Search results for: Decision Tree learning

Authors: Abbas Keramati, Abtin Boostani, Mohammad Jamal Sadeghi

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This paper examines the influence of knowledge management factors on organizational commitment for employees in the oil and gas drilling industry of Iran. We determine what knowledge factors have the greatest impact on the personnel loyalty and commitment to the organization using collected data from a survey of over 300 full-time personnel working in three large companies active in oil and gas drilling industry of Iran. To specify the effect of knowledge factors in the organizational commitment of the personnel in the studied organizations, the Principal Component Analysis (PCA) is used. Findings of our study show that the factors such as knowledge and expertise, in-service training, the knowledge value and the application of individuals’ knowledge in the organization as the factor “learning and perception of personnel from the value of knowledge within the organization” has the greatest impact on the organizational commitment. After this factor, “existence of knowledge and knowledge sharing environment in the organization”; “existence of potential knowledge exchanging in the organization”; and “organizational knowledge level” factors have the most impact on the organizational commitment of personnel, respectively.

Keywords: Knowledge management, organizational commitment, loyalty, drilling industry, principle component analysis.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila

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An extensive amount of work has been done in data clustering research under the unsupervised learning technique in Data Mining during the past two decades. Moreover, several approaches and methods have been emerged focusing on clustering diverse data types, features of cluster models and similarity rates of clusters. However, none of the single clustering algorithm exemplifies its best nature in extracting efficient clusters. Consequently, in order to rectify this issue, a new challenging technique called Cluster Ensemble method was bloomed. This new approach tends to be the alternative method for the cluster analysis problem. The main objective of the Cluster Ensemble is to aggregate the diverse clustering solutions in such a way to attain accuracy and also to improve the eminence the individual clustering algorithms. Due to the massive and rapid development of new methods in the globe of data mining, it is highly mandatory to scrutinize a vital analysis of existing techniques and the future novelty. This paper shows the comparative analysis of different cluster ensemble methods along with their methodologies and salient features. Henceforth this unambiguous analysis will be very useful for the society of clustering experts and also helps in deciding the most appropriate one to resolve the problem in hand.

Keywords: Clustering, Cluster Ensemble Methods, Coassociation matrix, Consensus Function, Median Partition.

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Authors: Chowdhury Dilruba Shoma

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Gender is about equal rights that both males and females having access to responsibilities and opportunities in decision making is a fundamental human right. It is also a precondition for, and a mark of, sustainable people-oriented development. In Bangladesh, women have fewer opportunities than men do to access credit from banks and financial institutions. Entrenched patriarchal attitudes, unequal inheritance rights, and male-dominated hierarchies in the financial system, plus high interest rates and a lack of security/collateral, make it harder for women to obtain bank loans. Limited access to institutional credit is a serious restraint on the productivity and income of women entrepreneurs, (and the wider economy). These gender-biased and structural barriers inhibit women’s access to fundamental economic rights. Using a liberal feminist theoretical lens, this study provides some useful insights into the relationship between gender inequality and entrepreneurship, leading to a better understanding of women’s entrepreneurship development in Bangladesh. Recently, the Bangladesh Government, the United Nations Capital Development Fund, and Bangladesh Bank opened up the Women Entrepreneur Support Fund (WESF) ‒ Credit Guarantee Scheme (CGS) pilot project to cover collateral shortfalls for women entrepreneurs in the small and medium enterprise sector. The aim is to improve gender equality and advance women’s rights in relation to receiving credit. This article examines the challenges and prospects of the WESF-CGS, and suggests that implementation of measures in WESF-CGS policymaking, coupled with a combination of legislatory and regulatory reforms that implement the fundamental tenets of liberal feminism, can lead to a comprehensive and effective credit policy to boost women’s agency and economic empowerment. This may ultimately lead to more sustainable development in Bangladesh.

Keywords: Bangladesh, CGS, liberal feminist theory, women entrepreneur support fund.

