Search results for: international classification of functioning

Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

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Authors: Aylin Mohammadalipour Tofighi

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Marine-based geoengineering methods, proposed to mitigate climate change, operate primarily through two mechanisms: reducing atmospheric carbon dioxide levels and diminishing solar absorption by the oceans. While these approaches promise beneficial outcomes, they are fraught with environmental, legal, ethical, and political challenges, necessitating robust international governance. This paper underscores the critical role of international cooperation within the governance framework, offering a focused analysis of existing international environmental mechanisms applicable to marine-based geoengineering governance. It evaluates the efficacy and limitations of current international legal structures, including treaties and organizations, in managing marine-based geoengineering, noting significant gaps such as the absence of specific regulations, dedicated international entities, and explicit governance mechanisms such as monitoring. To rectify these problems, the paper advocates for concrete steps to bolster international cooperation. These include the formulation of dedicated marine-based geoengineering guidelines within international agreements, the establishment of specialized supervisory entities, and the promotion of transparent, global consensus-building. These recommendations aim to foster governance that is environmentally sustainable, ethically sound, and politically feasible, thereby enhancing knowledge exchange, spurring innovation, and advancing the development of marine-based geoengineering approaches. This study emphasizes the importance of collaborative approaches in managing the complexities of marine-based geoengineering, contributing significantly to the discourse on international environmental governance in the face of rapid climate and technological changes.

Keywords: climate change, environmental law, international cooperation, international governance, international law, marine-based geoengineering, marine law, regulatory frameworks

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Authors: Annabelle Onyefulu-Kingston

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One of the major purposes of AI today is to evaluate and analyze millions of micro and macro data in order to determine what is relevant in a particular case and proffer it in an adequate manner. Microdata, as far as it relates to AI in international arbitration, is the millions of key issues specifically mentioned by either one or both parties or by their counsels, arbitrators, or arbitral tribunals in arbitral proceedings. This can be qualifications of expert witness and admissibility of evidence, amongst others. Macro data, on the other hand, refers to data derived from the resolution of the dispute and, consequently, the final and binding award. A notable example of this includes the rationale of the award and specific and general damages awarded, amongst others. This paper aims to critically evaluate and analyze the possibility of technological inclusion in international arbitration. This research will be imploring the qualitative method by evaluating existing literature on the consequence of applying AI to both micro and macro data in international arbitration, and how this can be of assistance to parties, counsels, and arbitrators.

Keywords: AI-based technologies, algorithms, arbitrators, international arbitration

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Authors: Neelum Maqsood

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This paper is a study of how institutional structures shape the policy-making activities of female legislators. The literature on this area indicates that if there is an institution created by men to secure elite interests, women will face constraints in legislative activities. This paper will analyze the institutional setting in Pakistan and document the conditions women face that both restrict or enable them from representing the general interests of other women. The main experimental design depends on the variation of international scrutiny that Pakistan faces in two different time periods that will be classified as high international scrutiny and low international scrutiny. A high international scrutiny period is one where Pakistan comes under the international lens because of a domestic event that has international ramifications, for example, in terms of gender equality. The argument is that women parliamentarians receive different treatment in periods of high international scrutiny. As Pakistan comes under scrutiny, women will be more active in their legislative activities than in low international scrutiny, as male parliamentarians will be less likely to influence or restrain women’s activities. Using this variation, the trends in memberships and support functions given to women in these two time periods will be studied. The second variation will comprise the analysis of male and female assignments, training, and funding on general seats across time, which will require data collection over this time of 12-15 years, including the years during the war when Pakistan was under high international scrutiny.

