Search results for: quest based learning

Authors: Mohammad Ali Alavidoost, Hossein Bozorgian

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Criterion-referenced tests (CRTs) are designed to measure student performance against a fixed set of predetermined criteria or learning standards. The reliability of such tests cannot be based on internal reliability. Threshold loss agreement is one way to calculate the reliability of CRTs. However, the selection of master and non-master in such agreement is determined by the threshold point. The problem is if the threshold point witnesses a minute change, the selection of master and non-master may have a drastic change, leading to the change in reliability results. Therefore, in this study, the Fuzzy logic approach is employed as a remedial procedure for data analysis to obviate the threshold point problem. Forty-one Iranian students were selected; the participants were all between 20 and 30 years old. A quantitative approach was used to address the research questions. In doing so, a quasi-experimental design was utilized since the selection of the participants was not randomized. Based on the Fuzzy logic approach, the threshold point would be more stable during the analysis, resulting in rather constant reliability results and more precise assessment.

Keywords: criterion-referenced tests, threshold loss agreement, threshold point, fuzzy logic approach

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Authors: Osagiede Mercy Afe

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After the internet explosion in the 90’s, Technology was immediately integrated into the school system. Technology which symbolizes advancement in human knowledge was seen as a setback by many educators many efforts have been made to help stem this erroneous believes and help educators realize the benefits of technology and ways of implementing it in the classrooms especially in the sciences. This advancement created a constantly expanding gap between the pupil’s perception on the use of technology within the learning atmosphere and the teacher’s perception and limitations hence the focus of this paper is on the need to refocus on the potentials of Science and Technology in enhancing children learning at school especially in science for sustainable development in Nigeria. The paper recommended measures for facilitating the sustenance of science and technology in Nigerian schools so as to enhance the potentials of our children in Science and Technology for a better tomorrow.

Keywords: children, information communication technology (ICT), potentials, sustainable development, science education

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Almaz R. Gazizov, Sergey S. Kharintsev, Myakzyum Kh. Salakhov

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This work deals with background signal suppression in tip-enhanced near-field optical microscopy (TENOM). The background appears because an optical signal is detected not only from the subwavelength area beneath the tip but also from a wider diffraction-limited area of laser’s waist that might contain another substance. The background can be reduced by using a taper probe with a grating on its lateral surface where an external illumination causes surface plasmon excitation. It requires the grating with parameters perfectly matched with a given incident light for effective light coupling. This work is devoted to an analysis of the light-grating coupling and a quest of grating parameters to enhance a near-field light beneath the tip apex. The aim of this work is to find the figure of merit of plasmon excitation depending on grating period and location of grating in respect to the apex. In our consideration the metallic grating on the lateral surface of the tapered plasmonic probe is illuminated by a plane wave, the electric field is perpendicular to the sample surface. Theoretical model of efficiency of plasmon excitation and propagation toward the apex is tested by fdtd-based numerical simulation. An electric field of the incident light is enhanced on the grating by every single slit due to lightning rod effect. Hence, grating causes amplitude and phase modulation of the incident field in various ways depending on geometry and material of grating. The phase-modulating grating on the probe is a sort of metasurface that provides manipulation by spatial frequencies of the incident field. The spatial frequency-dependent electric field is found from the angular spectrum decomposition. If one of the components satisfies the phase-matching condition then one can readily calculate the figure of merit of plasmon excitation, defined as a ratio of the intensities of the surface mode and the incident light. During propagation towards the apex, surface wave undergoes losses in probe material, radiation losses, and mode compression. There is an optimal location of the grating in respect to the apex. One finds the value by matching quadratic law of mode compression and the exponential law of light extinction. Finally, performed theoretical analysis and numerical simulations of plasmon excitation demonstrate that various surface waves can be effectively excited by using the overtones of a period of the grating or by phase modulation of the incident field. The gratings with such periods are easy to fabricate. Tapered probe with the grating effectively enhances and localizes the incident field at the sample.