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Authors: Hamed.Vezvaei, Sepideh.Ordibeheshti, Mehdi.Ardjmand

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Gasoline Octane Number is the standard measure of the anti-knock properties of a motor in platforming processes, that is one of the important unit operations for oil refineries and can be determined with online measurement or use CFR (Cooperative Fuel Research) engines. Online measurements of the Octane number can be done using direct octane number analyzers, that it is too expensive, so we have to find feasible analyzer, like ANFIS estimators. ANFIS is the systems that neural network incorporated in fuzzy systems, using data automatically by learning algorithms of NNs. ANFIS constructs an input-output mapping based both on human knowledge and on generated input-output data pairs. In this research, 31 industrial data sets are used (21 data for training and the rest of the data used for generalization). Results show that, according to this simulation, hybrid method training algorithm in ANFIS has good agreements between industrial data and simulated results.

Keywords: Adaptive Neuro-Fuzzy Inference Systems, GasolineOctane Number, Soft-sensor, Catalytic Naphtha Reforming

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Authors: Perpetua O. Ezeji, Kelechi E. Ezeji

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There has been considerable anxiety in society that social media distracts from education and reduces the social skills of young people. Following this, educators have sought ways to mitigate its negative effects on educational attainment while incorporating its positive aspects into the learning process. This study sought to examine the impact of social media on the study habits of students of Alvan Ikoku Federal College of Education, Owerri. The research design involved survey technique where questionnaires were used to collect data from a sample of the student population. Statistical package for social sciences (SPSS) was used to analyse the data. Spearman’s Rho was the specific tool used for analysis. It was presented in frequency tables and bar charts. Findings from variables investigated showed that at p<0.5, social media usage had a significant impact on the study habits of students of Alvan Ikoku Federal College of Education, Owerri. This indicated the need for stakeholders in the community to employ counselling and other proactive measures to ensure that students maintained proper focus on their primary assignment for schooling.

Keywords: Education, social media, study habits, technology.

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Authors: S. Srinivasa Moorthy, K. Manonmani

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The new, polymer composites consisting of e-glass fiber reinforcement with titanium oxide filler in the double bonded unsaturated polyester resin matrix were made. The glass fiber and titanium oxide reinforcement composites were made in three different fiber lengths (3cm, 5cm, and 7cm), filler content (2 wt%, 4 wt%, and 6 wt%) and fiber content (20 wt%, 40 wt%, and 60 wt%). 27 different compositions were fabricated and a sequence of experiments were carried out to determine tensile strength and impact strength. The vital influencing factors fiber length, fiber content and filler content were chosen as 3 factors in 3 levels of Taguchi’s L₉ orthogonal array. The influences of parameters were determined for tensile strength and impact strength by Analysis of variance (ANOVA) and S/N ratio. Using Artificial Neural Network (ANN) an expert system was devised to predict the properties of hybrid reinforcement GFRP composites. The predict models were experimentally proved with the maximum coincidence.

Keywords: Analysis of variance (ANOVA), Artificial neural network (ANN), Polymer composites, Taguchi’s orthogonal array.

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Authors: Rosnani Jusoh, Afifuddin Husairi Hussain, Fadzilah Abdul Rahman, Adi Irfan Che Ani, Haziyah Hussin

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New conceptualizations were introduced to address the emerging need to develop innovativeness and creativity attitudes in future engineering professional. Thus, entrepreneurial engineering education needs an environment where future engineers can be formed through practical learning and the interaction with forces, ideas and inspirations, in the final perspective of effectively identifying, acquiring, developing, and transferring technology into new business products and services. This paper aims to investigate the characteristics of a successful entrepreneur for engineering students. A questionnaire was used to measure the level of entrepreneurial competencies. The questionnaire was based on the Personal Entrepreneurial Competence Model, developed by McClelland Individual. Participants were recruited from the final year students of eight programs under the Faculty of Engineering. Of the 432 questionnaires distributed, 210 were collected back, giving a response rate of approximately 49%. Findings suggest that final year engineering students have respectable entrepreneurial attitudes and behaviors, and are competent to be engineerpreneur. The study also recommended that we need an environment that does not insist that engineers become entrepreneurs, but one where the two can meet, and business leaders can organize our nation effectively.

