Search results for: adaptive deep learning

Authors: A. M. Gsiea, J. P. Goss, P. R. Briddon, Ramadan. M. Al-habashi, K. M. Etmimi, Khaled. A. S. Marghani

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Using first-principles methods based on density functional theory and pseudopotentials, we have performed a details study of native defects in ZnO. Native point defects are unlikely to be cause of the unintentional n-type conductivity. Oxygen vacancies, which considered most often been invoked as shallow donors, have high formation energies in n-type ZnO, in edition are a deep donors. Zinc interstitials are shallow donors, with high formation energies in n-type ZnO, and thus unlikely to be responsible on their own for unintentional n-type conductivity under equilibrium conditions, as well as Zn antisites which have higher formation energies than zinc interstitials. Zinc vacancies are deep acceptors with low formation energies for n-type and in which case they will not play role in p-type coductivity of ZnO. Oxygen interstitials are stable in the form of electrically inactive split interstitials as well as deep acceptors at the octahedral interstitial site under n-type conditions. Our results may provide a guide to experimental studies of point defects in ZnO.

Keywords: DFT, native, n-type, ZnO

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Authors: Evgeny Pyshkin, Maxim Mozgovoy, Vladislav Volkov

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The paper follows a discourse on computer-assisted language learning. We examine problems of foreign language teaching and learning and introduce a metamodel that can be used to define learning models of language grammar structures in order to support teacher/student interaction. Special attention is paid to the concept of a virtual language lab. Our approach to language education assumes to encourage learners to experiment with a language and to learn by discovering patterns of grammatically correct structures created and managed by a language expert.

Keywords: computer-assisted instruction, language learning, natural language grammar models, HCI

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Authors: Zzibu Mark Brian

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As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

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Authors: Vijayan Narayananayar

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Identifying learning gaps is crucial in ensuring learners have the necessary knowledge and skills to succeed. The Learning and Teaching (L&T) approach requires tutors to identify gaps in knowledge and improvise learning activities to close them. One approach to identifying learning gaps is through diagnostic assessment, which uses well-structured questions and answer options. The paper focuses on the use of diagnostic assessment as a learning and teaching approach in a foundational module at a polytechnic. The study used diagnostic assessment over two semesters, including the COVID and post-COVID semesters, to identify gaps in learning. The design of the diagnostic activity, pedagogical intervention, and survey responses completed by learners were analyzed. Results showed that diagnostic assessment can be an effective tool for identifying learning gaps and designing interventions to address them. Additionally, the use of diagnostic assessment provides an opportunity for tutors to engage with learners on a one-to-one basis, tailoring teaching to individual needs. The paper also discusses the design of diagnostic questions and answer options, including characteristics that need to be considered in achieving the target of identifying learning gaps. The implications of using diagnostic assessment as a learning and teaching approach include bridging the gap between theory and practice, and ensuring learners are equipped with skills necessary for their future careers. This paper can be useful in helping educators and practitioners to incorporate diagnostic assessment into their L&T approach.

Keywords: assessment, learning & teaching, diagnostic assessment, analytics

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Authors: Annette McArthur

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The traditional model of teaching and learning, where ICT sits as a separate entity is not a model for a 21st century school. It is imperative that teaching and learning embraces technological advancements. The challenge in schools lies in shifting the mindset of teachers so they see ICT as integral to their teaching, learning and curriculum rather than a separate E-Learning curriculum stream. This research project investigates how the effective, planned, intentional integration of ICT into a STEM curriculum, can enable the shift in the teacher mindset. The project incorporated: • Developing a professional coaching relationship with key STEM teachers. • Facilitating staff professional development involving student centered project based learning pedagogy in the context of a STEM curriculum. • Facilitating staff professional development involving digital literacy. • Establishing a professional community where collaboration; sharing and reflection were part of the culture of the STEM community. • Facilitating classroom support for the effective delivery innovative STEM curriculum. • Developing STEM learning spaces where technologies were used to empower and engage learners to participate in student-centered, project-based learning.