Keywords: angular spectrum decomposition, efficiency, grating, surface plasmon, taper nanoantenna

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Authors: A. Pedro, T. Goldschmidt

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Socio-emotional development is considered to be an essential prerequisite for school readiness. To our best knowledge, research on socio-emotional development specifically from the views of teachers in the South African context is limited. This study explored preschool teachers’ understanding of the role that socio-emotional development plays in preparing the child for school. Using the social learning theory, a qualitative approach with an exploratory design was used for the study. A total of 12 preschool teachers from both community-based and school-based preschools were purposively recruited. Upon receiving ethics clearance from the University of the Western Cape and the Western Cape Education Department, semi-structured interviews were conducted and analysed by utilizing Braun and Clarke’s (2006) six phases of thematic analysis. Participants’ rights, anonymity, and confidentiality were upheld throughout the research process. Findings reveal that preschool teachers emphasise the importance of holistic development for school readiness. Teachers deemed socio-emotional development as absolutely crucial for preparing children for school as it eases the transition to formal schooling and adaptation to the classroom environment.

Keywords: early childhood, preschool teachers, school readiness, socio-emotional development

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Karthick Prasad Gunasekaran, B. Chase Babrich, Saurabh Shirodkar, Hee Hwang

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Construction preparation is crucial for the success of a construction project. By involving project participants early in the construction phase, project managers can plan ahead and resolve issues early, resulting in project success and satisfaction. This study uses quantitative data from construction management projects to determine the relationship between the pre-construction phase, construction schedule, and customer satisfaction. This study examined a total of 65 construction projects and 93 clients per job to (a) identify the relationship between the pre-construction phase and program reduction and (b) the pre-construction phase and customer retention. Based on a quantitative analysis, this study found a negative correlation between pre-construction status and project schedule in 65 construction projects. This finding means that the more preparatory work done on a particular project, the shorter the total construction time. The Net Promoter Score of 93 clients from 65 projects was then used to determine the relationship between construction preparation and client satisfaction. The pre-construction status and the projects were further analyzed, and a positive correlation between them was found. This shows that customers are happier with projects with a higher ready-to-build ratio than projects with less ready-to-build.

Keywords: NLP, BERT, LLM, deep learning, classification

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Shavindrie Cooray

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It is vital that students are supported in having balanced conversations about topics that might be controversial. This process is crucial to the development of critical thinking skills. This can be difficult to attain in e-learning environments, with some research finding students report a perceived loss in the quality of knowledge exchange and performance. This research investigated if Systems Theory could be applied to structure the discussion, improve information sharing, and reduce conflicts when students are working in online environments. This research involved 160 participants across four categories of student groups at a college in the Northeastern US. Each group was provided with a shared problem, and each group was expected to make a proposal for a solution. Two groups worked face-to-face; the first face to face group engaged with the problem and each other with no intervention from a facilitator; a second face to face group worked on the problem using Systems tools to facilitate problem structuring, group discussion, and decision-making. There were two types of virtual teams. The first virtual group also used Systems tools to facilitate problem structuring and group discussion. However, all interactions were conducted in a synchronous virtual environment. The second type of virtual team also met in real time but worked with no intervention. Findings from the study demonstrated that the teams (both virtual and face-to-face) using Systems tools shared more information with each other than the other teams; additionally, these teams reported an increased level of disagreement amongst their members, but also expressed more confidence and satisfaction with the experience and resulting decision compared to the other groups.