Keywords: Engineerpreneur, entrepreneurship, engineering students, higher education institutions.

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Authors: J. Alamelu Mangai, Satej Wagle, V. Santhosh Kumar

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The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.

Keywords: Medical Image Mining, Data Mining, Feature Weighting, Association Rule Mining, k nearest neighbor classifier.

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Authors: Anjan Babu G, Sumana G, Rajasekhar M

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multilayered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Further, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: Dialysis, Hereditary, Transplantation, Polycystic, Pathogenesis.

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Authors: W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sathasivam

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This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

Keywords: Convergence, Modeling, Neural Networks, Preconditioned Conjugate Gradient.

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Authors: F. Moghtaderi, M. Burke, J. Troelsen

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There has been a significant decline in active travel and a massive increase in the use of car dependent travel in many countries during the past two decades. Evidential risks for people’s physical and mental health problems are correlated with this increased use of motorized travel. These health related problems range from overweight and obesity to increased air pollution. In response to these rising concerns health professionals, traffic planers, local authorities and others have introduced a variety of initiatives to counterbalance the dominance of cars for daily journeys. However, the nature of travel behavior change interventions, which aim to reduce car use, are very complex and challenging regarding their interactions with human behavior. To change travel behavior at least two aspects have to be taken into consideration. First, how to alter attitudes and perceptions toward the sustainable and healthy modes of travel, in competition with experiences of private car use. And second, how to make these behavior change processes irreversible and sustainable. There are no comprehensive models available to guide policy interventions to increase the level of success of travel behavior change interventions across both these dimensions. A comprehensive theoretical framework is required in the effort to optimize how to facilitate and guide the processes of data collection and analysis to achieve the best possible guidelines for policy makers. Regarding the gaps in the travel behavior change research literature, this paper attempted to identify and suggest a multidimensional framework in order to facilitate planning the implemented travel behavior change interventions. A structured mixed-method model is suggested to improve the analytic power of the results according to the complexity of human behavior. In order to recognize people’s attitudes towards a specific travel mode, the Theory of Planned Behavior (TPB) was operationalized. But in order to capture decision making processes the Transtheoretical model of Behavior Change (TTM) was also used. Consequently, the combination of these two theories (TTM and TPB) has resulted in a synthesis with appropriate concepts to identify and design an implemented travel behavior change interventions.

Keywords: Behavior change theories, Theoretical framework, Travel behavior change interventions.

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Authors: M. L. Anitha, K. A. Radhakrishna Rao

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With the advent of modern computing technology, there is an increased demand for developing recognition systems that have the capability of verifying the identity of individuals. Recognition systems are required by several civilian and commercial applications for providing access to secured resources. Traditional recognition systems which are based on physical identities are not sufficiently reliable to satisfy the security requirements due to the use of several advances of forgery and identity impersonation methods. Recognizing individuals based on his/her unique physiological characteristics known as biometric traits is a reliable technique, since these traits are not transferable and they cannot be stolen or lost. Since the performance of biometric based recognition system depends on the particular trait that is utilized, the present work proposes a fusion approach which combines Inner knuckle print (IKP) trait of the middle, ring and index fingers with the geometrical features of hand. The hand image captured from a digital camera is preprocessed to find finger IKP as region of interest (ROI) and hand geometry features. Geometrical features are represented as the distances between different key points and IKP features are extracted by applying local binary pattern descriptor on the IKP ROI. The decision level AND fusion was adopted, which has shown improvement in performance of the combined scheme. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since both hand geometry and IKP features can be extracted from the palm region of the hand. The fusion of these features yields a false acceptance rate of 0.75%, false rejection rate of 0.86% for verification tests conducted, which is less when compared to the results obtained using individual traits. The results obtained confirm the usefulness of proposed approach and suitability of the selected features for developing biometric based recognition system based on features from palmar region of hand.

Keywords: Biometrics, hand geometry features, inner knuckle print, recognition.