Keywords: e-learning, ICT, project based learning, STEM

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Authors: Chechen Liao, Chin Yi Yang

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Although several factors that affect learning to program have been identified over the years, there continues to be no indication of any consensus in understanding why some students learn to program easily and quickly while others have difficulty. Seldom have researchers considered the problem of how to help the students enhance the programming learning outcome. The research had been conducted at a high school in Taiwan. Students participating in the study consist of 330 tenth grade students enrolled in the Basic Computer Concepts course with the same instructor. Two types of training methods-instruction-oriented and exploration-oriented were conducted. The result of this research shows that the instruction-oriented training method has better learning performance than exploration-oriented training method.

Keywords: learning performance, programming learning, TDD, training method

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Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

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When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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Authors: Nurul Yusnita

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Many studies have been developed to help the students to get good achievement in English learning. They can be from the teaching method or psychological ones. One of the psychological studies in educational research is learning style. In some ways, learning style can affect the achievement of the students. This study aimed to examine 4 (four) learning styles and their relations to English achievement among the students learning English in Language Training Center of Universitas Muhammadiyah Yogyakarta (LTC UMY). The method of this study was descriptive analytical. The sample consisted of 39 Accounting students in LTC UMY. The data was collected through questionnaires with Likert-scale. The achievement was obtained from the grade of the students. To analyze the questionnaires and to see the relation between the learning styles and the student achievement, SPSS statistical software of correlational analysis was used. The result showed that both visual and auditory had the same percentage of 35.9% (14 students). 3 students (7.7%) had kinaesthetic learning style and 8 students (20.5%) had visual and auditory ones. Meanwhile, there were 5 students (12.8%) who had visual learning style could increase their grades. Only 1 student (2.5%) who had visual and auditory could improve his grade. Besides grade increase, there were also grade decrease. Students with visual, auditory, visual and auditory, and kinaesthetic learning styles were 3 students (7.7%), 5 students (12%), 4 students (10.2%) and 1 student (2.5%) respectively. In conclusion, there was no significant relationship between learning style and English achievement. Most of the good achievers were the students with visual and auditory learning styles and most of them preferred visual method. The implication is the teachers and material designers could improve their method through visual things to achieve effective English teaching learning.

Keywords: accounting students, English achievement, language training centre, learning styles

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Authors: Bin Zhou, Tongli Lu, Jianwu Zhang, Hongtao Hao

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Dual clutch transmissions (DCT) offer a high comfort performance in terms of the gearshift. Hydraulic multi-disk clutches are the key components of DCT, its engagement determines the shifting comfort. The prefill of the clutches requests an initial engagement which the clutches just contact against each other but not transmit substantial torque from the engine, this initial clutch engagement point is called the touch point. Open-loop control is typically implemented for the clutch prefill, a lot of uncertainties, such as oil temperature and clutch wear, significantly affects the prefill, probably resulting in an inappropriate touch point. Underfill causes the engine flaring in gearshift while overfill arises clutch tying up, both deteriorating the shifting comfort of DCT. Therefore, it is important to enable an adaptive capacity for the clutch prefills regarding the uncertainties. In this paper, a dynamic model of the hydraulic actuator system is presented, including the variable force solenoid and clutch piston, and validated by a test. Subsequently, the open-loop clutch prefill is simulated based on the proposed model. Two control parameters of the prefill, fast fill time and stable fill pressure is analyzed with regard to the impact on the prefill. The former has great effects on the pressure transients, the latter directly influences the touch point. Finally, an adaptive method is proposed for the clutch prefill during gear shifting, in which clutch fill control parameters are adjusted adaptively and continually. The adaptive strategy is changing the stable fill pressure according to the current clutch slip during a gearshift, improving the next prefill process. The stable fill pressure is increased by means of the clutch slip while underfill and decreased with a constant value for overfill. The entire strategy is designed in the Simulink/Stateflow, and implemented in the transmission control unit with optimization. Road vehicle test results have shown the strategy realized its adaptive capability and proven it improves the shifting comfort.

