken weeks to obtain. Unfortunately, new techniques and technology are not the cure for the fundamental problems that have plagued the classroom ever since education was established. Class size has been an issue long debated in academia. While it is difficult to pin point an exact number, it is clear that in this case more does not mean better. By looking into the success and pitfalls of classroom size the true advantages of smaller classes will become clear. Previously, one class was comprised of 50 students. Being seventeen and eighteen- year- old students, sometimes it was quite difficult for them to stay focused. To help them understand and gain much knowledge, a researcher introduced “The Theory of Multiple Intelligence” and this, in fact, enabled students to learn according to their own learning preferences no matter how they were being taught. In this lesson, the researcher designed a cycle of learning activities involving all intelligences so that everyone had equal opportunities to learn.Keywords: multiple intelligences, role play, performance assessment, formative assessment
Procedia PDF Downloads 2693794 Jointly Learning Python Programming and Analytic Geometry
Authors: Cristina-Maria Păcurar
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The paper presents an original Python-based application that outlines the advantages of combining some elementary notions of mathematics with the study of a programming language. The application support refers to some of the first lessons of analytic geometry, meaning conics and quadrics and their reduction to a standard form, as well as some related notions. The chosen programming language is Python, not only for its closer to an everyday language syntax – and therefore, enhanced readability – but also for its highly reusable code, which is of utmost importance for a mathematician that is accustomed to exploit already known and used problems to solve new ones. The purpose of this paper is, on one hand, to support the idea that one of the most appropriate means to initiate one into programming is throughout mathematics, and reciprocal, one of the most facile and handy ways to assimilate some basic knowledge in the study of mathematics is to apply them in a personal project. On the other hand, besides being a mean of learning both programming and analytic geometry, the application subject to this paper is itself a useful tool for it can be seen as an independent original Python package for analytic geometry.Keywords: analytic geometry, conics, python, quadrics
Procedia PDF Downloads 2853793 Electrophysiological Correlates of Statistical Learning in Children with and without Developmental Language Disorder
Authors: Ana Paula Soares, Alexandrina Lages, Helena Oliveira, Francisco-Javier Gutiérrez-Domínguez, Marisa Lousada
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From an early age, exposure to a spoken language allows us to implicitly capture the structure underlying the succession of the speech sounds in that language and to segment it into meaningful units (words). Statistical learning (SL), i.e., the ability to pick up patterns in the sensory environment even without intention or consciousness of doing it, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language and possibly to lie behind the language difficulties exhibited by children with development language disorder (DLD). The research conducted so far has, however, led to inconsistent results, which might stem from the behavioral tasks used to test SL. In a classic SL experiment, participants are first exposed to a continuous stream (e.g., syllables) in which, unbeknownst to the participants, stimuli are grouped into triplets that always appear together in the stream (e.g., ‘tokibu’, ‘tipolu’), with no pauses between each other (e.g., ‘tokibutipolugopilatokibu’) and without any information regarding the task or the stimuli. Following exposure, SL is assessed by asking participants to discriminate between triplets previously presented (‘tokibu’) from new sequences never presented together during exposure (‘kipopi’), i.e., to perform a two-alternative-forced-choice (2-AFC) task. Despite the widespread use of the 2-AFC to test SL, it has come under increasing criticism as it is an offline post-learning task that only assesses the result of the learning that had occurred during the previous exposure phase and that might be affected by other factors beyond the computation of regularities embedded in the input, typically the likelihood two syllables occurring together, a statistic known as transitional probability (TP). One solution to overcome these limitations is to assess SL as exposure to the stream unfolds using online techniques such as event-related potentials (ERP) that is highly sensitive to the time-course of the learning in the brain. Here we collected ERPs to examine the neurofunctional correlates of SL in preschool children with DLD, and chronological-age typical language development (TLD) controls who were exposed to an auditory stream in which eight three-syllable nonsense words, four of which presenting high-TPs and the other four low-TPs, to further analyze whether the ability of DLD and TLD children to extract-word-like units from the steam was modulated by words’ predictability. Moreover, to ascertain if the previous knowledge of the to-be-learned-regularities affected the neural responses to high- and low-TP words, children performed the auditory SL task, firstly, under implicit, and, subsequently, under explicit conditions. Although behavioral evidence of SL was not obtained in either group, the neural responses elicited during the exposure phases of the SL tasks differentiated children with DLD from children with TLD. Specifically, the results indicated that only children from the TDL group showed neural evidence of SL, particularly in the SL task performed under explicit conditions, firstly, for the low-TP, and, subsequently, for the high-TP ‘words’. Taken together, these findings support the view that children with DLD showed deficits in the extraction of the regularities embedded in the auditory input which might underlie the language difficulties.Keywords: development language disorder, statistical learning, transitional probabilities, word segmentation
