Search results for: data collection

Authors: Ava Dodd, Jonathan Adams

Abstract:

The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represent another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: Early Warning System, Knowledge Management, Topic Modeling, Market Prediction.

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Authors: Vasily G. Moshnyaga

Abstract:

This paper presents an optimized MPEG2 video codec implementation, which drastically reduces the number of computations and memory accesses required for video compression. Unlike traditional scheme, we reuse data stored in frame memory to omit unnecessary coding operations and memory read/writes for unchanged macroblocks. Due to dynamic memory sharing among reference frames, data-driven macroblock characterization and selective macroblock processing, we perform less than 15% of the total operations required by a conventional coder while maintaining high picture quality.

Keywords: Data reuse, adaptive processing, video coding, MPEG

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Authors: Hyun-Woo Cho

Abstract:

The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.

Keywords: Diagnostics, batch process, nonlinear representation, data filtering, multivariate statistical approach

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Authors: Sarra Senhadji, Amar Kateb, Hafida Belbachir

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Data replication in data grid systems is one of the important solutions that improve availability, scalability, and fault tolerance. However, this technique can also bring some involved issues such as maintaining replica consistency. Moreover, as grid environment are very dynamic some nodes can be more uploaded than the others to become eventually a bottleneck. The main idea of our work is to propose a complementary solution between replica consistency maintenance and dynamic load balancing strategy to improve access performances under a simulated grid environment.

Keywords: Consistency, replication, data grid, load balancing.

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Authors: Myungraee Cha, Jun Seok Kim, Seung Hwan Park, Jun-Geol Baek

Abstract:

In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.

Keywords: Density estimation, Multivariate control chart, Oneclass classification, Support vector data description (SVDD)

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Authors: R. Rahmoun-Mrabet

Abstract:

It is acknowledged that our style of speaking changes according to a wide range of variables such as gender, setting, the age of both the addresser and the addressee, the conversation topic, and the aim of the interaction. These differences in style are noticeable in monolingual and multilingual speech communities. Yet, they are more observable in speech communities where two or more codes coexist. The linguistic situation in Algeria reflects a state of bilingualism because of the coexistence of Arabic and French. Nevertheless, like all Arab countries, it is characterized by diglossia i.e. the concomitance of Modern Standard Arabic (MSA) and Algerian Arabic (AA), the former standing for the ‘high variety’ and the latter for the ‘low variety’. The two varieties are derived from the same source but are used to fulfil distinct functions that is, MSA is used in the domains of religion, literature, education and formal settings. AA, on the other hand, is used in informal settings, in everyday speech. French has strongly affected the Algerian language and culture because of the historical background of Algeria, thus, what can easily be noticed in Algeria is that everyday speech is characterized by code-switching from dialectal Arabic and French or by the use of borrowings. Tamazight is also very present in many regions of Algeria and is the mother tongue of many Algerians. Yet, it is not used in the west of Algeria, where the study has been conducted. The present work, which was directed in the speech community of Tlemcen-Algeria, aims at depicting some of the outcomes of the contact of Arabic with French such as code-switching, borrowing and interference. The question that has been asked is whether Algerians are aware of their use of borrowings or not. Three steps are followed in this research; the first one is to depict the sociolinguistic situation in Algeria and to describe the linguistic characteristics of the dialect of Tlemcen, which are specific to this city. The second one is concerned with data collection. Data have been collected from 57 informants who were given questionnaires and who have then been classified according to their age, gender and level of education. Information has also been collected through observation, and note taking. The third step is devoted to analysis. The results obtained reveal that most Algerians are aware of their use of borrowings. The present work clarifies how words are borrowed from French, and then adapted to Arabic. It also illustrates the way in which singular words inflect into plural. The results expose the main characteristics of borrowing as opposed to code-switching. The study also clarifies how interference occurs at the level of nouns, verbs and adjectives.

Keywords: Bilingualism, borrowing, code-switching, interference, language contact.

