Search results for: assessment of learning

Authors: Chokri Slim

Abstract:

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.

Keywords: Neuro-fuzzy, Extended Kalman filter, nonlinear systems, financial time series.

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Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: Bayesian, Forecast, Stock, BART.

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Authors: Moulay A. Akhloufi, Abdelhakim Bendada

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Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA and LDA. In this work, we introduce a probabilistic Bayesian framework for face recognition in the infrared spectrum. In the infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. Bayesian approaches permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. This framework is compared with classical linear techniques. Non linear techniques we developed recently for infrared face recognition are also presented and compared to the Bayesian face recognition framework. A new approach for infrared face extraction based on SVM is introduced. Experimental results show that the Bayesian technique is promising and lead to interesting results in the infrared spectrum when a sufficient number of face images is used in an intrapersonal learning process.

Keywords: Face recognition, biometrics, probabilistic imageprocessing, infrared imaging.

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Authors: Beata Wentura-Dudek

Abstract:

In the United States in 2007-2010 before the crisis, the US labour market policy focused mainly on providing residents with unemployment insurance, after the recession this policy changed. The aim of the article was to present quantitative research presenting the most effective labor market instruments contributing to reducing unemployment during the crisis in the USA. The article presents research based on the analysis of available documents and statistical data. The results of the conducted research show that the most effective forms of counteracting unemployment at that time were: direct job creation, job search assistance, subsidized employment, training and employment promotion using new technologies, including social media.

Keywords: United States, financial crisis, unemployment, employment promotion, social media, job creation, training, labour market, employment agencies, lifelong learning, job search assistance, subsidized employment, companies, tax.

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Authors: John Walton, Vishal Parikh

Abstract:

Contact centres have been exemplars of scientific management in the discipline of operations management for more than a decade now. With the movement of industries from a resource based economy to knowledge based economy businesses have started to realize the customer eccentricity being the key to sustainability amidst high velocity of the market. However, as technologies have converged and advanced, so have the contact centres. Contact Centres have redirected the supply chains and the concept of retailing is highly diminished due to over exaggeration of cost reduction strategies. In conditions of high environmental velocity together with services featuring considerable information intensity contact centres will require up to date and enlightened agents to satisfy the demands placed upon them by those requesting their services. In this paper we examine salient factors such as Power Distance, Knowledge structures and the dynamics of job specialisation and enlargement to suggest critical success factors in the domain of contact centres.

Keywords: Post Taylorism, Knowledge Management, Power Distance, Organisational Learning

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Authors: Murat Koklu, Yavuz Unal

Abstract:

Data mining can be called as a technique to extract information from data. It is the process of obtaining hidden information and then turning it into qualified knowledge by statistical and artificial intelligence technique. One of its application areas is medical area to form decision support systems for diagnosis just by inventing meaningful information from given medical data. In this study a decision support system for diagnosis of illness that make use of data mining and three different artificial intelligence classifier algorithms namely Multilayer Perceptron, Naive Bayes Classifier and J.48. Pima Indian dataset of UCI Machine Learning Repository was used. This dataset includes urinary and blood test results of 768 patients. These test results consist of 8 different feature vectors. Obtained classifying results were compared with the previous studies. The suggestions for future studies were presented.

Keywords: Artificial Intelligence, Classifiers, Data Mining, Diabetic Patients.

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Authors: Peyman Shadman Heidari, Mohammad Khorasani

Abstract:

The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.

Keywords: Adaptive Neural Network Fuzzy Inference System, Wavelet Packet Transform, Response Spectrum.

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Authors: Belkacem Selma, Boumediene Selma, Samira Chouraqui, Hanifi Missoum, Tourkia Guerzou

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. Earthquake prediction to prevent the loss of human lives and even property damage is an important factor; that, is why it is crucial to develop techniques for predicting this natural disaster. This study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 104 J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines have been analyzed. The results obtained show that the ANN is able to predict earthquake parameters with  high accuracy; the classification accuracy through neural networks is more than 94%, and the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: Earthquake prediction, artificial intelligence, AI, Artificial Neural Network, ANN, seismic bumps.