Procedia PDF Downloads 1853792 Multi-Sensor Target Tracking Using Ensemble Learning
Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana
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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers
Procedia PDF Downloads 2543791 Role of Higher Education Commission (HEC) in Strengthening the Academia and Industry Relationships: The Case of Pakistan
Authors: Shah Awan, Fahad Sultan, Shahid Jan Kakakhel
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Higher education in the 21st century has been faced with game-changing developments impacting teaching and learning and also strengthening the academia and industry relationship. The academia and industry relationship plays a key role in economic development in developed, developing and emerging economies. The partnership not only explores innovation but also provide a real time experience of the theoretical knowledge. For this purpose, the paper assessing the role of HEC in the Pakistan and discusses the way in academia and industry contribute their role in improving Pakistani economy. Successive studies have reported the importance of innovation and technology , research development initiatives in public sector universities, and the significance of role of higher education commission in strengthening the academia and industrial relationship to improve performance and minimize failure. The paper presents the results of interviews conducted, using semi-structured interviews amongst 26 staff members of two public sector universities, higher education commission and managers from corporate sector.The study shows public sector universities face the several barriers in developing economy like Pakistan, to establish the successful collaboration between universities and industry. Of the participants interviewed, HEC provides an insufficient road map to improve organisational capabilities in facilitating and enhance the performance. The results of this study have demonstrated that HEC has to embrace and internalize support to industry and public sector universities to compete in the era of globalization. Publication of this research paper will help higher education sector to further strengthen research sector through industry and university collaboration. The research findings corroborate the findings of Dooley and Kirk who highlights the features of university-industry collaboration. Enhanced communication has implications for the quality of the product and human resource. Crucial for developing economies, feasible organisational design and framework is essential for the university-industry relationship.Keywords: higher education commission, role, academia and industry relationship, Pakistan
Procedia PDF Downloads 4623790 Assisting Dating of Greek Papyri Images with Deep Learning
Authors: Asimina Paparrigopoulou, John Pavlopoulos, Maria Konstantinidou
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Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. By experimenting with data obtained from the Collaborative Database of Dateable Greek Bookhands and the PapPal online collections of objectively dated Greek papyri, this study shows that deep learning dating models, pre-trained on generic images, can achieve accurate chronological estimates for a test subset (67,97% accuracy for book hands and 55,25% for documents). To compare the estimates of these models with those of humans, experts were asked to complete a questionnaire with samples of literary and documentary hands that had to be sorted chronologically by century. The same samples were dated by the models in question. The results are presented and analysed.Keywords: image classification, papyri images, dating
Procedia PDF Downloads 743789 Prosodic Transfer in Foreign Language Learning: A Phonetic Crosscheck of Intonation and F₀ Range between Italian and German Native and Non-Native Speakers
Authors: Violetta Cataldo, Renata Savy, Simona Sbranna
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Background: Foreign Language Learning (FLL) is characterised by prosodic transfer phenomena regarding pitch accents placement, intonation patterns, and pitch range excursion from the learners’ mother tongue to their Foreign Language (FL) which suggests that the gradual development of general linguistic competence in FL does not imply an equally correspondent improvement of the prosodic competence. Topic: The present study aims to monitor the development of prosodic competence of learners of Italian and German throughout the FLL process. The primary object of this study is to investigate the intonational features and the f₀ range excursion of Italian and German from a cross-linguistic perspective; analyses of native speakers’ productions point out the differences between this pair of languages and provide