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Authors: Kyoung-Mi Lee, Youn-Mi Lee

Abstract:

This paper proposes a way to track persons by making use of multiple non-overlapping cameras. Tracking persons on multiple non-overlapping cameras enables data communication among cameras through the network connection between a camera and a computer, while at the same time transferring human feature data captured by a camera to another camera that is connected via the network. To track persons with a camera and send the tracking data to another camera, the proposed system uses a hierarchical human model that comprises a head, a torso, and legs. The feature data of the person being modeled are transferred to the server, after which the server sends the feature data of the human model to the cameras connected over the network. This enables a camera that captures a person's movement entering its vision to keep tracking the recognized person with the use of the feature data transferred from the server.

Keywords: Person tracking, human model, networked cameras, vision-based surveillance.

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Authors: V. Hernandez-Perez, M. Abdulkadir, B. J. Azzopardi

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In this work, new experimental data for slugging frequency in inclined gas-liquid flow are reported, and a new correlation is proposed. Scale experiments were carried out using a mixture of air and water in a 6 m long pipe. Two different pipe diameters were used, namely, 38 and 67 mm. The data were taken with capacitance type sensors at a data acquisition frequency of 200 Hz over an interval of 60 seconds. For the range of flow conditions studied, the liquid superficial velocity is observed to influence the frequency strongly. A comparison of the present data with correlations available in the literature reveals a lack of agreement. A new correlation for slug frequency has been proposed for the inclined flow, which represents the main contribution of this work.

Keywords: slug frequency, inclined flow

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Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores, Valentin Soloiu

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This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development of an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The sensor used were the MyoWare Muscle sensor which contains two EMG electrodes used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor was performed using an Arduino Uno. Data processing algorithms were developed with the purpose of classifying the signals generated by the arm’s muscles when performing specific movements, namely: flexing, resting, and motion of the arm. With these arm motions roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification. To generate the input vector for the ANN and perform the classification, the root mean square and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained. Afterwards, classification results are used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.

Keywords: Biosensors, electromyography, Artificial Neural Network, Arduino, drone flight control, machine learning.

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Authors: Yu-Kyung Kang, Suk-Hyung Hwang, Kyoung-Mo Yang

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The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.

Keywords: Folksonomy data mining, formal concept analysis, collaborative tagging, conceptual knowledge discovery, classification.

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Authors: Nazaket Gazieva

Abstract:

Voice biometric data associated with physiological, psychological and other factors are widely used in forensic phonoscopy. There are various methods for identifying and verifying a person by voice. This article explores the minimum speech signal data as individual parameters of a speech signal. Monozygotic twins are believed to be genetically identical. Using the minimum data of the speech signal, we came to the conclusion that the voice imprint of monozygotic twins is individual. According to the conclusion of the experiment, we can conclude that the minimum indicators of the speech signal are more stable and reliable for phonoscopic examinations.

Keywords: Biometric voice prints, fundamental frequency, phonogram, speech signal, temporal characteristics.

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Authors: Ruchika Malhotra, Megha Khanna

Abstract:

The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.

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Authors: S. Fore, L. Zuze

Abstract:

Frequent machine breakdowns, low plant availability and increased overtime are a great threat to a manufacturing plant as they increase operating costs of an industry. The main aim of this study was to improve Overall Equipment Effectiveness (OEE) at a manufacturing company through the implementation of innovative maintenance strategies. A case study approach was used. The paper focuses on improving the maintenance in a manufacturing set up using an innovative maintenance regime mix to improve overall equipment effectiveness. Interviews, reviewing documentation and historical records, direct and participatory observation were used as data collection methods during the research. Usually production is based on the total kilowatt of motors produced per day. The target kilowatt at 91% availability is 75 Kilowatts a day. Reduced demand and lack of raw materials particularly imported items are adversely affecting the manufacturing operations. The company had to reset its targets from the usual figure of 250 Kilowatt per day to mere 75 per day due to lower availability of machines as result of breakdowns as well as lack of raw materials. The price reductions and uncertainties as well as general machine breakdowns further lowered production. Some recommendations were given. For instance, employee empowerment in the company will enhance responsibility and authority to improve and totally eliminate the six big losses. If the maintenance department is to realise its proper function in a progressive, innovative industrial society, then its personnel must be continuously trained to meet current needs as well as future requirements. To make the maintenance planning system effective, it is essential to keep track of all the corrective maintenance jobs and preventive maintenance inspections. For large processing plants these cannot be handled manually. It was therefore recommended that the company implement (Computerised Maintenance Management System) CMMS.