Keywords: e-learning, virtual teams, systems approach, conflicts

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Authors: Endina P. Purwandari, Yolanda Hervianti, Feri Noperman, Endang W. Winarni

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The earthquakes disaster is an event that can threaten at any moment and cause damage and loss of life. Game earthquake disaster mitigation is a useful educational game to enhance children insight, knowledge, and understanding in the response to the impact of the earthquake. This study aims to build an educational games application on the Android platform as a learning media for earthquake mitigation education and to determine the effect of the application toward children understanding of the earthquake disaster mitigation. The methods were research and development. The development was to develop edugame application for earthquakes mitigation education. The research involved elementary students as a research sample to test the developed application. The research results were valid android-based edugame application, and its the effect of application toward children understanding. The application contains an earthquake simulation video, an earthquake mitigation video, and a game consisting three stages, namely before the earthquake, when the earthquake occur, and after the earthquake. The results of the feasibility test application showed that this application was included in the category of 'Excellent' which the average percentage of the operation of applications by 76%, view application by 67% and contents of application by 74%. The test results of students' responses were 80% that showed that a positive their responses toward the application. The student understanding test results show that the average score of children understanding pretest was 71,33, and post-test was 97,00. T-test result showed that t value by 8,02 more than table t by 2,001. This indicated that the earthquakes disaster mitigation edugame application based on Android platform affects the children understanding about disaster earthquake mitigation.

Keywords: android, edugame, mitigation, earthquakes

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Authors: Li Jiuen Ong, Magdalin Cheong, Sri Rahayu, Lek Alexander, Pei Ting Tan

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Background: Previous programme evaluations from the diabetes training programme conducted in Changi General Hospital revealed that healthcare professionals (HCPs) are keen to receive advance diabetes training and education, specifically in medical, nutritional therapy. HCPs also expressed a preference for interactive activities over didactic teaching methods to enhance their learning. Since the War on Diabetes was initiated by MOH in 2016, HCPs are challenged to be actively involved in continuous education to be better equipped to reduce the growing burden of diabetes. Hence, streamlining training to incorporate an element of fun is of utmost importance. Aim: The nutrition programme incorporates game play using an interactive board game that aims to provide a more conducive and less stressful environment for learning. The board game could be adapted for training of community HCPs, health ambassadors or caregivers to cope with the increasing demand of diabetes care in the hospital and community setting. Methodology: Stages for game’s conception (Jaffe, 2001) were adopted in the development of the interactive board game ‘Sweet Score™ ’ Nutrition concepts and topics in diabetes self-management are embedded into the game elements of varying levels of difficulty (‘Easy,’ ‘Medium,’ ‘Hard’) including activities such as a) Drawing/ sculpting (Pictionary-like) b)Facts/ Knowledge (MCQs/ True or False) Word definition) c) Performing/ Charades To study the effects of game play on knowledge acquisition and perceived experiences, participants were randomised into two groups, i.e., lecture group (control) and game group (intervention), to test the difference. Results: Participants in both groups (control group, n= 14; intervention group, n= 13) attempted a pre and post workshop quiz to assess the effectiveness of knowledge acquisition. The scores were analysed using paired T-test. There was an improvement of quiz scores after attending the game play (mean difference: 4.3, SD: 2.0, P<0.001) and the lecture (mean difference: 3.4, SD: 2.1, P<0.001). However, there was no significance difference in the improvement of quiz scores between gameplay and lecture (mean difference: 0.9, 95%CI: -0.8 to 2.5, P=0.280). This suggests that gameplay may be as effective as a lecture in terms of knowledge transfer. All the13 HCPs who participated in the game rated 4 out of 5 on the likert scale for the favourable learning experience and relevance of learning to their job, whereas only 8 out of 14 HCPs in the lecture reported a high rating in both aspects. 16. Conclusion: There is no known board game currently designed for diabetes training for HCPs.Evaluative data from future training can provide insights and direction to improve the game format and cover other aspects of diabetes management such as self-care, exercise, medications and insulin management. Further testing of the board game to ensure learning objectives are met is important and can assist in the development of awell-designed digital game as an alternative training approach during the COVID-19 pandemic. Learning through gameplay increases opportunities for HCPs to bond, interact and learn through games in a relaxed social setting and potentially brings more joy to the workplace.

