Search results for: international standard industrial classification

Authors: Tatyana Kim

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This paper seeks to explore the academic and sociocultural adaptation experiences of international students studying in Kazakhstan. Using multiple case study design, the research will be undertaken at two private Kazakhstani universities having a relatively large and diverse body of international students. Thus, 20 full-time undergraduate international students from the sampled universities will be interviewed to identify factors that impede or, vice versa, facilitate their academic and sociocultural adaptation in Kazakhstan, as well as to reveal how universities support these students in the process of their adaptation. To investigate the issue more deeply, it was decided to explore the university administrators’ viewpoint of the issue. Thus, six university administrators who are in charge of recruiting and supporting international students and, thus, are particularly knowledgeable about their experiences, have been recruited for this study. Identification of both students’ and administrators’ perspectives on the matter may help reveal miscommunication, if any, and gain greater insight into the phenomenon. The data will be collected between November 5, 2019, and December 10, 2019. Preliminary findings will be presented at the conference. Lysgaard’s U-curve adjustment theory (1955) will be employed as a guiding framework to discuss and interpret the findings.

Keywords: academic adaptation, adaptation, higher education, international students, sociocultural adaptation

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Authors: Manoela Cabo da Silva, Elton Fernandes, Ricardo Pacheco, Heloisa Pires

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This study examined cointegration and causal relationships between economic growth and regular domestic and international passenger air transport in Brazil. Total passengers embarked and disembarked were used as a proxy for air transport activity and gross domestic product (GDP) as a proxy for economic development. The test spanned the period from 2000 to 2015 for domestic passenger traffic and from 1995 to 2015 for international traffic. The results confirm the hypothesis that there is cointegration between passenger traffic series and economic development, showing a bi-directional Granger causal relationship between domestic traffic and economic development and unidirectional influence by economic growth on international passenger air transport demand. Variance decomposition of the series showed that domestic air transport was far more important than international transport to promoting economic development in Brazil.

Keywords: air passenger transport, cointegration, economic growth, GDP, Granger causality

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Ali Arabkheradmand, Enayat A. Shabani, Shabnam Ranjbar Nikkhoo

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Historical and ancestral bonds of the countries in the Middle East have led to similarities in culture and context of their societies. In addition, economic resources, such as the oil industry, have generally been an integrative point in the region. Higher education of a country is influenced by different national and international factors and regarding the mentioned bonds, it is inviting to study the development of the countries of the Middle East in higher education and draw some practical implications which can be used in the educational policy-making of the region. This review includes a data analysis on the population of international students in the countries of the Middle East. As its second objective, a review study on the successful countries, that is those which host the highest number of international students and the strategies they have developed to reach this state among the countries of the region has been conducted. Suggestions are made as to the strategies in higher education systems of these countries which could prove useful and practical in the development of internationalization of higher education in the region, specifically with regard to the recruitment of international students.

Keywords: internationalization of higher education, international student recruitment, Middle East countries, educational policy making

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Tsedeke Lambore Gemecho, Ayele Taye Goshu

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The main objective of this study is to identify the determinants of the number of international migrants in a household and to compare regression models for count response. This study is done by collecting data from total of 2288 household heads of 16 randomly sampled districts in Hadiya and Kembata-Tembaro zones of Southern Ethiopia. The Poisson mixed models, as special cases of the generalized linear mixed model, is explored to determine effects of the predictors: age of household head, farm land size, and household size. Two ethnicities Hadiya and Kembata are included in the final model as dummy variables. Stepwise variable selection has indentified four predictors: age of head, farm land size, family size and dummy variable ethnic2 (0=other, 1=Kembata). These predictors are significant at 5% significance level with count response number of migrant. The Poisson mixed model consisting of the four predictors with random effects districts. Area specific random effects are significant with the variance of about 0.5105 and standard deviation of 0.7145. The results show that the number of migrant increases with heads age, family size, and farm land size. In conclusion, there is a significantly high number of international migration per household in the area. Age of household head, family size, and farm land size are determinants that increase the number of international migrant in households. Community-based intervention is needed so as to monitor and regulate the international migration for the benefits of the society.

