Search results for: continuous data

Authors: Azazhu Wassie

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This study tried to analyze the impact of the hydrologic and hydraulic parameters (catchment area, rainfall intensity, and runoff coefficient) on the referenced study area. The study was conducted in June 2023. The Aremenie River Dam has 30 years of record, which is reasonably sufficient data. It is a matter of common experience that, due to the failure of an instrument or the absence of a gauged river, the rainfall record at quite a number of stations is incomplete. From the analysis, the 50-year return period design flood is 62.685 m³/s at 1.2 hr peak time. This implies that for this watershed, the peak flood rate per km² area of the watershed is about this value, which ensures that high rainfall in the area can generate a higher rate of runoff per km² of the generating catchment. The Aremenie Rivers carry a large amount of sediment along with water. These sediments are deposited in the reservoir upstream of the dam because of the reduction in velocity. Sediment reduces the available capacity of the reservoir with continuous sedimentation; the useful life of the reservoir goes on decreasing.

Keywords: dam design, peak flood, rainfall, reservoir capacity, runoff

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Authors: Amy Gooden

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When human genomic data is considered, this is often done through only one dimension of the law, or the interplay between the various dimensions is not considered, thus providing an incomplete picture of the legal framework. This research considers and analyzes the various dimensions in South African law applicable to genomic sequence data – including property rights, personality rights, and intellectual property rights. The effective use of personal genomic sequence data requires the acknowledgement and harmonization of the rights applicable to such data.

Keywords: artificial intelligence, data, law, genomics, rights

Procedia PDF Downloads 138

Authors: Opeyemi A. Oyewo, Seshibe Makgato

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This paper presents the synthesis, characterization, and application of pelletized chitosan immobilized sawdust cellulose nanocrystals (PCCN) in a fixed-bed column for the continuous adsorption of methyl blue (MB) from water. The product was characterized using FT-IR, XRD, and SEM analysis. Microstructural examination revealed that the pellets are porous and spherical. XRD examination revealed phases that can be attributed to the presence of chitosan in PCCN. The effects of starting concentration, bed depth, and flow rate on synthetic water were explored. To identify MB breakthrough behaviour, performance indices such as bed volume, adsorbent exhaustion rate, and service time were investigated. Furthermore, the breakthrough data were incorporated into both the Thomas and Bohart-Adams models. The Thomas model was suitable for describing MB breakthrough curves. However, more research with diverse water matrices may be required to assess the resilience of PCCN.

Keywords: adsorption, dynamic, methyl blue, pelletization

Procedia PDF Downloads 30

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)

Procedia PDF Downloads 236

Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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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

Procedia PDF Downloads 591

Authors: Adelaide Rethabile Motshabi Pitso-Mbili

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Higher education institutions (HEIs) are under pressure in producing PhD graduates so that they can position themselves higher in terms of rankings. However, hospitality and tourism higher degree enrolment numbers are very low within South African universities. Furthermore, hospitality and tourism students choose these fields of study because they want to travel, be social, and have fun. Students in higher education are anxious about their future after studies as they are particularly worried about the job conditions in the hospitality and tourism sector, the remuneration, and the status perceptions of many jobs in the sector. Hence, there is a need for a continuous increase in the level of education with the formation of professional aptitudes and personnel quality. In order to understand the reasons for low enrolments, this study aims to explore the perceptions of the final year in tourism and hospitality diploma students towards enrolling for higher degree qualifications after their graduation. This paper utilised a quantitative research method, and data was gathered through questionnaires. In addition, the Linkert scale design was used for questionnaires. The questionnaires were distributed to 137 hospitality and tourism students who are on their exit level at Walter Sisulu University. SPSS was used to analyse data. The findings revealed that students do not wish to continue studying for higher degrees as they believe higher degrees are not important for the industry. Furthermore, families already have financial expectations as they think one qualification is enough. In addition, some students mentioned that they would love to study further, but lack of funding is a problem since NSFAS only funds the first qualification. Thus, it is recommended that students are made aware of the benefits and opportunities available after obtaining higher degrees.

Keywords: hospitality and tourism education, higher degrees, higher education, student’s perceptions, hospitality and tourism sector

Procedia PDF Downloads 117

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: A. Bentabet, A. Aydin, N. Fenineche

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The mean penetration depth has a most important in the absorption transport phenomena. Analytical model of light ion backscattering coefficients from solid targets have been made by Vicanek and Urbassek. In the present work, we showed a mathematical expression (deterministic model) for Z1/2. In advantage, in the best of our knowledge, relatively only one analytical model exit for electron or positron mean penetration depth in solid targets. In this work, we have presented a simple geometric spherical model of absorbed particles based on CSDA scheme. In advantage, we have showed an analytical expression of the mean penetration depth by combination between our model and the Vicanek and Urbassek theory. For this, we have used the Relativistic Partial Wave Expansion Method (RPWEM) and the optical dielectric model to calculate the elastic cross sections and the ranges respectively. Good agreement was found with the experimental and theoretical data.