Keywords: clutch prefill, clutch slip, dual clutch transmission, touch point, variable force solenoid

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Yen-Mei Lee

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Mobile learning (m-learning) has been applied in the education field not only because it is an alternative to web-based learning but also it possesses the ‘anytime, anywhere’ learning features. However, most studies focus on the technology-related issue, such as usability and functionality instead of addressing m-learning from the motivational perspective. Accordingly, the main purpose of the current paper is to integrate critical factors from different motivational theories and related findings to have a better understand the catalysts of an individual’s learning motivation toward m-learning. The main research question for this study is stated as follows: based on different motivational perspectives, what factors of applying mobile devices as medium can facilitate people’s learning motivations? Self-Determination Theory (SDT), Uses and Gratification Theory (UGT), Malone and Lepper’s taxonomy of intrinsic motivation theory, and different types of motivation concepts were discussed in the current paper. In line with the review of relevant studies, three motivating factors with five essential elements are proposed. The first key factor is autonomy. Learning on one’s own path and applying personalized format are two critical elements involved in the factor of autonomy. The second key factor is to apply a build-in instant feedback system during m-learning. The third factor is creating an interaction system, including communication and collaboration spaces. These three factors can enhance people’s learning motivations when applying mobile devices as medium toward learning. To sum up, in the currently proposed paper, with different motivational perspectives to discuss the m-learning is different from previous studies which are simply focused on the technical or functional design. Supported by different motivation theories, researchers can clearly understand how the mobile devices influence people’s leaning motivation. Moreover, instructional designers and educators can base on the proposed factors to build up their unique and efficient m-learning environments.

Keywords: autonomy, learning motivation, mobile learning (m-learning), motivational perspective

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: comparative genomics, adaptive evolution, bioinformatics, phylogenetics, genome mining

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Authors: Kornchanok Chidchaisuwan

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This project is a study of learning activities through experience to enhance career skills and technical abilities on the creative strategies for advertising course of undergraduate students. This instructional model consisted of study learning approaches: 1) Simulation-based learning: used to create virtual learning activities plans for work like working at advertising companies. 2) Project-based learning: Actual work based on the processed creating and focus on producing creative works to present on new media channels. The results of learning management found that there were effects on the students in various areas, including 1) The learners have experienced in the step by step of advertising work process. 2) The learner has the skills to work from the actual work (Learning by Doing), allowing the ability to create, present, and produce the campaign accomplished achievements and published on online media at a better level.

Keywords: technical, advertising, presentation, career skills, experience, simulation based learning

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Authors: Jatuphum Ketchatturat

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The objective of this research was to study the learning and innovation skills of undergraduate students after Research-Based Learning had been applied. Eighty research participants were selected from undergraduate students enrolled in Educational Research Program using the Purposive Sampling Method. Research Methodology was Descriptive Research, the research took one semester to complete. The research instruments consisted of (1) Research Skill Assessment Form, (2) Research Quality Assessment Form, (3) Scale of learning and innovation skills 25 items. The quantitative data were analysed using descriptive statistics including, frequency, percentage, average and standard deviation. The qualitative data were analyzed using content analysis. The research results were (1) The students were able to conduct research that focused on educational research, which has a fair to the excellent level of standards of a research learning outcome, research skills, and research quality. The student’s learning and innovation skills have relating to research skills and research quality. (2) The findings found that the students have been developed to be learning and innovation skills such as systematic thinking, analytical thinking, critical thinking, creative problem solving, collaborative, research-creation, communication, and knowledge and experience sharing to friends, community and society.

Keywords: learning and innovation skills, research based learning, research skills, undergraduate students

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Authors: Yaxian Chen, Yeonhee Park

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Driven by evolving FDA recommendations, modern clinical trials demand innovative designs that strike a balance between statistical rigor and ethical considerations. Covariate-adjusted response-adaptive (CARA) designs bridge this gap by utilizing patient attributes and responses to skew treatment allocation in favor of the treatment that is best for an individual patient’s profile. However, existing CARA designs for survival outcomes often hinge on specific parametric models, constraining their applicability in clinical practice. In this article, we address this limitation by introducing a CARA design for survival outcomes (CARAS) based on the Cox model and a variance estimator. This method addresses issues of model misspecification and enhances the flexibility of the design. We also propose a group sequential overlapweighted log-rank test to preserve type I error rate in the context of group sequential trials using extensive simulation studies to demonstrate the clinical benefit, statistical efficiency, and robustness to model misspecification of the proposed method compared to traditional randomized controlled trial designs and response-adaptive randomization designs.