Keywords: active learning, game, diabetes, nutrition

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Shivani Singla, Om Prakash Pandey, Gopi Sharma

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Metallic nanoparticle doped glasses has lead to rapid development in the field of optics. Large third order non-linearity, ultrafast time response, and a wide range of resonant absorption frequencies make these metallic nanoparticles more important in comparison to their bulk material. All these properties are highly dependent upon the size, shape, and surrounding environment of the nanoparticles. In a quest to find a suitable material for optical applications, several efforts have been devoted to improve the properties of such glasses in the past. In the present study, bismuth borate glass doped with different size gold nanoparticles (AuNPs) has been prepared using the conventional melt-quench technique. Synthesized glasses are characterized by X-ray diffraction (XRD) and Fourier Transformation Infrared spectroscopy (FTIR) to observe the structural modification in the glassy matrix with the variation in the size of the AuNPs. Glasses remain purely amorphous in nature even after the addition of AuNPs, whereas FTIR proposes that the main structure contains BO₃ and BO₄ units. Field emission scanning electron microscopy (FESEM) confirms the existence and variation in the size of AuNPs. Differential thermal analysis (DTA) depicts that prepared glasses are thermally stable and are highly suitable for the fabrication of optical fibers. The nonlinear optical parameters (nonlinear absorption coefficient and nonlinear refractive index) are calculated out by using the Z-scan technique with a Ti: sapphire laser at 800 nm. It has been concluded that the size of the nanoparticles highly influences the structural thermal and optical properties system.

Keywords: bismuth borate glass, different size, gold nanoparticles, nonlinearity

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Authors: Mohsine Jebbour

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Motivation and classroom interaction are of prime significance for second/foreign language learning to take place effectively. Thus, a considerable amount of motivation and classroom interaction helps ensure students’ success in and continuation of learning the TL. One way to enhance students’ motivation and classroom interaction in the Moroccan EFL classroom then is through the use of self-disclosure. For the purposes of this study, self-disclosure has been defined as the verbal communication of positive personal information including opinions, feelings, experiences, family and friendship stories to classmates and teachers. This paper is meant to demonstrate that positive self-disclosure can serve as an effective tool for helping students develop favorable attitudes toward the EFL classroom (i.e., English courses, teacher of English, and classroom activities) and promoting their intrinsic motivation (IM to know and IM toward stimulation). A further objective is that since self-disclosure is reciprocal, when teachers of English reveal their personal information, students will uncover their personal matters in return. This will help ensure effective classroom participation, foster teacher-student communication, and encourage students to practice and hence improve their oral proficiency (i.e., the speaking skill). A questionnaire was used to collect data in this study. 164 undergraduate students (99 females and 65 males) from the department of English at the faculty of letters and humanities, Dher el Mehraz, Sidi Mohammed Ben Abd Allah University completed a questionnaire that assessed self-disclosure in relation to motivation (i.e., attitudes toward the learning situation and intrinsic motivation) and classroom interaction (i.e., teacher-student interaction, participation, and out-of-class communication) on a 1 to 5 scale with (1) Strongly Disagree and (5) Strongly Agree. The level of agreement on the positive dimension of self-disclosure was ranked first by the respondents. The hypothesis set at the very beginning of the study, which posited that positive self-disclosure is essential to enhancing motivation and classroom interaction in the EFL context, was confirmed. In this regard, the findings suggest that implementing self-disclosure in the Moroccan EFL classroom may serve as an effective tool to have positive affect of teacher, class and classroom activities. This in turn will encourage the learners to attend classes, enjoy the language learning activity, complete classroom assignments, participate in class discussions, and interact with their teachers and classmates. It is hoped that teachers benefit from the results of this study and hence encourage the use of positive self-disclosure to develop English language learning in the Moroccan context where opportunities of using English outside the classroom are limited.

Keywords: EFL classroom, classroom interaction, motivation, self-disclosure

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Meruyert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova, Madina Ermaganbetova

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This research paper explores the process of creating a proctoring system by evaluating the implementation of practical face recognition algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As an outcome, a proctoring system will be created, enabling the conduction of tests and ensuring academic integrity checks within the system. Due to the correct operation of the system, test works are carried out. The result of the creation of the proctoring system will be the basis for the automation of the informational, educational portal developed by machine learning.