Keywords: Bentabet spherical geometric model, continuous slowing down approximation, stopping powers, ranges, mean penetration depth

Procedia PDF Downloads 641

Authors: Chong-Yang Qiao

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Mobile augmented reality (MAR) tracking targets from the surroundings and aids operators for interactive data and procedures visualization, potential equipment and system understandably. Operators remotely communicate and coordinate with each other for the continuous tasks, information and data exchange between control room and work-site. In the routine work, distributed control system (DCS) monitoring and work-site manipulation require operators interact in real-time manners. The critical question is the improvement of user experience in cooperative works through applying Augmented Reality in the traditional industrial field. The purpose of this exploratory study is to find the cognitive model for the multiple task performance by MAR. In particular, the focus will be on the comparison between different tasks and environment factors which influence information processing. Three experiments use interface and interaction design, the content of start-up, maintenance and stop embedded in the mobile application. With the evaluation criteria of time demands and human errors, and analysis of the mental process and the behavior action during the multiple tasks, heuristic evaluation was used to find the operators performance with different situation factors, and record the information processing in recognition, interpretation, judgment and reasoning. The research will find the functional properties of MAR and constrain the development of the cognitive model. Conclusions can be drawn that suggest MAR is easy to use and useful for operators in the remote collaborative works.

Keywords: mobile augmented reality, remote collaboration, user experience, cognition model

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Authors: Baljinder Singh, Navneet Sharma

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The skin can be used as the site for drug administration for continuous transdermal drug infusion into the systemic circulation. For the continuous diffusion/penetration of the drugs through the intact skin surface membrane-moderated systems, matrix dispersion type systems, adhesive diffusion controlled systems and micro reservoir systems have been developed. Various penetration enhancers are used for the drug diffusion through skin. In matrix dispersion type systems, the drug is dispersed in the solvent along with the polymers and solvent allowed to evaporate forming a homogeneous drug-polymer matrix. Matrix type systems were developed in the present study. In the present work, an attempt has been made to develop a matrix-type transdermal therapeutic system comprising of ondansetron-HCl with different ratios of hydrophilic and hydrophobic polymeric combinations using solvent evaporation technique. The physicochemical compatibility of the drug and the polymers was studied by infrared spectroscopy. The results obtained showed no physical-chemical incompatibility between the drug and the polymers. The patches were further subjected to various physical evaluations along with the in-vitro permeation studies using rat skin. On the basis of results obtained form the in vitro study and physical evaluation, the patches containing hydrophilic polymers i.e. polyvinyl alcohol and poly vinyl pyrrolidone with oleic acid as the penetration enhancer(5%) were considered as suitable for large scale manufacturing with a backing layer and a suitable adhesive membrane.

Keywords: transdermal drug delivery, penetration enhancers, hydrophilic and hydrophobic polymers, ondansetron HCl

Procedia PDF Downloads 322

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: Ndivhuho Tshikovhi

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The debate about indigenous people throughout the world has been necessitated by different circumstances that indigenous communities continue to suffer. Indigenous people of the world suffer chronic diseases, poor education, high unemployment and slow economic developments. This paper contributes to the continuous debate by studying the common elements of indigenous entrepreneur of the world and that of the South African indigenous entrepreneur. The research objective of this paper is to understand what constitute an indigenous status in the South African context as opposed to the indigenous people of the world. Furthermore, the study will explore the indigenous status through their entrepreneurial engagements. The paper will adopt a secondary data research method, by utilising the literature on indigenous entrepreneurship practice and theory of indigenous entrepreneurship. The implications of this paper is to bring about an African indigenous entrepreneurship debate rooted from the correct circumstances rather than generalised definitions. Recommendations for future research will be outlined, together with further readings on circumstantial evidence that necessitate indigenous entrepreneurs status in South Africa.

