Search results for: Survey data visualization.

Authors: Gökhan Silahtaroğlu

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Clustering large populations is an important problem when the data contain noise and different shapes. A good clustering algorithm or approach should be efficient enough to detect clusters sensitively. Besides space complexity, time complexity also gains importance as the size grows. Using hierarchies we developed a new algorithm to split attributes according to the values they have and choosing the dimension for splitting so as to divide the database roughly into equal parts as much as possible. At each node we calculate some certain descriptive statistical features of the data which reside and by pruning we generate the natural clusters with a complexity of O(n).

Keywords: Clustering, tree, split, pruning, entropy, gini.

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, Apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: Behavior, data mining technique, Apriori algorithm.

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Authors: T. Thulare, M. Herselman, A. Botha

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Poor system use, including inappropriate design of health information systems, causes difficulties in communication with patients and increased time spent by healthcare professionals in recording the necessary health information for medical records. System features like pop-up reminders, complex menus, and poor user interfaces can make medical records far more time consuming than paper cards as well as affect decision-making processes. Although errors associated with health information and their real and likely effect on the quality of care and patient safety have been documented for many years, more research is needed to measure the occurrence of these errors and determine the causes to implement solutions. Therefore, the purpose of this paper is to identify data integrity challenges in hospital information systems through a scoping review and based on the results provide recommendations on how to manage these. Only 34 papers were found to be most suitable out of 297 publications initially identified in the field. The results indicated that human and computerized systems are the most common challenges associated with data integrity and factors such as policy, environment, health workforce, and lack of awareness attribute to these challenges but if measures are taken the data integrity challenges can be managed.

Keywords: Data integrity, data integrity challenges, hospital information systems, South Africa.

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Authors: Sean Paulsen, Michael Casey

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In this work, we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: Transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training.

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai

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This paper proposes a novel feature extraction method, based on Discrete Wavelet Transform (DWT) and K-L Seperability (KLS), for the classification of Functional Data (FD). This method combines the decorrelation and reduction property of DWT and the additive independence property of KLS, which is helpful to extraction classification features of FD. It is an advanced approach of the popular wavelet based shrinkage method for functional data reduction and classification. A theory analysis is given in the paper to prove the consistent convergence property, and a simulation study is also done to compare the proposed method with the former shrinkage ones. The experiment results show that this method has advantages in improving classification efficiency, precision and robustness.

Keywords: classification, functional data, feature extraction, K-Lseperability, wavelet.

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Authors: Babajide S. Adeokin, Oguntoyinbo O. Kazeem

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The not so pleasing state of the nation's quality of education has been a major area of research. Many researchers have looked into various aspects of the educational system and organizational structure in relation to the quality of service delivery of the staff members. However, there is paucity of research in areas relating to the sense of competence and commitment in relation to leadership styles. Against this backdrop, this study investigated the impact of leadership style and sense of competence on the performance of post-primary school teachers in Oyo state Nigeria. Data were generated across public secondary schools in the city using survey design method. Ibadan as a metropolis has eleven local government areas contained in it. A systematic random sampling technique of the eleven local government areas in Ibadan was done and five local government areas were selected. The selected local government areas are Akinyele, Ibadan North, Ibadan North-East, Ibadan South and Ibadan South-West. Data were obtained from a range of two – three public secondary schools selected in each of the local government areas mentioned above. Also, these secondary schools are a representation of the variations in the constructs under consideration across the Ibadan metropolis. Categorically, all secondary school teachers in Ibadan were clustered into selected schools in those found across the five local government areas. In all, a total of 272 questionnaires were administered to public secondary school teachers, while 241 were returned. Findings revealed that transformational leadership style makes room for job commitment when compared with transactional and laissez-faire leadership styles. Teachers with a high sense of competence are more likely to demonstrate more commitment to their job than others with low sense of competence. We recommend that, it is important an assessment is made of the leadership styles employed by principals and school administrators. This guides administrators and principals in to having a clear, comprehensive knowledge of the style they currently adopt in the management of the staff and the school as a whole; and know where to begin the adjustment process from. Also to make an impact on student achievement, being attentive to teachers’ levels of commitment may be an important aspect of leadership for school principals.