Keywords: Poisson regression, GLM, number of migrant, Hadiya and Kembata Tembaro zones

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Authors: Youn-Sung Kim, Hye-Mi Kim, Sang-Hoon Seo, Jaden Cha

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This International standard IEC 61508 sets out a generic approach for all safety lifecycle activities for systems comprised of electrical/electronic/programmable electronic (E/E/PE) elements that are used to perform safety functions. The control unit in stage control system is safety related facilities to control state and speed for stage system running, and it performs safety-critical function by stage control system. The controller unit is part of safety loops corresponding to the IEC 61508 and classified as logic part in the safety loop. In this paper, we analyze using FMEDA (Failure Mode Effect and Diagnostic Analysis) to verification for fault tolerance methods and functional safety of control unit. Moreover, we determined SIL (Safety Integrity Level) for control unit according to the safety requirements defined in IEC 61508-2 based on an analyzed functional safety.

Keywords: safety function, failure mode effect, IEC 61508-2, diagnostic analysis, stage control system

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Authors: Khadiga Kahwa, Aniza Ismail

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Depression, anxiety, and stress are associated with decreased role functioning, productivity, and quality of life. International students are more prone to psychological distress as they face many stressors while studying abroad. The objectives of the study were to determine the prevalence and associated factors of depression, anxiety, and stress among international students, their help-seeking behavior, and their awareness of the available on-campus mental support services. A cross-sectional study with a purposive sampling method was performed on 280 international students at Universiti Kebangsaan Malaysia (UKM) between the age of 18 and 35 years. The Depression Anxiety Stress Scale-21 (DASS-21) questionnaire was used anonymously to assess the mental health of students. Socio-demographic, help-seeking behavior, and awareness data were obtained. Independent sample t-test, one-way ANOVA test, and multiple linear regression were used to explore associated factors. The overall prevalence of depression, anxiety, and stress among international students were 58.9%, 71.8%, and 53.9%, respectively. Age was significantly associated with depression and anxiety. Ethnicity showed a significant association with depression and stress. No other factors were found to be significantly associated with psychological distress. Only 9.6% of the international students had sought help from on-campus mental support services. Students who were aware of the presence of such services were only 21.4% of the participants. In conclusion, this study addressed the gap in the literature on the mental health of international students and provided data that could be used in intervention programs to improve the mental health of the increasing number of international students in Malaysia.

Keywords: anxiety, depression, stress, help-seeking behavior, students

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Authors: Julius Denafas, Irina Kliopova, Gintaras Denafas

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Three opportunities for implementation of industrial ecology principles in the real industrial production of c-Si solar cells and modules are presented in this study. It includes: material flow dematerialisation, product modification and industrial symbiosis. Firstly, it is shown how the collaboration between R&D institutes and industry helps to achieve significant reduction of material consumption by a) refuse from phosphor silicate glass cleaning process and b) shortening of SiNx coating production step. This work was performed in the frame of Eco-Solar project, where Soli Tek R&D is collaborating together with the partners from ISC-Konstanz institute. Secondly, it was shown how the modification of solar module design can reduce the CO2 footprint for this product and enhance waste prevention. It was achieved by implementing a frameless glass/glass solar module design instead of glass/backsheet with aluminium frame. Such a design change is possible without purchasing new equipment and without loss of main product properties like efficiency, rigidity and longevity. Thirdly, industrial symbiosis in the solar cell production is possible in such case when manufacturing waste (silicon wafer and solar cell breakage) are collected, sorted and supplied as raw-materials to other companies involved in the production chain of c-Si solar cells. The obtained results showed that solar cells produced from recycled silicon can have a comparable electrical parameters like produced from standard, commercial silicon wafers. The above mentioned work was performed at solar cell producer Soli Tek R&D in the frame of H2020 projects CABRISS and Eco-Solar.

Keywords: solar cells and solar modules, manufacturing, waste prevention, recycling

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Jonas Pedro Fabris, Alberth Almeida Amorim Souza, Maria Emilia Camargo, Suzana Leitão Russo

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With the law Nº. 9279, of May 14, 1996, the Brazilian government regulates rights and obligations relating to industrial property considering the economic development of the country as granting patents, trademark registration, registration of industrial designs and other forms of protection copyright. In this study, we show the application of the methodology of Box and Jenkins in the series of deposits of industrial design at the National Institute of Industrial Property for the period from May 1996 to April 2013. First, a graphical analysis of the data was done by observing the behavior of the data and the autocorrelation function. The best model found, based on the analysis of charts and statistical tests suggested by Box and Jenkins methodology, it was possible to determine the model number for the deposit of industrial design, SARIMA (2,1,0)(2,0,0), with an equal to 9.88% MAPE.