Keywords: indigenous entrepreneur, indigenous, entrepreneurship, indigenous people, entrepreneurship development

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Authors: Anisa Duraj

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As usually acknowledged, cultural heritage is the weakest component in EIA studies. The role of heritage impact assessment (HIA) in development projects is not often accounted for, and in those cases where it is, HIA is considered as a reactive response and not as a solutions provider. Because of continuous development projects, in most cases, heritage is unconsidered and often put under threat. Cultural protection and development challenges ask for prudent legal regulation and appropriate policy implementation. The challenges become even more peculiar in underdeveloped countries or endangered areas, which are generally characterized by numerous legal constraints. Therefore, the need for strategic proposals for HIA is of high importance. In order to trigger HIA as a proactive operation in the IA process and make sure to cover cultural heritage in the whole EIA framework, an appropriate system of evaluation of impacts should be provided. To obtain the required results for HIA, this last must be part of a regional policy, which will address and guide development projects toward a proper evaluation of their impacts affecting heritage. In order to get a clearer picture of existing gabs but also new possibilities for HIA, this paper will focus on the Western Balkans region and the undergoing changes that it faces. Concerning continuous development pressure in the region and within the aspiration of the Western Balkans countries to join the European Union (EU) as member states, attention should be paid to new development policies under the EU directives for conducting EIAs, and accurate support is required for the restructuration of existing policies as well as for the implementation of the UN Agenda for SDGs. In the framework of new emerging needs, if HIA is taken into account, the outcome would be an inclusive regional program that would help to overcome marginality issues of spaces and people.

Keywords: cultural heritage, impact assessment, SDGs, urban development, western Balkans, regional policy, HIA, EIA

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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

Procedia PDF Downloads 130

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: Segun Steven Bodunde

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Mudlogging is the continuous analysis of rock cuttings and drilling fluids to ascertain the presence or absence of oil and gas from the formation penetrated by the drilling bit. This research highlighted a case study of Well BSS-99ST from ‘Bisas Field’, Niger Delta, with depth extending from 1950m to 3640m (Measured Depth). It was focused on identifying the lithologies encountered at specified depth intervals and to accurately delineate the targeted potential reservoir on the field and prepare the lithology and Master log. Equipment such as the Microscope, Fluoroscope, spin drier, oven, and chemicals, which includes: hydrochloric acid, chloroethene, and phenolphthalein, were used to check the cuttings for their calcareous nature, for oil show and for the presence of Cement respectively. Gas analysis was done using the gas chromatograph and the Flame Ionization Detector, which was connected to the Total Hydrocarbon Analyzer (THA). Drilling Parameters and Gas concentration logs were used alongside the lithology log to predict and accurately delineate the targeted reservoir on the field. The result showed continuous intercalation of sand and shale, with the presence of small quantities of siltstone at a depth of 2300m. The lithology log was generated using Log Plot software. The targeted reservoir was identified between 3478m to 3510m after inspection of the gas analysis, lithology log, electric logs, and the drilling parameters. Total gas of about 345 units and five Alkane Gas components were identified in the specific depth range. A comparative check with the Gamma ray log from the well further confirmed the lithologic sequence and the accurate delineation of the targeted potential reservoir using mudlogging.

Keywords: mudlogging, chromatograph, drilling fluids, calcareous

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Authors: Manish Madappa, Madhavi Gayathri Raman

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The primary vehicle of human communication is language. A breakdown occurring in any aspect of communication may lead to frustration and isolation among the learners and the teachers. Over seven percent of the population in the world currently experience limitations and those children who exhibit a deviant/deficient language acquisition curve even when being in a language rich environment as their peers may be at risk of having a language disorder or language impairment. The difficulty may be in the word level [vocabulary/word knowledge] and/or the sentence level [syntax/morphology) Children with SLI appear to be developing normally in all aspects except for their receptive and/or expressive language skills. Thus, it is utmost importance to identify children with or at risk of SLI so that an early intervention can foster language and social growth, provide the best possible learning environment with special support for language to be explicitly taught and a step in providing continuous and ongoing support. The present study looks at Kannada English bilingual children and works towards identifying children at risk of “specific language impairment”. The study was conducted through an exploratory study which systematically enquired into the narratives of young Kannada-English bilinguals and to investigate the data for story structure in their narrative formulations. Oral narrative offers a rich source of data about a child’s language use in a relatively natural context. The fundamental objective is to ensure comparability and to be more universal and thus allows for the evaluation narrative text competence. The data was collected from 10 class three students at a primary school in Mysore, Karnataka and analyzed for macrostructure component reflecting the goal directed behavior of a protagonist who is motivated to carry out some kind of action with the intention of attaining a goal. The results show that the children exhibiting a deviation of -1.25 SD are at risk of SLI. Two learners were identified to be at risk of Specific Language Impairment with a standard deviation of more the 1.25 below the mean score.

