scholarly journals A Novel Approach for Outlier Detection in Multivariate Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Saima Afzal ◽  
Ayesha Afzal ◽  
Muhammad Amin ◽  
Sehar Saleem ◽  
Nouman Ali ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Multivariate Data ◽  
Evaluation Criteria ◽  
Real Life ◽  
Simulation Experiments ◽  
Novel Approach ◽  
Multiple Variables ◽  
Components Analysis ◽  
Detection Of Outliers ◽  
F Measure

Outlier detection is a challenging task especially when outliers are defined by rare combinations of multiple variables. In this paper, we develop and evaluate a new method for the detection of outliers in multivariate data that relies on Principal Components Analysis (PCA) and three-sigma limits. The proposed approach employs PCA to effectively perform dimension reduction by regenerating variables, i.e., fitted points from the original observations. The observations lying outside the three-sigma limits are identified as the outliers. This proposed method has been successfully employed to two real life and several artificially generated datasets. The performance of the proposed method is compared with some of the existing methods using different performance evaluation criteria including the percentage of correct classification, precision, recall, and F-measure. The supremacy of the proposed method is confirmed by abovementioned criteria and datasets. The F-measure for the first real life dataset is the highest, i.e., 0.6667 for the proposed method and 0.3333 and 0.4000 for the two existing approaches. Similarly, for the second real dataset, this measure is 0.8000 for the proposed approach and 0.5263 and 0.6315 for the two existing approaches. It is also observed by the simulation experiments that the performance of the proposed approach got better with increasing sample size.

scholarly journals A Review on Outliers-Detection Methods for Multivariate Data

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Sharifah Sakinah Syed Abd Mutalib ◽  
Siti Zanariah Satari ◽  
Wan Nur Syahidah Wan Yusoff
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Outlier Detection ◽  
High Dimension ◽  
Detection Method ◽  
Multivariate Data ◽  
Projection Pursuit ◽  
Detection Methods ◽  
And Cluster Analysis ◽  
Detection Of Outliers

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.

scholarly journals New fractional approaches for n-polynomial P-convexity with applications in special function theory

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shu-Bo Chen ◽  
Saima Rashid ◽  
Muhammad Aslam Noor ◽  
Zakia Hammouch ◽  
Yu-Ming Chu
Keyword(s):  
Fractional Calculus ◽  
Convex Functions ◽  
Special Functions ◽  
Real Life ◽  
Fractional Integrals ◽  
Digamma Function ◽  
Novel Approach ◽  
Inequality Theory ◽  
New Strategy ◽  
Significant Mechanism

Abstract Inequality theory provides a significant mechanism for managing symmetrical aspects in real-life circumstances. The renowned distinguishing feature of integral inequalities and fractional calculus has a solid possibility to regulate continuous issues with high proficiency. This manuscript contributes to a captivating association of fractional calculus, special functions and convex functions. The authors develop a novel approach for investigating a new class of convex functions which is known as an n-polynomial $\mathcal{P}$ P -convex function. Meanwhile, considering two identities via generalized fractional integrals, provide several generalizations of the Hermite–Hadamard and Ostrowski type inequalities by employing the better approaches of Hölder and power-mean inequalities. By this new strategy, using the concept of n-polynomial $\mathcal{P}$ P -convexity we can evaluate several other classes of n-polynomial harmonically convex, n-polynomial convex, classical harmonically convex and classical convex functions as particular cases. In order to investigate the efficiency and supremacy of the suggested scheme regarding the fractional calculus, special functions and n-polynomial $\mathcal{P}$ P -convexity, we present two applications for the modified Bessel function and $\mathfrak{q}$ q -digamma function. Finally, these outcomes can evaluate the possible symmetric roles of the criterion that express the real phenomena of the problem.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

Removing NOx Pollution by Photocatalytic Building Materials in Real-Life: Evaluation of Existing Field Studies

2021 ◽  
Vol 02 ◽  
Author(s):  
Pernille D. Pedersen ◽  
Nina Lock ◽  
Henrik Jensen
Keyword(s):  
Air Pollutants ◽  
Building Materials ◽  
Evaluation Criteria ◽  
Real Life ◽  
Ambient Air ◽  
Well Being ◽  
Good Health ◽  
Objective Evaluation ◽  
Field Studies ◽  
Hazardous Air Pollutants