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Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: Neural networks, Noise, Speech Recognition.

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Authors: Hui Fang Huang Su, Jia Borror

Abstract:

This study compared two different interventions for math instruction among preschoolers with autism spectrum disorder (ASD). The first intervention, a combination of discrete trial teaching and Strategies for Teaching Based on Autism Research (STAR), was the regular math curriculum utilized at the preschool. The second activity-based, naturalistic intervention was Project Mind, also known as Math is Not Difficult. The curricular interventions were randomly assigned to four preschool classrooms with ASD students and implemented over three months for Project MIND. Measurements gained during the same three months for the STAR intervention were used. A quasi-experimental, pre-test/post-test design was selected to compare which intervention was the most effective in increasing mathematical knowledge and skills among preschoolers with ASD. Standardized pre and post-test instruments included the Bracken Basic Concept Scale-3 Receptive, the Applied Problems and Calculation subtests of the Woodcock-Johnson IV Tests of Achievement, and the TEMA 3: Test of Early Mathematics Ability – Third Edition. The STAR assessment is typically administered to all preschoolers at the study site three times per year, and those results were used in this study. We anticipated that the implementation of these two approaches would lead to improvement in the mathematical knowledge and skills of children with ASD. Still, it is essential to see whether a behavioral or naturalistic teaching approach leads to more significant results.

Keywords: Autism, mathematics, preschool, special education.

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Authors: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

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In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.

Keywords: Image classification, multi-level neural networks, feature extraction, wavelets decomposition.

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Authors: M. Karassartova, D. Shormanbayeva, A. Beissenova, S.Balshikeyev

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Forming a legal culture among citizens is a complicated and lengthy process, influencing all spheres of social life. It includes promoting justice, learning rights and duties, the introduction of juridical norms and knowledge, and also a process of developing a system of legal acts and constitutional norms. Currently, the evaluative and emotional influence of attempts to establish a legal culture among the citizens of Kazakhstan is limited by real legal practice. As a result, the values essential to a sound civil society are absent from the consciousness of the Kazakh people who are thus, in turn, not able to develop respect for these values. One of the disadvantages of the modern Kazakh educational system is a tendency to underrate the actual forces shaping the worldview of Kazakh youths. The mass-media, which are going through a personnel crisis, cannot provide society with the legal and political information necessary to form the sort of legal culture required for a true civil society.

Keywords: Kazakhstan society, Legal education, legal culture.

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Authors: Ismail Karaca, Ridvan Saracoglu, Omer Soner

Abstract:

Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.

Keywords: Calculation of risk factor, fuzzy logic, fuzzy programming for ship, safe navigation of ships.

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Authors: C. Lanzerstorfer

Abstract:

Liquid sprays of water are frequently used in air pollution control for gas cooling purposes and for gas cleaning. Twin-fluid nozzles with internal mixing are often used for these purposes because of the small size of the drops produced. In these nozzles the liquid is dispersed by compressed air or another pressurized gas. In high efficiency scrubbers for particle separation, several nozzles are operated in parallel because of the size of the cross section. In such scrubbers, the scrubbing water has to be re-circulated. Precipitation of some solid material can occur in the liquid circuit, caused by chemical reactions. When such precipitations are detached from the place of formation, they can partly or totally block the liquid flow to a nozzle. Due to the resulting unbalanced supply of the nozzles with water and gas, the efficiency of separation decreases. Thus, the nozzles have to be cleaned if a certain fraction of blockages is reached. The aim of this study was to provide a tool for continuously monitoring the status of the nozzles of a scrubber based on the available operation data (water flow, air flow, water pressure and air pressure). The difference between the air pressure and the water pressure is not well suited for this purpose, because the difference is quite small and therefore very exact calibration of the pressure measurement would be required. Therefore, an equation for the reference air flow of a nozzle at the actual water flow and operation pressure was derived. This flow can be compared with the actual air flow for assessment of the status of the nozzles.