models for the Target Language (TL). A following crosscheck compares the L2 productions in Italian and German by non-native speakers to the Target Language models, in order to verify the occurrence of prosodic interference phenomena, i.e., type, degree, and modalities. Methodology: The subjects of the research are university students belonging to two groups: Italian native speakers learning German as FL and German native speakers learning Italian as FL. Both of them have been divided into three subgroups according to the FL proficiency level (beginners, intermediate, advanced). The dataset consists of wh-questions placed in situational contexts uttered in both speakers’ L1 and FL. Using a phonetic approach, analyses have considered three domains of intonational contours (Initial Profile, Nuclear Accent, and Terminal Contour) and two dimensions of the f₀ range parameter (span and level), which provide a basis for comparison between L1 and L2 productions. Findings: Results highlight a strong presence of prosodic transfer phenomena affecting L2 productions in the majority of both Italian and German learners, irrespective of their FL proficiency level; the transfer concerns all the three domains of the contour taken into account, although with different modalities and characteristics. Currently, L2 productions of German learners show a pitch span compression on the domain of the Terminal Contour compared to their L1 towards the TL; furthermore, German learners tend to use lower pitch range values in deviation from their L1 when improving their general linguistic competence in Italian FL proficiency level. Results regarding pitch range span and level in L2 productions by Italian learners are still in progress. At present, they show a similar tendency to expand the pitch span and to raise the pitch level, which also reveals a deviation from the L1 possibly in the direction of German TL. Conclusion: Intonational features seem to be 'resistant' parameters to which learners appear not to be particularly sensitive. By contrast, they show a certain sensitiveness to FL pitch range dimensions. Making clear which the most resistant and the most sensitive parameters are when learning FL prosody could lay groundwork for the development of prosodic trainings thanks to which learners could finally acquire a clear and natural pronunciation and intonation.Keywords: foreign language learning, German, Italian, L2 prosody, pitch range, transfer
Procedia PDF Downloads 2823788 Dermoscopy Compliance: Improving Melanoma Detection Pathways Through Quality Improvement
Authors: Max Butler
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Melanoma accounts for 80% of skin cancer-related deaths globally. The poor prognosis and increasing incidence of melanoma impose a significant burden on global healthcare systems. Early detection, precise diagnosis, and preventative strategies are critical to improving patient outcomes. Dermoscopy is the gold standard for specialist assessments of pigmented skin lesions, as it can differentiate between benign and malignant growths with greater accuracy than visual inspection. In the United Kingdom, guidelines from the National Institute of Clinical Excellence (NICE) state dermoscopy should be used in all specialist assessments of pigmented skin lesions. Compliance with this guideline is low, resulting in missed and delayed melanoma diagnoses. To address this problem, a quality improvement project was initiated at Buckinghamshire Healthcare Trust (BHT) within the plastic surgery department. The target group was a trainee and consultant plastic surgeons conducting outpatient skin cancer clinics. Analysis of clinic documentation over a one-month period found that only 62% (38/61) of patients referred with pigmented skin lesions were examined using dermoscopy. To increase dermoscopy rates, teaching was delivered to the department highlighting national guidelines and the evidence base for dermoscopic examination. In addition, clinic paperwork was redesigned to include a text box for dermoscopic examination. Reauditing after the intervention found a significant increase in dermoscopy rates (52/61, p = 0.014). In conclusion, implementing a quality improvement project with targeted teaching and documentation template templates successfully increased dermoscopy rates. This is a promising step toward improving early melanoma detection and patient outcomes.Keywords: melanoma, dermoscopy, plastic surgery, quality improvement
Procedia PDF Downloads 683787 Voting Representation in Social Networks Using Rough Set Techniques
Authors: Yasser F. Hassan
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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices
Procedia PDF Downloads 3893786 Web 2.0 in Higher Education: The Instructors’ Acceptance in Higher Educational Institutes in Kingdom of Bahrain
Authors: Amal M. Alrayes, Hayat M. Ali
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Since the beginning of distance education with the rapid evolution of technology, the social network plays a vital role in the educational process to enforce the interaction been the learners and teachers. There are many Web 2.0 technologies, services and tools designed for educational purposes. This research aims to investigate instructors’ acceptance towards web-based learning systems in higher educational institutes in Kingdom of Bahrain. Questionnaire is used to investigate the instructors’ usage of Web 2.0 and the factors affecting their acceptance. The results confirm that instructors had high accessibility to such technologies. However, patterns of use were complex. Whilst most expressed interest in using online technologies to support learning activities, learners seemed cautious about other values associated with web-based system, such as the shared construction of knowledge in a public format. The research concludes that there are main factors that affect instructors’ adoption which are security, performance expectation, perceived benefits, subjective norm, and perceived usefulness.Keywords: Web 2.0, higher education, acceptance, students' perception
