Search results for: Application based learning

Authors: Shuen-Tai Wang, Yu-Ching Lin, Hsi-Ya Chang

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Virtualization technologies are experiencing a renewed interest as a way to improve system reliability, and availability, reduce costs, and provide flexibility. This paper presents the development on leverage existing cloud infrastructure and virtualization tools. We adopted some virtualization technologies which improve portability, manageability and compatibility of applications by encapsulating them from the underlying operating system on which they are executed. Given the development of application virtualization, it allows shifting the user’s applications from the traditional PC environment to the virtualized environment, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. Users no longer need to burden the platform maintenances and drastically reduces the overall cost of hardware and software licenses. Moreover, this flexible and web-based application virtualization service represents the next significant step to the mobile workplace, and it lets user executes their applications from virtually anywhere. 

Keywords: Cloud service, application virtualization, virtual machine, elastic environment.

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Authors: Viktorie Janečková, Petr Šnapka, Marie Mikušová

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This paper aims to initiate an analytical account of the issues of compliance with economy condition for incentive pay system application in an enterprise. Economy is considered one of the conditions for effective incentive pay system application another condition being the achievement of desired efficiency level of the incentive pay system application. Bonus pay system is discussed as an example.

Keywords: Cost analysis, economy, incentive pay system.

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Authors: Thitaporn Thoopputra, Tipaporn Pongmesa, Shuchuen Li

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Aims: To evaluate the application of non-invasive diabetes risk assessment tool in community pharmacy setting. Methods: Thai diabetes risk score was applied to assess individuals at risk of developing type 2 diabetes. Interactive computer-based risk screening (IT) and paper-based risk screening (PT) tools were applied. Participants aged over 25 years with no known diabetes were recruited in six participating pharmacies. Results: A total of 187 clients, mean aged (+SD) was 48.6 (+10.9) years. 35% were at high risk. The mean value of willingness-to-pay for the service fee in IT group was significantly higher than PT group (p=0.013). No significant difference observed for the satisfaction between groups. Conclusions: Non-invasive risk assessment tool, whether paper-based or computerized-based can be applied in community pharmacy to support the enhancing role of pharmacists in chronic disease management. Long term follow up is needed to determine the impact of its application in clinical, humanistic and economic outcomes.

Keywords: Community pharmacy, intervention, prevention, risk assessment, type 2 diabetes.

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Authors: H. Nikzad, S. Yoshitomi

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This paper presents the design application and reinforcement detailing of 15 storied reinforced concrete shear wall-frame structure based on linear static analysis. Databases are generated for section sizes based on automated structural optimization method utilizing Active-set Algorithm in MATLAB platform. The design constraints of allowable section sizes, capacity criteria and seismic provisions for static loads, combination of gravity and lateral loads are checked and determined based on ASCE 7-10 documents and ACI 318-14 design provision. The result of this study illustrates the efficiency of proposed method, and is expected to provide a useful reference in designing of RC shear wall-frame structures.

Keywords: Structural optimization, linear static analysis, ETABS, MATLAB, RC shear wall-frame structures.

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Authors: Mohammad Reza Karami Nejad

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A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP-Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature.

Keywords: Routing, Quality of Service, Multicaset, Learning Automata, Genetic, Next Generation Networks.

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Authors: C. Rogerson, E. Scott

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The purpose of this paper is to consider the introduction of online courses to replace the current classroom-based staff training. The current training is practical, and must be completed before access to the financial computer system is authorized. The long term objective is to measure the efficacy, effectiveness and efficiency of the training, and to establish whether a transfer of knowledge back to the workplace has occurred. This paper begins with an overview explaining the importance of staff training in an evolving, competitive business environment and defines the problem facing this particular organization. A summary of the literature review is followed by a brief discussion of the research methodology and objective. The implementation of the alpha version of the online course is then described. This paper may be of interest to those seeking insights into, or new theory regarding, practical interventions of online learning in the real world.

Keywords: Computer-based courses, e-learning, online training, workplace training.

