Search results for: computer assisted learning

Authors: M. V. A. Raju Bahubalendruni, Bibhuti Bhusan Biswal, B. B. V. L. Deepak

Abstract:

Achieving an appropriate assembly sequence needs deep verification for its physical feasibility. For this purpose, industrial engineers use several assembly predicates; namely, liaison, geometric feasibility, stability and mechanical feasibility. However, testing an assembly sequence for these predicates requires huge assembly information. Extracting such assembly information from an assembled product is a time consuming and highly skillful task with complex reasoning methods. In this paper, computer aided methods are proposed to extract all the necessary assembly information from computer aided design (CAD) environment in order to perform the assembly sequence planning efficiently. These methods use preliminary capabilities of three-dimensional solid modelling and assembly modelling methods used in CAD software considering equilibrium laws of physical bodies.

Keywords: assembly automation, assembly attributes, assembly, CAD

Procedia PDF Downloads 290

Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

Abstract:

Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

Procedia PDF Downloads 290

Authors: Abdelaaty A. Shahat, Mansour S. Alsaid

Abstract:

Four Acetylated flavonol glycosides were isolated from Centaurium spicatum (L.) Fritsch (Gentianaceae). Structure elucidation, especially the localization of the acetyl groups, and complete 1H and 13C NMR assignments of these biologically active compounds were carried out using one- and two-dimensional NMR methods, including CNMR, DEPT-135 and DEPT-90 and gradient-assisted experiments such as DQF-COSY, TOCSY, HSQC and HMBC experiments. The antioxidant activities of the new acetylated flavonoid glycosides using DPPH• assay were determined. The compounds tested showed a good DPPH• activity compared with control, but their activity was lower than that of their corresponding aglycone, quercetin.

Keywords: Centaurium spicatum, flavonoids, biological activity, isolation, glycosides

Procedia PDF Downloads 403

Authors: Nik Siti Hanifah Nik Ahmad

Abstract:

This research discuss about teaching cupping therapy or hijama by using an online 3D Virtual Learning Environment. The experimental platform was using of flash and Second Life as 2D and 3D comparison. 81 samples have been used in three experiments with 21 in the first and 30 in each second and third. The design of the presentation was tested in five categories such as effectiveness, ease of use, efficacy, aesthetic and users’ satisfaction. The results from three experiments had shown promising outcome for usage of the technique to be implement in teaching Cupping Therapy as well as other alternative or conventional medicine knowledge especially for training.

Keywords: medical and health, cupping therapy or hijama, second life, online 3D VLE, virtual worlds

Procedia PDF Downloads 416

Authors: Abdi Omar Shuriye

Abstract:

A social business is a business designed to address socio-economic problems to enhance the welfare of the communities involved. Lately, social business, with its focus on innovative ideas, is capturing the interest of educational institutions, governments, and non-governmental organizations. Social business uses a business model to achieve a social goal, and in the last few decades, the idea of imbuing social business into the education system of higher learning institutions has spurred much excitement. This is due to the belief that it will lead to job creation and increased social resilience. One of the higher learning institutions which have invested immensely in the idea is Albukhari International University; it is a private education institution, on a state-of-the-art campus, providing an advantageous learning ecosystem. The niche area of this institution is social business, and it graduates job creators, not job seekers; this Malaysian institution is unique and one of its kind. The objective of this paper is to develop a work plan, direction, and milestone as well as the focus area for the infusion of social business into higher learning institutions with special reference to Al-Bukhari International University. The purpose is to develop a prototype and model full-scale to enable higher learning education institutions to construct the desired curriculum fermented with social business. With this model, major predicaments faced by these institutions could be overcome. The paper sets forth an educational plan and will spell out the basic tenets of social business, focusing on the nature and implementational aspects of the curriculum. It will also evaluate the mechanisms applied by these educational institutions. Currently, since research in this area remains scarce, institutions adopt the process of experimenting with various methods to find the best way to reach the desired result on the matter. The author is of the opinion that social business in education is the main tool to educate holistic future leaders; hence educational institutions should inspire students in the classroom to start up their own businesses by adopting creative and proactive teaching methods. This proposed model is a contribution in that direction.