Keywords: female representation, gender equality, democratic institutions, quota seats

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Authors: Mualla McManus, Jenna Luche Thaye

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World Health Organisation International Classification of Disease codes were created to define disease including infections in order to guide and educate diagnosticians. Most infectious diseases such as syphilis are clearly defined by their ICD 10 codes and aid/help to educate the clinicians in syphilis diagnosis and treatment globally. However, current ICD 10 codes for relapsing fever Borreliosis and Lyme disease are less clearly defined and can impede appropriate diagnosis especially if the clinician is not familiar with the symptoms of these infectious diseases. This is despite substantial number of scientific articles published in peer-reviewed journals about relapsing fever and Lyme disease. In the USA there are estimated 380,000 people annually contacting Lyme disease, more cases than breast cancer and 6x HIV/AIDS cases. This represents estimated 0.09% of the USA population. If extrapolated to the global population (7billion), 0.09% equates to 63 million people contracting relapsing fever or Lyme disease. In many regions, the rate of contracting some form of infection from tick bite may be even higher. Without accurate and appropriate diagnostic codes, physicians are impeded in their ability to properly care for their patients, leaving those patients invisible and marginalized within the medical system and to those guiding public policy. This results in great personal hardship, pain, disability, and expense. This unnecessarily burdens health care systems, governments, families, and society as a whole. With accurate diagnostic codes in place, robust data can guide medical and public health research, health policy, track mortality and save health care dollars. Better defined ICD codes are the way forward in educating the diagnosticians about relapsing fever and Lyme diseases.

Keywords: WHO ICD codes, relapsing fever, Lyme diseases, World Health Organisation

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Authors: Aleksandra Chiniaeva

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The following paper describes the activity of national and international parliamentary assemblies of the European region in protection and promotion of human rights. It may be said that parliamentarians have a “double mandate” — as members of the international assembly and of their respective national parliaments. In other words, parliamentarization at both international and national level provides a situation for parliamentarians, where they link people, national governments and international organizations. The paper is aimed towards demonstrating that the activity of the main international parliamentary assemblies of the European region have a real positive impact on the human rights situation in the European region. In addition, the paper describes the assemblies that include protection of human rights in their Agenda as one of the main subjects: the EP, the PACE, the OSCE PA and the IPA CIS. Co-operation activities such as joint election observation; participation in inter-parliamentary associations, such as the IPU; conclusion agreements allow assemblies to provide observation of human right situation in the states that are not members of the particular organization and as consequence make their impact broader.

Keywords: human rights, international parliamentary assembly, IPU, EP, PACE, OSCE, international election observation

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Authors: Anthony Vandarakis

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Today’s International Service Learning practices require an update: modern technologies, fresh educational frameworks, and a new operating system to accountably prosper. This paper describes a model of International Service Learning (ISL), which combines current technological hardware, electronic platforms, and asynchronous communications that are grounded in inclusive pedagogy. This model builds on the work around collaborative field trip learning, extending the reach to international partnerships across continents. Mobile technology, 21^st century skills and summit-basecamp modeling intersect to support novel forms of learning that tread lightly on fragile natural ecosystems, affirm local reciprocal partnership in projects, and protect traveling participants from common yet avoidable cultural pitfalls.

Keywords: International Service Learning, ISL, field experiences, mobile technology, out there in here, summit basecamp pedagogy

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Authors: Nurul Aini Abdul Wahab, Nor Syamim Halidin, Sayidatina Aisah Masnan, Nur Izzati Romli

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There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes.

Keywords: classification model, discriminant function, principle component analysis, variable reduction

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Authors: Oustani Mabrouka, Halilat Md Tahar, Hannachi Slimane

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This study was conducted on representative and contrasting soils of arid regions. It focuses on the compared influence of two organic fertilizers: poultry manure (PM) and bovine manure (BM) on improving the microbial functioning of non-saline (SS) and saline (SSS) soils, in particularly, the process of mineralization of nitrogen and carbon. The microbiological activity was estimated by respirometric test (CO2–C emissions) and the extraction of two forms of mineral nitrogen (NH4+-N and NO3--N). Thus, after 56 days of incubation under controlled conditions (28 degrees and 80 per cent of the field capacity), the two types of manures showed that the mineralization activity varies according to type of soil and the organic substrate itself. However, the highest cumulative quantities of CO2–C, NH4+–N and NO3-–N obtained at the end of incubation were recorded in non-saline (SS) soil treated with poultry manure with 1173.4, 4.26 and 8.40 mg/100 g of dry soil, respectively. The reductions in rates of release of CO2–C and of nitrification under saline conditions were 21 and 36, 78 %, respectively. The influence of organic substratum on the microbial density shows a stimulating effect on all microbial groups studied. The whole results show the usefulness of two types of manures for the improvement of the microbiological functioning of arid soils.