Keywords: Maintenance, Manufacturing, Overall Equipment Effectiveness

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Authors: Marzieh Moosavi-Nasab, Mohsen Gavahian, Ali R. Yousefi, Hamed Askari

Abstract:

Dextran is a D-glucose polymer which is produced by Leuconostoc mesenteroides grown in a sucrose-rich media. The organism was obtained from the Persian Type Culture Collection (PTCC) and was transferred in MRS broth medium at 30°C and pH 6.8 for 24 h. After preparation of inoculums, organisms were inoculated into five liquid fermentation media containing either molasses or cheese whey or different combinations of cheese whey and molasses. After certain fermentation period, the produced dextran was separated and dried. Dextran yield was calculated and significant differences in different media were observed. Furthermore, FT-IR analysis was performed and the results showed that there were no significant differences in the produced dextran structures.

Keywords: Dextran, Leuconostoc mesenteroides, Molasses, Whey

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Authors: Kitti Koonsanit, Chuleerat Jaruskulchai, Poonsak Miphokasap, Apisit Eiumnoh

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In the end of the day, meteorological data and environmental data becomes widely used such as plant varieties selection system. Variety plant selection for planted area is of almost importance for all crops, including varieties of sugarcane. Since sugarcane have many varieties. Variety plant non selection for planting may not be adapted to the climate or soil conditions for planted area. Poor growth, bloom drop, poor fruit, and low price are to be from varieties which were not recommended for those planted area. This paper presents plant varieties selection system for planted areas in Thailand from meteorological data and environmental data by the use of decision tree techniques. With this software developed as an environmental data analysis tool, it can analyze resulting easier and faster. Our software is a front end of WEKA that provides fundamental data mining functions such as classify, clustering, and analysis functions. It also supports pre-processing, analysis, and decision tree output with exporting result. After that, our software can export and display data result to Google maps API in order to display result and plot plant icons effectively.

Keywords: Plant varieties selection system, decision tree, expert recommendation.

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Authors: Tsunwai Gary Yip

Abstract:

Phase locked loops for data links operating at 10 Gb/s or faster are low phase noise devices designed to operate with a low jitter reference clock. Characterization of their jitter transfer function is difficult because the intrinsic noise of the device is comparable to the random noise level in the reference clock signal. A linear model is proposed to account for the intrinsic noise of a PLL. The intrinsic noise data of a PLL for 10 Gb/s links is presented. The jitter transfer function of a PLL in a test chip for 12.8 Gb/s data links was determined in experiments using the 400 MHz reference clock as the source of simultaneous excitations over a wide range of frequency. The result shows that the PLL jitter transfer function can be approximated by a second order linear model.

Keywords: Intrinsic phase noise, jitter in data link, PLL jitter transfer function, high speed clocking in electronic circuit

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

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In the present world, predicting rainfall is considered to be an essential and also a challenging task. Normally, the climate and rainfall are presumed to have non-linear as well as intricate phenomena. For predicting accurate rainfall, we necessitate advanced computer modeling and simulation. When there is an enhanced understanding of the spatial and temporal distribution of precipitation then it becomes enrichment to applications such as hydrologic, climatic and ecological. Conversely, there may be some kind of challenges occur in the community due to some application which results in the absence of consistent precipitation observation in remote and also emerging region. This survey paper provides a multifarious collection of methodologies which are epitomized by various researchers for predicting the rainfall. It also gives information about some technique to forecast rainfall, which is appropriate to all methods like numerical, traditional and statistical.

Keywords: Satellite Image, Segmentation, Feature Extraction, Classification, Clustering, Precipitation Estimation.

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Authors: Md Rabiul Islam, Ben Wadham

Abstract:

This paper describes how indigenous students face challenges with various school activities due to inadequate equity and diversity principles in mainstream primary schools in Bangladesh. This study focuses on indigenous students’ interactions with mainstream class teachers and students through teaching-learning activities at public primary schools. Ethnographic research methods guided data collection under a case study methodology in Chittagong Hill Tracts (CHTs) region where maximum indigenous peoples’ inhabitants. The participants (class teachers) shared information through in-depth interviews about their experiences in the four selecting schools. The authors also observed the effects of school activities by use of equity and diversity lens for indigenous students’ situations in those schools. The authors argue that the socio-economic situations of indigenous families are not supportive of the educational development of their children. Similarly, the Bangladesh government does not have enough initiative programs based on equity and diversity principles for fundamental education of indigenous children at rural schools level. Besides this, the conventional teaching system cannot improve the diversification among the students in classrooms. The principles of equity and diversity are not well embedded in professional development of teachers, and using teaching materials in classrooms. The findings suggest that implementing equitable education; there are needed to arrange teachers’ education with equitable knowledge and introducing diversified teaching materials, and implementing teaching through students centered activities that promote the diversification among the multicultural students.

