Search results for: classification society

Authors: Ashwini Mohan, Haris Batley

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An orthodontic index is used to rate or categorise an individual’s occlusion using a numeric or alphanumeric score. Indexing of malocclusions and their correction is important in epidemiology, diagnosis, communication between clinicians as well as their patients and assessing treatment outcomes. Many useful indices have been put forward, but to the author’s best knowledge, no one method to this day appears to be equally suitable for the use of epidemiologists, public health program planners and clinicians. This article describes the common clinical orthodontic indices and classifications used in United Kingdom.

Keywords: classification, indices, orthodontics, validity

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Authors: Mohamad Saifudin Mohamad Saleh

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This paper provides a discussion of the importance of sustainability education among the Malaysian media. Without doubt, media play a crucial role in promoting the sustainable or so called “eco-system” society for a better future. Since 2002, the role of media as one of the vital stakeholders particularly in educating the society in three main areas of sustainable education including on environment, economy and society has been clearly highlights on the World Summit for Sustainable Development (WSSD) that was held in Johannesburg. In this paper, six media practitioners from two local Malaysia newspapers organization were interviewed by the researcher in order to identify their understanding about sustainability education; their perception about the pivotal role in sustainability education and the challenges faced by them in the process of educating society about sustainability issues. The findings of this study showed that most of Malaysian media practitioners have displayed clear understanding about sustainability education and they also realize their huge responsibility for not only informing but also educating society in having a sustainable lifestyle. The ultimate challenge in sustainability education faced by the media is to make the public really understand the importance of sustainable lifestyle. Overall, from this study, it is hoped to provide more possible direction in sustainability education not only among the Malaysian media but also all media in the entire world, particularly the developing and Southeast Asian countries.

Keywords: media, sustainability education, Malaysia

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Authors: Mai Al-Subaie

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This research suggests that women in traditional families of Saudi Arabia are divided into two groups, the one who conform to the society and the new type of women that has been emerged due to the changing and development of the culture, who do not want to conform to the rules. The factors underlying the differences were explored by using a test and an interview. That concluded some of the main factors that were a real effect of why some women still want to follow the society and traditional rules, and other want to break free.

Keywords: conformity, non conformity, females, Saudi Arabia

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Sonny Dewi Judiasih

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The principle of a marriage is to achieve a happy and eternity family based on the willing of the God. The family has a fundamental role in the society as a social individual and as a nuclear family consists of father, mother, and children. Thus, each family always would like to have children who will continue the family. However, not all family will be blessed with children and consequently, there is family without children. Therefore, the said the certain family will do any effort to fulfill the wish to have children. One of the ways is to adopt children. The implementation of child adoption is conducted by the family who does not have children but sometimes child adoption is conducted by a family who has already children. The implementation of child adoption is based on the interest of the welfare and the intellectual of the said child. Moreover, it should be based on the social liability of the individual in accordance with the developing of the traditional values as part of the nation culture. The child adoption is conducted for the welfare of the child demonstrates that a change on the basic motive (value) whereby in the past the child adoption is to fulfill the wish of foster parent (to have children in the family). Nowadays the purpose of child adoption is not merely for the interest of foster parent but in particular for the interest, welfare and the future of the child. The development of the society has caused the occurrence of changes of perspective in the society which lead to a need for new law. The court of justice has an impact of such changes. It is evidenced by the court order for child adoption in the legal framework of certainty of law. The changes of motives (value) of the child adoption in the society can be fully understood in the event that the society fully understand that the ultimate purpose of Indonesia nation is to achieve a justice and prosperity society, i.e., social welfare for all Indonesian people.

Keywords: child adoption, family law, legal protection, children

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Authors: Yuichi Miyamoto

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In regards to the energy sector in the modern period, two points were raised. First is a vast and growing energy demand, and second is an environmental impact associated with it. The enormous consumption of fossil fuel to the mobile unit is leading to its rapid depletion. Nuclear power is not the only problem. A modal shift that utilizes personal transporters and independent power, in order to realize a sustainable society, is very effective. The paper proposes that the world will continue to work on this. Energy of the future society, innovation in battery technology and the use of natural energy is a big key. And it is also necessary in order to save on energy consumption.

Keywords: natural energy, modal shift, personal transportation, battery

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Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

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A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

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Authors: K. P. Sandesh, M. H. Suman

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Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures.