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Authors: M. Michael Gromiha, A. Mary Thangakani, Sandeep Kumar, D. Velmurugan

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The main cause of several neurodegenerative diseases such as Alzhemier, Parkinson and spongiform encephalopathies is formation of amyloid fibrils and plaques in proteins. We have analyzed different sets of proteins and peptides to understand the influence of sequence based features on protein aggregation process. The comparison of 373 pairs of homologous mesophilic and thermophilic proteins showed that aggregation prone regions (APRs) are present in both. But, the thermophilic protein monomers show greater ability to ‘stow away’ the APRs in their hydrophobic cores and protect them from solvent exposure. The comparison of amyloid forming and amorphous b-aggregating hexapeptides suggested distinct preferences for specific residues at the six positions as well as all possible combinations of nine residue pairs. The compositions of residues at different positions and residue pairs have been converted into energy potentials and utilized for distinguishing between amyloid forming and amorphous b-aggregating peptides. Our method could correctly identify the amyloid forming peptides at an accuracy of 95-100% in different datasets of peptides.

Keywords: Aggregation prone regions, amyloids, thermophilic proteins, amino acid residues, machine learning.

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Authors: Brehme D’napoli Reis de Mesquita, Marcus Vin´ıcius de Souza Almeida, Caio Vin´ıcius Silva do Carmo

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Educational robotics has emerged as a pedagogical tool, utilizing technological artifacts to engage students’ curiosity and interest. It fosters active learning of STEM education competencies while also cultivating essential behavioral skills. Robotic competitions provide students with platforms to collaboratively devise diverse solutions to shared problems, fostering experience exchange, collaboration, and personal growth. Despite the prevalence of current robotic competitions, especially in Brazil, simulating real-world challenges like natural disasters, there is a notable absence of industry-related tasks. This article presents an educational robotics initiative centered around material transportation within smart manufacturing using automated guided vehicles. The proposed robotics challenge was executed in a competition held in Ac¸ailˆandia city, Maranh˜ao, Brazil, yielding satisfactory results and inspiring teams to develop time-limited solution strategies.

Keywords: Educational robotics, STEM education, robotic competitions, material transportation, smart manufacturing.

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Authors: K. Akilandeswari, G. M. Nasira

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Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable recognition accuracy. It is a vital step affecting pattern recognition system performance. This study presents a new Binary Particle Swarm Optimization (BPSO) based feature selection algorithm. Multi-layer Perceptron Neural Network (MLPNN) classifier with backpropagation training algorithm and Levenberg-Marquardt training algorithm classify selected features.

Keywords: Brain-Computer Interfaces (BCI), Feature Selection (FS), Walsh–Hadamard Transform (WHT), Binary Particle Swarm Optimization (BPSO), Multi-Layer Perceptron (MLP), Levenberg–Marquardt algorithm.

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Authors: C. S. Caires, A. Barbosa, W. Hanyou

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In this paper, we present the main results achieved during a five-week international workshop on Interactive Furniture for the Classroom, with 22 Chinese design students, in Jiangmen city (Guangdong province, China), and five teachers from Portugal, France, Iran, Macao SAR, and China. The main goal was to engage design students from China with new skills and practice methodologies towards interactive design research for furniture and product design for the classroom. The final results demonstrate students' concerns on improving Chinese furniture design for the classrooms, including solutions related to collaborative learning and human-interaction design for interactive furniture products. The findings of the research led students to the fabrication of five original prototypes: two for kindergartens ('Candy' and 'Tilt-tilt'), two for primary schools ('Closer' and 'Eks(x)'), and one for art/creative schools ('Wave'). From the findings, it was also clear that collaboration, personalization, and project-based teaching are still neglected when designing furniture products for the classroom in China. Students focused on these issues and came up with creative solutions that could transform this educational field in China.

Keywords: Product design, interface design, interactive design, collaborative education and design research, design prototyping.

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: Neural networks, motion detection, signature detection, convolutional neural network.