Keywords: social business, curriculum, skills, university

Procedia PDF Downloads 84

Authors: Davoud Masoumi, Maryam Bourbour

Abstract:

By characterizing children’s actions in the scaffolding process, which is often undermined and ignored in the studies reviewed, this study aimed to examine children’s different (re)actions in relation to the teachers’ actions in a context where digital technologies are used. Over five months, 22 children aged 4-6 with five preschool teachers were video observed. The study brought in rich details of the children’s actions in relation to the teacher’s actions in the scaffolding process. The findings of the study reveal thirteen (re)actions, including Giving short response; Explaining; Participating in the activities; Examining; Smiling and laughing; Pointing and showing; Working together; Challenging each other; Problem-solving skills; Developing vocabulary; Choosing the activity; Expressing of the emotions; and Identifying the similarities and differences. Our findings expanded and deepened the understanding of the scaffolding process, which can contribute to the notion of scaffolding and help us to gain further understanding about scaffolding of children’s learning. Characterizing the children’s (re)action in relation to teacher’s scaffolding actions further can contribute to ongoing discussions about how teachers can scaffold children’s learning using digital technologies in the learning process.

Keywords: children’ (re)actions, scaffolding process, technologies, preschools

Procedia PDF Downloads 75

Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

Abstract:

The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

Procedia PDF Downloads 156

Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

Abstract:

Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

Procedia PDF Downloads 63

Authors: Manjitsingh Rajput

Abstract:

Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

Procedia PDF Downloads 85

Authors: Si Chen, Quanhong Jiang

Abstract:

In the increasingly mature posture recognition technology, human movement information is widely used in sports rehabilitation, human-computer interaction, medical health, human posture assessment, and other fields today; this project uses the most original ideas; it is proposed to use the collection equipment for the collection of myoelectric data, reflect the muscle posture change on a degree of freedom through data processing, carry out data-muscle three-dimensional model joint adjustment, and realize basic pose recognition. Based on this, bionic aids or medical rehabilitation equipment can be further developed with the help of robotic arms and cutting-edge technology, which has a bright future and unlimited development space.

Keywords: pose recognition, 3D animation, electromyography, machine learning, bionics

Procedia PDF Downloads 68

Authors: B. Mahfoud, A. Bendjaghlouli

Abstract:

Natural convection is simulated in a truncated cone filled with nanofluid. Inclined and top walls have constant temperature where the heat source is located on the bottom wall of the conical container which is thermally insulated. A finite volume approach is used to solve the governing equations using the SIMPLE algorithm for different parameters such as Rayleigh number, inclination angle of inclined walls of the enclosure and heat source length. The results showed an enhancement in cooling system by using a nanofluid, when conduction regime is assisted. The inclination angle of inclined sidewall and heat source length affect the heat transfer rate and the maximum temperature.

Keywords: heat source, truncated cone, nanofluid, natural convection

Procedia PDF Downloads 304

Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

Abstract:

In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method Web-App auto-generated twin data structure replication. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi" has been developed. A special login form has been developed with a special instance of data validation; this verification process secures the web application from its early stages. The system has been tested and validated, up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database

Procedia PDF Downloads 143

Authors: Geoffrey Sinha

Abstract:

This paper is primarily addressed to teachers who stand on the threshold of bringing technology and new media into their classrooms. Technology and new media, such as smart phones and tablets have changed the face of communication in general and of language teaching more specifically. New media has widespread appeal among young people in particular, so it is in the teacher’s best interests to bring new media into their lessons. It is the author’s firm belief that technology will never replace the teacher, but it is without question that the twenty-first century teacher must employ technology and new media in some form, or run the risk of failure. The level that one chooses to incorporate new media within their class is entirely in their hands.

