Search results for: Problem Based Learning

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 processes, robot control learning, tracked vehicles.

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Authors: Hrudaya Ku. Tripathy, B. K. Tripathy, Pradip K. Das

Abstract:

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. In this paper we presented the current status of research on applying rough set theory to KDD, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how rough set and rough set analysis can be effectively used to extract knowledge from large databases.

Keywords: Data mining, Data tables, Knowledge discovery in database (KDD), Rough sets.

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Authors: K. -H. Yang

Abstract:

Supplier selection problem is one of the important issues of supply chain problems. Two categories of methodologies include qualitative and quantitative approaches which can be applied to supplier selection problems. However, due to the complexities of the problem and lacking of reliable and quantitative data, qualitative approaches are more than quantitative approaches. This study considers operational cost and supplier’s reliability factor and solves the problem by using a quantitative approach. A mixed integer programming model is the primary analytic tool. Analyses of different scenarios with variable cost and reliability structures show that the effectiveness of this approach to the supplier selection problem.

Keywords: Mixed integer programming, quantitative approach, supplier’s reliability, supplier selection.

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Authors: Felix Brito

Abstract:

With the growth of online learning, several higher education institutions have attempted to incorporate technology in their curriculum. Successful technology implementation projects really on technology infrastructure and on the acceptance of education professionals towards innovation. This research study is aimed at illustrating the relevance of the human component in technology implementation projects in higher education by describing the Learning Management System implementation project executed by instructional designers working for a higher education institution in the southeast region of the United States. An analysis of the Transformative Leadership Theory, the Technology Acceptance Model, and the Diffusion of Innovation Process provide the support for a solid understanding of this issue and address recommendations for future technology implementation projects in higher education institutions.

Keywords: Learning management systems, transformative leadership theory, technology acceptance model, diffusion of innovation process, leadership, instructional design, online learning.

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Authors: O. F. Elkommos

Abstract:

Teaching English as a Second Language (TESL) has become a very rich area of research. Practitioners or teachers of English as a foreign or a second language are now promoting both collaborative learning and collaborative teaching. Students learning a language collaboratively and cooperatively are learning in a better environment of team work where they learn from each other. Further, teaching English collaboratively also creates an enriching environment that is also very enriching to students’ and teachers’ experiences of learning and teaching. Moreover, action research stems from actual teacher concerns and students’ needs. Reflection in turn, on the experience of the material taught and the delivery of material is becoming an integral part of the teaching and learning experience self- evaluation and self-development. In this case, the concern of the research field in the area of TESL will be the development of teaching delivery, material and quality of learning. In the present research, the TESL module taught to year two students in the Faculty of Arts and Humanities, British University in Egypt (BUE) will be evaluated reflexively by the students and teachers. The module was taught to students in two different specialisms. It was taught and delivered through collaborative teaching and was evaluated by both teachers and students as very successful and enjoyable. The reflections of both teachers and students as well as student results confirm that it was a success.

Keywords: Action research, addressing differentiation, collaborative teaching, reflective teaching and learning, reflexive learning, reflexive teaching, self-development, self-evaluation, TESL.

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Authors: S. Kavitha, Nirmala P. Ratchagar

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This paper presents the novel deterministic dynamic programming approach for solving optimization problem with quadratic objective function with linear equality and inequality constraints. The proposed method employs backward recursion in which computations proceeds from last stage to first stage in a multi-stage decision problem. A generalized recursive equation which gives the exact solution of an optimization problem is derived in this paper. The method is purely analytical and avoids the usage of initial solution. The feasibility of the proposed method is demonstrated with a practical example. The numerical results show that the proposed method provides global optimum solution with negligible computation time.

Keywords: Backward recursion, Dynamic programming, Multi-stage decision problem, Quadratic objective function.

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Authors: Flur R. Ismagilov, Vyacheslav E. Vavilov, Denis V. Gusakov

Abstract:

This paper is devoted to solve the problem of increasing the electrification of aircraft engines by installing a synchronous generator at high pressure shaft. Technical solution of this problem by various research centers is discussed. A design solution of the problem was proposed. To evaluate the effectiveness of the proposed cooling system, thermal analysis was carried out in ANSYS software.