Procedia PDF Downloads 3303785 An Energy Efficient Clustering Approach for Underwater Wireless Sensor Networks
Authors: Mohammad Reza Taherkhani
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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.Keywords: underwater sensor networks, clustering, learning automata, energy consumption
Procedia PDF Downloads 3563784 Inversely Designed Chipless Radio Frequency Identification (RFID) Tags Using Deep Learning
Authors: Madhawa Basnayaka, Jouni Paltakari
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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.Keywords: artificial intelligence, chipless RFID, deep learning, machine learning
Procedia PDF Downloads 393783 FMR1 Gene Carrier Screening for Premature Ovarian Insufficiency in Females: An Indian Scenario
Authors: Sarita Agarwal, Deepika Delsa Dean
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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation
Procedia PDF Downloads 1203782 Process Driven Architecture For The ‘Lessons Learnt’ Knowledge Sharing Framework: The Case Of A ‘Lessons Learnt’ Framework For KOC
Authors: Rima Al-Awadhi, Abdul Jaleel Tharayil
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On a regular basis, KOC engages into various types of Projects. However, due to very nature and complexity involved, each project experience generates a lot of ‘learnings’ that need to be factored into while drafting a new contract and thus avoid repeating the same mistakes. But, many a time these learnings are localized and remain as tacit leading to scope re-work, larger cycle time, schedule overrun, adjustment orders and claims. Also, these experiences are not readily available to new employees leading to steep learning curve and longer time to competency. This is to share our experience in designing and implementing a process driven architecture for the ‘lessons learnt’ knowledge sharing framework in KOC. It high-lights the ‘lessons learnt’ sharing process adopted, integration with the organizational processes, governance framework, the challenges faced and learning from our experience in implementing a ‘lessons learnt’ framework.Keywords: lessons learnt, knowledge transfer, knowledge sharing, successful practices, Lessons Learnt Workshop, governance framework
Procedia PDF Downloads 5723781 A Case Study on How Biomedical Engineering (BME) Outreach Programmes Serve as An Alternative Educational Approach to Form and Develop the BME Community in Hong Kong
Authors: Sum Lau, Wing Chung Cleo Lau, Wing Yan Chu, Long Ching Ip, Wan Yin Lo, Jo Long Sam Yau, Ka Ho Hui, Sze Yi Mak
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Biomedical engineering (BME) is an interdisciplinary subject where knowledge about biology and medicine is applied to novel applications, solving clinical problems. This subject is crucial for cities such as Hong Kong, where the burden on the medical system is rising due to reasons like the ageing population. Hong Kong, who is actively boosting technological advancements in recent years, sets BME, or biotechnology, as a major category, as reflected in the 2018-19 Budget, where biotechnology was one of the four pillars for development. Over the years, while resources in terms of money and space have been provided, there has been a lack of talents expressed by both the academia and industry. While exogenous factors, such as COVID, may have hindered talents from outside Hong Kong to come, endogenous factors should also be considered. In particular, since there are already a few local universities offering BME programmes, their curriculum or style of education requires to be reviewed to intensify the network of the BME community and support post-academic career development. It was observed that while undergraduate (UG) studies focus on knowledge teaching with some technical training and postgraduate (PG) programmes concentrate on upstream research, the programmes are generally confined to the academic sector and lack connections to the industry. In light of that, a “Biomedical Innovation and Outreach Programme 2022” (“B.I.O.2022”) was held to connect students and professors from academia with clinicians and engineers from the industry, serving as a comparative approach to conventional education methods (UG and PG programmes from tertiary institutions). Over 100 participants, including undergraduates, postgraduates, secondary school students, researchers, engineers, and clinicians, took part in various outreach events such as conference and site visits, all held from June to July 2022. As a case study, this programme aimed to tackle the aforementioned problems with the theme of “4Cs” (connection, communication, collaboration, and commercialisation). The effectiveness of the programme is investigated by its ability to serve as an adult and continuing education and the effectiveness of causing social change to tackle current societal challenges, with the focus on tackling the lack