Keywords: Case study research, equity and diversity, Indigenous children.

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: Decision support system, data mining, knowledge discovery, data discovery, fuzzy logic.

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Authors: S. El-Gamal, P. Hossam, A. Abd El Aziz, R. Mahmoud, A. Hassan, D. Hilal, E. Ayman, H. Haytham, O. Khamis

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Several concerns fall upon the supply chain especially in cold supply chains. These concerns are mainly in the distribution and storage phases. This research focuses on the storage area, which contains several activities such as the picking activity that faces a lot of obstacles and challenges. The implementation of IoT solutions enables businesses to monitor the temperature of food items, which is perhaps the most critical parameter in cold chains. Therefore, the research at hand proposes a practical solution that would help in eliminating the problems related to ineffective picking for products especially fish and seafood products by using IoT technology, most notably LiFi technology; thus, guaranteeing sufficient picking, reducing waste, and consequently lowering costs. A prototype was specially designed and examined. This research is a single case study research. Two methods of data collection were used; observation and semi-structured interviews. Semi-structured interviews were conducted with managers and a decision maker at one of the biggest retail stores Carrefour, Alexandria, Egypt to validate the problem and the proposed practical solution using IoT and LiFi technology. A total of three interviews were conducted. As a result, a SWOT analysis was achieved in order to highlight all the strengths and weaknesses of using the recommended LiFi solution in the picking process. According to the investigations, it was found that, the use of IoT and LiFi technology is cost effective, efficient, and reduces human errors, minimizes the percentage of product waste and thus saves money and cost. Therefore, increasing customer satisfaction and profits could be achieved.

Keywords: Cold supply chain, IoT, LiFi, warehousing operation, picking process.

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Authors: Moth'd Belal. Al-Daoud

Abstract:

Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.

Keywords: clustering, k-means, data mining.

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop

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Frequent pattern discovery over data stream is a hard problem because a continuously generated nature of stream does not allow a revisit on each data element. Furthermore, pattern discovery process must be fast to produce timely results. Based on these requirements, we propose an approximate approach to tackle the problem of discovering frequent patterns over continuous stream. Our approximation algorithm is intended to be applied to process a stream prior to the pattern discovery process. The results of approximate frequent pattern discovery have been reported in the paper.

Keywords: Frequent pattern discovery, Approximate algorithm, Data stream analysis.

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Authors: Zhou Yu, Jiang Feng

Abstract:

An adaptive Chinese hand-talking system is presented in this paper. By analyzing the 3 data collecting strategies for new users, the adaptation framework including supervised and unsupervised adaptation methods is proposed. For supervised adaptation, affinity propagation (AP) is used to extract exemplar subsets, and enhanced maximum a posteriori / vector field smoothing (eMAP/VFS) is proposed to pool the adaptation data among different models. For unsupervised adaptation, polynomial segment models (PSMs) are used to help hidden Markov models (HMMs) to accurately label the unlabeled data, then the "labeled" data together with signerindependent models are inputted to MAP algorithm to generate signer-adapted models. Experimental results show that the proposed framework can execute both supervised adaptation with small amount of labeled data and unsupervised adaptation with large amount of unlabeled data to tailor the original models, and both achieve improvements on the performance of recognition rate.

Keywords: sign language recognition, signer adaptation, eMAP/VFS, polynomial segment model.

