Data Analysis (Engineering)

Numerical methods assist us in solving problems swiftly and effectively on comparison with analytic solutions. Either while integrating or when solving complex differential equations, numerical methods are easier and accurate whereas it may be quite difficult using analytical mathematics or simple algebra. Numerical analysis is a proficient tool to handle large systems of equations, nonlinearities, and complicated geometries which are omnipresent in engineering disciplines and which are often non-viable or difficult to interpret analytically. Numerical methods are highly recommended tools for engineers to solve problems in their domains.

Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month the rainfall occurs in India which also cause damage to the farming industry as the crop seeds gets damaged. In the proposed study the future rainfall is predicted using Linear Regression Technique as we also compared various model such as SVM, Random Forest Regression, Neural Network model so we obtained better results in Linear Regression so we further consider this model for future prediction of rainfall.

International Journal on Bioinformatics & Biosciences (IJBB) is a Quarterly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Bioinformatics & Biosciences. Bioinformatics is the application of information science and technology to the field of biology to increase the understanding of all biological process. The aim of this journal is to publish all the latest and outstanding research articles in all areas of bioinformatics and Biometrics. Researchers and scientists from the fields of biology, computer science, mathematics, statistics, and physics are invited to share their developments and new techniques in the fields of Biometrics and Bioinformatics.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred. But it is observed that these methods were mostly resulted with high misclassification ratio. To overcome this problem, this study proposes a three-layered neural network (NN) architecture with a backpropagation algorithm. NN architecture was trained by using two different data sets which were OMRON data set (collected by OMRON) and 2016-2020 ES.LV data set (collected by the authors) separately. For the made of this study firstly the most relevant classification method (Gaussian Naive Bayes Algorithm) and the most relevant neural network method (Scaled Conjugate Gradient Backpropagation Algorithm) was chosen and both data sets were trained by using each method seperately for the purpose of observing which type of modelling architecture would give better results. Experimental results of this study showed that the neural network approach is useful for predicting whether a project is risky or not risky.

Global interests in organic foods are of importance to researchers and the food industry. Traditional questionnaire-based methods do not provide a broad picture. To meet this need, worldwide interests in organic foods were studied by integrating query data from the Google search engine and deep learning methods. The results show that organic oil, organic milk, organic chicken, and organic apples are the most interested organic foods; people from Singapore, US, New Zealand, Australia, United Kingdom and Canada care about organic foods the most; consumers' interest in organic foods has no correlation with GDP and life expectancy but has significant correlations with other dimensions of culture such as individualism, uncertainty avoidance, and long-term orientation. A recurrent neural network (RNN) model structure is useful in predicting people's interests in major organic foods over time.

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and
heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems
operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing
with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the
availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints
for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly
scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for
information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios
and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large
dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently
constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale
model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and
analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different
exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction
and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough
theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we
provide a performance evaluation and a empirical user study employing real datasets.

Databases are vulnerable. Public statements by Target, Home Depot, and Anthem following their extremely advertised data breaches are each uniform and succinct on how their breaches unfolded: unauthorized access to those systems that ultimately led to the extraction of sensitive information. A comprehensive strategy to secure a database is over data security. Usually, security events will be related to the later action: illegitimate access to data confidentiality damage, injury to the integrity of knowledge, and loss of data accessibility (Discover). Loss of privacy of data, creating them accessible to others without a right of access is not visible within the database and does not need changes deductible database. This paper addresses these events to confirm database security.

This contribution presents a brief history of softwares for identifying of
typical daily load pro¯les developed in the Brazilian electric power sector since the
eighties. In order to tell this history we present the features of three softwares that
have been used by the Brazilian electric distribution utilities, all of then with data
mining techniques for clustering.