Keywords: Leadership style, sense of competence, teachers, public secondary schools, Ibadan.

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Authors: M. Pandi, K. Premalatha

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A DNA microarray technology is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. It is handled by clustering which reveals the natural structures and identifying the interesting patterns in the underlying data. In this paper, gene based clustering in gene expression data is proposed using Cuckoo Search with Differential Evolution (CS-DE). The experiment results are analyzed with gene expression benchmark datasets. The results show that CS-DE outperforms CS in benchmark datasets. To find the validation of the clustering results, this work is tested with one internal and one external cluster validation indexes.

Keywords: DNA, Microarray, genomics, Cuckoo Search, Differential Evolution, Gene expression data, Clustering.

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Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

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In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: Anthraquinones, physiologically active substances, phytopreparation, Ramon.

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Authors: Carla Layana Luis Diambra

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Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.

Keywords: circadian rhythms, clustering, gene expression, PCA.

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Authors: S. Soommat, S. Patamatamkul, T. Prempridi, M. Sritulyachot, P. Ineure, S. Yimman

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Currently, slider process of Hard Disk Drive Industry become more complex, defective diagnosis for yield improvement becomes more complicated and time-consumed. Manufacturing data analysis with data mining approach is widely used for solving that problem. The existing mining approach from combining of the KMean clustering, the machine oriented Kruskal-Wallis test and the multivariate chart were applied for defective diagnosis but it is still be a semiautomatic diagnosis system. This article aims to modify an algorithm to support an automatic decision for the existing approach. Based on the research framework, the new approach can do an automatic diagnosis and help engineer to find out the defective factors faster than the existing approach about 50%.

Keywords: Slider process, Defective diagnosis and Data mining.

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Authors: Osamah M. Al-Qershi, Khoo Bee Ee

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In recent years image watermarking has become an important research area in data security, confidentiality and image integrity. Many watermarking techniques were proposed for medical images. However, medical images, unlike most of images, require extreme care when embedding additional data within them because the additional information must not affect the image quality and readability. Also the medical records, electronic or not, are linked to the medical secrecy, for that reason, the records must be confidential. To fulfill those requirements, this paper presents a lossless watermarking scheme for DICOM images. The proposed a fragile scheme combines two reversible techniques based on difference expansion for patient's data hiding and protecting the region of interest (ROI) with tamper detection and recovery capability. Patient's data are embedded into ROI, while recovery data are embedded into region of non-interest (RONI). The experimental results show that the original image can be exactly extracted from the watermarked one in case of no tampering. In case of tampered ROI, tampered area can be localized and recovered with a high quality version of the original area.

Keywords: DICOM, reversible, ROI-based, watermarking.

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on hyperspectral image (HSI) dataset on Indian Pines. The results confirm the capability of the proposed method.

Keywords: Continual learning, data reconstruction, remote sensing, hyperspectral image segmentation.

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Authors: Mritha Ramalingam

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Computer technology and the Internet have made a breakthrough in the existence of data communication. This has opened a whole new way of implementing steganography to ensure secure data transfer. Steganography is the fine art of hiding the information. Hiding the message in the carrier file enables the deniability of the existence of any message at all. This paper designs a stego machine to develop a steganographic application to hide data containing text in a computer video file and to retrieve the hidden information. This can be designed by embedding text file in a video file in such away that the video does not loose its functionality using Least Significant Bit (LSB) modification method. This method applies imperceptible modifications. This proposed method strives for high security to an eavesdropper-s inability to detect hidden information.

