Search results for: ensemble class

Authors: Bina Kumari, Subir K. Sarkar, Pradipta Bandyopadhyay

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The autocorrelation function of the density fluctuation is studied in each of the two phases in a Gibbs Ensemble Monte Carlo (GEMC) simulation of the problem of phase separation for a square well potential with various values of its range. We find that the normalized autocorrelation function is described very well as a linear combination of an exponential function with a time scale τ₂ and a stretched exponential function with a time scale τ₁ and an exponent α. Dependence of (α, τ₁, τ₂) on the parameters of the GEMC algorithm and the range of the square well potential is investigated and interpreted. We also analyse the issue of how to choose the parameters of the GEMC simulation optimally.

Keywords: autocorrelation function, density fluctuation, GEMC, simulation

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Kassim A. Jassim

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In the present paper, we introduce and discuss a new class Kp(λ,α) of meromorphic multivalent functions in the punctured unit disk U*={z∈¢:0<|z|<1} defined by Ruscheweyh derivative. We obtain some sufficient conditions for the functions belonging to the class Kp(λ,α).

Keywords: meromorphic multivalent function, Ruscheweyh derivative, hadamard product

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Authors: Prasan Briboonnanggoul

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The major objective of this research study was to explore some aspects of the performance culture of musical folk drama called Ligkey. This study was undertaken in an effect to focus on the specific functions of orchestra which accompanied Ligkey on Thai musical instruments in Chachoengsao Province. The process of study and exploration consisted of questionnaire, interview, a tape recording of an interview and photographs of performances which all of them were analyzed for the finding. The information obtained from the study indicated that Ligkey still received stable attention from people despite lesser performances affected by economics crisis. Almost all of the performances were organized and supported by both the public sector and the private sector. Based on the summary and finding of this study, a) there were ten Ligkey ensemble and ten orchestra which were Mon orchestra, not the precedent and the predecessor known as Thai orchestra; b) a variety of functions performed by musicians must harmonize discipline, punctuality, patience, no negligence, proficiency in performance; c) folklore melodies known as Plengnapad were performed as usual, but folklore melodies and songs known as Plangsongchan got lesser and got a tendency towards extinction because of the plot which corresponded with a market-driven entertainment. Therefore, a purpose-built schema of the preservation of Thai folklore songs was that they should have been recognized by both the performers and the audiences and patronized by the public sector via the government media to publicize the value of popular art form.

Keywords: Pipat Ensemble, Ligkey, Amphur Muaeng, Chachoengsao Province

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Authors: Chothmal, Basant Agarwal

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Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: feature selection methods, machine learning, NB, one-class SVM, sentiment analysis, support vector machine

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Authors: Kamalpreet Kaur, Renu Dhir

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Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%.

Keywords: climate, satellite images, prediction, classification

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Ting Shen, Spyros Konstantopoulos

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During the past three decades, class size effects have been a focal debate in education. The idea of having smaller class is enormously popular among parents, teachers and policy makers. The rationale of its popularity is that small classroom could provide a better learning environment in which there would be more teacher-pupil interaction and more individualized instruction. This early stage benefits would also have a long-term positive effect. It is a common belief that reducing class size may result in increases in student achievement. However, the empirical evidence about class-size effects from experimental or quasi-experimental studies has been mixed overall. This study sheds more light on whether class size reduction impacts reading achievement in eight European countries: Bulgaria, Germany, Hungary, Italy, Lithuania, Romania, Slovakia, and Slovenia. We examine class size effects on reading achievement using national probability samples of fourth graders. All eight European countries had participated in the Progress in International Reading Literacy Study (PIRLS) in 2001, 2006 and 2011. Methodologically, the quasi-experimental method of instrumental variables (IV) has been utilized to facilitate causal inference of class size effects. Overall, the results indicate that class size effects on reading achievement are not significant across countries and years. However, class size effects are evident in Romania where reducing class size increases reading achievement. In contrast, in Germany, increasing class size seems to increase reading achievement. In future work, it would be valuable to evaluate differential class size effects for minority or economically disadvantaged student groups or low- and high-achievers. Replication studies with different samples and in various settings would also be informative. Future research should continue examining class size effects in different age groups and countries using rich international databases.

Keywords: class size, reading achievement, instrumental variables, PIRLS

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Authors: Aanuoluwapo Olusola Adesanya, Adesina Joseph

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This paper examined the class-size and instructional materials as correlates of pupils learning and academic achievement in primary school. The population of the study comprised 198 primary school pupils in three selected schools in Ogun State, Nigeria. Data were collected through questionnaire and were analysed with the use of multiple regression and ANOVA to analysed the correlation between class-size, instructional materials (independent variables) and learning achievement (dependent variable). The findings revealed that schools having an average class-size of 30 and below with use of instructional materials obtained better results than schools having more than 30 and above. The main score were higher in the school in schools having 30 and below than schools with 30 and above. It was therefore recommended that government, stakeholders and NGOs should provide more classrooms and supply of adequate instructional materials in all primary schools in the state to cater for small class-size.