of talents engaging in biomedical engineering. In this study, B.I.O.2022 is found to be able to complement the traditional educational methods, particularly in terms of knowledge exchange between the academia and the industry. With enhanced communications between participants from different career stages, there were students who followed up to visit or even work with the professionals after the programme. Furthermore, connections between the academia and industry could foster the generation of new knowledge, which ultimately pointed to commercialisation, adding value to the BME industry while filling the gap in terms of human resources. With the continuation of events like B.I.O.2022, it provides a promising starting point for the development and relationship strengthening of a BME community in Hong Kong, and shows potential as an alternative way of adult education or learning with societal benefits.Keywords: biomedical engineering, adult education for social change, comparative methods and principles, lifelong learning, faced problems, promises, challenges and pitfalls
Procedia PDF Downloads 1103780 Heart Ailment Prediction Using Machine Learning Methods
Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula
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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting
Procedia PDF Downloads 413779 Turkish Graduate Students' Perceptions of Drop Out Issues in Massive Open Online Courses
Authors: Harun Bozna
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MOOC (massive open online course) is a groundbreaking education platform and a current buzzword in higher education. Although MOOCs offer many appreciated learning experiences to learners from various universities and institutions, they have considerably higher dropout rates than traditional education. Only about 10% of the learners who enroll in MOOCs actually complete the course. In this case, perceptions of participants and a comprehensive analysis of MOOCs have become an essential part of the research in this area. This study aims to explore the MOOCs in detail for better understanding its content, purpose and primarily drop out issues. The researcher conducted an online questionnaire to get perceptions of graduate students on their learning experiences in MOOCs and arranged a semi- structured oral interview with some participants. The participants are Turkish graduate level students doing their MA and Ph.D. in various programs. The findings show that participants are more likely to drop out courses due to lack of time and lack of pressure.Keywords: distance education, MOOCs, drop out, perception of graduate students
Procedia PDF Downloads 2353778 StockTwits Sentiment Analysis on Stock Price Prediction
Authors: Min Chen, Rubi Gupta
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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing
Procedia PDF Downloads 1463777 A Comprehensive Survey of Artificial Intelligence and Machine Learning Approaches across Distinct Phases of Wildland Fire Management
Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran
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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities
Procedia PDF Downloads 633776 Adaption Model for Building Agile Pronunciation Dictionaries Using Phonemic Distance Measurements
Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath
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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.Keywords: pronunciation variations, dynamic programming, machine learning, natural language processing
Procedia PDF Downloads 1663775 Improving the Training for Civil Engineers by Introducing Virtual Reality Technique
Authors: Manar Al-Ateeq
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The building construction industry plays a major role in the economy of the word and the state of Kuwait. This paper evaluates existing new civil site engineers, describes a new system for improvement and insures the importance of prequalifying and developing for new engineers. In order to have a strong base in engineering, educational institutes and workplaces should be responsible to continuously train engineers and update them with new methods and techniques in engineering. As to achieve that, school of engineering should constantly update computational resources to be used in the professions. A survey was prepared for graduated Engineers based on stated objectives to understand the status of graduate engineers in both the public and private sector. Interviews were made with different sectors in Kuwait, and several visits were made to different training centers within different workplaces in Kuwait to evaluate training process and try to improve it. Virtual Reality (VR) technology could be applied as a complement to three-dimensional (3D) modeling, leading to better communication whether in job training, in education or in professional practice. Techniques of 3D modeling and VR can be applied to develop the models related to the construction process. The 3D models can support rehabilitation design as it can be considered as a great tool for monitoring failure and defaults in structures; also it can support decisions based on the visual analyses of alternative solutions. Therefore, teaching computer-aided design (CAD) and VR techniques in school will help engineering students in order to prepare them to site work and also will assist them to consider these technologies as important supports in their later professional practice. This teaching technique will show how the construction works developed, allow the visual simulation of progression of each type of work and help them to know more about the necessary equipment needed for tasks and how it works on site.Keywords: three dimensional modeling (3DM), civil engineers (CE), professional practice (PP), virtual reality (VR)
