A Novel Approach for Detecting Outliers by Using Isolation Forest with Reducing Under Fitting Issue

The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during the training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is good knowledge that running time for ML tasks is significantly impacted by conditions as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection of connected supervised learning for binary classification. We carry out in-depth tests on various datasets and demonstrate that in our latest outlier finding technique compare with the old way. Our approach yields superior outcomes in terms of accuracy, precision, recall & F-1 score. Additionally, we successfully lowered the machine learning algorithms' under fitting issue

DETECCIÓN DE PATRONES Y TENDENCIAS MEDIANTE BUSINESS INTELLIGENCE EN EL ÁREA DE VENTAS EN LA PASTEURIZADORA QUITO PLANTA TULCÁN

La tecnología a nivel mundial avanza, por lo que la toma de decisiones es devital importancia para la supervivencia de la empresa, los altos ejecutivos, gerentes, administradores, requieren conocer y disponer de información exacta, precisa, real y completa para que esto posibilite el crecimiento de su organización. El desconocimiento que existe en las empresas sobre la utilización de herramientas que ayuden en la toma de decisiones a nivel gerencial, hace que pierdan tiempo y dinero en sus ventas, y más aún el no contar con banco de datos que les permita analizar la problemática que existe en la misma y tomar sus decisiones de manera óptima para que la empresa a su cargo vaya creciendo en el mercado. Para el desarrollo del proyecto se utilizó la metodología de Ralph Kimball. Uno de los resultados que se ha logrado obtener con esta investigación, es tener conocimiento de los procesos de venta que se realizan actualmente mediante el sistema transaccional que tiene la empresa. Además de ello, con la implementación de la herramienta se logró que los parámetros de búsqueda de información sean más eficientes al momento de presentar los datos, tanto con gráficas como con datos tabulares, esto ayuda a que el área de ventas y el gerente puedan tomar decisiones sobre las comercializaciones que se efectuaron en períodos de tiempo y pueda hacer una proyección de las negociaciones futuras

DETECCIÓN DE PATRONES Y TENDENCIAS MEDIANTE BUSINESS INTELLIGENCE EN EL ÁREA DE VENTAS EN LA PASTEURIZADORA QUITO PLANTA TULCÁN

La tecnología a nivel mundial avanza a pasos agigantados, por lo que la toma de decisiones es de vital importancia para la supervivencia de la empresa, los altos ejecutivos, gerentes, administradores. El desconocimiento que existe en las empresas sobre la utilización de herramientas que ayuden en la toma de decisiones. Para el desarrollo del proyecto se utilizó la metodología de Ralph Kimball. Además de ello, con la implementación de la herramienta se logró que los parámetros de búsqueda de información sean más eficientes al momento de presentar los datos, tanto con gráficas como con datos tabulares, esto ayuda a que el área de ventas y el gerente puedan tomar decisiones sobre las comercializaciones que se efectuaron en períodos de tiempo y pueda hacer una proyección de las negociaciones futuras

Exploratory data analysis and data envelopment analysis of construction and demolition waste management in the European Economic Area

This paper deals with the efficiency and sustainability of Construction and Demolition Waste (CDW) management in 30 Member States of the European Economic Area (EEA) (the 28 European Union countries plus Norway and Iceland) for the period 2010-2016 using Exploratory Data Analytics (EDA) and Data Envelopment Analysis (DEA). The first stage of the proposed methodology is EDA with already available (the CDW recovery rate) and suggested indicators (e.g., building stock characterization, dwelling occupancy ratio, macroeconomic ratios and CDW breakdown) to characterize the efficiency and sustainability of CDW management. The second stage is to assess the efficiency of countries using DEA through two original CDW production models, one for sustainability, measuring the efficiency of the construction sector for reducing itsCDW, and the second a model to score the efficiency of maximizing the CDW recovery rate. The main outcome of the paper is the proposed methodology, which is a candidate for replacing current indicators in order to evaluate the performance of CDW policy, due to is adaptive nature, promoting the continuous improvement and overcoming the limitations of the poor quality of metrics, data and parametric indicators. The methodology has been experimentally validated using Eurostat data for 30 Member States of EEA, ranking them according to the two DEA model scores, to point out the countries considered efficient among those of their scale, as a reference for sustainable and efficient practices.info:eu-repo/semantics/publishedVersio