Keywords: ARIMA models, autocorrelation, Box and Jenkins Models, industrial design, MAPE, time series

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Fedorova Elena

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In this article, we draw a distinction between two important functions of public policy in private international law. The first function is widely recognized and relates to the prevention of application of foreign laws and enforcement of foreign court judgments whenever their effects are incompatible with the domestic legal system of the forum. This effectively protects sovereign rights of the forum state as it allows to resist against the undesirable effects of foreign law-making and law-enforcement policies. The second function is less obvious, but not less important. As the internal private legal relationships, international private relationships are usually governed by rules of public policy, to which the parties can not derogate by mutual agreement. Thefore, for international private law relations public policy has a different function than previously mentioned: in this case, the public policy acts as a defense against unacceptable effects of the party autonomy. Thus, this second function of public policy consists in the limitation of the party autonomy wich effects would be unacceptable for the local legal system. In the frame of this second function the author will analyse two types of public policy which can limit the party autonomy: « substantial » public policy (which regulates the substance of international legal relationship) and « conflictual » public policy (which regulates the party autonomy to choose the law applicable for the substance of relationship). The author provides an analysis of these functions of the public policy in the field of international contract law because of the important role of the principle of party autonomy for international contract relations.

Keywords: public policy, general theory of private international law, substantial public policy, conflictual public policy

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Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Akbar

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Pros and cons of the capital punishment in Indonesia have been out of the date. The discourse of capital punishment has no relevance to the theory of punishment and theories of cultural relativism. In fact, the provisions of exceptions to the right to life by administering the death penalty against the perpetrators of serious crimes in Indonesia is a narrow perspective that does not pay attention to the development of the punishment of the crime. This thing is aggravated by an error to understand the natural right and legal right where the prohibition of those rights is result from a failure to distinguish the characteristic of the rights and to remember the raison d’être of law. To parse the irrational above, this paper will try to analyze normatively the error referring to the complementary theory between the sources of international law and the sources of municipal law of Indonesia. Both sources of the law above should be understood in the mutually reinforcing relationship enforceability because of false perceptions against those will create the disintegration between international law and municipal law of Indonesia. This disintegration is explicit not only contrary to the integrative theory of international law but also integrative theory of municipal law of Indonesia.

Keywords: capital punishment, municipal law, right to life, international law, the raison d’être of law, complementary theory, integrative theory

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

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Authors: Emmanuel Mugiraneza

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Rwanda uses defence diplomacy to pursue international collective security through different mechanisms. This paper shows that with an intent of promoting international collective security, Rwanda has constituted its defense diplomacy policy in three standpoints. First, Rwanda has formed strategic cooperation alliances with state actors, regional and international Organizations that enables her to participate in and promote international collective peace, security and cooperation. Secondary, Rwanda uses defence diplomacy to foster cooperation in to pre-empt, minimize and neutralize potential triggers that would lead to the outbreak of international conflict. Thirdly, Rwanda implements defence diplomacy policy strategy through internationally recognized operational and tactical standards while dispelling hostilities, assisting the friendly nation’s forces and or building and maintaining public confidence and trust in the areas where Rwanda Defence Force deploys for peacekeeping missions in Sudan, South Sudan, Central African Republic and Mozambique for a counterterrorism mission.

Keywords: defence diplomacy, collective security, Rwanda, Peacekeeping

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Authors: Rahul Arora, S. S. Dhami

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This paper implements the design structure of industrial robot along with the different transmission components like gear assembly and analysis of complete industrial robot. In this paper, it gives the overview on the most efficient types of modeling and different analysis results that can be obtained for an industrial robot. The investigation is executed in regards to two classifications i.e. the deformation and the stress tests. SolidWorks is utilized to design and review the 3D drawing plan while ANSYS Workbench is utilized to execute the FEA on an industrial robot and the designed component. The CAD evaluation was conducted on a disentangled model of an industrial robot. The study includes design and drafting its transmission system. In CAE study static, modal and dynamic analysis are presented. Every one of the outcomes is divided in regard with the impact of the static and dynamic analysis on the situating exactness of the robot. It gives critical data with respect to parts of the industrial robot that are inclined to harm under higher high force applications. Therefore, the mechanical structure under different operating conditions can help in optimizing the manipulator geometry and in selecting the right material for the same. The FEA analysis is conducted for four different materials on the same industrial robot and gear assembly.