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Authors: S. Assi, M. Haffar

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Teaching statistics is a critical and challenging issue especially to students from multidisciplinary and diverse postgraduate backgrounds. Postgraduate research students require statistics not only for the design of experiments; but also for data analysis. Students often perceive statistics as a complex and technical subject; thus, they leave data analysis to the last moment. The lecture needs to be simple and inclusive at the same time to make it comprehendible and address the learning needs of each student. Therefore, the aim of this work was to design a simple and comprehendible statistics lecture to postgraduate research students regarding ‘Research plan, design and data collection’. The lecture adopted the constructive alignment learning theory which facilitated the learning environments for the students. The learning environment utilized a student-centered approach and used interactive learning environment with in-class discussion, handouts and electronic voting system handsets. For evaluation of the lecture, formative assessment was made with in-class discussions and poll questions which were introduced during and after the lecture. The whole approach showed to be effective in creating a learning environment to the students who were able to apply the concepts addressed to their individual research projects.

Keywords: Teaching, statistics, lecture, multidisciplinary, postgraduate, learning theory, learning environment, student-centered approach, data analysis.

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: Flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning.

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Authors: S. Umarani, D. Sharmila

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A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a target server, it is necessary to classify the legitimate access and attacks. In this paper, a novel method is proposed to detect DDoS attacks from the traces of traffic flow. An access matrix is created from the traces. As the access matrix is multi dimensional, Principle Component Analysis (PCA) is used to reduce the attributes used for detection. Two classifiers Naive Bayes and K-Nearest neighborhood are used to classify the traffic as normal or abnormal. The performance of the classifier with PCA selected attributes and actual attributes of access matrix is compared by the detection rate and False Positive Rate (FPR).

Keywords: Distributed Denial of Service (DDoS) attack, Application layer DDoS, DDoS Detection, K- Nearest neighborhood classifier, Naive Bayes Classifier, Principle Component Analysis.

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Authors: Vasanthi Muniasamy, Intisar Magboul Ejalani, M. Anandhavalli, K. Gauthaman

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Social Media (SM) is websites increasingly popular and built to allow people to express themselves and to interact socially with others. Most SMT are dominated by youth particularly College students. The proliferation of popular social media tools, which can accessed from any communication devices has become pervasive in the lives of today’s student life. Connecting traditional education to social media tools are a relatively new era and any collaborative tool could be used for learning activities. This study focuses (i) how the social media tools are useful for the learning activities of the students of faculty of medicine in King Khalid University (ii) whether the social media affects the collaborative learning with interaction among students, among course instructor, their engagement, perceived ease of use and perceived ease of usefulness (TAM) (iii) overall, the students satisfy with this collaborative learning through Social media.

Keywords: Social Media, Web 2.0, Perceived ease of use, perceived usefulness, Collaborative Learning.

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Authors: Abdelghani Boudiaf

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The profusion of studies relating to the concept of organizational learning shows the importance that has been given to this concept in the management sciences. A few years ago, companies leaned towards ISO 9001 certification; this requires the implementation of the quality management system (QMS). In order for this objective to be achieved, companies must have a set of skills, which pushes them to develop learning through continuous training. The results of empirical research have shown that implementation of the QMS in the company promotes the development of learning. It should also be noted that several types of learning are developed in this sense. Given the nature of skills development is normative in the context of the quality demarche, companies are obliged to qualify and improve the skills of their human resources. Continuous training is the keystone to develop the necessary learning. To carry out continuous training, companies need to be able to identify their real needs by developing training plans based on well-defined engineering. The training process goes obviously through several stages. Initially, training has a general aspect, that is to say, it focuses on topics and actions of a general nature. Subsequently, this is done in a more targeted and more precise way to accompany the evolution of the QMS and also to make the changes decided each time (change of working method, change of practices, change of objectives, change of mentality, etc.). To answer our problematic we opted for the method of qualitative research. It should be noted that the case study method crosses several data collection techniques to explain and understand a phenomenon. Three cases of companies were studied as part of this research work using different data collection techniques related to this method.

Keywords: Changing mentalities, continuous training, organizational learning, quality management system, skills development.

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Authors: Byoung-Tak Zhang, Chan-Hoon Park

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Hypernetworks are a generalized graph structure representing higher-order interactions between variables. We present a method for self-organizing hypernetworks to learn an associative memory of sentences and to recall the sentences from this memory. This learning method is inspired by the “mental chemistry" model of cognition and the “molecular self-assembly" technology in biochemistry. Simulation experiments are performed on a corpus of natural-language dialogues of approximately 300K sentences collected from TV drama captions. We report on the sentence completion performance as a function of the order of word-interaction and the size of the learning corpus, and discuss the plausibility of this architecture as a cognitive model of language learning and memory.

Keywords: Linguistic recall memory, sentence completion task, self-organizing hypernetworks, cognitive learning and memory.