Keywords: cox model, log-rank test, optimal allocation ratio, overlap weight, survival outcome

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Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Muhammad Afzal Malik

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Traditional Learning & Distance based learning are the two systems prevailing in Pakistan. These systems affect the level of education standard. The purpose of this study was to compare the traditional classroom learning and distance learning in Pakistan: (a) To explore the effectiveness of the traditional to Distance learning in Pakistan; (b) To identify the factors that affect traditional and distance learning. This review found that, on average, students in traditional classroom conditions performed better than those receiving education in and distance learning. The difference between student outcomes for traditional Classroom and distance learning classes —measured as the difference between treatment and control means, divided by the pooled standard deviation— was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. This research was conducted to highlight the impact of distance learning education system on education standard. The education standards were institutional support, course development, learning process, student support, faculty support, evaluation and assessment. A well developed questionnaire was administered and distributed among 26 faculty members of GCET, H-9 and Virtual University of Pakistan from each. Data was analyzed through correlation and regression analysis. Results confirmed that there is a significant relationship and impact of DLE system on education standards. This will also provide baseline for future research. It will add value to the existing body of knowledge.

Keywords: distance learning education, higher education, education standards, student performance

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Authors: Mohammed A. Zidan, Alaa Sagheer, Nasser Metwally

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Real-time learning is an important goal that most of artificial intelligence researches try to achieve it. There are a lot of problems and applications which require low cost learning such as learn a robot to be able to classify and recognize patterns in real time and real-time recall. In this contribution, we suggest a model of quantum competitive learning based on a series of quantum gates and additional operator. The proposed model enables to recognize any incomplete patterns, where we can increase the probability of recognizing the pattern at the expense of the undesired ones. Moreover, these undesired ones could be utilized as new patterns for the system. The proposed model is much better compared with classical approaches and more powerful than the current quantum competitive learning approaches.

Keywords: competitive learning, quantum gates, quantum gates, winner-take-all

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Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

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Artificial Intelligence (AI) in the Engineering, Procurement, and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Nevertheless, AI has become an attractive technology for organizations looking to automate repetitive tasks to reduce manual work. Meanwhile, the current AI market has started offering various solutions and services. The contribution of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to a specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Keywords: artificial intelligence, machine learning, deep learning, innovation, engineering, procurement and construction industry, AI in the EPC industry

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Authors: Majid Rezayi, Sh. Shahaboddin, HNM E. Mahmud, A. Yadollah, A. Saeid, A. Yatimah

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In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study.

Keywords: adaptive neuro fuzzy inference, PVC sensor, titanium (III) ions, Nerntian slope

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Authors: Fahri Benli, Sitalakshmi Venkartraman, Ye Wei, Fiona Wahr

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A flipped classroom which is a form of blended learning shifts the focus from a teacher-centered approach to a learner-centered approach. However, not all learners are ready to take the active role of knowledge and skill acquisition through a flipped classroom and they continue to delve in a passive mode of learning. This challenges educators in designing, scaffolding and facilitating in-class activities for students to have active learning experiences in a flipped classroom environment. Experiential learning theories have been employed by educators in the past in physical classrooms based on the principle that knowledge could be actively developed through direct experience. However, with more of online teaching witnessed recently, there are inherent limitations in designing and simulating an experiential learning activity for an online environment. In this paper, we explore enhancing experiential learning using smart digital tools that could be employed in a flipped classroom within a higher education setting. We present the use of smart collaborative tools online to enhance the experiential learning activity to teach higher-order cognitive concepts of business process modelling as a case study.