Keywords: class-size, instructional materials, learning, academic achievement

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

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Authors: T. Hari Prasath, P. Ithaya Rani

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In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods.

Keywords: detecting face, Gabor filter, multi-class AdaBoost classifier, Z-score normalization

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Authors: Saba Riaz, Syed A. Hussain

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This paper is addressing the estimation method of the unknown population variance of the variable of interest. A new generalized class of estimators of the finite population variance has been suggested using the auxiliary information. To improve the precision of the proposed class, known population variance of the auxiliary variable has been used. Mathematical expressions for the biases and the asymptotic variances of the suggested class are derived under large sample approximation. Theoretical and numerical comparisons are made to investigate the performances of the proposed class of estimators. The empirical study reveals that the suggested class of estimators performs better than the usual estimator, classical ratio estimator, classical product estimator and classical linear regression estimator. It has also been found that the suggested class of estimators is also more efficient than some recently published estimators.

Keywords: study variable, auxiliary variable, finite population variance, bias, asymptotic variance, percent relative efficiency

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Authors: Fatma Susilawati Mohamad, Azizah Abdul Manaf, Fadhillah Ahmad, Zarina Mohamad, Wan Suryani Wan Awang

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A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification.

Keywords: multi-class, linear classifier, nearest neighbor, linear discriminant analysis

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Authors: Irfan Mehmood

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This paper examines the Marxist ideology in Toni Morrison's novel, Beloved. Toni Morrison writes about the society she inhabits and doesn't knowingly or emotionally leave. Marxism emphasizes the working class' dire economic status as well as the bourgeoisie as the dominant capitalist class. Using the Marxist literary theory promoted by Louis Althusser in his well-known book On the Reproduction of Capitalism, the chosen Toni Morrison piece is evaluated (1976). This essay explores how Morrison uses Marxist theory in her literary work to highlight the oppression of the Afro-American society and how the upper class ruled the lower class through the use of interpellation. In spite of hegemony and interpellation, certain major characters in this essay battle with the discriminatory ruling order.

Keywords: Marxism, social class, Toni Morrison, African American literature.

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Authors: Myunghoun Jang

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Most engineering colleges in South Korea have engineering design classes in order to develop and enhance a student's creativity and problem-solving ability. Many cases about engineering design class are shown in journals and magazines, but a case lasting many years is few. The engineering design class in the Department of Architectural Engineering, Jeju National University was open in 2009 and continues to this year. 3-5 teams in every year set up their problems found their solutions and produced good results. Three of the results obtained patents. The class also provides students with opportunities to improve communication skill because they have many discussions in solving their problems.

Keywords: engineering design, architectural engineering, team-based learning, construction safety

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Authors: Saad Al-Baddai, Karema Al-Subari, Elmar Lang, Bernd Ludwig

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Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.

Keywords: empirical mode decomposition (EMD), mode mixing, sifting process, over-sifting

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Authors: Chi-Lun Liu

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Requirements modeling and analysis are important in successful information systems' maintenance. Unified Modeling Language (UML) class diagrams are useful standards for modeling information systems. To our best knowledge, there is a lack of a systems development methodology described by the organism metaphor. The core concept of this metaphor is adaptation. Using the knowledge representation and reasoning approach and ontologies to adopt new requirements are emergent in recent years. This paper proposes an organic methodology which is based on constructivism theory. This methodology is a knowledge representation and reasoning approach to analyze new requirements in the class diagrams maintenance. The process and rules in the proposed methodology automatically analyze inconsistencies in the class diagram. In the big data era, developing an automatic tool based on the proposed methodology to analyze large amounts of class diagram data is an important research topic in the future.

Keywords: knowledge representation, reasoning, ontology, class diagram, software engineering

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Authors: Hui Liu, Xiaozan Wang, Jiarong Zhong, Ziming Shao