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Authors: P. Kumsawat, N. Pimpru, K. Attakitmongcol, A.Srikaew

Abstract:

In this paper, we proposed an efficient data compression strategy exploiting the multi-resolution characteristic of the wavelet transform. We have developed a sensor node called “Smart Sensor Node; SSN". The main goals of the SSN design are lightweight, minimal power consumption, modular design and robust circuitry. The SSN is made up of four basic components which are a sensing unit, a processing unit, a transceiver unit and a power unit. FiOStd evaluation board is chosen as the main controller of the SSN for its low costs and high performance. The software coding of the implementation was done using Simulink model and MATLAB programming language. The experimental results show that the proposed data compression technique yields recover signal with good quality. This technique can be applied to compress the collected data to reduce the data communication as well as the energy consumption of the sensor and so the lifetime of sensor node can be extended.

Keywords: Wireless sensor network, wavelet transform, data compression, ZigBee, skipped high-pass sub-band.

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Authors: Uzma Bashir, Jamaludin Md. Ali

Abstract:

This study is concerned with the visualization of monotone data using a piecewise C1 rational trigonometric interpolating scheme. Four positive shape parameters are incorporated in the structure of rational trigonometric spline. Conditions on two of these parameters are derived to attain the monotonicity of monotone data and othertwo are leftfree. Figures are used widely to exhibit that the proposed scheme produces graphically smooth monotone curves.

Keywords: Trigonometric splines, Monotone data, Shape preserving, C1 monotone interpolant.

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Authors: A. Banovcinova, M. Zakova

Abstract:

Labor market activity and paid employment should be a key factor in protecting individuals and families from falling into poverty and providing them with sufficient resources to meet the needs of their members. However, due to various processes in the labor market as well as the influence of individual factors and often insufficient social capital, there is a relatively large group of households that cannot eliminate paid employment and find themselves in a state of so-called working poverty. The aim of the research was to find out what strategies families use in managing poverty and meeting their needs and which of these strategies prevail in the Slovak population. A quantitative research strategy was chosen. The method of data collection was a structured interview focused on finding out the use of individual management strategies and also selected demographic indicators. The research sample consisted of members of families in which at least one member has a paid job. The condition for inclusion in the research was that the family's income did not exceed 60% of the national median equalized disposable income. The analysis of the results showed 5 basic areas to which management strategies are related - work, financial security, needs, social contacts and perception of the current situation. The prevailing strategies were strategies aimed at increasing and streamlining labor market activity and the planned and effective management of the family budget. Strategies that were rejected were mainly related to debt creation. The results make it possible to identify the preferred ways of managing poverty in individual areas of life, as well as the factors that influence this behavior. This information is important for working with families living in a state of working poverty and can help professionals develop positive ways of coping for families.

Keywords: Copying strategies, family, in-work poverty, quantitative research.

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Authors: Pham Van Nha, Le Cam Binh

Abstract:

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: Analysis of optimization, artificial intelligence-based optimization, optimization for learning and data analysis, global optimization.

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Authors: David M. Lucas, Charles W. Jarrett

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In the culture of Thailand, the Yak serve as a mediated icon representing strength, power, and mystical protection not only for the Buddha, but for population of worshipers. Originating from the forests of China, the Yak continues to stand guard at the gates of Buddhist temples. The Yak represents Thai culture in the hearts of Thai people. This paper presents a qualitative study regarding the curious mix of media, culture, and religion that projects the Yak of Thailand as a larger than life message throughout the political, cultural, and religious spheres. The gate guardians, or gods as they are sometimes called, appear throughout the religious temples of Asian cultures. However, the Asian cultures demonstrate differences in artistic renditions (or presentations) of such sentinels. Thailand gate guards (the Yak) stand in front of many Buddhist temples, and these iconic figures display unique features with varied symbolic significance. The temple (or wat), plays a vital role in every community; and, for many people, Thailand’s temples are the country’s most endearing sights. The authors applied folknography as a methodology to illustrate the importance of the Thai Yak in serving as meaningful icons that transcend not only time, but the culture, religion, and mass media. The Yak represents mythical, religious, artistic, cultural, and militaristic significance for the Thai people. Data collection included interviews, focus groups, and natural observations. This paper summarizes the perceptions of the Thai people concerning their gate sentries and the relationship, communication, connection, and the enduring respect that Thai people hold for their guardians of the gates.

Keywords: Communication, Culture, Folknography, Icon, Image, Media, Protection, Religion, Yak.

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Authors: M. Govindarajan, R. M.Chandrasekaran

Abstract:

Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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