Keywords: artificial intelligence, education portal, face recognition, machine learning, proctoring

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Authors: Mahdi Bazarganigilani

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Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

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Authors: Halilu Babaji, Adamu Buba

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The proliferation of small arms and light weapons (SALW) and its illicit trafficking in West Africa and Nigeria in particular, pose a major threat to peace, security and development in the Sub-region. The high circulation of these weapons in the region is a product of the interplay of several factors, which derives principally from the internal socio-economic and political dynamics compounded by globalization. The process of globalization has congealed both time and space making it easier for ideas, goods, persons, services, information, products and money to move across borders with fewer restrictions. And this has a negative effect in the entire region making it easier for arms, ammunition, insurgents, criminal and drugs to flow within national boundaries. The failure of public security in most parts of Nigeria has lead communities to indulge in different forms of ‘self-help ‘security measures, ranging from vigilante groups to community-owned arms stockpiling. Having lost confidence in the Nigerian state, parties to some of these conflicts have become entangled in a security dilemma. The quest to procure more arms to guarantee personal and community protection from perceived and real enemies is fuelling the ‘domestic arms race ‘. Therefore, as small arms remain-and proliferate – development is impeded. The impact of SALW on economic well being and national development in Nigeria is of vast significant. Therefore the need to collect these arms in circulation in Nigeria particularly the volatile area of North-east is of very important. This will hopefully contribute to government effort in building a free, secured and peaceful society.

Keywords: arms, development, proliferation, security

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Authors: Nuhu Bitrus Mailabari

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Nigeria is a highly multifarious nation trapped between affluence and affliction. On one hand, the state has vast territorial size, economic strength, relative internal cohesion, and good external linkages. On the other, it is bedeviled with enormous challenges. It is common knowledge that the North-East geo-political zone has suffered colossal destruction for the most part of the last ten years due to the activities of the insurgent group Boko Haram. Several factors (political, economic, religious, socio-cultural) have been credited with the heightened insecurity in the region. Without a doubt, the security crisis in the region has rekindled several discussions critical to Nigeria’s security architecture. However, the debate on finding an enduring solution to the devastation in the North East continually neglects the nexus between cross border migration and national security. Using content analysis, this paper debates two main issues that continue to affect security in the North East. One, the cumulative impact of the Economic Community of West African States (ECOWAS) protocol on the free movement of people and goods. Two, the porous nature of Nigeria’s borders. Theoretically, the paper will rely on the systems theory because of its broad focus on structure, linkage, and process. The work concludes in twofold. First, that cross border migration and poor border management processes further worsened the political and socio-economic conditions of a region that is already in a bad state. Secondly, in addition to the existing strategies, Nigeria must develop a holistic approach including new methods of handling cross border movements in solving the security issues.

Keywords: border, cross border, migration, Nigeria, northeast region, security

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Authors: Assel Jaxylykova, Alexnder Pak

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This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits.

Keywords: named entity recognition, natural language processing, self-organizing map, CONLL-2003, semantics

