Search results for: data scarcity

Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

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Authors: N. Y. H. Konagano

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Riverside population of Brazilian amazon suffers drinking water scarcity, seeking alternative water resources such as well and rivers, ordinary polluted. Although Amazon Region holds high annual river inflow and enough available of underground water, human activities have compromised the conservation of water resources. In addition, decentralized rural households make difficult to access of potable water. Main objective is to analyze quantitatively the potential of rainwater harvesting to human consumption at Marupaúba community, located in northeast of Amazon region, Brazil. Methods such as historical rainfall data series of municipality of Tomé-Açu at Pará state were obtained from Hydrological Information System of National Water Agency (ANA). Besides, Rippl method was used to calculate, mainly, volume of the reservoir based on difference of water demand and volume available through rainwater using as references two houses (CA I and CA II) as model of rainwater catchment and supply. Results presented that, from years 1984 to 2017, average annual precipitation was 2.607 mm, average maximum precipitation peak was 474 mm on March and average minimum peak on September was 44 mm. All months, of a year, surplus volume of water have presented in relation to demand, considering catchment area (CA) I = 134.4m² and demand volume =0.72 m³/month; and, CA II = 81.84 m² and demand volume = 0.48 m³/month. Based on results, it is concluded that it is feasible to use rainwater for the supply of the rural community Marupaúba, since the access of drinking water is a human right and the lack of this resource compromises health and daily life of human beings.

Keywords: Amazon Region, rainwater harvesting, rainwater resource, rural community

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Mary Jane Suarez

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A massive educational overhaul has taken place at the onset of the 21st century addressing the mismatches of employability skills with that of scholastic skills taught in schools. For a community to thrive in an ever-developing economy, the teaching of the necessary skills for job competencies should be realized by every educational institution. However, in harnessing 21st-century skills amongst learners, teachers, who often lack familiarity and thorough insights into the emerging 21st-century skills, are chained with the restraint of the need to comprehend the physiognomies of 21st-century skills learning and the requisite to implement the tenets of 21st-century skills teaching. With the endeavor to espouse 21st-century skills learning and teaching, a United States-based national coalition called Partnership 21st Century Skills (P21) has identified the four most important skills in 21st-century learning: critical thinking, communication, collaboration, and creativity and innovation with an established framework for 21st-century skills standards. Assessment of skills is the lifeblood of every teaching and learning encounter. It is correspondingly crucial to look at the 21st century standards and the assessment guides recognized by P21 to ensure that learners are 21st century ready. This mixed-method study sought to discover and describe what classroom assessments were used by English teachers in a public secondary school in the Philippines with course offerings on science, technology, engineering, and mathematics (STEM). The research evaluated the assessment tools implemented by English teachers and how these assessment tools were congruent to the 21st assessment standards of P21. A convergent parallel design was used to analyze assessment tools and practices in four phases. In the data-gathering phase, survey questionnaires, document reviews, interviews, and classroom observations were used to gather quantitative and qualitative data simultaneously, and how assessment tools and practices were consistent with the P21 framework with the four Cs as its foci. In the analysis phase, the data were treated using mean, frequency, and percentage. In the merging and interpretation phases, a side-by-side comparison was used to identify convergent and divergent aspects of the results. In conclusion, the results yielded assessments tools and practices that were inconsistent, if not at all, used by teachers. Findings showed that there were inconsistencies in implementing authentic assessments, there was a scarcity of using a rubric to critically assess 21st skills in both language and literature subjects, there were incongruencies in using portfolio and self-reflective assessments, there was an exclusion of intercultural aspects in assessing the four Cs and the lack of integrating collaboration in formative and summative assessments. As a recommendation, a harmonized assessment scheme of P21 skills was fashioned for teachers to plan, implement, and monitor classroom assessments of 21st-century skills, ensuring the alignment of such assessments to P21 standards for the furtherance of the institution’s thrust to effectively integrate 21st-century skills assessment standards to its curricula.