Keywords: Flur R. Ismagilov, Vyacheslav E. Vavilov, Denis V. Gusakov

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Authors: Minghui Wang

Abstract:

Based on the classical algorithm LSQR for solving (unconstrained) LS problem, an iterative method is proposed for the least-squares like-minimum-norm symmetric solution of AXB+CYD=E. As the application of this algorithm, an iterative method for the least-squares like-minimum-norm biymmetric solution of AXB=E is also obtained. Numerical results are reported that show the efficiency of the proposed methods.

Keywords: Matrix equation, bisymmetric matrix, least squares problem, like-minimum norm, iterative algorithm.

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Authors: Anis Fatmi, Amur Al Yahmadi, Lazhar Khriji, Nouri Masmoudi

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One of the long standing challenging aspect in mobile robotics is the ability to navigate autonomously, avoiding modeled and unmodeled obstacles especially in crowded and unpredictably changing environment. A successful way of structuring the navigation task in order to deal with the problem is within behavior based navigation approaches. In this study, Issues of individual behavior design and action coordination of the behaviors will be addressed using fuzzy logic. A layered approach is employed in this work in which a supervision layer based on the context makes a decision as to which behavior(s) to process (activate) rather than processing all behavior(s) and then blending the appropriate ones, as a result time and computational resources are saved.

Keywords: Behavior based navigation, context based coordination, fuzzy logic, mobile robots.

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

Abstract:

Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humans – including asset maintenance operations – in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: Degradation models, ageing, anomaly detection, soft sensor, incremental learning.

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Authors: F. Simba, L. Trojer, N.H. Mvungi, B.M. Mwinyiwiwa, E.M. Mjema

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In response to address different development challenges, Tanzania is striving to achieve its fourth attribute of the National Development Vision, i.e. to have a well educated and learned society by the year 2025. One of the most cost effective methods that can reach a large part of the society in a short time is to integrate ICT in education through e-learning initiatives. However, elearning initiatives are challenged by limited or lack of connectivity to majority of secondary schools, especially those in rural and remote areas. This paper has explores the possibility for rural secondary school to access online e-Learning resources from a centralized e- Learning Management System (e-LMS). The scope of this paper is limited to schools that have computers irrespective of internet connectivity, resulting in two categories schools; those with internet access and those without. Different connectivity configurations have been proposed according to the ICT infrastructure status of the respective schools. However, majority of rural secondary schools in Tanzania have neither computers nor internet connection. Therefore this is a challenge to be addressed for the disadvantaged schools to benefit from e-Learning initiatives.

Keywords: connectivity, configuration, e-Learning, replication, rural.

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Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

Abstract:

Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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Authors: Mario Gonzalez, David Dominguez, Francisco B. Rodriguez

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An attractor neural network on the small-world topology is studied. A learning pattern is presented to the network, then a stimulus carrying local information is applied to the neurons and the retrieval of block-like structure is investigated. A synaptic noise decreases the memory capability. The change of stability from local to global attractors is shown to depend on the long-range character of the network connectivity.

Keywords: Hebbian learning, image recognition, small world, spatial information.