Keywords: Salinity, Organic matter, Microorganisms, Mineralization, Nitrogen, Carbon, Arid regions

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Authors: Mohamed R. Mhereeg

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The paper discusses the design of a .NET Windows Service based agent system called MACS (Multi-Agent Classification System). MACS is a system aims to accurately classify spread-sheet developers competency over a network. It is designed to automatically and autonomously monitor spread-sheet users and gather their development activities based on the utilization of the software Multi-Agent Technology (MAS). This is accomplished in such a way that makes management capable to efficiently allow for precise tailor training activities for future spread-sheet development. The monitoring agents of MACS are intended to be distributed over the WWW in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus methodology is used for the design of the agents of MACS. Prometheus has been used to undertake this phase of the system design because it is developed specifically for specifying and designing agent-oriented systems. Additionally, Prometheus specifies also the communication needed between the agents in order to coordinate to achieve their delegated tasks.

Keywords: classification, design, MACS, MAS, prometheus

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Authors: Charles Simplice Mbatsogo Mebo

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With growth picking up again, Africa seems increasingly attractive for its own nationals who return home through new opportunities available for them. The purpose of our research paper is to understand the role of the international partners in Cameroon, with regards to their support for the return and reintegration of migrants. We, therefore, questioned the relevance and effectiveness and efficacy of international instruments in reintegrating returnees to Cameroon. After our analysis that was conducted on the basis of a documentary exploration, interviews, and field surveys, it appears that the contribution of the international partners in Cameroon is proven in relation to their participation in the financing and placement of returned experts. However, their contribution remains insufficient due to their low level of deployment and the insignificant impact of their investments on the reintegration of Cameroonian Diasporas. The research also reveals some exogenous and endogenous constraints that hinder international institutions' actions in terms of accompanying migrants returning to Cameroon. Finally, for a better management of the returnees' issue, it is necessary to set up a mechanism to raise awareness and a coordination system of all international actors involved. It is also relevant to reform the migration policy, build institutional capacities, and improve the juridical-administrative and economic environment so as to favor co-development in Cameroon.

Keywords: international partners, returnees, diaspora, migration policy, co-development

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Hakam Junus, Natassha Chrysanti

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The international relations theory currently is dominated by the western theoretical perspectives. Although the western theories are often used by many scholars as the universal perspective to explain the phenomena that occur in the world, sometimes the existing theories are failed to explain various issues that occur in the non-western world, for example, in the studies concerning on terrorism issues. Using inappropriate theories to explain the international issues such as terrorism will cause a failure in the decision-making process. The lack of understanding regarding Islamic perspective could be one of the factors that make international society unable to eradicate violent terrorism in the name of religion. Thus, this paper is argued that considering Islamic perspective as one of the major studies in international relations is significant to build a bridge between the Islamic world and the western world. It is believed that enhancing the study of Islamic perspective will create better understanding of the Islamic world and will enrich the study of international relations. This paper is conducted through a qualitative approach, in which data is obtained from the literature analysis. Considering Islamic perspective is important because Islam is listed as one of the major religions in the world. It is also due to the geopolitical spread of the Muslim in the world and the likelihood of the Islamic perspective to shape and influence Muslim’s behavior in the international level. The study of Islamic perspective in the international level is neither to contempt nor to oppose the existing western theories; rather it is needed in order to broaden the perspective in the international relations studies. The Islamic perspective is different compared to the non-western school of thought such as realism, and liberalism in some respects. The Islamic perspective cannot be explained through the lens of rationalist approaches. Compares to the post-positivism international relations perspectives, Islamic perspective is probably closer to the constructivist school of thought. However, the Islamic perspective offers some uniqueness that is not limited to the socially constructed ideas as in the constructivist arguments. This paper will be developed according to the discussion of three aspects that make Islamic perspective different with the existing international relations theories. The first aspect is the main actors in the international level. The second aspect is regarding on what appears to be the most important point for the actors in the international relations. The third aspect is regarding the pattern of relationship between the actors in the international level. In addition, this paper will briefly discuss the perspective of Islam in economics compare to the existing theories in the realm of international political economy.