Procedia PDF Downloads 1673774 The Effect of Aerobics Course on Fitness Ability of the University Students
Authors: Hui-Fang Lee, Hsuan-Jung Hsieh, Wen-Chi Lu, Meng-Chu Liu
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The purpose of the study was to examine abnormal BMI students of Chien Hsin University of Science and Technology, implement teaching aerobics course through elementary and advanced curriculum design, dietary education and three-day dietary record, analyze participant fitness improvement, an 10-week course as well as pre-test and post-test were carried out to evaluate the effect of the aerobics course on the fitness ability. The actual participate elementary and advanced courses each of 40 people, with low participation deduction course unfinished fitness testing, access to elementary curriculum valid samples 35 (87.5%) people, advanced courses valid samples 38(95%) people, 16 students participated in two consecutive courses. The fitness activities included sit-bending, one-minute sit-ups, standing long jump, and three minutes to board the stage. Analysis and comparison to the average three-day dietary record difference, an independent samples t-test was conducted to analyze the differences in the four activities between pre-test and post-test. The results showed that the elementary course had significant effects on females’ sit-bending and one minute sit-ups, the females also had high fat intake in three-day dietary record. The advanced course had significant effects on males’ sit-bending and on females’ BMI, sit-bending and standing long jump, males and females in three-day dietary record carbohydrate intake slightly low, slightly higher protein and fat intake. In conclusion, aerobics course teaching, dietary education and three-day, dietary record implementation can significantly enhance the physical fitness indicators, and continued to participate in advanced courses better. In the practice of sport should be the future course planning elementary and advanced courses, while introducing dietary education, achieve concrete results in improving physical fitness.Keywords: physical fitness, aerobics course, dietary education, three-day dietary record
Procedia PDF Downloads 3113773 Forensic Analysis of Thumbnail Images in Windows 10
Authors: George Kurian, Hongmei Chi
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Digital evidence plays a critical role in most legal investigations. In many cases, thumbnail databases show important information in that investigation. The probability of having digital evidence retrieved from a computer or smart device has increased, even though the previous user removed data and deleted apps on those devices. Due to the increase in digital forensics, the ability to store residual information from various thumbnail applications has improved. This paper will focus on investigating thumbnail information from Windows 10. Thumbnail images of interest in forensic investigations may be intact even when the original pictures have been deleted. It is our research goal to recover useful information from thumbnails. In this research project, we use various forensics tools to collect left thumbnail information from deleted videos or pictures. We examine and describe the various thumbnail sources in Windows and propose a methodology for thumbnail collection and analysis from laptops or desktops. A machine learning algorithm is adopted to help speed up content from thumbnail pictures.Keywords: digital forensic, forensic tools, soundness, thumbnail, machine learning, OCR
Procedia PDF Downloads 1213772 Design and Implementation of an AI-Enabled Task Assistance and Management System
Authors: Arun Prasad Jaganathan
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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization
Procedia PDF Downloads 473771 Using Balanced Scorecard Performance Metrics in Gauging the Delivery of Stakeholder Value in Higher Education: the Assimilation of Industry Certifications within a Business Program Curriculum
Authors: Thomas J. Bell III
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This paper explores the value of assimilating certification training within a traditional course curriculum. This innovative approach is believed to increase stakeholder value within the Computer Information System program at Texas Wesleyan University. Stakeholder value is obtained from increased job marketability and critical thinking skills that create employment-ready graduates. This paper views value as first developing the capability to earn an industry-recognized certification, which provides the student with more job placement compatibility while allowing the use of critical thinking skills in a liberal arts business program. Graduates with industry-based credentials are often given preference in the hiring process, particularly in the information technology sector. And without a pioneering curriculum that better prepares students for an ever-changing employment market, its educational value is dubiously questioned. Since certifications are trending in the hiring process, academic programs should explore the viability of incorporating certification training into