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Introduction: Related research shows that students don’t concentrate on teacher’s speaking in the classroom. The d2 attention test is a time-limited test about selective attention. The d2 attention test can be used to evaluate individual selective attention. Purpose: To use the d2 attention test tool to measure the difference between the attention level of the experimental class and the control class before and after Brain-Break and to explore the effect of Brain-Break in the classroom on students' selective attention. Methods: According to the principle of no difference in pre-test data, two classes in the fourth- grade of Shenzhen Longhua Central Primary School were selected. After 20 minutes of class in the third class in the morning and the third class in the afternoon, about 3-minute Brain-Break intervention was performed in the experimental class for 10 weeks. The normal class in the control class did not intervene. Before and after the experiment, the d2 attention test tool was used to test the attention level of the two-class students. The paired sample t-test and independent sample t-test in SPSS 23.0 was used to test the change in the attention level of the two-class classes around 10 weeks. This article only presents results with significant differences. Results: The independent sample t-test results showed that after ten-week of Brain-Break, the missed errors (E1 t = -2.165 p = 0.042), concentration performance (CP t = 1.866 p = 0.05), and the degree of omissions (Epercent t = -2.375 p = 0.029) in experimental class showed significant differences compared with control class. The students’ error level decreased and the concentration increased. Conclusions: Adding Brain-Break interventions in the classroom can effectively improve the attention level of fourth-grade primary school students to a certain extent, especially can improve the concentration of attention and decrease the error rate in the tasks. The new sport's learning model is worth promoting

Keywords: cultural class, micromotor, attention, D2 test

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Authors: Jehad Al Dallal

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Class cohesion is a key object-oriented software quality attribute that is used to evaluate the degree of relatedness of class attributes and methods. Researchers have proposed several class cohesion measures. However, the effect of considering the special methods (i.e., constructors, destructors, and access and delegation methods) in cohesion calculation is not thoroughly theoretically studied for most of them. In this paper, we address this issue for three popular connectivity-based class cohesion measures. For each of the considered measures we theoretically study the impact of including or excluding special methods on the values that are obtained by applying the measure. This study is based on analyzing the definitions and formulas that are proposed for the measures. The results show that including/excluding special methods has a considerable effect on the obtained cohesion values and that this effect varies from one measure to another. For each of the three connectivity-based measures, the proposed theoretical study recommended excluding the special methods in cohesion measurement.

Keywords: object-oriented class, software quality, class cohesion measure, class cohesion, special methods

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Authors: Alfrina Marwan, Erna Sulistyawati

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Class III malocclusion shows both skeletal and dentoalveolar component. Sketal Class III malocclusion can have variants in different region, maxilla or mandibular. Skeletal Class III malocclusion during growth period is considered to treat to prevent its severity in adulthood. Orthopedics treatment of skeletal Class III malocclusion in growing patient can be treated by using reverse twin block with expansion screw to modify the growth pattern. The objective of this case report was to describe the functional correction of skeletal Class III maloclussion using reverse twin block with expansion screw in growing patient. A patient with concave profile came with a chief complaint of aesthetic problems. The cephalometric analysis showed that patient had skeletal Class III malocclusion (ANB -5⁰, SNA 75º, Wits appraisal -3 mm) with anterior cross bite and deep bite (overjet -3 mm, overbite 6 mm). In this case report, the patient was treated with reverse twin block appliance with expansion screw. After three months of treatment, the skeletal problems have been corrected (ANB -1°), overjet, overbite and aesthetic were improved. Reverse twin block appliance with expansion screw can be used as orthopedics treatment for skeletal Class III malocclusion in growing patient and can improve the aesthetic with great satisfaction which was the main complaint in this patient.

Keywords: maxilla retrognatism, reverse twin block, skeletal class III malocclusion, growing patient

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Authors: Tebogo Emma Makaba, Barnabas Ndlovu Gatsheni

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The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending.

Keywords: bagging ensemble methods, confusion matrix, multi-layer perceptron, vehicle traffic flow

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Yuqing Sun

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Nowadays, English has been taught in many countries as a second language. One of the major ways to learn this language is through the class teaching. As in the field of second language acquisition, there are many factors to affect its acquisition processes, such as the target language itself, a learner’s personality, cognitive factor, language transfer, and the outward factors (teaching method, classroom, environmental factor, teaching policy, social environment and so on). Flipped Classroom as a newly developed classroom model has been widely used in language teaching classroom, which was, to some extent, accepted by teachers and students for its effect. It distinguishes itself from the traditional classroom for its focus on the learner and its great importance attaching to the personal learning process and the application of technology. The class becomes discussion-targeted, and the class order is somewhat inverted since the teaching process is carried out outside the class, while the class is only for knowledge-internalization. This paper will concentrate on the influences of the flipped classroom, as a classroom affecting factor, on the the process of English acquisition by the way of case studies (English teaching class in China), and the analysis of the mechanism of the flipped classroom itself to propose some feasible advice of promoting the the effectiveness of English acquisition.