Keywords: Twin-fluid nozzles, operation data, condition monitoring, flow equation.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

Abstract:

Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.

Keywords: Bioinformatics, Protein-protein interactions, One-Class Support Vector Machines

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Authors: R. Litt, A. Brown

Abstract:

Aims: We aimed to assess the efficiency of ‘Attend Anywhere’ orthodontic clinics within a district general hospital during COVID- 19. Our secondary aim was to pilot a questionnaire to assess patient satisfaction with virtual orthodontic appointments. Design: The study design is a service evaluation including pilot questionnaire. Methods: The average number of patients seen per virtual clinic and the number of patients failing to attend was compared to face-to-face clinics. The capability of virtual appointments to be successful in preventing the need for a face-to-face appointment was assessed. Patients were invited to complete a telephone pilot questionnaire focusing on patient satisfaction and accessibility. Results: There was a small increase in the number of patients failing to attend virtual appointments, with a third of the patients who did not attend failing to receive the appointment link. 81.9% of virtual clinic appointments were successful and prevented the need for a face-to-face appointment. Overall patients were very satisfied with their virtual orthodontic appointment and the majority required no assistance to access the service. Conclusions: The use of ‘Attend Anywhere’ clinics in orthodontics offers patients and clinicians an effective and efficient alternative to face-to-face appointments that patients on average find easy to use and completely satisfactory.

Keywords: Clinics, COVID-19, orthodontics, patient satisfaction, virtual.

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

Abstract:

In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are class balancing, data shuffling, and standardization, were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the sequential model and ReLU activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: Spectroscopy, soluble solid content, pineapple, neural network.

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Authors: O. Lasaga

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Defining professional objectives and the search for work are some of the greatest stress factors for final year university students and recent graduates. To manage correctly the stress brought about by the uncertainty, confusion and frustration this process often generates, a course to control stress based on mindfulness has been designed and taught. This course provides tools based on relaxation, mindfulness and meditation that enable students to address personal and professional challenges in the transition to the job market, eliminating or easing the anxiety involved. The course is extremely practical and experiential, combining theory classes and practical classes of relaxation, meditation and mindfulness, group dynamics, reflection, application protocols and session integration. The evaluation of the courses highlighted on the one hand the high degree of satisfaction and, on the other, the usefulness for the students in becoming aware of stressful situations and how these affect them and learning new coping techniques that enable them to reach their goals more easily and with greater satisfaction and well-being.

Keywords: Employability, meditation, mindfulness, relaxation techniques, stress.

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Authors: Ikumapayi Catherine Mayowa

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The well been of human beings on construction site is very important, many man power had been lost through accidents which kills or make workers physically unfit to carry out construction activities, these in turn have multiple effects on the whole economy. Thus it is necessary to put all safety items and regulations in place before construction activities can commence. This study was carried out in Ondo state of Nigeria to known and analyse the state of health and safety of construction workers in the state. The study was done using first hand observation method, 50 construction project sites were visited in 10 major towns of Ondo state, questionnaires were distributed and the results were analysed. The result show that construction workers are being exposed to a lot of construction site hazards due to lack of inadequate safety programmes and nonprovision of appropriate safety materials for workers on site. From the data gotten for each site visited and the statistical analysis, it can be concluded that occurrence of accident on construction sites depends significantly on the available safety facilities on the sites. The result of the regression statistics show that the level of significant of the dependence of occurrence of accident on the availability of safety items on site is 0.0362 which is less than 0.05 maximum significant level required. Therefore a vital way of sustaining our building strategy is by given a detail attention to provision of adequate health and safety items on construction sites which will reduce the occurrence of accident, loss of man power and death of skilled workers among others.

Keywords: Construction sites, health, safety, welfare.