Keywords: Data hiding, LSB, Stego machine, VideoSteganography

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Authors: Tuti Haryati Jasimin, Hishamuddin Mohd Ali

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Malaysia’s green building development is gaining momentum and green buildings have become a key focus area, especially within the commercial sector with the encouragement of government legislation and policy. Due to the emerging awareness among the market players’ views of the benefits associated with the ownership of green buildings in Malaysia, there is a need for valuers to incorporate consideration of sustainability into their assessments of property market value to ensure the green buildings continue to increase in the market. This paper analyses the valuers’ current perception on the valuation practices with regard to the green issues in Malaysia. The study was based on a survey of registered real estate valuers and the experts whose work related to valuation in the Klang Valley area to rate their view regarding the perception on valuation of green building. The findings present evidence that even though Malaysian valuers have limited knowledge of green buildings, they recognise the importance of incorporating the green features in the valuation process. The inclusion of incorporating the green features in valuations in practice was hindered by the inadequacy of sufficient transaction data in the market. Furthermore, valuers experienced difficulty in identifying what are the various input parameters of green building and how to adjust it in order to reflect the benefit of sustainability features correctly in the valuation process. This paper focuses on the present challenges confronted by Malaysian valuers with regards to incorporating the green features in their valuation.

Keywords: Green commercial office building, Malaysia, valuers’ perception, valuation.

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Authors: Mikel Niño, Roberto V. Zicari, Todor Ivanov, Kim Hee, Naveed Mushtaq, Marten Rosselli, Concha Sánchez-Ocaña, Karsten Tolle, José Miguel Blanco, Arantza Illarramendi, Jörg Besier, Harry Underwood

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One of the application fields for data analysis techniques and technologies gaining momentum is the area of social good or “common good”, covering cases related to humanitarian crises, global health care, or ecology and environmental issues, among others. The promotion of data-driven projects in this field aims at increasing the efficacy and efficiency of social initiatives, improving the way these actions help humanity in general and people in need in particular. This application field, however, poses its own barriers and challenges when developing data-driven projects, lagging behind in comparison with other scenarios. These challenges derive from aspects such as the scope and scale of the social issue to solve, cultural and political barriers, the skills of main stakeholders and the technological resources available, the motivation to be engaged in such projects, or the ethical and legal issues related to sensitive data. This paper analyzes the application of data projects in the field of social good, reviewing its current state and noteworthy initiatives, and presenting a framework covering the key aspects to analyze in such projects. The goal is to provide guidelines to understand the main challenges and opportunities for this type of data project, as well as identifying the main differential issues compared to “classical” data projects in general. A case study is presented on the initial steps and stakeholder analysis of a data project for the inclusion of refugees in the city of Frankfurt, Germany, in order to empirically confront the framework with a real example.

Keywords: Data-Driven projects, humanitarian operations, personal and sensitive data, social good, stakeholders analysis.

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Authors: Mustafa Qaseem Hielat, Ahmad Mohammad Al-Shabatat

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Jordan exerts many efforts to nurture their academically gifted students in special schools since 2001. During the past nine years of launching these schools, their learning and excellence environments were believed to be distinguished compared to public schools. This study investigated the environments of gifted students compared with other non-gifted, using a survey instrument that measures the dimensions of family, peers, teachers, school- support, society, and resources –dimensions rooted deeply in supporting gifted education, learning, and achievement. A total number of 109 were selected from excellence schools for academically gifted students, and 119 non-gifted students were selected from public schools. Around 8.3% of the non-gifted students reported that they “Never" received any support from their surrounding environments, 14.9% reported “Seldom" support, 23.7% reported “ Often" support, 26.0% reported “Frequent" support, and 32.8% reported “Very frequent" support. Where the gifted students reported more “Never" support than the non-gifted did with 11.3%, “Seldom" support with 15.4%, “Often" support with 26.6%, “Frequent" support with 29.0%, and reported “Very frequent" support less than the non-gifted students with 23.6%. Unexpectedly, statistical differences were found between the two groups favoring non-gifted students in perception of their surrounding environments in specific dimensions, namely, school- support, teachers, and society. No statistical differences were found in the other dimensions of the survey, namely, family, peers, and resources. As the differences were found in teachers, school- support, and society, the nurturing environments for the excellence schools need to be revised to adopt more creative teaching styles, rich school atmosphere and infrastructures, interactive guiding for the students and their parents, promoting for the excellence environments, and re-build successful identification models. Thus, families, schools, and society should increase their cooperation, communication, and awareness of the gifted supportive environments. However, more studies to investigate other aspects of promoting academic giftedness and excellence are recommended.