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Authors: Jayanthi Rajendran

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Experience is what makes a man perfect. Though we tend to learn many a different things in life through practice still we need to go an extra mile to gain experience which would be profitable only when it is integrated with regular practice. A clear phenomenal idea is that every teacher is a learner. The centralized idea of this paper would focus on the integrated practices carried out among the students of Jizan University which enhances learning through experiences. Integrated practices like student-directed activities, balanced curriculum, phonological based activities and use of consistent language would enlarge the vision and mission of students to earn experience through learning. Students who receive explicit instruction and guidance could practice the skills and strategies through student-directed activities such as peer tutoring and cooperative learning. The second effective practice is to use consistent language. Consistent language provides students a model for talking about the new concepts which also enables them to communicate without hindrances. Phonological awareness is an important early reading skill for all students. Students generally have phonemic awareness in their home language can often transfer that knowledge to a second language. And also a balanced curriculum requires instruction in all the elements of reading. Reading is the most effective skill when both basic and higher-order skills are included on a daily basis. Computer based reading and listening skills will empower students to understand language in a better way. English language learners can benefit from sound reading instruction even before they are fully proficient in English as long as the instruction is comprehensible. Thus, if students have to be well equipped in learning they should foreground themselves in various integrated practices through multifarious experience for which teachers are moderators and trainers. This type of learning prepares the students for a constantly changing society which helps them to meet the competitive world around them for better employability fulfilling the vision and mission of the institution.

Keywords: Consistent language, employability, phonological awareness, balanced curriculum.

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Authors: Verónica Díaz

Abstract:

A Cartesian graph, as a mathematical object, becomes a tool for configuration of change. Its best comprehension is done through everyday life problem-solving associated with its representation. Despite this, the current educational framework favors general graphs, without consideration of their argumentation. Students are required to find the mathematical function without associating it to the development of graphical language. This research describes the use made by students of configurations made prior to Cartesian graphs with regards to an everyday life problem related to a time and distance variation phenomenon. The theoretical framework describes the function conditions of study and their modeling. This is a qualitative, descriptive study involving six undergraduate case studies that were carried out during the first term in 2016 at University of Los Lagos. The research problem concerned the graphic modeling of a real person’s movement phenomenon, and two levels of analysis were identified. The first level aims to identify local and global graph interpretations; a second level describes the iconicity and referentiality degree of an image. According to the results, students were able to draw no figures before the Cartesian graph, highlighting the need for students to represent the context and the movement of which causes the phenomenon change. From this, they managed Cartesian graphs representing changes in position, therefore, achieved an overall view of the graph. However, the local view only indicates specific events in the problem situation, using graphic and verbal expressions to represent movement. This view does not enable us to identify what happens on the graph when the movement characteristics change based on possible paths in the person’s walking speed.

Keywords: Cartesian graphs, higher education, movement modeling, problem solving.

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Authors: Hsing-Kuo Pao, John Case

Abstract:

Biological sequences from different species are called or-thologs if they evolved from a sequence of a common ancestor species and they have the same biological function. Approximations of Kolmogorov complexity or entropy of biological sequences are already well known to be useful in extracting similarity information between such sequences -in the interest, for example, of ortholog detection. As is well known, the exact Kolmogorov complexity is not algorithmically computable. In prac-tice one can approximate it by computable compression methods. How-ever, such compression methods do not provide a good approximation to Kolmogorov complexity for short sequences. Herein is suggested a new ap-proach to overcome the problem that compression approximations may notwork well on short sequences. This approach is inspired by new, conditional computations of Kolmogorov entropy. A main contribution of the empir-ical work described shows the new set of entropy-based machine learning attributes provides good separation between positive (ortholog) and nega-tive (non-ortholog) data - better than with good, previously known alter-natives (which do not employ some means to handle short sequences well).Also empirically compared are the new entropy based attribute set and a number of other, more standard similarity attributes sets commonly used in genomic analysis. The various similarity attributes are evaluated by cross validation, through boosted decision tree induction C5.0, and by Receiver Operating Characteristic (ROC) analysis. The results point to the conclu-sion: the new, entropy based attribute set by itself is not the one giving the best prediction; however, it is the best attribute set for use in improving the other, standard attribute sets when conjoined with them.

Keywords: compression, decision tree, entropy, ortholog, ROC.

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Authors: Wanatchapong Kongkaew

Abstract:

This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

 

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness.

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Authors: A. Shayeghi, H. Shayeghi, H. Eimani Kalasar

Abstract:

In recent years, tuned mass damper (TMD) control systems for civil engineering structures have attracted considerable attention. This paper emphasizes on the application of particle swarm application (PSO) to design and optimize the parameters of the TMD control scheme for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using the PSO technique which has a story ability to find the most optimistic results. An 11- story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed method. The results analysis through the time-domain simulation and some performance indices reveals that the designed PSO based TMD controller has an excellent capability in reduction of the seismically excited example building.