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Authors: Rosalyn R. Porle, Ali Chekima, Farrah Wong, G. Sainarayanan

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Arms detection is one of the fundamental problems in human motion analysis application. The arms are considered as the most challenging body part to be detected since its pose and speed varies in image sequences. Moreover, the arms are usually occluded with other body parts such as the head and torso. In this paper, histogram-based skin colour segmentation is proposed to detect the arms in image sequences. Six different colour spaces namely RGB, rgb, HSI, TSL, SCT and CIELAB are evaluated to determine the best colour space for this segmentation procedure. The evaluation is divided into three categories, which are single colour component, colour without luminance and colour with luminance. The performance is measured using True Positive (TP) and True Negative (TN) on 250 images with manual ground truth. The best colour is selected based on the highest TN value followed by the highest TP value.

Keywords: image colour analysis, image motion analysis, skin, wavelet transform.

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Authors: Christina Van Wingerden

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The aim of this paper is to propose pedagogical design for learning management systems (LMS) that offers greater inclusion for students based on a number of theoretical perspectives and delineated through an example. Considering the impact of COVID-19, including on student mental health, the research suggesting the importance of student sense of belonging on retention, success, and student well-being, the author describes intentional LMS design incorporating theoretically based practices informed by critical theory, feminist theory, indigenous theory and practices, and new materiality. This article considers important aspects of these theories and practices which attend to inclusion, identities, and socially just learning environments. Additionally, increasing student sense of belonging and mental health through LMS design influenced by adult learning theory and the community of inquiry model are described.  The process of thinking through LMS pedagogical design with inclusion intentionally in mind affords the opportunity to allow LMS to go beyond course use as a repository of documents, to an intentional community of practice that facilitates belonging and connection, something much needed in our times. In virtual learning environments it has been harder to discern how students are doing, especially in feeling connected to their courses, their faculty, and their student peers. Increasingly at the forefront of public universities is addressing the needs of students with multiple and intersecting identities and the multiplicity of needs and accommodations. Education in 2020, and moving forward, calls for embedding critical theories and inclusive ideals and pedagogies to the ways instructors design and teach in online platforms. Through utilization of critical theoretical frameworks and instructional practices, students may experience the LMS as a welcoming place with intentional plans for welcoming diversity in identities.

Keywords: Belonging, critical pedagogy, instructional design, Learning Management System, LMS.

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Authors: M. D. Stelescu, E. Manaila, G. Pelin, M. Georgescu, M. Sonmez

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This paper presents studies on the development and characterization of nanocomposites based on ethylene-propylene terpolymer rubber (EPDM), chlorobutyl rubber (IIR-Cl) and organically modified montmorillonite (OMMT). Mixtures were made containing 0, 3 and 6 phr (parts per 100 parts rubber) OMMT, respectively. They were obtained by melt intercalation in an internal mixer - Plasti-Corder Brabender, in suitable blending parameters, at high temperature for 11 minutes. Curing agents were embedded on a laboratory roller at 70-100 ºC, friction 1:1.1, processing time 5 minutes. Rubber specimens were obtained by compression, using a hydraulic press at 165 ºC and a pressing force of 300 kN. Curing time, determined using the Monsanto rheometer, decreases with the increased amount of OMMT in the mixtures. At the same time, it was noticed that mixtures containing OMMT show improvement in physical-mechanical properties. These types of nanocomposites may be used to obtain rubber seals for the space application or for other areas of application.

Keywords: Chlorobutyl rubber, ethylene-propylene-diene terpolymers, montmorillonite, rubber seals, space application.

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: Fall detection, machine learning, deep learning, pose estimation, tracking.

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Authors: Nobue T. Ellis

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This preliminary study attempts to see if a learning environment influences instructor’s teaching strategies and learners’ in-class activities in a foreign language class at a university in Japan. The class under study was conducted in a computer room, while the majority of classes of the same course were offered in traditional classrooms without computers. The study also sees if the unplanned blended learning environment, enhanced, or worked against, in achieving course goals, by paying close attention to in-class artefacts, such as computers. In the macro-level analysis, the course syllabus and weekly itinerary of the course were looked at; and in the microlevel analysis, nonhuman actors in their environments were named and analyzed to see how they influenced the learners’ task processes. The result indicated that students were heavily influenced by the presence of computers, which lead them to disregard some aspects of intended learning objectives.

Keywords: Computer-assisted language learning, actor-network theory, English as a foreign language, task-based teaching.