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Authors: Moulay A. Akhloufi

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In recent years, we see an increase of interest for efficient tracking systems in surveillance applications. Many of the proposed techniques are designed for static cameras environments. When the camera is moving, tracking moving objects become more difficult and many techniques fail to detect and track the desired targets. The problem becomes more complex when we want to track a specific object in real-time using a moving Pan and Tilt camera system to keep the target within the image. This type of tracking is of high importance in surveillance applications. When a target is detected at a certain zone, the possibility of automatically tracking it continuously and keeping it within the image until action is taken is very important for security personnel working in very sensitive sites. This work presents a real-time tracking system permitting the detection and continuous tracking of targets using a Pan and Tilt camera platform. A novel and efficient approach for dealing with occlusions is presented. Also a new intelligent forget factor is introduced in order to take into account target shape variations and avoid learning non desired objects. Tests conducted in outdoor operational scenarios show the efficiency and robustness of the proposed approach.

Keywords: Tracking, surveillance, target detection, Pan and tilt.

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Authors: Jayendra Krishna, Laxmi Srivastava

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Power flow (PF) study, which is performed to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state operating condition of a system, is very important and is the most frequently carried out study by power utilities for power system planning, operation and control. In this paper, a counterpropagation neural network (CPNN) is proposed to solve power flow problem under different loading/contingency conditions for computing bus voltage magnitudes and angles of the power system. The counterpropagation network uses a different mapping strategy namely counterpropagation and provides a practical approach for implementing a pattern mapping task, since learning is fast in this network. The composition of the input variables for the proposed neural network has been selected to emulate the solution process of a conventional power flow program. The effectiveness of the proposed CPNN based approach for solving power flow is demonstrated by computation of bus voltage magnitudes and voltage angles for different loading conditions and single line-outage contingencies in IEEE 14-bus system.

Keywords: Admittance matrix, counterpropagation neural network, line outage contingency, power flow

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Authors: Chu-Ching Hsu

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This paper focuses on how the government-led language policies and the political changes in Taiwan manipulate the languages choice in translations and what translation strategies are employed by the translator to show his or her language ideology behind the power struggles and decision-making. Therefore, framed by Lefevere’s theoretical concept of translating as rewriting, and carried out a diachronic and chronological study, this paper specifically sets out to investigate the language ideology and translator’s idiolect of Chinese language translations of Anglo-American novels. The examples drawn to explore these issues were taken from different versions of Chinese renditions of Mark Twain’s English-language novel The Adventures of Huckleberry Finn in which there are several different dialogues originally written in the colloquial language and dialect used in the American state of Mississippi and reproduced in Mark Twain’s works. Also, adapted corpus methodology, many examples are extracted as instances from the translated texts and source text, to illuminate how the translators in Taiwan deal with the dialectal features encoded in Twain’s works, and how different versions of Chinese translations are employed by Taiwanese translators to confirm the language polices and to express their language identity textually in different periods of the past five decades, from the 1960s onward. The finding of this study suggests that the use of Taiwanese dialect and language patterns in translations does relate to the movement of the mother-tongue language and language ideology of the translator as well as to the issue of language identity raised in the island of Taiwan. Furthermore, this study confirms that the change of political power in Taiwan does bring significantly impact in language policy-- assimilationism, pluralism or multiculturalism, which also makes Taiwan from a monolingual to multilingual society, where the language ideology and identity can be revealed not only in people’s daily communication but also in written translations.

Keywords: Language politics and policies, literary translation, mother-tongue, multiculturalism, translator’s ideology.