Keywords: bilingual, oral narratives, SLI, macrostructure

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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

Procedia PDF Downloads 484

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: U. Datta

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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection

Procedia PDF Downloads 134

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

Procedia PDF Downloads 433

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

Procedia PDF Downloads 348

Authors: Fariza Yunus, Jasmee Jaafar, Zamalia Mahmud, Nurul Nisa’ Khairul Azmi, Nursalleh K. Chang, Nursalleh K. Chang

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Variation of air temperature from one place to another is cause by air temperature controls. In general, the most important control of air temperature is elevation. Another significant independent variable in estimating air temperature is the location of meteorological stations. Distances to coastline and land use type are also contributed to significant variations in the air temperature. On the other hand, in homogeneous terrain direct interpolation of discrete points of air temperature work well to estimate air temperature values in un-sampled area. In this process the estimation is solely based on discrete points of air temperature. However, this study presents that air temperature controls also play significant roles in estimating air temperature over homogenous terrain of Peninsular Malaysia. An Inverse Distance Weighting (IDW) interpolation technique was adopted to generate continuous data of air temperature. This study compared two different datasets, observed mean monthly data of T, and estimation error of T–T’, where T’ estimated value from a multiple regression model. The multiple regression model considered eight independent variables of elevation, latitude, longitude, coastline, and four land use types of water bodies, forest, agriculture and build up areas, to represent the role of air temperature controls. Cross validation analysis was conducted to review accuracy of the estimation values. Final results show, estimation values of T–T’ produced lower errors for mean monthly mean air temperature over homogeneous terrain in Peninsular Malaysia.

Keywords: air temperature control, interpolation analysis, peninsular Malaysia, regression model, air temperature

Procedia PDF Downloads 374

Authors: Ebrahim Panah, Muhammad Yasir Babar

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Thanks to the development of online technology and social networks, people are able to communicate as well as learn. WhatsApp is a popular social network which is growingly gaining popularity. This app can be used for communication as well as education. It can be used for instructor-learner, learner-learner, and learner-content interactions; however, very little knowledge is available on these potentials of WhatsApp. The current study was undertaken to investigate university students’ perceptions of WhatsApp used as a tool for instructor-learner dialogue, learner-content dialogue, and learner-learner dialogue. The study adopted a survey approach and distributed the questionnaire developed by Google Forms to 54 (11 males and 43 females) university students. The obtained data were analyzed using SPSS version 20. The result of data analysis indicates that students have positive attitudes towards WhatsApp as a tool for Instructor-Learner Dialogue: it easy to reach the lecturer (4.07), the instructor gives me valuable feedback on my assignment (4.02), the instructor is supportive during course discussion and offers continuous support with the class (4.00). Learner-Content Dialogue: WhatsApp allows me to academically engage with lecturers anytime, anywhere (4.00), it helps to send graphics such as pictures or charts directly to the students (3.98), it also provides out of class, extra learning materials and homework (3.96), and Learner-Learner Dialogue: WhatsApp is a good tool for sharing knowledge with others (4.09), WhatsApp allows me to academically engage with peers anytime, anywhere (4.07), and we can interact with others through the use of group discussion (4.02). It was also found that there are significant positive correlations between students’ perceptions of Instructor-Learner Dialogue (ILD), Learner-Content Dialogue (LCD), Learner-Learner Dialogue (LLD) and WhatsApp Application in classroom. The findings of the study have implications for lectures, policy makers and curriculum developers.

Keywords: instructor-learner dialogue, learners-contents dialogue, learner-learner dialogue, whatsapp application

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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

Procedia PDF Downloads 779

Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

Procedia PDF Downloads 235

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

Abstract:

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

Procedia PDF Downloads 295

Authors: A. B. Benshak, M. G. Yilkangnha, V. Y. Nanle

Abstract:

Social crises and violent conflict can inflict direct (short-term) impact on the environment like poisoning water bodies, climate change, deforestation, destroying the chemical component of the soil due to the chemical and biological weapons used. It can also impact the environment indirectly (long-term), e.g., the destruction of political and economic infrastructure to manage the environmental resources and breaking down traditional conservation practices, population displacement and refugee flows which puts pressure on the already inadequate resources, infrastructure, facilities, amenities, services etc. This study therefore examines the impact of social crises on the environment in Jos Plateau State with emphasis on the long-term impact, analyze the relationship between crises and the environment and assess the perception of people on social crises because much work have concentrated on other repercussions such as the economy, health etc that are more politically expedient. The data for this research were collected mostly through interviews, questionnaire, dailies and reports on the subject matter. The data and findings were presented in tables and results showed that the environment is directly and indirectly impacted by crises and that these impacts can in turn result to a continuous cycle of violent activities if not addressed because of the inadequacies in the supply of infrastructural facilities, resources and so on caused by the inflow of displaced population. Recommendations were made on providing security to minimize conflict occurrences in Jos and its environs, minimizing the impact of social crises on the environment, provision of adequate infrastructural facilities to carter for population rise, renewal and regeneration schemes, etc. which will go a long way in mitigating the impact of crises on the environment.

Keywords: environment, impact, long-term, social crises

Procedia PDF Downloads 341

Authors: Behnam Tavakkol

Abstract:

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

Procedia PDF Downloads 215