Keywords: Academic giftedness, Supportive environment, Excellence schools, Gifted grouping, Gifted nurturing.

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Authors: Rohan Putatunda, Aryya Gangopadhyay

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Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Keywords: FFT, ICA, vehicle classification, multi-feature DNN, CNN, MLP.

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

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Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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Authors: Lilac A. E. Al-Safadi

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This paper proposes an auto-classification algorithm of Web pages using Data mining techniques. We consider the problem of discovering association rules between terms in a set of Web pages belonging to a category in a search engine database, and present an auto-classification algorithm for solving this problem that are fundamentally based on Apriori algorithm. The proposed technique has two phases. The first phase is a training phase where human experts determines the categories of different Web pages, and the supervised Data mining algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the categorization phase where a web crawler will crawl through the World Wide Web to build a database categorized according to the result of the data mining approach. This database contains URLs and their categories.

Keywords: Information Processing on the Web, Data Mining, Document Classification.

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Authors: Md. Sayeed Akhter, Md. Akhtar Hossain Mazumder, Syeda Afreena Mamun

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Bangladesh is a patriarchal and male dominated country. Traditional, cultural, social, and religious values and practices have reinforced the lower status of women accorded to them in society and have limited their opportunities for education, technical and vocational training, and involvement with earning activities outside their households. After independence numbers of women are doing job outside their households. This study attempts to find out the reasons of engaging in earning activities outside households and difficulties faced by upper and lower class working women in Bangladesh. To explore the objectives and research questions of the study descriptive techniques had been used. Survey was conducted among the women who were working in Rajshahi city of Bangladesh and face-to-face interviews were conducted to collect data. Findings of the study illustrates that most of the upper class working women engaged into job because they wanted to utilized their education and to bring solvency in the family, and they spend their income for meeting the needs of all the members of the family. On the other hand, most of the lower class working women involved into earning activities outside their households because they want to bring solvency in their families and spend their income on household expenditure. Both classes became tensed for their children because they had to stay at their working place for long time. Therefore, day care center should be established besides their working place for their children.

Keywords: Working Women, Reasons for Doing Jobs, Working Environment, Difficulties Faced.

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Authors: Andrzej Śluzek

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To illustrate diversity of methods used to extract relevant (where the concept of relevance can be differently defined for different applications) visual data, the paper discusses three groups of such methods. They have been selected from a range of alternatives to highlight how hardware and software tools can be complementarily used in order to achieve various functionalities in case of different specifications of “relevant data". First, principles of gated imaging are presented (where relevance is determined by the range). The second methodology is intended for intelligent intrusion detection, while the last one is used for content-based image matching and retrieval. All methods have been developed within projects supervised by the author.

Keywords: Relevant visual data, gated imaging, intrusion detection, image matching.

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Authors: Jintana Khemprasit, Vatcharaporn Esichaikul

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Crime is a major societal problem for most of the world's nations. Consequently, the police need to develop new methods to improve their efficiency in dealing with these ever increasing crime rates. Two of the common difficulties that the police face in crime control are crime investigation and the provision of crime information to the general public to help them protect themselves. Crime control in police operations involves the use of spatial data, crime data and the related crime data from different organizations (depending on the nature of the analysis to be made). These types of data are collected from several heterogeneous sources in different formats and from different platforms, resulting in a lack of standardization. Moreover, there is no standard framework for crime data collection, integration and dissemination through mobile devices. An investigation into the current situation in crime control was carried out to identify the needs to resolve these issues. This paper proposes and investigates the use of service oriented architecture (SOA) and the mobile spatial information service in crime control. SOA plays an important role in crime control as an appropriate way to support data exchange and model sharing from heterogeneous sources. Crime control also needs to facilitate mobile spatial information services in order to exchange, receive, share and release information based on location to mobile users anytime and anywhere.

Keywords: Crime Control, Geographic Information System (GIS), Mobile GIS, Service Oriented Architecture (SOA).