Keywords: international relations, Islam, non-western theories, societies

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Authors: Vadivel Ayyasamy

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The present approach deals with the identification of Emotions and classification of Emotional patterns at Phrase-level with respect to Positive and Negative Orientation. The proposed approach considers emotion triggered terms, its co-occurrence terms and also associated sentences for recognizing emotions. The proposed approach uses Part of Speech Tagging and Emotion Actifiers for classification. Here sentence patterns are broken into phrases and Neuro-Fuzzy model is used to classify which results in 16 patterns of emotional phrases. Suitable intensities are assigned for capturing the degree of emotion contents that exist in semantics of patterns. These emotional phrases are assigned weights which supports in deciding the Positive and Negative Orientation of emotions. The approach uses web documents for experimental purpose and the proposed classification approach performs well and achieves good F-Scores.

Keywords: emotions, sentences, phrases, classification, patterns, fuzzy, positive orientation, negative orientation

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: N. Samarinas, C. Evangelides, C. Vrekos

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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

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Authors: S. S. Patil, Sachidanand Kini

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Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found.

Keywords: Mahalanobis distance, minimum distance, supervised, unsupervised, user classification accuracy, producer's classification accuracy, maximum likelihood, kappa coefficient

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Authors: Nina V. Zaitseva, Pavel Z. Shur, Nina G. Atiskova

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Nowadays harmonization of sanitary and hygienic standards of environmental quality with international standards is crucial part of integration of Russia into the international community. Harmonization of Russian and international ambient air quality standards may be realized by risk-based standards development. In this paper approaches to risk-based standards development and examples of these approaches implementation are presented.

Keywords: harmonization, health risk assessment, evolutionary modelling, benchmark level, nickel, manganese

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Majid Abdolrazzagh-Nezhad, Salwani Abdullah

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The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.

Keywords: job-shop scheduling, classification, constraints, objective functions

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Authors: Yuliang Liu, Thomas Lavallee, Mary Weishaar, Gretchen Fricke, Huaibo Xin

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The longitudinal study was designed to investigate the impacts of the International Training Program in Pedagogy (ITPP) at a midwestern institution in the United States on the visiting scholars from China from 2012-18. The study used the survey research method and involved 48 visiting scholars from Northwest Normal University in China in those eight ITPP cohorts. The results of both quantitative and qualitative data were critically examined and indicated both types of data sources revealed similar findings. It was found that the ITPP has significantly affected all scholars' instruction in China. International implications resulted from the study.

Keywords: international training program in pedagogy, visiting scholars, survey research method, International implications

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: K. Matušínská, Z. Bednarčík, M. Klepek

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This paper examines international marketing in business practice of Czech exporting small and medium-sized enterprises (SMEs) with regard to the strategic perspectives. Research was focused on Czech exporting SMEs from Moravian-Silesia region and their behaviour on international markets. For purpose of collecting data, a questionnaire was given to 262 SMEs involved in international business. Statistics utilized in this research included frequency, mean, percentage, and chi-square test. Data were analysed by Statistical Package for the Social Sciences software. The research analysis disclosed that there is certain space for improvement in strategic marketing especially in marketing research, perception of cultural and social differences, product adaptation and usage of marketing communication tools.

Keywords: international marketing, marketing mix, marketing research, small and medium-sized enterprises, strategic marketing

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Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: Geetika Barman, B. S. Daya Sagar

