Volume 6 | Issue 1 |

 Abstract

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Womanist, African-American, historiography, retelling, myths, slavery, counterdiscourse, Blues, revisioning, reinterpretation

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 Abstract

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Indian automobile sales, Gross Domestic Savings, Lending-rate, prices of fuel and power, Unemployment, Time Series Models, Granger Causality

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In this paper we have done some investigation on some concepts of the theory of metric space to analyze fractal objects. If we iteratively apply a finite set of contraction mappings to any point on a compact metric space, we will come arbitrarily close to a set of points in the space which is very often fractal. The present work addresses the problem of how iterated function systems may be used to construct such fractal objects. For this purpose, we discuss two algorithms producing fractals, namely that of deterministic algorithm, and random iterated algorithm. We have also discussed about the connection between Hausdorff dimension and iterated function systems.

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Affine transformation, Attractor, Contraction factor, Contractive mapping, Fractal, Haudorff dimension, Iterated Function Systems, Metric space.

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Data Mining (referred as extracting knowledge from data) is the process of discovering patterns, associations, and links in the huge stack of data which is based on the analysis done through different perspectives. There are many disciplines which are found under data mining some of them are clustering analysis, regression analysis, classification analysis etc. To analyze the data, we need some sort of software which can be used to perform the required analysis on data set. In this paper, we are using WEKA library (.jar file) for data mining and implementing the outlier detection algorithm to detect the outliers in the data set. Outliers are the points in our data set which is distinct from the remaining data set and including of these points may result in inappropriate outcomes. Outliers are the observation that deviates so much from other observation as to arouse suspicion that it was generated by a different mechanism. In this paper, we are studying various data mining techniques to find outliers in our data set and also finding the best suited algorithm for different sample survey data of India (comprises of Inflation rate, Demonetization, UIDAI, and Pan Card) that will help us to find the set of outliers for our data set.

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Outliers, Clustering, Survey, K-mean clustering, Hierarchical clustering, Spectral clustering, Partition clustering, Density-based clustering, Quadrant, Grubb value

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: Smart Phones Users Are Drastically Increasing From 1998 Onwards To Till Date .Various Applications And Tools Are Introduced During This Recent Years For User Comfort. For This Increasing Always Changing Industry. One Common Problem Is Battery Life Of A Smart Phone .Due To Heavy Uses Always There Is A Battery Charging Issue Is Come In To The Picture. So We Proposed One Electronic Based Solution For This Problem. The Solution For This Problem Include Various Sensors And Phototransistors For Temperature, Pressure And Voice Are Used To Get The Analog Signal. Analog To Digital Converter (ADC) And Digital To Analog Converter (DAC)For Conversion Of Signals. Digital Signal Processor Which Processes Only Digital Signals. Those Digital Signal Converted Into Electricity By Using V2I Convertor.

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signal processor, analog to digital converter(ADC), Digital to analog converter(DAC), sensors, photodiodes, Digital signals, analog signals, Amplifier, Battery, V2I Converter

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This journal is about Faculty Management (FMag) which is developed to help faculty in reminding their class schedules, academic work by giving an alert message. It is develop as the faculty may have a chance of forgetting their stipulated class schedules which impacts in lagging of syllabus. FMag provides solution to this problem through an android application which has the features like setting alerts, viewing and posting of the timetables, academic calendar, leave management and substitutions.

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FMag- Faculty Management.

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Brain-computer interfaces (BCI), level analyzer unit (LAU), blink muscle contraction.

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Data mining is an enactment of inspecting the enormous preceding databases in order to produce new information. In this paper we will be detecting communities from trajectories. In existing algorithm trajectory clustering is performed based on a single information source such as location data, regrettably additional information are ignored, due to these discovering the communities in trajectory data sets are not trustfully. To overcome these we proposed trajectory community for the multi-source scattered modeling based on the user recommendation. It combines additional information with raw trajectory data and fabricate the scattered process on multiple similitude metrics. Based on these scattered modeling we will be constructing the multi-modal scattered process and optimizing the heat kernel to learn the ordered kernel. Then compact sub-graph detection is used to discover the set of diverse communities. At last based on this information, we proposed a novel model for user recommendation.

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community, trajectory, clustering, scattering

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With the increased use of Internet and information technol-ogy all over the world, there is an increased amount of criminal activities that involve computing and digital data. These digital crimes (e-crimes) impose new challenges on prevention, detection, investigation, and pros-ecution of the corresponding o?ences. Computer forensics (also known as cyberforensics) is an emerging research area that applies computer in-vestigation and analysis techniques to help detection of these crimes and gathering of digital evidence suitable for presentation in courts. This new area combines the knowledge of information technology, forensics science, and law and gives rise to a number of interesting and challeng-ing problems related to computer security and cryptography that are yet to be solved.To be able to examine large amounts of data in a timely manner in search of important evidence during crime in-vestigations is essential to the success of computer forensic examinations. The limitations in time and resources, both computational and human, have a negative impact in the results obtained. Thus, better use of the resources avail-able are necessary, beyond the capabilities of the currently used forensic tools. Herein, we describe the use of Artificial Intelligence in computer forensics through the development of a multiagent system and case-based reasoning. This sys-tem is composed of specialized intelligent agents that act based on the experts knowledge of the technical domain. Their goal is to analyze and correlate the data contained in the evidences of an investigation and based on its ex-pertise, present the most interesting evidence to the human examiner, thus reducing the amount of data to be person-ally analyzed. The correlation feature helps to find links between evidences that can be easily overlooked by a hu-man expert, specially due to the amount of data involved.

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computer forensics, artificial intelligence, multiagent sys-tems, digital investigation,digital crimes, forensics technology

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The Balanced Incomplete Block Designs (BIBD) is play an important role in design of experiments and especially in the ?eld of agricultural experiments. In this design ensure that treatments are compared with equal precision. The several methods are available in the literature to construction of BIBDs. In this paper, we have proposed a method for construction of balanced incomplete block designs using Mutually Orthogonal Latin Squares (MOLS) of order six is a special case developed through Galois Field (GF) theory.

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LSD, MOLS, Orthogonal, Galois Field, BIBD

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Creative Commons Attribution 4.0 and The Open Definition