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Authors: Nur Atiqah Rochin Demong, Jie Lu, Farookh Khadeer Hussain

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Property investment in the real estate industry has a high risk due to the uncertainty factors that will affect the decisions made and high cost. Analytic hierarchy process has existed for some time in which referred to an expert-s opinion to measure the uncertainty of the risk factors for the risk analysis. Therefore, different level of experts- experiences will create different opinion and lead to the conflict among the experts in the field. The objective of this paper is to propose a new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach. The propose technique consist of a basic framework which includes four modules: user, technology, end-user access tools and applications. The property investment risk analysis defines as a micro level analysis as the features of the property will be considered in the analysis in this paper.

Keywords: Uncertainty factors, data mining, multidimensional data model, risk analysis.

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Authors: Sara Jelodarian

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Organic agriculture is a kind of living and dynamic agriculture that was introduced in the early 20^th century. The fundamental basis for organic agriculture is in harmony with nature. This version of farming emphasizes removing growth hormones, chemical fertilizers, toxins, radiation, genetic manipulation and instead, integration of modern scientific techniques (such as biologic and microbial control) that leads to the production of healthy food and the preservation of the environment and use of agricultural products such as forage and manure. Supports from governments for the markets producing organic products and taking advantage of the experiences from other successful societies in this field can help progress the positive and effective aspects of this technology, especially in developing countries. This research proves that till 2030, 25% of the global agricultural lands would be covered by organic farming. Consequently Iran, due to its rich genetic resources and various climates, can be a pioneer in promoting organic products. In addition, for sustainable farming, blend of organic and other innovative systems is needed. Important limitations exist to accept these systems, also a diversity of policy instruments will be required to comfort their development and implementation. The paper was conducted to results of compilation of reports, issues, books, articles related to the subject with library studies and research. Likewise we combined experimental and survey to get data.

Keywords: Development, production markets, progress, strategic role, technology.

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Authors: Michal Cerny

Abstract:

We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.

Keywords: Linear regression, interval-censored data, computational complexity.

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Authors: Soon-Hyun Park, Takami Matsuo

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This paper presents an adaptive differentiator of sequential data based on the adaptive control theory. The algorithm is applied to detect moving objects by estimating a temporal gradient of sequential data at a specified pixel. We adopt two nonlinear intensity functions to reduce the influence of noises. The derivatives of the nonlinear intensity functions are estimated by an adaptive observer with σ-modification update law.

Keywords: Adaptive estimation, parameter adjustmentlaw, motion detection, temporal gradient, differential filter.

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Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

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Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: Ecosystem, business model, personal data, preventive healthcare.

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Authors: Ali M. Jawarneh

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Beam and diffuse radiation data are extracted analytically from previous measured data on a horizontal surface in Zarqa city. Moreover, radiation data on a tilted surfaces with different slopes have been derived and analyzed. These data are consisting of of beam contribution, diffuse contribution, and ground reflected contribution radiation. Hourly radiation data for horizontal surface possess the highest radiation values on June, and then the values decay as the slope increases and the sharp decreasing happened for vertical surface. The beam radiation on a horizontal surface owns the highest values comparing to diffuse radiation for all days of June. The total daily radiation on the tilted surface decreases with slopes. The beam radiation data also decays with slopes especially for vertical surface. Diffuse radiation slightly decreases with slopes with sharp decreases for vertical surface. The groundreflected radiation grows with slopes especially for vertical surface. It-s clear that in June the highest harvesting of solar energy occurred for horizontal surface, then the harvesting decreases as the slope increases.

Keywords: Beam and Diffuse Radiation, Zarqa City

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Authors: Xun Ge, Jianhua Gong

Abstract:

 

ankings for output of Chinese main agricultural commodity in the world for 1978, 1980, 1990, 2000, 2006, 2007 and 2008 have been released in United Nations FAO Database. Unfortunately, where the ranking of output of Chinese cotton lint in the world for 2008 was missed. This paper uses sequential data mining methods with decision rules filling this gap. This new data mining method will be help to give a further improvement for United Nations FAO Database.

Keywords: Ranking, output of the main agricultural commodity, gross domestic product, decision table, information system, data mining, decision rule

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