The indirect approach previously proposed for extracting the bridge frequencies from a passing vehicle works mainly for the first frequency. In order to extract bridge frequencies of higher modes, the vehicle response will first be processed by the empirical mode decomposition (EMD) to generate the intrinsic mode functions (IMFs), and then by the fast Fourier transform. One feature with the EMD technique is that frequencies of higher modes can be made more visible by the sifting process. To verify the feasibility of such an approach, the vehicle response generated by the finite element simulations will first be analyzed, with results compared with the analytical ones. Then the same procedure will be adopted to extract bridge frequencies from the recorded response of a passing vehicle, with results compared with those from an ambient vibration test. It was demonstrated that using the IMFs computed from the vehicle response, rather than based on the original data, bridge frequencies of higher modes can be successfully extracted. Future directions of research are highlighted.

Cloud computing recently offers immense benefits. But higher education institutions (HEIs) are hesitating to adopt. As a result they are not benefited more from such technology. This is because key adoption capabilities are not explored in detail in HEIs context. In addition to this, there is no comprehensive empirically validated adoption readiness assessment model which helps them to adopt Cloud solutions efficiently. Hence, to fill this gap this study identifies the critical capabilities and develops a model to assess Cloud computing adoption readiness of higher education institutions before initiating the adoption. Resource based view theory was used as theoretical lens to identify capabilities and to build the model. The conceptual model considers adoption readiness and adoption success from technological, human, organizational, financial and external environment dimensions. The model proposes that the degree to which an organization holds these capabilities is related to the degree to which they are ready to deploy and use computing resources, thereby increasing the possibility of adoption success. Further Empirical research will be used to validate and refine the conceptual model.

Abstract Performance monitoring using wireless sensors is now common practice in building operation and maintenance and generates a large amount of building specific data. However, it is difficult for occupants, owners and operators to explore such data and ...

Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. The main goal is to make the process of introducing new medical interventions to patients more efficient by reducing the cost and the time associated with evaluating their safety and efficacy. The principal question is how should adaptation be performed so as to minimize the chance of distorting the outcome of the trial. We propose a novel method for achieving this. Unlike previous work our approach focuses on trial adaptation by sample size adjustment. We adopt a recently proposed stratification framework based on collected auxiliary data and show that this information together with the primary measured variables can be used to make a probabilistically informed choice of the particular sub-group a sample should be removed from. Experiments on simulated data are used to illustrate the effectiveness of our method and its application in practice.

The typical load profiles are used in formulating the electricity distribution tariffs. Usually, clustering
algorithms are used in identification of typical load profiles from a sample with load curves
measurements. These methods group together load curves having similar shapes, from the which are
extracted the typical profiles. The various clustering algorithms can be applied to identify load profiles
but only the Self-Organising Map also known as Kohonen Map, a kind of neural network, can
organize a map where the clusters with similar load diagrams appears together in the map. The map
shows simultaneously all clusters and allows the visual identification of the load profiles. This paper
shows some results of a case study performed with Kohonen map applied on a sample with 125 load
curves.

Providing of safe human life is very connected with research and analysis of different disasters. Among of different disasters, there is a special place for natural disasters (ND), because ND have significant influence on human life. One of the methods to prevent uncontrolled ND influence on human life is wide data research which are connected with appearance and distribution of ND. Thus, in this work researched the whole methodology of data analysis of ND. Basis of this methodology is statistic model of ND data analysis and development of forecast ranking of ND development.

We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large real-world data sets demonstrate that the proposed methods vastly outperform the existing alternatives.

Industries’ projects are associated with large amounts of data throughout the project lifecycle. As the size of the projects grows, so does the data associate with the projects. To generate useful information from Big Data, appropriate predictive analysis and data visualisation tools are required. Therefore, the use of visualisation techniques will target this expected evolution of big data. Thus, this study aims to give an overview of the migrations of an existing computer-based application, used to analyse multidata sources of enterprises, to a web-based application. Essential development aspects were concerned with the process of application development, platform development, software utilisation, and efficient display interface according to the enterprise’s requirement. Two solutions such as Microsoft Power Business Intelligence (BI) and the Dynamic Web System have been presented that proven to be widespread because these solutions have met the enterprises’ requirements in terms of expenses, policy, security, maintenance, and interactive interface. Also, an assessment of these solutions and challenges to adopting has been presented. Eventually, the system developed on the web overcomes computer-based systems in solving their problems such as deploying and maintaining applications and providing efficient and richer user interfaces for users. The web-based visualisation application has wide beneficial features; it provides high-performance real-time data analysis, data multimodal visualisation, and friendly to use. Web-based applications will increase confidence in decision-making based on data visualisation, which will positively reflect on comprehensive data analysis processes for project success.