Keywords: TMD, Particle Swarm Optimization, Tall Buildings, Structural Dynamics.

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Authors: Ja'far Alqatawna, Jawed Siddiqi, Babak Akhgar, Mohammad Hjouj Btoush

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A great deal of research works in the field information systems security has been based on a positivist paradigm. Applying the reductionism concept of the positivist paradigm for information security means missing the bigger picture and thus, the lack of holism which could be one of the reasons why security is still overlooked, comes as an afterthought or perceived from a purely technical dimension. We need to reshape our thinking and attitudes towards security especially in a complex and dynamic environment such as e- Business to develop a holistic understanding of e-Business security in relation to its context as well as considering all the stakeholders in the problem area. In this paper we argue the suitability and need for more inductive interpretive approach and qualitative research method to investigate e-Business security. Our discussion is based on a holistic framework of enquiry, nature of the research problem, the underling theoretical lens and the complexity of e-Business environment. At the end we present a research strategy for developing a holistic framework for understanding of e-Business security problems in the context of developing countries based on an interdisciplinary inquiry which considers their needs and requirements.

Keywords: e-Business Security, Complexity, Methodological considerations, interpretive qualitative research and Case study method.

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Authors: Sameerchand Pudaruth, Nawsheen Bibi Jannnoo

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In this paper, we give an overview of an online elearning tool which has been developed for kids aged from nine to eleven years old in Mauritius for the self-study of Mathematics in order to prepare them for the CPE examination. The software does not intend to render obsolete the existing pedagogical approaches. Nowadays, the teaching-learning process is mainly focused towards the class-room model. Moreover, most of the e-learning platforms that exist are simply static ways of delivering resources using the internet. There is nearly no interaction between the learner and the tool. Our application will enable students to practice exercises online and also work out sample examination papers. Another interesting feature is that the kid will not have to wait for someone to correct the work as the correction will be done online and on the spot. Additional feedback is also provided for some exercises.

Keywords: CPE, e-learning, Mauritius, primary education

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Authors: Gholamreza Ghodrati Amiri, Mojtaba Jafarian Abyaneh, Ali Zare Hosseinzadeh

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This paper presents an effective model updating strategy for damage localization and quantification in frames by defining damage detection problem as an optimization issue. A generalized version of the Modal Residual Force (MRF) is employed for presenting a new damage-sensitive cost function. Then, Grey Wolf Optimization (GWO) algorithm is utilized for solving suggested inverse problem and the global extremums are reported as damage detection results. The applicability of the presented method is investigated by studying different damage patterns on the benchmark problem of the IASC-ASCE, as well as a planar shear frame structure. The obtained results emphasize good performance of the method not only in free-noise cases, but also when the input data are contaminated with different levels of noises.

Keywords: Frame, grey wolf optimization algorithm, modal residual force, structural damage detection.

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Authors: Norazah Mohd Suki, Norbayah Mohd Suki

Abstract:

Descriptive statistics was performed with the aim to achieve research objective of to investigate lecturers- usage of the mobile technology for teaching. A representative sample of 20 lecturers from the Faculty of Industrial Art & Design Technology of Universiti Industri Selangor (UNISEL), Malaysia was selected as the respondents. The result attested that lecturers fully accept the concept of mobility in learning and game play is appealing concept to support classroom learning. Subsequently, analogous experience on small size of keypad, screen resolution, and navigation could be the major problematic factors to students and affect their mobile learning process. Recommendation for future research is also presented.

Keywords: Academics, Mobile e-learning, Mobile technology, Readiness.

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

Abstract:

Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: Clustering, k-means, categorical datasets, pattern recognition, unsupervised learning, knowledge discovery.

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Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

Abstract:

The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic Language acquisition and learning, natural language processing, morphological analyzer, part-of-speech.