Keywords: document classification, document clustering, entropy, accuracy, classifiers, clustering algorithms

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Authors: Liane Marcia Freitas Silva, Fernando Augusto Silva Marins, Maria Silene Alexandre Leite

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The adoption of a network structure, like in the supply chains, favors the increase of dependence between companies and, by consequence, their vulnerability. Environment disasters, sociopolitical and economical events, and the dynamics of supply chains elevate the uncertainty of their operation, favoring the occurrence of events that can generate break up in the operations and other undesired consequences. Thus, supply chains are exposed to various risks that can influence the profitability of companies involved, and there are several previous studies that have proposed risk classification models in order to categorize the risks and to manage them. The objective of this paper is to analyze and discuss thirty of these risk classification models by means a theoretical survey. The research method adopted for analyzing and discussion includes three phases: The identification of the types of risks proposed in each one of the thirty models, the grouping of them considering equivalent concepts associated to their definitions, and, the analysis of these risks groups, evaluating their similarities and differences. After these analyses, it was possible to conclude that, in fact, there is more than thirty risks types identified in the literature of Supply Chains, but some of them are identical despite of be used distinct terms to characterize them, because different criteria for risk classification are adopted by researchers. In short, it is observed that some types of risks are identified as risk source for supply chains, such as, demand risk, environmental risk and safety risk. On the other hand, other types of risks are identified by the consequences that they can generate for the supply chains, such as, the reputation risk, the asset depreciation risk and the competitive risk. These results are consequence of the disagreements between researchers on risk classification, mainly about what is risk event and about what is the consequence of risk occurrence. An additional study is in developing in order to clarify how the risks can be generated, and which are the characteristics of the components in a Supply Chain that leads to occurrence of risk.

Keywords: sisks classification, survey, supply chain management, theoretical discussion

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Subham Kharel, Sudha Ravindranath, A. Vidya, B. Chandrasekaran, K. Ganesha Raj, T. Shesadri

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Digitization of urban features is a tedious and time-consuming process when done manually. In addition to this problem, Indian cities have complex habitat patterns and convoluted clustering patterns, which make it even more difficult to map features. This paper makes an attempt to classify urban objects in the satellite image using object-oriented classification techniques in which various classes such as vegetation, water bodies, buildings, and shadows adjacent to the buildings were mapped semi-automatically. Building layer obtained as a result of object-oriented classification along with already available building layers was used. The main focus, however, lay in the extraction of high-rise buildings using spatial technology, digital image processing, and modeling, which would otherwise be a very difficult task to carry out manually. Results indicated a considerable rise in the total number of buildings in the city. High-rise buildings were successfully mapped using satellite imagery, spatial technology along with logical reasoning and mathematical considerations. The results clearly depict the ability of Remote Sensing and GIS to solve complex problems in urban scenarios like studying urban sprawl and identification of more complex features in an urban area like high-rise buildings and multi-dwelling units. Object-Oriented Technique has been proven to be effective and has yielded an overall efficiency of 80 percent in the classification of high-rise buildings.

Keywords: object oriented classification, shadow extraction, high-rise buildings, satellite imagery, spatial technology

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

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Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.

Keywords: big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review

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Authors: Kamel Belammi, Houria Fatrim

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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

Abstract:

DNA Barcode, a short mitochondrial DNA fragment, made up of three subunits; a phosphate group, sugar and nucleic bases (A, T, C, and G). They provide good sources of information needed to classify living species. Such intuition has been confirmed by many experimental results. Species classification with DNA Barcode sequences has been studied by several researchers. The classification problem assigns unknown species to known ones by analyzing their Barcode. This task has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. To make this type of analysis feasible, heuristics, like progressive alignment, have been developed. Another tool for similarity search against a database of sequences is BLAST, which outputs shorter regions of high similarity between a query sequence and matched sequences in the database. However, all these methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. This method permits to avoid the complex problem of form and structure in different classes of organisms. On empirical data and their classification performances are compared with other methods. Our system consists of three phases. The first is called transformation, which is composed of three steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. The second is called approximation, which is empowered by the use of Multi Llibrary Wavelet Neural Networks (MLWNN).The third is called the classification of DNA Barcodes, which is realized by applying the algorithm of hierarchical classification.