In this paper, an integrated mapping of the georeferenced data is presented using the QGIS and GMT scripting tool set. The study area encompasses the Bolivian Andes, South America, notable for complex geophysical and geological parameters and high seismicity. A data integration was performed for a detailed analysis of the geophysical and geological setting. The data included the raster and vector datasets captured from the open sources: the IRIS seismic data (2015 to 2021), geophysical data from satellite-derived gravity grids based on CryoSat, topographic GEBCO data, geoid undulation data from EGM-2008, and geological georeferences’ vector data from the USGS. The techniques of data processing included quantitative and qualitative evaluation of the seismicity and geophysical setting in Bolivia. The result includes a series of thematic maps on the Bolivian Andes. Based on the data analysis, the western region was identified as the most seismically endangered area in Bolivia with a high risk of earthquake hazards in Cordillera Occidental, followed by Altiplano and Cordillera Real. The earthquake magnitude here ranges from 1.8 to 7.6. The data analysis shows a tight correlation between the gravity, geophysics, and topography in the Bolivian Andes. The cartographic scripts used for processing data in GMT are available in the author’s public GitHub repository in open-access with the provided link. The utility of scripting cartographic techniques for geophysical and topographic data processing combined with GIS spatial evaluation of the geological data supported automated mapping, which has applicability for risk assessment and geological hazard mapping of the Bolivian Andes, South America.

Analysis of information retrieved from microblogging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two underlying approaches for sentiment analysis are dictionary-based and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods.

An automated, real-time, multiple sensor data source relying and globally applicable earthquake loss model and visualiser is desirable for post-event earthquake analysis. To achieve this there is a need to support rapid data ingestion, loss estimation and integration of data from multiple data sources and rapid visualisation at multiple geographic levels. In this paper, the design and development of the Automated Post-Event Earthquake Loss Estimation and Visualisation (APE-ELEV) system for real-time estimation and visualisation of insured losses incurred due to earthquakes is presented. A model for estimating ground up and net of facultative losses due to earthquakes in near real-time is implemented. Since post-event data is often available immediately from multiple disparate sources, a geo-browser is employed to facilitate the visualisation and integration of earthquake hazard, exposure and loss data. The feasibility of APE-ELEV is demonstrated using a test case earthquake that occurred in Tohoku, Japan (2011). The APE-ELEV model is further validated for ten global earthquakes using industry loss data.

Abstract
A new EU Bathing Water Directive was implemented in March 2006, which sets a series of stringent microbiological standards. One of the main requirements of the new Directive is to provide the public with information on conditions likely to lead to short-term coastal pollution. The paper describes how numerical models have been combined with Artificial Neural Networks (ANNs) to develop an accurate and rapid tool for assessing the bathing water status of the Ribble Estuary, UK. Faecal coliform was used as the water quality indicator. In order to provide enough data for training and testing the neural networks, a calibrated hydrodynamic and water quality model was run for various river flow and tidal conditions. In developing the neural network model a novel data analysis tool called WinGamma was used in the model identification process. WinGamma is capable of determining the data noise level, even with the underlying function unknown, and whether or not a smooth model can be developed. Model predictions based on this technique show a good generalisation ability of the neural networks. Details are given of a series of experiments being undertaken to test the ANN model performance for different numbers of input parameters. The main focus has been to quantify the impact of including time series inputs of faecal coliform on the neural network performance. The response time of the receiving water quality to the river boundary conditions, obtained from the hydrodynamic model, has been shown to provide valuable knowledge for developing accurate and efficient neural networks.

: In the last two years, data analysis has been rated as the top job in the world, according to Jeffrey Strickland, a data scientist, and adjunct faculty from George Washington University, United States.