Keywords: new media, social media, technology, education, language learning

Procedia PDF Downloads 328

Authors: Odigie Veronica

Abstract:

The paper focused on the application of Information and Communication Technology (ICT) in effective service delivery in view of the ongoing COVID-19 experience. It adopted the exploratory research method with three research objectives captured. Consequently, the objectives were to ascertain the meaning of online education, understand the concept of COVID-19 and to determine the relevance of online education in effective service delivery in institutions of learning. It is evident from the findings that through ICT, online mode of learning can be adopted in schools which helps greatly in promoting continual education. Online mode of education is practiced online; it brings both the teacher and learners from different places together, without any physical boundary/contact (at least 75%); and has helped greatly in human development in countries where it has been practiced. It is also a welcome development owing to its many benefits such as exposure to digital learning, having access to works of great teachers and educationists such as Socrates, Plato, Dewey, R.S. Peters, J. J. Rosseau, Nnamdi Azikwe, Carol Gilligan, J. I. Omoregbe, Jane Roland Martin, Jean Piaget, among others; and the facilitation of uninterrupted learning for class promotion and graduation of students. Developing the learners all round is part of human development which helps in developing a nation. These and many more are some benefits online education offers which make ICT very relevant in our contemporary society

Keywords: online education, COVID-19 pandemic, effective service delivery, human development

Procedia PDF Downloads 91

Authors: D. Kokt, T. F. Dreyer

Abstract:

The main objective of the study was to design a mentoring programme aimed at developing the soft skills of mentors. The mentors are all employed by a multinational corporation. The company had a mentoring plan in place that did not yield the required results, especially related to the development of soft skills. This prompted the researchers to conduct an extensive literature review followed by a mixed methods approach to ascertain the best practices in developing the soft skills of mentors. The outcomes of the study led to the development of a structured mentoring programme using 25 modules to be completed by mentors. The design incorporated a blended modular approach using both face-to-face teaching and teaching supported by Information Communication Technology (ICT). Blended learning was ideal as the ICT component helped to minimise instructor-mentor physical contact as part of the health measures during the Covid-19 pandemic. The blended learning approach also allowed instructors and mentors an online or offline mode, so that mentors could have more time for creative and cooperative exercises. A range of delivery methodologies were spread out across the different modules to ensure mentor engagement and accelerate mentor development. This included concept development through in-person instructor-led training sessions, concept development through virtual instructor-led training sessions, simulations, case studies, e-learning, role plays, interactive learning using mentoring toolkits, and experiential learning through application. The mentor development journey included formal modular competency assessments. All modules contained post-competency assessment consisting of 10 questions (comprising of a combination of explanatory questions and multiple-choice questions) to ensure understanding and deal with identified competency gaps. The minimum pass mark for all modular competency assessments was 80%. Mentors were allowed to retake the assessment if they scored less than 80% until they demonstrated understanding at the required level.

Keywords: mentor, mentee, soft skills, mentor development, blended learning, modular approach

Procedia PDF Downloads 24

Authors: Kenneth Philip Jones

Abstract:

This dissertation investigates the potential of developing educational-based video games to motivate and engage African American males. The study employed a qualitative methodological approach by investigating African American males who are avid video game players and are currently enrolled at a college or university. The participants were individually and collectively video and audio recorded during the interviews and observations. Situated Learning theory analyzed how motivation and engagement can transfer from a video game to an educational context. The research aims to address the disparities in our educational systems when it comes to providing a culture, climate, and atmosphere that will enable the academic development of African American males. The primary objective of the findings is based on the participants’ responses and the data collected to provide recommendations to educators and scholars on how to address the issues that have demoralized African American males in education and provide a platform that will allow for equality in educational development and advancement.

Keywords: video games, motivation, behavioral, learning transfer

Procedia PDF Downloads 111

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

Abstract:

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 180

Authors: Manik Sharma, Ganapathy Krishnamurthi

Abstract:

The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

Procedia PDF Downloads 102

Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

Abstract:

This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

Procedia PDF Downloads 168

Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

Abstract:

In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

Procedia PDF Downloads 74

Authors: Mario Gianni, Manuel A. Ruiz Garcia, Fiora Pirri

Abstract:

In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV.

Keywords: Dirichlet processes, gaussian mixture models, learning motion patterns, tracked robots for urban search and rescue

Procedia PDF Downloads 442

Authors: Willy Mbenza, Sho Kenjiro

Abstract:

Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation.