Keywords: DNA barcode, electron-ion interaction pseudopotential, Multi Library Wavelet Neural Networks (MLWNN)

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Authors: Ly Thi Hai Yen

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The Internet in Vietnam has developed significantly over the last 20 years and made impressive advances, permeating into every part of daily life, changing people's habits as well as the way people's communication. Under this situation, social media have become useful tools for the citizen to get and share information. About 10 years ago, most Vietnamese people got the information through state media, but thanks to the development of the Internet, nowadays, social media have been giving them rich sources of information at any area of society. By using case study research methods, documentary research method and in-depth interviews conducted with social media users, NGO activists, and Vietnamese scholars in 2017, the paper found that social media has been bringing a cyber public sphere to whom wish to discuss social and political issues. And, more importantly, it gives them a chance to connect with the government and policy makers quickly and effectively. Moreover, people use social media to supervise activities in society to prevent social evils and corruption in Vietnam. However, besides these positive points, people also have to cope with the rising situation of fake news which causing consequences for the society, such as pulling off crimes or cheating money and others. The paper concluded that social media plays important roles in the change of Vietnamese society, giving citizens a cyber public sphere unrestrictedly to discuss social and political issues but also bringing many bad risks to citizens.

Keywords: Vietnamese, social media, politics-society

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Authors: A. Elsehemy, M. Abdeen , T. Nazmy

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Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification.

Keywords: Arabic text classification, ontology based retrieval, Arabic semantic web, information retrieval, Arabic ontology

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Authors: Raj Kumar Kama, Rajshree Kamat

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Traffic congestion is a serious problem that is to be considered, and it is increasing day-by-day in urban areas that is seriously affecting the urban society. From the study, it is understood that increased urbanization and growth of population are the principal causes of congestion. It has adverse effects on society, economy, environment, and health. This study mainly focussed on studying and understanding the causes of congestion, consequences faced by urban society, and planning solutions to mitigate congestion. Techniques like transit oriented development (TOD) and integrated transport systems are more effective in mitigating traffic congestion.

Keywords: traffic congestion, transit oriented development, integrated transport system, urbanization

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Authors: Mohinder Kumar

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Sports are considered as the very prominent social institution in almost every society because it reflects the mores, values, and general culture of a society. Sports activity tend to pave the foundation for learning acceptable values and beliefs and for acquiring desirable character traits such as self-discipline, sportsmanship, and an appreciation for hard work, fairness, self-respect, leadership, followership, justice, perseverance, competition, and goal attainment. The present study focuses on ideal ways of socializing youngsters into sports. Influences of some socializing agents (e.g. family, school, community) are reviewed and suggestions made as to how these socializing agents can be oriented and made effective in carrying out functional processes toward target ends.

Keywords: sports, socializing, family, community, society

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Authors: Weihsuan Lin

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The Swiss Bethlehem Mission Society (BMS) in Taiwan has influenced and made an important contribution to religion and social work in Taidong. This German-speaking Catholic missionary society is located in Taidong, which is the political and economic periphery of Taiwan but is the cultural center of the Chinese and many different Austronesian ethnic groups, including Amis, Paiwan, Puyuma, Yami, Bunun, and Rukai. Through document analysis and fieldwork, this research aims to explore the result of the confrontation, exchange, and innovation between the BMS and other ethnic groups. Further, based upon Michael Foucault’s discussion of two modalities of constructing individuals, namely ‘discipline’ and ‘care of the self,’ this research will analyze the ‘discipline’ and ‘care of the self’ mechanisms of and between BMS Fathers, Brothers, and Church followers at the scale of individuals. At the scale of groups, the ‘autonomy’ and ‘been governed’ of the BMS in relationship to the Catholic Church in Taiwan and the world will also be examined.

Keywords: Bethlehem Mission Society, Religion and Geography, Spirituality, Foucault

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Authors: Ramin Barati, Atefeh Salemi

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In Victorian era, the society had been surrounded by extreme class ranking in order to identify the people of the classes and to intensify power relationships due to the growth of industry in Charles Dickens's (1812-1870) Great Expectations (2003) in which he illustrates the clash and dichotomy in the 19th century London. The classes in Victorian period have socially divided the population into two parts, the lower and the upper class of the community. In such a panopticon society, the major character Pip was the best example of the working class who was under the domination of violence, malice, and abuse of Miss Havisham as a member of the ruling class in order to take revenge on her failures. The conflict and disunity represented in vindictiveness and the sense of revenge applied by Miss Havisham against his victim Pip, made him experience alienation and eventually suffer from identity crisis. This paper considers New Historicism based on the theories of the French critic Michel Foucault (1926-1984). The social concept, panopticism, was called after the panopticon society, basically elaborated by Foucault in his book Discipline and Punish (1975) and he considers the panopticon as a sign of punitive community of surveillance. This paper evaluates the problems of a dual society to show that the people of the lower class are under the domination of capitalist society.