Professor Strickland is now teaching a month-long course in Data Analytics for post- graduate students of VIT University. “The role of a data scientist is to get relevant data, turn it into information for better decision,” he told reporters during an interaction on Thursday. For the last two years, the job of data scientists had been rated as number one in the world, he added.

The field, according to him, had great prospects. “However, presently, there is inadequate number of data scientists in the world. It is a multi-disciplinary job as candidates require skills in computer science, mathematics and statistics. There is a great demand for data scientists now,” he added.

On the skills required by data scientists, he said the debate whether the scientists required skills or conceptual knowledge was still on. “One school says data scientists require skills to work with tools, while another school of thought says they need to understand conceptual ideas. I would say they need both,” Professor Strickland, who has served in the United States Military for 24 years, said.

He added that data scientists needed to understand concepts and also know how tools worked. “They need to apply the right technique for arriving at a solution,” he observed. Internationally, there was a qualification exam too to certify professional analysts, he noted. One of the main challenges for data scientists was security of data. “They should know ways to make data more secure. There could be breaches in security and they should know how to keep it secure,” he said.

On his experience of teaching students in India and the USA, he said there was no difference between the two groups. “Students here are very informed and have knowledge about the subject. They have a higher level of thought and prepare for classes,” he said.

Aswani Kumar Cherukuri, professor and dean, School of Information Technology and Engineering, VIT University, said the international faculty member was teaching fourth year students of the five-year integrated programme – M. Tech Software Engineering - from September 1 to 30.

“Our school already has 10 adjunct faculty members from USA, Australia, Japan and Europe. The aim is to provide students the expertise of the international faculty members,” he said.

In this paper we address the problem of matching sets of vectors embedded in the same input space. We propose an approach which is motivated by canonical correlation analysis (CCA), a statistical technique which has proven successful in a wide variety of pattern recognition problems. Like CCA when applied to the matching of sets, our extended canonical correlation analysis (E-CCA) aims to extract the most similar modes of variability within two sets. Our first major contribution is the formulation of a principled framework for robust inference of such modes from data in the presence of uncertainty associated with noise and sampling randomness. E-CCA retains the efficiency and closed form computability of CCA, but unlike it, does not possess free parameters which cannot be inferred directly from data (inherent data dimensionality, and the number of canonical correlations used for set similarity computation). Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA). Theoretical contributions of this paper are followed by an empirical evaluation of its premises on the task of face recognition from sets of rasterized appearance images. The results demonstrate that our approach, E-CCA, already outperforms both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for all values of their free parameters. An even greater improvement is achieved with the discriminative variant, DE-CCA.

The load data quality is fundamental to the short-term load forecasting. In general, the load data
quality is poor, since the load records often have outliers, discontinuities and gaps resulting from
failures in the measuring system. The presence of bad data in historical load data contributes to poor
specification of a forecasting model and therefore affects the accuracy of load forecasts. Thus, the
construction of a forecasting model should be preceded by the treatment of the load records. This
paper presents a methodology based in statistical and data mining techniques to automatically cleanse
outliers and fill missing values in the historical load records.

By letting a test vehicle move over a bridge, the frequencies of the bridge can be identified by picking the peaks in the Fourier response spectra of the test vehicle. One problem with this approach is that the vehicle frequency (undesired) usually appears as a dominant peak in the spectrum, making it difficult to identify the bridge frequencies (desired). To enhance the visibility of bridge frequencies, an effective filter is needed to filter out the vehicle frequency. As a preliminary attempt in this study, three filters are adopted, i.e., the band-pass filter (BPF), singular spectrum analysis (SSA), and the singular spectrum analysis with band-pass filter (SSA-BPF), proposed herein as a combination of the above two. Through the numerical study on two cases with the vehicle frequency smaller or larger than the 1st bridge frequency, the SSA-BPF technique is demonstrated to be most effective for extracting the bridge frequencies, due to its salient feature that there is no need to select the number of singular values, as required by SSA, while the unexpected peaks encountered by the BPF are avoided.