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Authors: Petronella Jonck, Freda van der Walt

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Despite the benefits of innovation diffusion in the South African public service, implementation thereof seems to be problematic, particularly with regard to e-governance which would enhance the quality of service delivery, especially accessibility, choice, and mode of operation. This paper reports on differences between the public service and the private sector in terms of innovation diffusion. Innovation diffusion will be investigated to explore identified obstacles that are hindering successful implementation of e-governance. The research inquiry is underpinned by the diffusion of innovation theory, which is premised on the assumption that innovation has a distinct channel, time, and mode of adoption within the organisation. A comparative thematic document analysis was conducted to investigate organisational differences with regard to innovation diffusion. A similar approach has been followed in other countries, where the same conceptual framework has been used to guide document analysis in studies in both the private and the public sectors. As per the recommended conceptual framework, three organisational characteristics were emphasised, namely the external characteristics of the organisation, the organisational structure, and the inherent characteristics of the leadership. The results indicated that the main difference in the external characteristics lies in the focus and the clientele of the private sector. With regard to organisational structure, private organisations have veto power, which is not the case in the public service. Regarding leadership, similarities were observed in social and environmental responsibility and employees’ attitudes towards immediate supervision. Differences identified included risk taking, the adequacy of leadership development, organisational approaches to motivation and involvement in decision making, and leadership style. Due to the organisational differences observed, it is recommended that differentiated strategies be employed to ensure effective innovation diffusion, and ultimately e-governance. It is recommended that the results of this research be used to stimulate discussion on ways to improve collaboration between the mentioned sectors, to capitalise on the benefits of each sector.

Keywords: E-governance, ICT, innovation diffusion, comparative analysis.

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Authors: Wanghongshu Zhou

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This study aims to explore the relationship between the second language motivational self-system (L2MSS) and the willingness to communicate (WTC) among Chinese non-English major students in order to provide pedagogical implications for English as a Foreign Language (EFL) classrooms in Chinese universities. By employing a mixed methods approach, we involved 103 Chinese non-English major students from a typical university in China, conducted questionnaire survey to measure their levels of L2WTC and L2MSS level, and then analyzed the correlation between the two above mentioned variables. Semi-structured interviews were conducted with eight participants to provide a deeper understanding and explanation of the questionnaire data. Findings show that 1) Chinese non-English major students’ ideal L2 self and L2 learning experience could positively predict their L2 WTC in EFL class; 2) Chinese non-English major students’ ought-to L2 self might have no significant impact on their L2 WTC in EFL class; and 3) self-confidence might be another main factor that will influence Chinese non-English major students’ L2 WTC in EFL class. These findings might shed light on the second language acquisition field and provide pedagogical recommendations for pre-service as well as in-service EFL teachers.

Keywords: Chinese non-English major students, L2 Motivation, L2 willingness to communicate, self-confidence.

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Authors: Eftah Bte. Moh Hj Abdullah, Izazol Binti Idris, Abd Aziz Bin Abd Shukor

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This study aims to identify the understanding expectations of school administrators concerning school assessment. The researcher utilized a qualitative descriptive study on 19 administrators from three secondary schools in the North Kinta district. The respondents had been interviewed on their understanding expectations of school assessment using the focus group discussion method. Overall findings showed that the administrators’ understanding expectations of school assessment was weak; especially in terms of content focus, articulation across age and grade, transparency and fairness, as well as the pedagogical implications. Findings from interviews indicated that administrators explained their understanding expectations of school assessment from the aspect of school management, and not from the aspect of instructional leadership or specifically as assessment leaders. The study implications from the administrators’ understanding expectations may hint at the difficulty of the administrators to function as assessment leaders, in order to reduce their focus as manager, and move towards their primary role in the process of teaching and learning. The administrator, as assessment leaders, would be able to reach assessment goals via collaboration in identifying and listing teacher assessment competencies, how to construct assessment capacity, how to interpret assessment correctly, the use of assessment and how to use assessment information to communicate confidently and effectively to the public.

Keywords: Assessment leaders, assessment goals, instructional leadership, understanding expectation of assessment.

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Authors: M. R. Mustafa, M. H. Isa, R. B. Rezaur

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The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water resources engineering are being increasing rapidly. Infrastructural applications of ANN in terms of selection of inputs, architecture of networks, training algorithms, and selection of training parameters in different types of neural networks used in water resources engineering have been reported. ANN modeling conducted for water resources engineering variables (river sediment and discharge) published in high impact journals since 2002 to 2011 have been examined and presented in this review. ANN is a vigorous technique to develop immense relationship between the input and output variables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of examples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data before network modeling, which can facilitate to design an efficient network. An appropriate training based ANN model is able to adopt the physical understanding between the variables and may generate more effective results than conventional prediction techniques.