Keywords: CAD, CAE, FEA, robot, static, dynamic, modal, gear assembly

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Alaa Alateeqi, Sara Aldhulaiee, Sara Alibraheem, Noura Alsaleh

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For the past few decades or so, Kuwait's local food industry has grown remarkably due to increase in demand for processed or semi processed food products in the market. It is important that the ever increasing food manufacturing/processing units maintain the required quality standards as per regional and to some extent international quality requirements. It has been realized that all Kuwait food manufacturing units should understand and follow the international standard practices, and moreover a set of guidelines must be set for quality assurance such that any new business in this area is aware of the minimum requirements. The current study has been undertaken to identify the gaps in Kuwait food industries in following the Good Manufacturing Practices (GMP) in terms of quality planning, control and quality assurance. GMP refers to Good Manufacturing Practices, which are a set of rules, laws or regulations that certify producing products within quality standards and ensuring that it is safe, pure and effective. The present study therefore reports about a ‘case study’ in a reputed food manufacturing unit in Kuwait; starting from assessment of the current practices followed by diagnosis, report of the diagnosis and road map and corrective measures for GMP implementation in the unit. The case study has also been able to identify the best practices and establish a benchmarking data for other companies to follow, through measuring the selected company's quality, policies, products and strategies and compare it with the established benchmarking data. A set of questionnaires and assessment mechanism has been established for companies to identify their ‘benchmarking score’ in relation to the number of non-conformities and conformities with the GMP standard requirements.

Keywords: good manufacturing practices, GMP, benchmarking, Kuwait Food Industries, food quality

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Authors: Lina Fernanda González

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Through this essay, an attempt will be made to make visible the work of the international network of the Women in Black (henceforth WB), on the one hand. On the other hand, the work of Women International Courts as a political practice will be showed as well, focusing their work into generating a collective identity - becoming thusly a peace building space, rescuing in this way the symbolic value of their practices consisting in peaceful resistance as political scenarios, that serve, too, a pedagogical and healing purposes.

Keywords: collective actions, women, peace, human rights and humanitarian international law

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Authors: Muhammad Haris Aziz, Shoaib Sarfraz, Chanchal Saha

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The basic functions of a university are research and education. Research develops theories and education provides the link between the theory and the practical. Being an applied science, the link between theory and practice needs to be strong in engineering disciplines. But there remains a gap between industry and academy due lack of understanding and awareness from both sides. This gap is been shorten with an industrial engineering graduate class composed of a mix of students from industrial background and from theoretical background. Results are four industrial case studies which are the outcome of group projects in a course on operations research.

Keywords: industrial-academia linkage, operations research, industrial engineering, engineering education.

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Hugo M. Gonzales, Ke Ni Chai, Deanne Mary King

Abstract:

International students are underrepresented in Australian health literature, and this population is especially vulnerable to the well-documented negative impacts associated with racial microaggressions in their adjustment to settling in the new society, as well as to the many challenges they already face as international students. This study investigated the prevalence of racial microaggressions among international students and their impact on stress and psychological well-being. This research was conducted during the COVID-19 pandemic, which has been documented to contribute to anti-Asian racism. Participants included 54 international students, of which 72% were Asian. The Racial and Ethnic Microaggressions Scale (REMS), Perceived Stress Scale (PSS), and the Perceived General Wellbeing Indicator (PGWBI) were used to measure the participants’ responses. All participants reported experiencing racial microaggression in the last six months, and significant correlations and regression models were found between REMS, certain elements of the PSS scale, and time in Australia. Despite the small sample size, this research corroborated outcomes from recent studies and provided insight into the prevalence and impact of racial microaggressions among such populations, highlighting the need for further exploration.

Keywords: racial microaggressions, international students, racism, REMS, microaggressions in Australia, stress, psychological wellbeing

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Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

Abstract:

Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

Procedia PDF Downloads 95