Keywords: second language acquisition, English, flipped classroom, case

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Authors: Tahar Djebbar Aziza

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The American society, like all societies, is fractured by social divisions between different groups of people. It is divided by race, class, gender, and other social and cultural characteristics. Social divisions affect the way and the manner welfare is delivered for citizens within the American society. The welfare state exists to guarantee the promotion of well –being for all the different components within a society without taking into account their age, gender, their ethnicity/race, or their social belonging (class). Race, class, and even gender issues are the main factors that affected the formal structure, the nature, as well as the evolution of the American welfare state and led to its uniqueness. They have affected the structure and the evolution of the American welfare state since its creation in the 1930s, and led to its uniqueness in an international level. This study aims therefore at enhancing the readers’ awareness of social divisions: race, class, gender and their implications for the distribution of welfare resources and life chances in the USA from the early 1930s to the late 1990s.

Keywords: African Americans, class, gender, minority groups, race, social divisions, social policy, U.S. welfare state

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Authors: A. B. M. Najmus Sakib

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Being one of the most densely populated cities in the world, Dhaka is facing diversified types of crimes every day. Limitations of resources insert serious strains among the inhabitants of this city. This paper aims to analyze the correlation between crime and class, more especially the violent crime in connection with social class. Following the stratified random sampling technique, one of the police divisions among eight of the Dhaka Metropolitan Police (DMP) will be selected. The data will be collected by evaluating the case files found in the police report. First, this paper discusses the nature and pattern of violent crimes in Dhaka city. Then, it assesses the socio-economic status of the accused persons considering their professions. Furthermore, by testing hypothesis, it will identify how the social classes are connected with violent crimes. Finally, the paper will ascertain the particular class that has an impact on violent crime in Dhaka City and will be ended by suggesting possible measures should take by the law enforcement agencies of Bangladesh.

Keywords: social class, violent crime, crime prevention, nature of crime

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Authors: M. Laura Vazquez Maggio, Lilia Dominguez Villalobos, Jan Luka Frey

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This paper contributes to the understanding of the dynamic and the relational nature of identities formation among skilled middle-class migrants. Following the idea that identities are never singular, multifaceted and have a necessarily processual character, the authors specifically analyze three dimensions of the identity of qualified Mexican migrants in the US and the interplay between them. Drawing on semi-structured interviews with skilled Mexican middle-class migrants in the US, the paper explores how skilled Mexican migrants preserve their ethno-national identity (their ‘Mexicanness’) in reaction to a hostile socio-political reception context in the US. It further shows how these migrants recreate their class identity and show tendencies to distance themselves from what they perceive as lower-class Mexican migrants and the dominant popular Mexican and Latin-American cultural expressions. In a final step, it examines how the lived experience of migration itself impacts the migrants’ identities, their concept of self and feelings/modes of being and belonging.

Keywords: ethno-national identity, middle class identity, middle-class migration, migrants’ identity, skilled migration

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

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With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

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Authors: Karen Bell

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It is vital to integrate a working-class perspective into the just transition to an inclusive and sustainable society because of the particular expertise and interests that working-class people bring to the debates and actions. In class societies, those who are not well represented in the current structures of power can find it easier to see when the system is not working. They are also more likely to be impacted by the environmental crises because wealthier people can change their dwelling places, jobs and other aspects of their lives in the face of risks. Therefore, challenging the ‘post-material values thesis’, this paper argues that, if enabled to do so, working-class people are more likely to identify what needs to be addressed and changed in transition and can be more motivated to make the changes necessary than other social groups. However, they are often excluded from environmental decision-making and environmental social movements. The paper is based on a mixed methodology; drawing on secondary data, interview material, participant observation and documentary analysis. It is based on years of research and activism on environmental issues in working-class communities. The analysis and conclusion discusses the seven kinds of change required to address this problem: 1) organizational change - participatory practice (2) legislative change - make class an equalities and human rights issue (3) policy change - reduce inequality (4) social movement change - radicalize the environmental movement and support the environmental working-class (5) political change - create an eco-social state based on sharing (6) cultural change - integrate social and environmental justice, and (7) revolutionary change - dismantle capitalism.

Keywords: environmentalism, just transition, sustainability, working class

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Authors: Nursuhaila Ibrahim, Noor Hanim Rahmat, Nurul Ain Hasni, Nursyuhada Zakaria

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Past studies have shown that class discussions in the ESL classrooms not only helped students exchange ideas with their peers. Students also learn turn taking skills throughout the discussion. Through interacting with their peers, learners learn to use turn-taking strategies. They learn to wait for their turn, to listen to the ideas of others, to disagree, or to agree, and to add on more points in the conversation. This quantitative research explores students’ perception on class discussions and how the discussions influence their choice of turn-taking strategies. Findings of this research will have interesting implications towards the use of class discussions in the ESL classrooms.

Keywords: ESL, class discussions, turn-taking skills, peers

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