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In this article, we proposed a pixel-wise classification of hyperspectral images using a mathematical morphology operator-based distance metric called “dilation distance” and “erosion distance”. This method involves measuring the spatial distance between the spectral features of a hyperspectral image across the bands. The key concept of the proposed approach is that the “dilation distance” is the maximum distance a pixel can be moved without changing its classification, whereas the “erosion distance” is the maximum distance that a pixel can be moved before changing its classification. The spectral signature of the hyperspectral image carries unique class information and shape for each class. This article demonstrates how easily the dilation and erosion distance can measure spatial distance compared to other approaches. This property is used to calculate the spatial distance between hyperspectral image feature vectors across the bands. The dissimilarity matrix is then constructed using both measures extracted from the feature spaces. The measured distance metric is used to distinguish between the spectral features of various classes and precisely distinguish between each class. This is illustrated using both toy data and real datasets. Furthermore, we investigated the role of flat vs. non-flat structuring elements in capturing the spatial features of each class in the hyperspectral image. In order to validate, we compared the proposed approach to other existing methods and demonstrated empirically that mathematical operator-based distance metric classification provided competitive results and outperformed some of them.

Keywords: dilation distance, erosion distance, hyperspectral image classification, mathematical morphology

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Authors: Heeyong Noh, Seongryong Kang, Sungjoo Lee

Abstract:

As the world becomes increasingly interconnected, firms have tried to explore market opportunities not only in the domestic market but also abroad. In particular, transactions of intangible assets in the global market now take on great importance. Accordingly, technology transfer activities such as patent licensing, copyright transfer, or workforce trainings which are considered significant to leverage an organization’s internal capabilities, are occurring more frequently and briskly across the world than ever before. Though a number of studies have addressed the issues regarding technology transfer, most of them have focused on university-industry technology transfer. Of course, some have investigated international technology transfer phenomenon but used patent citations data as a proxy. In order to understand the phenomena more clearly, it would be necessary to collect and analyze data that can measure technology transfer activities between firms more directly. Therefore, this study aims to examine the patterns of international trade in technology by employing data about international technology in-licensing activities in Korean firms. We also investigate the effect of international technology in-licensing strategy on a firm’s innovation performance. The research findings are expected to help R&D managers understand how firms have absorbed technological knowledge from foreign firms in the form of licensing and further develop effective international collaboration strategies. In addition, significant implications can be offered for political decision-making regarding technology trade within increasing international interconnections.

Keywords: international technology trade, technology trade effect, technology transfer, R&D managers

Procedia PDF Downloads 378

Authors: Mehak Nagpal, T. S. Sathyanarayan Rao

Abstract:

Background: Sexual functioning is a neglected aspect of health in women with diabetes, though it contributes greatly towards quality of life and feeling of wellbeing. Also women with DM are at higher risk than men of developing sexual dysfunction and depression. Materials and Methods: Cross-sectional comparison study. Sample size: 100 previously diagnosed type 2DM patients attending Outpatient Diabetic Clinic at Medicine department JSS Hospital Mysore; aged 20-65 years and 60 normal healthy female subjects for Control group. Data was collected with ethical approval over a period of 2 years. Tools Used: 1) Hamilton Depression Rating Scale (HAMD – 17 item) 2) Female Sexual Functioning Index (FSFI) 3) Arizona Sexual Experience Scale (ASEX-F) for female-for screening. 4) The Appraisal of Diabetes Scale (ADS). Results: Statistically significant differences were observed in prevalence rate and severity of depression between diabetic group (45% vs 11% syndromal depression) and controls. Depression scores correlated significantly with glycaemic control, adherence to treatment, BMI and the cognitive appraisal of diabetes. There was significantly greater impairment in the sexual functioning of women with type 2 diabetes mellitus as compared to controls; both prevalence (62% vs 38.3%) and severity (p value < 0.01). Arousal (74.2% vs 53.3%), Desire (76.3% vs 50%) and Satisfaction (76.7% vs 63.7%) were most affected and 64.5% were affected in 2 or more domains. A negative illness appraisal on ADS correlated significantly with poor glycaemic control, higher rates of depression and also more severe female sexual dysfunction (p value < 0.05). Conclusion: Diabetes specific factors that correlated significantly with FSD in this study included the psychological appraisal of diabetes, duration of diabetes, presence of complications and BMI.

Keywords: depression, female sexual dysfunction, India, type 2 diabetes mellitus

Procedia PDF Downloads 329

Authors: Kemal Polat

Abstract:

In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

Procedia PDF Downloads 249