Keywords: ANN, discharge, modeling, prediction, sediment,

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models, on two different real-world electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, Machine Learning, imputation, laboratory variables, algorithmic bias.

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Authors: Thiang, Handry Khoswanto, Rendy Pangaldus

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Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.

Keywords: Artificial neural network, back propagation, inverse kinematics, manipulator, robot.

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Authors: Jheng-Long Wu, Pei-Chann Chang, Shih-Ling Chang, Liang-Chih Yu, Jui-Feng Yeh, Chin-Sheng Yang

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The Major Depressive Disorder has been a burden of medical expense in Taiwan as well as the situation around the world. Major Depressive Disorder can be defined into different categories by previous human activities. According to machine learning, we can classify emotion in correct textual language in advance. It can help medical diagnosis to recognize the variance in Major Depressive Disorder automatically. Association language incremental is the characteristic and relationship that can discovery words in sentence. There is an overlapping-category problem for classification. In this paper, we would like to improve the performance in classification in principle of no overlapping-category problems. We present an approach that to discovery words in sentence and it can find in high frequency in the same time and can-t overlap in each category, called Association Language Features by its Category (ALFC). Experimental results show that ALFC distinguish well in Major Depressive Disorder and have better performance. We also compare the approach with baseline and mutual information that use single words alone or correlation measure.

Keywords: Association language features, Emotion Classification, Overlap-Category Feature, Nature Language Processing.

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Authors: Suprijanto, Linda Sari, Vebi Nadhira , IGN. Merthayasa. Farida I.M

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Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback.

Keywords: Multimodal Emotion Detection, EEG, Facial Image, Optical Flow, compass mapping, Brain Wave

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Authors: Pierre Kancir, Jean-Philippe Diguet, Marc Sevaux

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One of the main difficulties in developing multi-robot systems (MRS) is related to the simulation and testing tools available. Indeed, if the differences between simulations and real robots are too significant, the transition from the simulation to the robot won’t be possible without another long development phase and won’t permit to validate the simulation. Moreover, the testing of different algorithmic solutions or modifications of robots requires a strong knowledge of current tools and a significant development time. Therefore, the availability of tools for MRS, mainly with flying drones, is crucial to enable the industrial emergence of these systems. This research aims to present the most commonly used tools for MRS simulations and their main shortcomings and presents complementary tools to improve the productivity of designers in the development of multi-vehicle solutions focused on a fast learning curve and rapid transition from simulations to real usage. The proposed contributions are based on existing open source tools as Gazebo simulator combined with ROS (Robot Operating System) and the open-source multi-platform autopilot ArduPilot to bring them to a broad audience.

Keywords: ROS, ArduPilot, MRS, simulation, drones, Gazebo.

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Authors: Sufian Ashraf Mazhari, Surendra Kumar

Abstract:

In this paper performance of Puma 560 manipulator is being compared for hybrid gradient descent and least square method learning based ANFIS controller with hybrid Genetic Algorithm and Generalized Pattern Search tuned radial basis function based Neuro-Fuzzy controller. ANFIS which is based on Takagi Sugeno type Fuzzy controller needs prior knowledge of rule base while in radial basis function based Neuro-Fuzzy rule base knowledge is not required. Hybrid Genetic Algorithm with generalized Pattern Search is used for tuning weights of radial basis function based Neuro- fuzzy controller. All the controllers are checked for butterfly trajectory tracking and results in the form of Cartesian and joint space errors are being compared. ANFIS based controller is showing better performance compared to Radial Basis Function based Neuro-Fuzzy Controller but rule base independency of RBF based Neuro-Fuzzy gives it an edge over ANFIS

Keywords: Neuro-Fuzzy, Robotic Control, RBFNF, ANFIS, Hybrid GA.

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