Implementation of green buildings in Afghanistan, barriers and solutions

Green building (GB) is receiving full acceptance as a workable option in the construction sector to meet the increasing demands for environmentally sustainable. Green standards are one of the most relevant energy-saving strategies, which define best practices in sustainable building. They also have significant impacts on resource conservation as well as on the saving of the climate. The demand toward a more sustainable construction has prompted, and green specifications are the best way to emerge and adapt. However, in industrialised economies, there are several types of problems due to green building implementation, including high costs, lack of green products, information, and awareness. The difficulties of adopting sustainable buildings in Afghanistan presently include original costs associated with construction. Construction companies also face a massive hurdle in understanding and applying the World Green Building Council Rating System. Implementing green building understanding in Afghanistan is not widely practised and most providers do not have appropriate training. This study evaluated the feasibility of green building implementation in Afghanistan, identifying green building implementation for indoor air pollution control, and assess barriers as well as green building solutions in Afghanistan. Thirty-three (33) sets of questionnaire were distributed to project stakeholders including consultants, contractors, real estate developers, and project management consultancies. The responses were analysed quantitatively using SPSS and Ms Excel Software. Findings showed that 75% of the participants had knowledge about green building and declared that green building could reduce the negative impact of climate change on the environment. Majority of participants construction firms acknowledged that the green building implementation procedure was excellent therefore green building implementation in Afghanistan is feasible and can be one of the most effective ways for reduction of the negative environmental impacts as the participants pointed out. Similarly 38% of respondent acknowledged that green building implementation effectiveness is eminent for the healthy and comfortable built environment. Green buildings implementation is also believed to reduce indoor air pollution related sicknesses of the residents due to controlled indoor air pollution. Result showed that knowledge of green building advantages among the Afghan construction firms was not adequate. Therefore, awareness, mutual benefits and establishing an award as an incentive for construction firms to apply green building characteristics in their projects are crucial. The findings of this study useful to all related parties towards improving green building implementation in Afghanistan

Hillview:A trillion-cell spreadsheet for big data

Hillview is a distributed spreadsheet for browsing very large datasets that cannot be handled by a single machine. As a spreadsheet, Hillview provides a high degree of interactivity that permits data analysts to explore information quickly along many dimensions while switching visualizations on a whim. To provide the required responsiveness, Hillview introduces visualization sketches, or vizketches, as a simple idea to produce compact data visualizations. Vizketches combine algorithmic techniques for data summarization with computer graphics principles for efficient rendering. While simple, vizketches are effective at scaling the spreadsheet by parallelizing computation, reducing communication, providing progressive visualizations, and offering precise accuracy guarantees. Using Hillview running on eight servers, we can navigate and visualize datasets of tens of billions of rows and trillions of cells, much beyond the published capabilities of competing systems

A comprehensive review of tools for exploratory analysis of tabular industrial datasets

Exploratory data analysis plays a major role in obtaining insights from data. Over the last two decades, researchers have proposed several visual data exploration tools that can assist with each step of the analysis process. Nevertheless, in recent years, data analysis requirements have changed significantly. With constantly increasing size and types of data to be analyzed, scalability and analysis duration are now among the primary concerns of researchers. Moreover, in order to minimize the analysis cost, businesses are in need of data analysis tools that can be used with limited analytical knowledge. To address these challenges, traditional data exploration tools have evolved within the last few years. In this paper, with an in-depth analysis of an industrial tabular dataset, we identify a set of additional exploratory requirements for large datasets. Later, we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis. We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process. We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets. Finally, we identify and present a set of research opportunities in the field of visual exploratory data analysis. Keywords: Exploratory data analysis, Industrial tabular data, Interactive visualization, Systematic literature review, Research opportunitie