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Authors: Adesina, Olusola Joseph

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The use of Technological tools in the classroom setting has drawn the interest of researchers all over the world in the recent time. Technology has been identified in the recent time as potentials tools to aid learning especially during early childhood stage. The main objective of this is to assist the upcoming younger generations to acquire necessary skills for cognitive development which later enhances effective teaching learning process. The integration of Technology in early childhood requires a careful selection of devices that will both assist the children and the teachers or care givers. This paper therefore, examines some selected literature evidences and highlighted the efficacy of various technologies tools in enhancing the development and learning of children (0 – 5 years). Conclusion and recommendations were also drawn in this paper. 

Keywords: Development, Efficacy, Learning, Technological Device.

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: Machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation.

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Authors: Kari Björn

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Introduction of games has expanded the application area of computer-aided learning tools to wide variety of age groups of learners. Serious games engage the learners into a real-world -type of simulation and potentially enrich the learning experience. Institutional background of a Bachelor’s level engineering program in Information and Communication Technology is introduced, with detailed focus on one of its majors, Health Technology. As part of a Customer Oriented Software Application thematic semester, one particular course of “eHealth Business and Solutions” is described and reflected in a gamified framework. Learning a consistent view into vast literature of business management, strategies, marketing and finance in a very limited time enforces selection of topics relevant to the industry. Health Technology is a novel and growing industry with a growing sector in consumer wearable devices and homecare applications. The business sector is attracting new entrepreneurs and impatient investor funds. From engineering education point of view the sector is driven by miniaturizing electronics, sensors and wireless applications. However, the market is highly consumer-driven and usability, safety and data integrity requirements are extremely high. When the same technology is used in analysis or treatment of patients, very strict regulatory measures are enforced. The paper introduces a course structure using gamification as a tool to learn the most essential in a new market: customer value proposition design, followed by a market entry game. Students analyze the existing market size and pricing structure of eHealth web-service market and enter the market as a steering group of their company, competing against the legacy players and with each other. The market is growing but has its rules of demand and supply balance. New products can be developed with an R&D-investment, and targeted to market with unique quality- and price-combinations. Product cost structure can be improved by investing to enhanced production capacity. Investments can be funded optionally by foreign capital. Students make management decisions and face the dynamics of the market competition in form of income statement and balance sheet after each decision cycle. The focus of the learning outcome is to understand customer value creation to be the source of cash flow. The benefit of gamification is to enrich the learning experience on structure and meaning of financial statements. The paper describes the gamification approach and discusses outcomes after two course implementations. Along the case description of learning challenges, some unexpected misconceptions are noted. Improvements of the game or the semi-gamified teaching pedagogy are discussed. The case description serves as an additional support to new game coordinator, as well as helps to improve the method. Overall, the gamified approach has helped to engage engineering student to business studies in an energizing way.

Keywords: Engineering education, integrated curriculum, learning experience, learning outcomes.

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: Generic knowledge representation, toolkit, toolroom, pervasive computing.

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: Creative problem-based play, educational games, flexible thinking, tertiary education.

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Authors: A. Akın, H. Çoban

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This study was carried out to determine the effects of Control (C), 1/3 Cluster Tip Reduction (1/3 CTR), 1/6 Cluster Tip Reduction (1/6 CTR), 1/9 Cluster Tip Reduction (1/9 CTR), 1/3 CTR + Boric Acid (BA), 1/6 CTR + BA, 1/9 CTR + BA applications on yield and yield components of four years old Alphonse Lavallee grape variety (Vitis vinifera L.) grown on grafted 110 Paulsen rootstock in Konya province in Turkey in the vegetation period in 2015. According to the results, the highest maturity index 21.46 with 1/9 CTR application; the highest grape juice yields 736.67 ml with 1/3 CTR + BA application; the highest L* color value 32.07 with 1/9 CTR application; the highest a* color value 1.74 with 1/9 CTR application; the highest b* color value 3.72 with 1/9 CTR application were obtained. The effects of applications on grape fresh yield, cluster weight and berry weight were not found statistically significant.

Keywords: Alphonse Lavallee grape cultivar, different cluster tip reduction (1/3, 1/6, 1/9), foliar boric acid application, yield, quality.