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Authors: S. Raja Balachandar, K.Kannan

Abstract:

This paper presents a heuristic approach to solve the Generalized Assignment Problem (GAP) which is NP-hard. It is worth mentioning that many researches used to develop algorithms for identifying the redundant constraints and variables in linear programming model. Some of the algorithms are presented using intercept matrix of the constraints to identify redundant constraints and variables prior to the start of the solution process. Here a new heuristic approach based on the dominance property of the intercept matrix to find optimal or near optimal solution of the GAP is proposed. In this heuristic, redundant variables of the GAP are identified by applying the dominance property of the intercept matrix repeatedly. This heuristic approach is tested for 90 benchmark problems of sizes upto 4000, taken from OR-library and the results are compared with optimum solutions. Computational complexity is proved to be O(mn2) of solving GAP using this approach. The performance of our heuristic is compared with the best state-ofthe- art heuristic algorithms with respect to both the quality of the solutions. The encouraging results especially for relatively large size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.

Keywords: Combinatorial Optimization Problem, Generalized Assignment Problem, Intercept Matrix, Heuristic, Computational Complexity, NP-Hard Problems.

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Authors: Bahloul Amel

Abstract:

The present study debates students’ perceptions of the use of technology in learning English as a Foreign Language. Its aim is to explore and understand students’ preparation and presentation of Posters, PowerPoint and Animated Videos by drawing attention to visual and oral elements. The data is collected through observations and semi-structured interviews and analyzed through phenomenological data analysis steps. The themes emerged from the data, visual learning satisfaction in using information and communication technology, providing structure to oral presentation, learning from peers’ presentations, draw attention to using Posters, PowerPoint and Animated Videos as each supports visual learning and organization of thoughts in oral presentations.

Keywords: Animated Videos, EFL, Posters, PowerPoint presentations, Visual Learning.

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Authors: Wernhuar Tarng, Wen-Shin Tsai, Yu-Si Lin, Chen-Kai Shiu

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Ecological ponds can be a good teaching tool for science teachers, but they must be built and maintained properly to provide students with a safe and suitable learning environment. Hence, many schools do not have the ability to build an ecological pond. This study used virtual reality technology to develop a webbased virtual ecological pond. Supported by situated learning theory and the instructional design of “Aquatic Life" learning unit, elementary school students can actively explore in the virtual ecological pond to observe aquatic animals and plants and learn about the concept of ecological conservation. A teaching experiment was conducted to investigate the learning effectiveness and practicability of this instructional design, and the results showed that students improved a great deal in learning about aquatic life. They found the virtual ecological pond interesting, easy to operate and helpful to understanding the aquatic ecological system. Therefore, it is useful in elementary science education.

Keywords: Virtual reality, virtual ecological ponds, situated learning, instructional design, science education.

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Authors: A. Shirzadeh Chaleshtari, Sh. Shadrokh

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Renewable and non-renewable resource constraints have been vast studied in theoretical fields of project scheduling problems. However, although cumulative resources are widespread in practical cases, the literature on project scheduling problems subject to these resources is scant. So in order to study this type of resources more, in this paper we use the framework of a resource constrained project scheduling problem (RCPSP) with finish-start precedence relations between activities and subject to the cumulative resources in addition to the renewable resources. We develop a branch and bound algorithm for this problem customizing precedence tree algorithm of RCPSP. We perform extensive experimental analysis on the algorithm to check its effectiveness and performance for solving different instances of the problem in question.

Keywords: Resource constrained project scheduling problem, cumulative resources, branch and bound algorithm, precedence tree.

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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Authors: Yucel Ugurlu

Abstract:

This paper explains a novel approach to human interactive e-learning systems using head posture images. Students- face and hair information are used to identify a human presence and estimate the gaze direction. We then define the human-computer interaction level and test the definition using ten students and seventy different posture images. The experimental results show that head posture images provide adequate information for increasing human-computer interaction in e-learning systems.

Keywords: E-learning, image segmentation, human-presence, gaze-direction, human-computer interaction, LabVIEW

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