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: Degree, initial cluster center, k-means, minimum spanning tree.

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Authors: Kawai Nobuaki

Abstract:

This paper describes the design and implementation of web system for continuable and viable collaboration. This study proposes the improvement of the system based on a result of a certain practice. As contemporary higher education information environments transform, this study highlights the significance of university identity and college identity that are formed continuously through independent activities of the students. Based on these discussions, the present study proposes a practical media environment design which facilitates the processes of organizational identity formation based on a continuous and cyclical model. Even if users change by this system, the communication system continues operation and cooperation. The activity becomes the archive and produces new activity. Based on the result, this study elaborates a plan with a re-design by a system from the viewpoint of second-order cybernetics. Systems theory is a theoretical foundation for our study.

Keywords: Collaborative work, learning management system, second-order cybernetics, systems theory, user generated contents, viable system model.

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: Cross-language analysis, machine learning, machine translation, sentiment analysis.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

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The protein domain structure has been widely used as the most informative sequence feature to computationally predict protein-protein interactions. However, in a recent study, a research group has reported a very high accuracy of 94% using hydrophobicity feature. Therefore, in this study we compare and verify the usefulness of protein domain structure and hydrophobicity properties as the sequence features. Using the Support Vector Machines (SVM) as the learning system, our results indicate that both features achieved accuracy of nearly 80%. Furthermore, domains structure had receiver operating characteristic (ROC) score of 0.8480 with running time of 34 seconds, while hydrophobicity had ROC score of 0.8159 with running time of 20,571 seconds (5.7 hours). These results indicate that protein-protein interaction can be predicted from domain structure with reliable accuracy and acceptable running time.

Keywords: Bioinformatics, protein-protein interactions, support vector machines, protein features.

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Authors: Ilario Gabriele Gerloni, Vincenza Carchiolo, Alessandro Longheu, Ugo Becciani, Eva Sciacca, Fabio Vitello

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ICT tools and platforms endorse more and more educational process. Many models and techniques for people to be educated and trained about specific topics and skills do exist, as classroom lectures with textbooks, computers, handheld devices and others. The choice to what extent ICT is applied within learning contexts is related to personal access to technologies as well as to the infrastructure surrounding environment. Among recent techniques, the adoption of Virtual Reality (VR) and Augmented Reality (AR) provides significant impulse in fully engaging users senses. In this paper, an application of AR/VR within Geographic Information Systems (GIS) context is presented. It aims to provide immersive environment experiences for educational and training purposes (e.g. for civil protection personnel), useful especially for situations where real scenarios are not easily accessible by humans. First acknowledgments are promising for building an effective tool that helps civil protection personnel training with risk reduction.

Keywords: Education, virtual reality, augmented reality, civil protection.

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Authors: Marut Pichetvit

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The research objective aims to search information about storytelling and fable associated with fireflies in Amphawa community, in order to design and create a story book which is appropriate for the interests of children in early childhood. This book should help building the development of learning about the natural environment, imagination, and creativity among children, which then, brings about the promotion of the development, conservation and dissemination of cultural values and uniqueness of the Amphawa community. The population used in this study were 30 students in early childhood aged between 6-8 years-old, grade 1-3 from the Demonstration School of Suan Sunandha Rajabhat University. The method used for this study was purposive sampling and the research conducted by the query and analysis of data from both the document and the narrative field tales and fable associated with the fireflies of Amphawa community. Then, using the results to synthesize and create a conceptual design in a form of 8 visual images which were later applied to 1 illustrated children’s book and presented to the experts to evaluate and test this media.

Keywords: Children’s illustrated book, Fireflies, Amphawa.