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Authors: Argaw T. Gurmu, Muhammad N. Mahmood

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The objective of this research is to identify the architecture and construction management students’ learning challenges of the building measurement. This research used the survey data obtained collected from the students who completed the building measurement unit. NVivo qualitative data analysis software was used to identify relevant themes. The analysis of the qualitative data revealed the major learning difficulties such as inadequacy of practice questions for the examination, inability to work as a team, lack of detailed understanding of the prerequisite units, insufficiency of the time allocated for tutorials and incompatibility of lecture and tutorial schedules. The output of this research can be used as a basis for improving the teaching and learning activities in construction measurement units.

Keywords: Building measurement, construction management, learning challenges, evaluate survey.

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Authors: Samson T. Obafemi, Seraphin D. Eyono Obono

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Information and Communication Technologies (ICTs) are pervasive nowadays, including in education where they are expected to improve the performance of learners. However, the hope placed in ICTs to find viable solutions to the problem of poor academic performance in schools in the developing world has not yet yielded the expected benefits. This problem serves as a motivation to this study whose aim is to examine the perceptions of educators on the advantages and disadvantages of e-learning. This aim will be subdivided into two types of research objectives. Objectives on the identification and design of theories and models will be achieved using content analysis and literature review. However, the objective on the empirical testing of such theories and models will be achieved through the survey of educators from different schools in the Pinetown District of the South African Kwazulu-Natal province. SPSS is used to quantitatively analyse the data collected by the questionnaire of this survey using descriptive statistics and Pearson correlations after assessing the validity and the reliability of the data. The main hypothesis driving this study is that there is a relationship between the demographics of educators’ and their adherence to learning theories on one side, and their perceptions on the advantages and disadvantages of e-learning on the other side, as argued by existing research; but this research views these learning theories under three perspectives: educators’ adherence to self-regulated learning, to constructivism, and to progressivism. This hypothesis was fully confirmed by the empirical study except for the demographic factor where teachers’ level of education was found to be the only demographic factor affecting the perceptions of educators on the advantages and disadvantages of e-learning.

Keywords: Academic performance, e-learning, Learning theories, Teaching and Learning.

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

Abstract:

Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity and aflatoxinogenic capacity of the strains, topography, soil and climate parameters of the fig orchards are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high-performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques i.e., dimensionality reduction on the original dataset (Principal Component Analysis), metric learning (Mahalanobis Metric for Clustering) and K-nearest Neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson Correlation Coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: K. M. Faraoun, A. Boukelif

Abstract:

In the present work, we propose a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism. The K-means algorithm is first applied to the training dataset to reduce the amount of samples to be presented to the neural network, by automatically selecting an optimal set of samples. The obtained results demonstrate that the proposed technique performs exceptionally in terms of both accuracy and computation time when applied to the KDD99 dataset compared to a standard learning schema that use the full dataset.

Keywords: Neural networks, Intrusion detection, learningenhancement, K-means clustering

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Authors: M. Tahmasebi, R.A. Rahman, M. Mailah, M. Gohari

Abstract:

The control of sprayer boom undesired vibrations pose a great challenge to investigators due to various disturbances and conditions. Sprayer boom movements lead to reduce of spread efficiency and crop yield. This paper describes the design of a novel control method for an active suspension system applying proportional-integral-derivative (PID) controller with an active force control (AFC) scheme integration of an iterative learning algorithm employed to a sprayer boom. The iterative learning as an intelligent method is principally used as a method to calculate the best value of the estimated inertia of the sprayer boom needed for the AFC loop. Results show that the proposed AFC-based scheme performs much better than the standard PID control technique. Also, this shows that the system is more robust and accurate.

Keywords: Active force control, sprayer boom, active suspension, iterative learning.

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Authors: Fahad Suleiman

Abstract:

The aim of this paper is to study students’ view on mathematics learning in Katsina State Senior Secondary Schools of Nigeria, such as their conceptions of mathematics, attitudes toward mathematics learning, etc. A questionnaire was administered to a random sample of 1,225 senior secondary two (SS II) students of Katsina State in Nigeria. The data collected showed a clear picture of the hurdles that affect the teaching and learning of mathematics in our schools. Problems such as logistics and operational which include shortage of mathematics teachers, non–availability of a mathematics laboratory, etc. were identified. It also depicted the substantial trends of changing views and attitudes toward mathematics across secondary schools. Students’ responses to the conception of mathematics were consistent and they demonstrated some specific characteristics of their views in learning mathematics. This survey has provided useful information regarding students’ needs and aspirations in mathematics learning for curriculum planners and frontline teachers for future curriculum reform and implementation.

Keywords: Attitude, education, mathematics, students.

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