Keywords: machine learning (ML), predictions, rainfall events, regional variables

Procedia PDF Downloads 80

Authors: Shaheeb Abdul Azeez, Siong-Choy Chong

Abstract:

Background: Talent retention is increasingly becoming a major challenge for many industries due to the high turnover rate. Public higher learning institutions in the Maldives have a similar situation with the turnover of their employees'. This paper is to identify whether Human Resource Management (HRM) practices have any impact on employee retention in public higher learning institutions in the Maldives. Purpose: This paper aims to identify the influence of HRM practices on employee retention in public higher learning institutions in the Maldives. A total of 15 variables used in this study; 11 HRM practices as independent variables (leadership, rewards, salary, employee participation, compensation, training and development, career development, recognition, appraisal system and supervisor support); job satisfaction and motivation as mediating variables; demographic profile as moderating variable and employee retention as dependent variable. Design/Methodology/Approach: A structured self-administered questionnaire was used for data collection. A total of 300 respondents were selected as the study sample, representing the academic and administrative from public higher learning institutions using a stratified random sampling method. AMOS was used to test the hypotheses constructed. Findings: The results suggest that there is no direct effect between the independent variable and dependent variable. Also, the study concludes that no moderate effects of demographic profile between independent and dependent variables. However, the mediating effects of job satisfaction and motivation in the relationship between HRM practices and employee retention were significant. Salary had a significant influence on job satisfaction, whilst both compensation and recognition have significant influence on motivation. Job satisfaction and motivation were also found to significantly influence employee retention. Research Limitations: The study consists of many variables more time consuming for the respondents to answer the questionnaire. The study is focussed only on public higher learning institutions in the Maldives due to no participation from the private sector higher learning institutions. Therefore, the researcher is unable to identify the actual situation of the higher learning industry in the Maldives. Originality/Value: To our best knowledge, no study has been conducted using the same framework throughout the world. This study is the initial study conducted in the Maldives in this study area and can be used as a baseline for future researches. But there are few types of research conducted on the same subject throughout the world. Some of them concluded with positive findings while others with negative findings. Also, they have used 4 to 7 HRM practices as their study framework.

Keywords: human resource management practices, employee retention, motivation, job satisfaction

Procedia PDF Downloads 148

Authors: Mary Deyo, Zmara Harrison

Abstract:

This study reports the outcomes of an 8-week experiential learning program for 6 adults with Intellectual and Developmental Disabilities (IDD) at a day habilitation program. The intervention foci for this program include executive function, language learning in the domains of expressive, receptive, and pragmatic language, and quality of life. The interprofessional collaboration aimed at supporting adults with IDD to reach person-centered, functional goals across skill domains is critical. This study is a significant addition to the speech-language pathology literature in that it examines a therapy method that potentially meets this need while targeting domains within the speech-language pathology scope of practice. Communication therapy was provided during highly valued and meaningful hands-on learning experiences, referred to as the Garden Club, which incorporated all aspects of planting and caring for a garden as well as related journaling, sensory, cooking, art, and technology-based activities. Direct care staff and an undergraduate research assistant were trained by SLP to be impactful language guides during their interactions with participants in the Garden Club. SLP also provided direct therapy and modeling during Garden Club. Research methods used in this study included a mixed methods analysis of a literature review, a quasi-experimental implementation of communication therapy in the context of experiential learning activities, Quality of Life participant surveys, quantitative pre- post- data collection and linear mixed model analysis, qualitative data collection with qualitative content analysis and coding for themes. Outcomes indicated overall positive changes in expressive vocabulary, following multi-step directions, sequencing, problem-solving, planning, skills for building and maintaining meaningful social relationships, and participant perception of the Garden Project’s impact on their own quality of life. Implementation of this project also highlighted supports and barriers that must be taken into consideration when planning similar projects. Overall findings support the use of experiential learning projects in day habilitation programs for adults with IDD, as well as additional research to deepen understanding of best practices, supports, and barriers for implementation of experiential learning with this population. This research provides an important contribution to research in the fields of speech-language pathology and other professions serving adults with IDD by describing an interprofessional experiential learning program with positive outcomes for executive function, language learning, and quality of life.