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Authors: Sahithi Yarlagadda

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The design of antenna is constrained by mathematical and geometrical parameters. Though there are diverse antenna structures with wide range of feeds yet, there are many geometries to be tried, which cannot be customized into predefined computational methods. The antenna design and optimization qualify to apply evolutionary algorithmic approach since the antenna parameters weights dependent on geometric characteristics directly. The evolutionary algorithm can be explained simply for a given quality function to be maximized. We can randomly create a set of candidate solutions, elements of the function's domain, and apply the quality function as an abstract fitness measure. Based on this fitness, some of the better candidates are chosen to seed the next generation by applying recombination and permutation to them. In conventional approach, the quality function is unaltered for any iteration. But the antenna parameters and geometries are wide to fit into single function. So, the weight coefficients are obtained for all possible antenna electrical parameters and geometries; the variation is learnt by mining the data obtained for an optimized algorithm. The weight and covariant coefficients of corresponding parameters are logged for learning and future use as datasets. This paper drafts an approach to obtain the requirements to study and methodize the evolutionary approach to automated antenna design for our past work on Vivaldi antenna as test candidate. The antenna parameters like gain, directivity, etc. are directly caged by geometries, materials, and dimensions. The design equations are to be noted here and valuated for all possible conditions to get maxima and minima for given frequency band. The boundary conditions are thus obtained prior to implementation, easing the optimization. The implementation mainly aimed to study the practical computational, processing, and design complexities that incur while simulations. HFSS is chosen for simulations and results. MATLAB is used to generate the computations, combinations, and data logging. MATLAB is also used to apply machine learning algorithms and plotting the data to design the algorithm. The number of combinations is to be tested manually, so HFSS API is used to call HFSS functions from MATLAB itself. MATLAB parallel processing tool box is used to run multiple simulations in parallel. The aim is to develop an add-in to antenna design software like HFSS, CSTor, a standalone application to optimize pre-identified common parameters of wide range of antennas available. In this paper, we have used MATLAB to calculate Vivaldi antenna parameters like slot line characteristic impedance, impedance of stripline, slot line width, flare aperture size, dielectric and K means, and Hamming window are applied to obtain the best test parameters. HFSS API is used to calculate the radiation, bandwidth, directivity, and efficiency, and data is logged for applying the Evolutionary genetic algorithm in MATLAB. The paper demonstrates the computational weights and Machine Learning approach for automated antenna optimizing for Vivaldi antenna.

Keywords: machine learning, Vivaldi, evolutionary algorithm, genetic algorithm

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Authors: Usama Shahid

Abstract:

Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Jiangyong Liu, Xiangxiang Xu, Bote Luo, Xiaoxue Luo, Jiang Zhu, Lingzhi Yi

Abstract:

To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the Least Square Support Vector Machine optimized by an Improved Sparrow Search Algorithm combined with the Variational Mode Decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of Intrinsic Mode Functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the least Square Support Vector Machine. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy.

Keywords: load forecasting, variational mode decomposition, improved sparrow search algorithm, least square support vector machine

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

Abstract:

The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Dr. Rebecca Kimelman

Abstract:

Teaching students to self-advocate has become a central topic in special education literature and practice. However, many special education programs do not address this important skill area. To this end, I created and implemented the Self Advocacy Made Easy (S.A.M.E.) program of instruction, intended to enhance the self-advocacy skills of young adults with mild to moderate disabilities. The effectiveness of S.A.M.E., the degree to which self-advocacy skills were acquired and demonstrated by the students, the level of parental support, and the impact of culture on the process, and teachers’ beliefs and attitudes about the role of self-advocacy skills for their students were measured using action research that employed mixed methodology. Conducted at an overseas American International School, this action research study sought answers to these questions by providing an in-depth portrayal of the S.A.M.E. program, as well as the attitudes and perceptions of the stakeholders involved in the study (thirteen students, their parents, teachers and counsellors). The findings of this study were very positive. The S.A.M.E. program was found to be a valid and valuable instructional tool for teaching self-advocacy skills to students with learning disabilities and ADHD. The study showed participation in the S.A.M.E. program led to an increased understanding of the important elements of self-advocacy, an increase in students’ skills and abilities to self-advocate, and a positive increase in students’ feelings about themselves. Inclusion in the Student-Led IEP meetings, an authentic student assessment within the S.A.M.E. program, also yielded encouraging results, including a higher level of ownership of one’s profile and learning needs, a higher level of student engagement and participation in the IEP meeting, and a growing student awareness of the relevance of the document and the IEP process to their lives. Without exception, every parent believed that participating in the Student-Led IEP led to a growth in confidence in their children, including that it taught them how to ‘own’ their disability and an improvement in their communication skills. Teachers and counsellors that participated in the study felt the program was worthwhile, and led to an increase in the students’ ability to acknowledge their learning profile and to identify and request the accommodations (such as extended time or use of a calculator) they need to overcome or work around their disability. The implications for further research are many, and include an examination of the degree to which participation in S.A.M.E. fosters student achievement, the long-term effects of participation in the program, and the degree to which student participation in the Student-Led IEP meeting increases parents’ level of understanding and involvement.