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Authors: Huay Lit Woo, Qiyun Wang

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Weblog is an Internet tool that is believed to possess great potential to facilitate learning in education. This study wants to know if weblog can be used to promote students- critical thinking. It used a group of secondary two students from a Singapore school to write weblogs as a means of substitution for their traditional handwritten assignments. The topics for the weblogging are taken from History syllabus but modified to suit the purpose of this study. Weblogs from the students were collected and analysed using a known coding system for measuring critical thinking. Results show that the topic for blogging is crucial in determining the types of critical thinking employed by the students. Students are seen to display critical thinking traits in the areas of information sourcing, linking information to arguments and viewpoints justification. Students- criticalness is more profound when the information for writing a topic is readily available. Otherwise, they tend to be less critical and subjective. The study also found that students lack the ability to source for external information suggesting that students may need to be taught information literacy in order to widen their use of critical thinking skills.

Keywords: Affordance, blog, critical thinking, perception, weblog.

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Authors: Pedro Barbosa Cabral, Neuza Pedro, Ana Mafalda Gonçalves

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The purpose of this research is the analysis of the impact of ICT-related training in the adoption of a learning management systems (LMS) for teaching practicesby faculties in a higher education institution. Based on comparative analyses the impact will be obtained by the number of LMS courses created and managed by participants in ICT for teaching workshops and those who have not attended to any workshops. Involving near 1320 LMS courses and 265 faculties, the results evidence that(i) faculties who have not attend any workshop present a larger distribution of empty courses and (ii) faculties who have attended three or more workshops managed a higher distribution of courses with a considerable level of use intensity, when compared to the others groups. These findings supportthe idea that faculty training is a crucial factor in the process of LMS integration in higher education institutions and that faculties who have been enrolled in three or more workshops develop a higher level of technical and pedagogical proficiency in LMS.

Keywords: Higher Education, Faculty Training, LMS, Comparative Analyses

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Features selection, learning with kernels, support vector machine, genetic algorithms and classification.

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Authors: Madhuri Bhavsar, Anupam K Singh, Shrikant Pradhan

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Long term rainfall analysis and prediction is a challenging task especially in the modern world where the impact of global warming is creating complications in environmental issues. These factors which are data intensive require high performance computational modeling for accurate prediction. This research paper describes a prototype which is designed and developed on grid environment using a number of coupled software infrastructural building blocks. This grid enabled system provides the demanding computational power, efficiency, resources, user-friendly interface, secured job submission and high throughput. The results obtained using sequential execution and grid enabled execution shows that computational performance has enhanced among 36% to 75%, for decade of climate parameters. Large variation in performance can be attributed to varying degree of computational resources available for job execution. Grid Computing enables the dynamic runtime selection, sharing and aggregation of distributed and autonomous resources which plays an important role not only in business, but also in scientific implications and social surroundings. This research paper attempts to explore the grid enabled computing capabilities on weather indices from HOAPS data for climate impact modeling and change detection.

Keywords: Climate model, Computational Grid, GridApplication, Heterogeneous Grid

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Authors: Rhadinia Tayag-Relanes, Felina C. Young

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This study was conducted to analyze the noodle production process at Yan Hu Food Manufacturing for the basis of production improvement. The study utilized the Plan, Do, Check, Act (PDCA) approach and record review in the gathering of data for the calendar year 2019, specifically from August to October, focusing on the noodle products miki, canton, and misua. A causal-comparative research design was employed to establish cause-effect relationships among the variables, using descriptive statistics and correlation to compute the data gathered. The findings indicate that miki, canton, and misua production have distinct cycle times and production outputs in every set of its production processes, as well as varying levels of wastage. The company has not yet established a formal allowable rejection rate for wastage; instead, this paper used a 1% wastage limit. We recommended the following: machines used for each process of the noodle product must be consistently maintained and monitored; an assessment of all the production operators should be conducted by assessing their performance statistically based on the output and the machine performance; a root cause analysis must be conducted to identify solutions to production issues; and, an improved recording system for input and output of the production process of each noodle product should be established to eliminate the poor recording of data.

Keywords: Production, continuous improvement, process, operations, Plan, Do, Check, Act approach.

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