Keywords: 21st-century skills, 21st-century skills assessments, assessment standards, congruency, four Cs

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Zakieh Piri, Shahla Damanabi, Peyman Rezaii Hachesoo

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Introduction: Healthcare organizations like other organizations suffer from a number of disorders such as Business Sponsor Disorder, Business Acceptance Disorder, Cultural/Political Disorder, Data Disorder, etc. As quality in healthcare care mostly depends on the quality of data, we aimed to identify data disorders and its symptoms in two teaching hospitals. Methods: Using a self-constructed questionnaire, we asked 20 questions in related to quality and usability of patient data stored in patient records. Research population consisted of 150 managers, physicians, nurses, medical record staff who were working at the time of study. We also asked their views about the symptoms and treatments for any data disorders they mentioned in the questionnaire. Using qualitative methods we analyzed the answers. Results: After classifying the answers, we found six main data disorders: incomplete data, missed data, late data, blurred data, manipulated data, illegible data. The majority of participants believed in their important roles in treatment of data disorders while others believed in health system problems. Discussion: As clinicians have important roles in producing of data, they can easily identify symptoms and disorders of patient data. Health information managers can also play important roles in early detection of data disorders by proactively monitoring and periodic check-ups of data.

Keywords: data disorders, quality, healthcare, treatment

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

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Authors: D. Sarkar, M. Pal, A. K. Sarkar

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Asphaltic concrete for pavement construction in India are produced by using crushed stone, gravels etc. as aggregate. In north-Eastern region of India, there is a scarcity o f stone aggregate. Therefore the road engineers are always in search of an optional material as aggregate which can replace the regularly used material. The purpose of this work was to evaluate the utilization of substandard or marginal aggregates in flexible pavement construction. The investigation was undertaken to evaluate the effects of using lower quality aggregates such as over burnt brick aggregate on the preparation of asphalt concrete for flexible pavements. The scope of this work included a review of available literature and existing data, a laboratory evaluation organized to determine the effects of marginal aggregates and potential techniques to upgrade these substandard materials, and a laboratory evaluation of these upgraded marginal aggregate asphalt mixtures. Over burnt brick aggregates are water susceptible and can leads to moisture damage. Moisture damage is the progressive loss of functionality of the material owing to loss of the adhesion bond between the asphalt binder and the aggregate surface. Hence, zycosoil as an anti striping additive were evaluated in this study. This study summarizes the results of the laboratory evaluation carried out to investigate the properties of asphalt concrete prepared with zycosoil modified over burnt brick aggregate. Marshall specimen were prepared with stone aggregate, zycosoil modified stone aggregate, over burnt brick aggregate and zycosoil modified over burnt brick aggregate. Results show that addition of zycosoil with stone aggregate increased stability by 6% and addition of zycosoil with over burnt brick aggregate increased stability by 30%.

Keywords: asphalt concrete, over burnt brick aggregate, marshall stability, zycosoil

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Furry A. Wilis, Dewi Larasati, Suhendri

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Around 96% of architecture firms in Indonesia are classified as small-medium enterprises (SME). This number shows that the SME firms have an important role in architecture, engineering, and construction (AEC) industry in Indonesia. Some of them are still using conventional system (2D based) in arranging construction project documents. This system is fragmented and not fully well-coordinated, so causes many changes in the whole project cycle. Building information modeling (BIM), as a new developed system in Indonesian construction industry, has been assumed can decrease changes in the project. But BIM has not fully implemented in Indonesian AEC industry, especially in SME architecture firms. This article identifies the challenges of implementing BIM in SME architecture firms in Indonesia. Quantitative-explorative research with questionnaire was chosen to achieve the goal of this article. The scarcity of skilled BIM user, low demand from client, high investment cost, and the unwillingness of the firm to switch into BIM were found as the result of this paper.