Keywords: class, identity crisis, violence, panoptic society, domination

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: Rohtash

Abstract:

Academically, the concept of Justice in the literature is vast, and theories are voluminous and definitions are numerous but it is very difficult to define. Through the ages, justice has been evolving and developing reasoning that how individuals and communities do the right thing that is just and fair to all in that society. Justice is a relative and dynamic concept, not absolute one. It is different in different societies based on their morality and ethics. The idea of justice cannot arise from a single morality but interaction of competing moralities and contending perspectives. Justice is the conditional and circumstantial term. Therefore, justice takes different meanings in different contexts. Justice is the application of the Laws. It is a values-based concept in order to protect the rights and liberties of the people. It is a socially created concept that has no physical reality. It exists in society on the basis of the spirit of sharing by the communities and members of society. The conception of justice in society or among communities and individuals is based on their social coordination. It can be effective only when people’s judgments are based on collective reasoning. Their behavior is shaped by social values, norms and laws. People must accept, share and respect the set of principles for delivering justice. Thus justice can be a reasonable solution to conflicts and to coordinate behavior in society. The subject matter of distributive justice is the Public Good and societal resources that should be evenly distributed among the different sections of society on the principles developed and established by the State through legislation, public policy and Executive orders. The Socioeconomic transformation of the society is adopted by the constitution within the limit of its morality and gives a new dimension to transformative justice. Therefore, both Procedural and Transformative justice is part of Distributive justice. Distributive justice is purely an economic phenomenon. It concerns the allocation of resources among the communities and individuals. The subject matter of distributive justice is the distribution of rights, responsibilities, burdens and benefits in society on the basis of the capacity and capability of individuals.

Keywords: distributive justice, constitutionalism, institutionalism, constitutional morality

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Authors: Taufik Hidayah

Abstract:

Wayang is a traditional puppet show originated from Java. This traditional art is characterized by distinctive Hinduism influence. Wayang reflects the social life of the Javanese society. It contains Javanese philosophy, myths, magical stories, and religion, as well as educational media and transmission for noble values of Javanese society conveyed through the story. Nowadays, the performance of wayang has faced a new challenge to maintain its existence in the public life of Javanese society. Modernity has penetrated into every shape of culture. Many people consider traditional culture as old fashioned, particularly the young generation. That is one of the reasons why many people have left traditional culture. For maintaining the existence of wayang, a new art called ‘wayang hip hop’ has arisen. Wayang hip hop seeks to modify wayang show into a more modern form, but without removing any principles and main functions of wayang art. This article will discuss theoretically the changes and traditional continuity in wayang hip hop based on a literature review and qualitative approaches. Wayang hip hop uses hip-hop music as the background music in the show. It will discuss about the impact that comes with the existential strengthening of wayang hip hop especially among the Javanese society and discuss the opportunities that arise regarding the function of wayang hip-hop as a medium of education, social criticism, and cultural revitalization of the Javanese society.

Keywords: cultural revitalization, social criticism, education, continuity and change

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Authors: Muqarrab Akbar

Abstract:

Pakistan is considered as a state confronting different internal and external challenges. Extremism is one of the most vital internal challenges faced by Pakistani society. The state’s contradictory policies, political instability, socio-economic injustice, absence of the rule of law are the major reasons behind the proliferation of violence and extremism in society. The fall of the Shah of Iran, the Iranian revolution, the 1979 Afghan war of 1979, the emergence of Al-Qaeda, Talibanisation, war against terrorism, and involvement of Saudia and Iran have further aggravated the culture of violence and extremism in Pakistan. The absence of a narrative of peaceful coexistence and harmony has created a vacuum for youth in Pakistani society. In the contemporary era, civil society and the government of Pakistan has initiated different steps to introduce a narrative to counter violent extremism. These narratives have helped a lot in creating community resilience to promote peace and harmony among Pakistani society in general and to bridge the gap between the Sunni Shia divide in particular. This paper will highlight those factors in detail that threw the society into extremism and violence, particularly with reference to Sunni Shia divide in Pakistan. This paper explores the impact of sectarian violence in Pakistan and highlights the different initiatives and their impacts on Pakistani society at large. A quantitative method has been adopted to explore the results. Empirical study used in the paper was based on the survey conducted by distributing questionnaires among 300 people from both community Sunni and Shia in Pakistan. Some interviews of the religious scholars of both communities are also conducted for this research. The recent developments on the government level and society levels have created community resilience. The results of the survey show that Pakistani society in the contemporary era is more peaceful and tolerant as compared to the past. The research concludes that the counter-narrative approach is positively affecting the peaceful environment in Pakistan.

Keywords: extremism, Pakistan, Shia, Sunni, violence

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

Abstract:

In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

Abstract:

Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

Procedia PDF Downloads 421