Keywords: experiential learning, flipped classroom, smart software tools, online learning higher-order learning attributes

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

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Authors: Heba Mustafa Abdullah

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The research aimed at examining the effect of a using a quest based learning strategy on developing EFL Pre-Master Students. The study adopted the quasi-experimental design. The sample of the research consists of a group of 30 students enrolled in Pre-Master program, Curriculum and EFL Instruction Department, Faculty of Graduate Studies in Education Tools of the study included a EFL research skills checklist and EFL research skills test. Results revealed that there were statistically significant differences at 0.01 levels with regard to some research skills. Results were discussed in relation to several factors that affected the language learning process. Finally, the research provided beneficial contributions in relation to manipulating e-learning technologies in general and Quest based learning strategy in particular with respect to EFL research skills.

Keywords: English as foreign language, e-Learning, research skills, quest based learning

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Authors: Precious Ehiomogue, Ifechukwude Israel Ahuchaogu, Isiguzo Edwin Ahaneku

Abstract:

Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Several works have verified the adsorption of toxic metals using vermicompost but the application is still scarce for the retention of organic compounds. This research brings to knowledge the effectiveness of earthworm waste (vermicompost) for the remediation of crude oil contaminated soils. The remediation methods adopted in this study were two soil washing methods namely, batch and column process which represent laboratory and in-situ remediation. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental results. This study also investigated the application of machine learning models [Artificial neural network (ANN), Adaptive neuro fuzzy inference system (ANFIS). ANN and ANFIS were evaluated using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency obtained from the Box-Behnken design experiment ranged from 29% to 98.9% for batch process remediation. Optimization of the experimental factors carried out using numerical optimization techniques by applying desirability function method of the response surface methodology (RSM) produce the highest removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively, showing the agreement between experimental and predicted results. For batch and column precess, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between experimental and projected findings. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. It can be concluded that machine learning models can predict the removal of crude oil from polluted soil using vermicompost. Therefore, it is recommended to use machines learning models to predict the removal of crude oil from contaminated soil using vermicompost.

Keywords: ANFIS, ANN, crude-oil, contaminated soil, remediation and vermicompost

Procedia PDF Downloads 111

Authors: Gunmala Suri

Abstract:

The purpose of this study was to investigate the relationship between various dimensions of Knowledge Management and Learning Organizations. On the basis of the dimensions of Learning Organization, Hypothesis were formulated. Knowledge Management (KM) is taken as the independent variable and Learning Organization (LO) as a dependent variable. KM had 5 dimensions and LO had 7. For this study, a total of 92 participants took part and answered the questionnaire. The respondents were selected using Judgemental and Snowball sampling. The respondents were from SMEs in and around Chandigarh. SPSS was used to for the data analysis purposes. The results showed that the dimensions of KM had a positive influence on the dimensions of LO. The hypothesis were accepted.

Keywords: knowledge management leadership, knowledge management, learning organization, knowledge management culture

Procedia PDF Downloads 418

Authors: Erteki̇n Kotbaş

Abstract:

Teaching English as a Foreign Language (EFL) is on the front burner of many countries in the world, in particular for English language teaching departments that train EFL teachers. Under the head of motivational theories in foreign language education, there are numerous researches in literature. However; researches comprising English language self-efficacy and teachers’ learning goal orientation which has a positive impact on learning teachings skills are scarce. Examination of these English language self-efficacy beliefs and learning goal orientations of pre-service EFL teachers may broaden the horizons, considering the importance of self-efficacy and goal orientation on learning and teaching activities. At this juncture, present study aims to investigate the strong relationship between English language self efficacy and teachers’ learning goal orientation from Turkish context in addition to teacher students’ grade factor.

Keywords: English language, learning goal orientation, self efficacy, pre-service teachers

Procedia PDF Downloads 462

Authors: Heba Mustafa Abdullah

Abstract:

The research aimed at examining the effect of using a suggested blended learning (BL) strategy on developing EFL pre- intermediate students. The study adopted the quasi-experimental design. The sample of the research consisted of a group of 26 EFL pre- intermediate students. Tools of the study included a listening comprehension checklist and a pre-post listening comprehension test. Results were discussed in relation to several factors that affected the language learning process. Finally, the research provided beneficial contributions in relation to manipulating BL strategy with respect to language learning process in general and oral language learning in particular.

Keywords: blended learning, english as a foreign language, listening comprehension, oral language instruction

Procedia PDF Downloads 561