Keywords: architecture consultants, BIM, SME, Indonesia

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Imessaoudene Y., Mouhouche B., Sengouga A., Kadir M.

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The objective of this work is to determine the virtual water of main crops grown in the province of Djelfa and water use efficiency (W.U.E.), Which is essential to approach the application and better integration with the offer in the region. In the case of agricultural production, virtual water is the volume of water evapo-transpired by crops. It depends on particular on the expertise of its producers and its global production area, warm and dry climates induce higher consumption. At the scale of the province, the determination of the quantities of virtual water is done by calculating the unit water requirements related to water irrigated hectare and total rainfall over the crop using the Cropwat 8.0 F.A.O. software. Quantifying the volume of agricultural virtual water of crops practiced in the study area demonstrates the quantitative importance of these volumes of water in terms of available water resources in the province, so the advantages which can be the concept of virtual water as an analysis tool and decision support for the management and distribution of water in scarcity situation.

Keywords: virtual water, water use efficiency, water requirements, Djelfa

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Athulya Jayakumar, M. Manjula

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Though there has been a shift in the understanding of homosexuality from being a sin, crime or pathology in the medical and legal perspectives, the acceptance of homosexuality still remains very scanty in the Indian subcontinent. The present case study is a 24-year-old female who has completed a diploma in polytechnic engineering and residing in the state of Kerala. She initially presented with her husband with complaints of lack of sexual desire and non-cooperation from the index client. After an initial few sessions, the client revealed, in an individual session, about her homosexual orientation which was unknown to her family. She has had multiple short-term relations with females and never had any heterosexual orientation/interest. During her adolescence, she was wondering if she could change herself into a male. However, currently, she accepts her gender. She never wanted a heterosexual marriage; but, had to succumb to the pressure of mother, as a result of a series of unexpected incidents at home and had to agree for the marriage, also with a hope that she may change herself into a bi-sexual. The client was able to bond with the husband emotionally but the multiple attempts at sexual intercourse, at the insistence of the husband, had always been non-pleasurable and induced a sense of disgust. Currently, for several months, there has not been any sexual activity. Also, she actively avoids any chance to have a warm communication with him so that she can avoid chances of him approaching her in a sexual manner. The case study is an attempt to highlight the culture and the struggles of a homosexual individual who comes to therapy for wanting to be a ‘normal wife’ despite having knowledge of legal rights and scenario. There is a scarcity of Indian literature that has systematically investigated issues related to homosexuality. Data on prevalence, emotional problems faced and clinical services available are sparse though it is crucial for increasing understanding of sexual behaviour, orientation and difficulties faced in India.

Keywords: case study, culture, cognitive behavior therapy, female homosexuality

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: Deepa Das, Susmita Das

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Cognitive Radio (CR) is an emerging technology to combat the spectrum scarcity issues. This is achieved by consistently sensing the spectrum, and detecting the under-utilized frequency bands without causing undue interference to the primary user (PU). In soft decision fusion (SDF) based cooperative spectrum sensing, various evolutionary algorithms have been discussed, which optimize the weight coefficient vector for maximizing the detection performance. In this paper, we propose the hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm as a fast and global optimization method, which improves the detection probability with a lesser sensing time. Then, the efficiency of this algorithm is compared with the standard invasive weed optimization (IWO), particle swarm optimization (PSO), genetic algorithm (GA) and other conventional SDF based methods on the basis of convergence and detection probability.