Keywords: experiential learning, adults, intellectual and developmental disabilities, expressive language, receptive language, pragmatic language, executive function, communication therapy, day habilitation, interprofessionalism, quality of life

Procedia PDF Downloads 116

Authors: Meera Rajeev Kumar

Abstract:

An overall social, emotional, and cultural development in a child is what a parent expects. There is no point in comparing the generations of 70’s or 80’s with that of the children of today as the trends are changing drastically. Technology has enabled them to become smart as well as over smart in one way or the other. Children today are quite ignorant of today’s values or ethics and are imbibing different cultures around them and ultimately confused on what to follow. The researcher has gained experience in transmitting or imparting the traditional culture through performing arts. It is understood that the children undergo a transformation from what they knew to what the truth is, through learning and experience. Through performing arts, the child develops an emotional, quick learning, abundant creativity, and ultimately self-realisation on what is right and wrong. The child also gains good organising skills, good decision making skills, therefore summing up to a holistic development. The sample study is 50, and a random sampling technique is adopted to differentiate between a normal child and a child learning an art. The study is conducted in Tamil Nadu, in India.

Keywords: creativity, cultural, emotional, empower

Procedia PDF Downloads 195

Authors: Chandrakala Varatharajoo, Adelina Binti Asmawi, Nabeel Abdallah Mohammad Abedalaziz

Abstract:

This study investigated the impact of inflectional and derivational morphemic analysis awareness on ESL secondary school students’ vocabulary learning strategy. The quasi-experimental study was conducted with 106 low proficiency secondary school students in two experimental groups (inflectional and derivational) and one control group. The students’ vocabulary acquisition was assessed through two measures: Morphemic Analysis Test and Vocabulary- Morphemic Test in the pretest and posttest before and after an intervention programme. Results of ANCOVA revealed that both the experimental groups achieved a significant score in Morphemic Analysis Test and Vocabulary-Morphemic Test. However, the inflectional group obtained a fairly higher score than the derivational group. Thus, the results indicated that ESL low proficiency secondary school students performed better on inflectional morphemic awareness as compared to derivatives. The results also showed that the awareness of inflectional morphology contributed more on the vocabulary acquisition. Importantly, learning inflectional morphology can help ESL low proficiency secondary school students to develop both morphemic awareness and vocabulary gain. Theoretically, these findings show that not all morphemes are equally useful to students for their language development. Practically, these findings indicate that morphological instruction should at least be included in remediation and instructional efforts with struggling learners across all grade levels, allowing them to focus on meaning within the word before they attempt the text in large for better comprehension. Also, by methodologically, by conducting individualized intervention and assessment this study provided fresh empirical evidence to support the existing literature on morphemic analysis awareness and vocabulary learning strategy. Thus, a major pedagogical implication of the study is that morphemic analysis awareness strategy is a definite boon for ESL secondary school students in learning English vocabulary.

Keywords: ESL, instruction, morphemic analysis, vocabulary

Procedia PDF Downloads 389

Authors: Elisabeth Desiana Mayasari, Laurensia Aptik Evanjeli, Brigitta Erlita Tri Anggadewi

Abstract:

The condition of the COVID-19 pandemic demands adaptation in various aspects of human life, including in the field of education. Teachers are expected to do distance learning or Learning From Home (LFH). The teacher said that he experienced stress, anxiety, feeling depressed, and afraid based on the interview. Learning adaptations and pandemic situations can impact the mental health of teachers, so the purpose of this study is to determine the mental health of teachers while teaching online. This research was conducted with a quantitative approach using a survey method. The subjects in this study were 330 elementary school teachers under the auspices of a foundation in Yogyakarta. Teachers' mental health was measured using the Indonesian version of The Mental Health Inventory (MHI-38), which has a reliability of 0.888. The results showed that the teachers generally had a good mental health condition marked by a lower negative aspect score than the positive aspect. In addition, the overall mental health aspect shows that some teachers have better mental health when compared to the average score, as well as higher positive aspect scores in all sub-aspects.

Keywords: mental health, teacher, COVID-19 pandemic, MHI-38

Procedia PDF Downloads 176

Authors: Heichia Wang, Yalan Chao

Abstract:

Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

Procedia PDF Downloads 118

Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

Abstract:

Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

Procedia PDF Downloads 75

Authors: C. Manjula, Lilly Florence

Abstract:

Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

Procedia PDF Downloads 325