teaching pedagogy and courses curriculum. This study will examine the use of the balanced scorecard across four performance dimensions (financial, customer, internal process, and innovation) to measure the stakeholder value of certification training within a traditional course curriculum. The balanced scorecard as a strategic management tool may provide insight for leveraging resource prioritization and decisions needed to achieve various curriculum objectives and long-term value while meeting multiple stakeholders' needs, such as students, universities, faculty, and administrators. The research methodology will consist of quantitative analysis that includes (1) surveying over one-hundred students in the CIS program to learn what factor(s) contributed to their certification exam success or failure, (2) interviewing representatives from the Texas Workforce Commission to identify the employment needs and trends in the North Texas (Dallas/Fort Worth) area, (3) reviewing notable Workforce Innovation and Opportunity Act publications on training trends across several local business sectors, and (4) analyzing control variables to identify specific correlations between industry alignment and job placement to determine if a correlation exists. These findings may provide helpful insight into impactful pedagogical teaching techniques and curriculum that positively contribute to certification credentialing success. And should these industry-certified students land industry-related jobs that correlate with their certification credential value, arguably, stakeholder value has been realized.Keywords: certification exam teaching pedagogy, exam preparation, testing techniques, exam study tips, passing certification exams, embedding industry certification and curriculum alignment, balanced scorecard performance evaluation
Procedia PDF Downloads 1003770 Deep Learning for Renewable Power Forecasting: An Approach Using LSTM Neural Networks
Authors: Fazıl Gökgöz, Fahrettin Filiz
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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.Keywords: deep learning, long short term memory, energy, renewable energy load forecasting
Procedia PDF Downloads 2583769 Mobile Technology Use by People with Learning Disabilities: A Qualitative Study
Authors: Peter Williams
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Mobile digital technology, in the form of smart phones, tablets, laptops and their accompanying functionality/apps etc., is becoming ever more used by people with Learning Disabilities (LD) - for entertainment, to communicate and socialize, and enjoy self-expression. Despite this, there has been very little research into the experiences of such technology by this cohort, it’s role in articulating personal identity and self-advocacy and the barriers encountered in negotiating technology in everyday life. The proposed talk describes research funded by the British Academy addressing these issues. It aims to explore: i) the experiences of people with LD in using mobile technology in their everyday lives – the benefits, in terms of entertainment, self-expression and socialising, and possible greater autonomy; and the barriers, such as accessibility or usability issues, privacy or vulnerability concerns etc. ii) how the technology, and in particular the software/apps and interfaces, can be improved to enable the greater access to entertainment, information, communication and other benefits it can offer. It is also hoped that results will inform parents, carers and other supporters regarding how they can use the technology with their charges. Rather than the project simply following the standard research procedure of gathering and analysing ‘data’ to which individual ‘research subjects’ have no access, people with Learning Disabilities (and their supporters) will help co-produce an accessible, annotated and hyperlinked living e-archive of their experiences. Involving people with LD as informants, contributors and, in effect, co-researchers will facilitate digital inclusion and empowerment. The project is working with approximately 80 adults of all ages who have ‘mild’ learning disabilities (people who are able to read basic texts and write simple sentences). A variety of methods is being used. Small groups of participants have engaged in simple discussions or storytelling about some aspect of technology (such as ‘when my phone saved me’ or ‘my digital photos’ etc.). Some individuals have been ‘interviewed’ at a PC, laptop or with a mobile device etc., and asked to demonstrate their usage and interests. Social media users have shown their Facebook pages, Pinterest uploads or other material – giving them an additional focus they have used to discuss their ‘digital’ lives. During these sessions, participants have recorded (or employed the researcher to record) their observations on to the e-archive. Parents, carers and other supporters are also being interviewed to explore their experiences of using mobile technology with the cohort, including any difficulties they have observed their charges having. The archive is supplemented with these observations. The presentation will outline the methods described above, highlighting some of the special considerations required when working inclusively with people with LD. It will describe some of the preliminary findings and demonstrate the e-archive with a commentary on the pages shown.Keywords: inclusive research, learning disabilities, methods, technology
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