Keywords: cognitive radio, spectrum sensing, soft decision fusion, GA, PSO, IWO, hybrid IWO/PSO

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Syed Hammad Ali, Rijan Bhakta Kayastha, Ahuti Shrestha, Iram Bano

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The hydrology of Upper Indus basin is not recognized well due to the intricacies in the climate and geography, and the scarcity of data above 5000 meters above sea level where most of the precipitation falls in the form of snow. The main objective of this study is to measure the contributions of different components of runoff in Upper Indus basin. To achieve this goal, the Modified positive degree-day model (MPDDM) was used to simulate the runoff and investigate its components in two catchments of Upper Indus basin, Hunza and Gilgit River basins. These two catchments were selected because of their different glacier coverage, contrasting area distribution at high altitudes and significant impact on the Upper Indus River flow. The components of runoff like snow-ice melt and rainfall-base flow were identified by the model. The simulation results show that the MPDDM shows a good agreement between observed and modeled runoff of these two catchments and the effects of snow-ice are mainly reliant on the catchment characteristics and the glaciated area. For Gilgit River basin, the largest contributor to runoff is rain-base flow, whereas large contribution of snow-ice melt observed in Hunza River basin due to its large fraction of glaciated area. This research will not only contribute to the better understanding of the impacts of climate change on the hydrological response in the Upper Indus, but will also provide guidance for the development of hydropower potential, water resources management and offer a possible evaluation of future water quantity and availability in these catchments.

Keywords: future discharge projection, positive degree day, regional climate model, water resource management

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Authors: Amir Hamzeh Haghiabi

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Water resource management is of global significance as it plays a key role in the socioeconomic development of all nations. On account of the fact that Iran is situated in a highly pressurized belt in the world, precipitation is limited, so that the average annual precipitation in the country is about 250 mm, only about one third to one quarter of the world average for rainfall. Karkheh basin is located in the semiarid and arid regions of Western Iran, an area with severe water scarcity. 70 % of rainfall is directly evaporated. The potential annual evaporation of the southern and northern regions is 3,600 mm 1,800 mm, respectively. In this paper, Some aspects of water resources management for this region, the specifications of the Karkheh reservoir dam & hydroelectric power plant as the biggest dam in history of Iran with total volume of reservoir 7.3 Bm3 are illustrated. Also the situation of water availability in the basin, surface and groundwater potential are considered.

Keywords: Iran, water availability, water resources, Zagros

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Authors: A. Lianah, B. Peter Sopade, C. Krisantini

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Walikadep/Tetrastigma glabratum (Blume) Planch is a traditional herb that has been used by people of Blumah village; it is believed to have a stimulant effect and ailments for many illnesses. Our survey demonstrated that the people of Blumah village has exploited walikadep from Protected Forest of Mount Prau. More than 10% of 448 households at Blumah village have used walikadep as traditional herb or jamu. Part of the walikadep plants used is the liquid extract of the stem. The population of walikadep is getting scarce and it is rarely found now. The objectives of this study are to examine the stimulant effect of walikadep, to measure growth and exploitation rate of walikadep, and to find ways to effectively propagate the plants, as well as identifying the impact on the environment through field experiments and explorative survey. Stimulant effect was tested using open-field and hole-board test. Data were collected through field observation and experiment, and data were analysed using lab test and Anova. Rate of exploitation and plant growth was measured using Regression analysis; comparison of plant growth in-situ and ex-situ used descriptive analysis. The environmental impact was measured by population structure vegetation analysis method by Shannon Weinner. The study revealed that the walikadep exudates did not have a stimulant effect. Exploitation of walikadep and the long time required to reach harvestable size resulted in the scarcity of the plant in the natural habitat. Plant growth was faster in-situ than ex-situ; and fast growth was obtained from middle part cuttings treated with vermicompost. Biodiversity index after exploitation was higher than before exploitation, possibly due to the toxic and allellopathic effect (phenolics) of the plant. Based on these findings, further research is needed to examine the toxic effects of the leave and stem extract of walikadep and their allelopathic effects. We recommend that people of Blumah village to stop using walikadep as the stimulant. The local people, village government in the regional and central levels, and perhutani should do an integrated efforts to conserve walikadep through Pengamanan Terpadu Konservasi Walikadep Lestari (PTKWL) program, so this population of this plant in the natural habitat can be maintained.

Keywords: utilization, medical plants, traditional, Tetastigma glabratum

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

Abstract:

With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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