Keywords: self-advocacy, learning disabilities, ADHD, student-led IEP process

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm

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Authors: Yi Meng, Yun Gao

Abstract:

Having spent significant periods studying and working in North America and Europe, we, as two Chinese art educators, have been profoundly shaped by both Eastern and Western cultures. Consequently, our ambition is to enrich students' learning experiences by delving into and merging both cultural perspectives for innovative, creative expressions. This exposition draws on our action research study on students' visual literacy practices in a visual literacy course at a prominent Chinese university. The central premise was to explore innovative art forms by cross-utilizing various aspects of diverse cultures. By examining distinct cultural elements, we encouraged students to break away from familiar approaches and forge new paths in their creative endeavors. In implementing our curriculum, we utilized a multimodal pedagogy that deviated from the predominant print-based presentations typically employed in our classroom settings. This pedagogical approach effectively encouraged students to critically analyze the artifact, imbue it with their understanding and perspectives, and then produce an original piece. This approach also motivated students to leverage the semiotic potential of various communicative modes to address diverse cultural issues through their multimodal designs. To demonstrate the potential for cultural amalgamation, we utilized the artwork of Hong Kong-based artist Tik Ka. His works epitomize the fusion of Chinese traditions with Western pop culture, which served as a visual and conceptual reference point for students. Seeing how these distinct cultural elements could coexist and enrich each other in Tik Ka's work was inspiring and motivating for the students. Taken together, these pedagogical strategies helped create a dialogical space where students could actively experience, analyze, and negotiate complex modes of expression. This environment fostered active learning, encouraging students to apply their knowledge, question their assumptions, and reconsider their perspectives. Overall, such a unique approach to visual literacy education has the potential to reshape students' understanding of both cultures. By encouraging them to critically engage with their multimodal designs, we promoted an in-depth, nuanced appreciation of these diverse cultural heritages. The students no longer just interpreted and replicated images—they actively contributed to a dynamic and ongoing conversation between cultures.

Keywords: multimodal pedagogy, creative expressions, visual literacy education, multimodal designs

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Authors: Bhaskar Basu

Abstract:

Universities and higher education institutes are finding it increasingly difficult to engage students fruitfully through traditional pedagogic tools. Web 2.0 technologies comprising social networking sites (SNSs) offer a platform for students to collaborate and share information, thereby enhancing their learning experience. Despite the potential and reach of SNSs, its use has been limited in academic settings promoting higher education. The purpose of this paper is to assess the perception of social networking sites among business school students in India and analyze its role in enhancing quality of student experiences in a business school leading to the proposal of an agenda for future research. In this study, more than 300 students of a reputed business school were involved in a survey of their preferences of different social networking sites and their perceptions and attitudes towards these sites. A questionnaire with three major sections was designed, validated and distributed among  a sample of students, the research method being descriptive in nature. Crucial questions were addressed to the students concerning time commitment, reasons for usage, nature of interaction on these sites, and the propensity to share information leading to direct and indirect modes of learning. It was further supplemented with focus group discussion to analyze the findings. The paper notes the resistance in the adoption of new technology by a section of business school faculty, who are staunch supporters of the classical “face-to-face” instruction. In conclusion, social networking sites like Facebook and LinkedIn provide new avenues for students to express themselves and to interact with one another. Universities could take advantage of the new ways  in which students are communicating with one another. Although interactive educational options such as Moodle exist, social networking sites are rarely used for academic purposes. Using this medium opens new ways of academically-oriented interactions where faculty could discover more about students' interests, and students, in turn, might express and develop more intellectual facets of their lives. hitherto unknown intellectual facets.  This study also throws up the enormous potential of mobile phones as a tool for “blended learning” in business schools going forward.

Keywords: business school, India, learning, social media, social networking, university

Procedia PDF Downloads 253