: The NOx gasses (NO and NO2) are among the most important air pollutants, due to the toxicity of NO2, as well as the role of NOx in the tropospheric oxidation of Volatile Organic Carbons (VOCs), contributing to the formation of other hazardous air pollutants. Air pollution is one of the biggest health threats world-wide, hence reducing NOx levels is an important objective of the UN sustainable development goals, e.g. #3, “Good health and well-being” and #11 “Sustainable cities and communities”. Photocatalysis using TiO2 and light is a promising technique for removing NOx along with other pollutants, as demonstrated on laboratory scale. Furthermore, a long range of real-life test studies of varying scales have been conducted during the past two decades. The results of these studies have been conflicting, with some studies reporting no effect on the ambient air quality and others reporting significant reductions of NOx level. However, the studies are very difficult to compare and assess due to the very different approaches used, which consequently vary in quality. In this review, we aim to develop a set of objective evaluation criteria to assess the quality of the individual studies in order to simplify the interpretation and comparison of the existing studies. Moreover, we propose some guidelines for future test-studies. Furthermore, the approaches and main conclusions from 23 studies are independently assessed and discussed herein.

scholarly journals An Advanced Educational Tool for Digital Forensic Engineering

2016 ◽  
Vol 11 (03) ◽  
pp. 15 ◽  
Author(s):  
Primož Cigoj ◽  
Borka Jerman Blažič
Keyword(s):  
Digital Forensics ◽  
Real Life ◽  
University Education ◽  
Educational Tool ◽  
Digital Tool ◽  
College And University ◽  
Novel Approach ◽  
Wide Range ◽  
E Learning ◽  
Forensic Engineering

This paper presents a novel approach to education in the area of digital forensics based on a multi-platform cloud-computer infrastructure and an innovative computer based tool. The tool is installed and available through the cloud-based infrastructure of the Dynamic Forensic Education Alliance. Cloud computing provides an efficient mechanism for a wide range of services that offer real-life environments for teaching and training cybersecurity and digital forensics. The cloud-based infrastructure, the virtualized environment and the developed educational tool enable the construction of a dynamic e-learning environment making the training very close to reality and to real-life situations. The paper presents the Dynamic Forensic Digital tool named EduFors and describes the different levels of college and university education where the tool is introduced and used in the training of future investigators of cybercrime events.

scholarly journals Intricacies of Social Movement Outcome Research and beyond: “How can you Tell” Social Movements Prompt Changes?

2012 ◽  
Vol 17 (4) ◽  
pp. 92-102 ◽  
Author(s):  
Doowon Suh
Keyword(s):  
Social Movements ◽  
Outcome Research ◽  
Evaluation Criteria ◽  
Combined Effects ◽  
Comprehensive Understanding ◽  
Specific Context ◽  
Invariant Model ◽  
Historical Progress ◽  
Collective Actors ◽  
Multiple Variables

Most scholars of social movements have been drawn to research on the politically contentious behavior of collective actors because of the conviction that social movements sometimes generate significant historical progress and social change. Yet movement outcome research has been least developed in the literature. This irony emanates from methodological and causal intricacies that fail to clearly explicate how social movements create change. The challenges encompass the heaped typologies, mutual inconsistencies, causal heterogeneities, and conflictive evaluation criteria of movement outcomes. To overcome these quandaries, this paper proposes that (1) any attempt to find an invariant model or general theorization of a movement outcome is inevitably futile; (2) instead, attention to the specific context of time and place in which social movements produce outcomes is necessary; and (3) a comprehensive understanding of the origins of a movement outcome becomes possible when multiple variables are considered and their combined effects are analyzed.

scholarly journals An under sampled k-means approach for handlingimbalanced data using diversified distribution

2018 ◽  
Vol 7 (1.8) ◽  
pp. 113 ◽  
Author(s):  
G Shobana ◽  
Bhanu Prakash Battula
Keyword(s):  
Research Issue ◽  
Minority Class ◽  
Effective Utilization ◽  
Specific Strategy ◽  
Open Research ◽  
Novel Approach ◽  
Under Sampling ◽  
F Measure

Some true applications uncover troubles in taking in classifiers from imbalanced information. Albeit a few techniques for enhancing classifiers have been presented, the distinguishing proof of conditions for the effective utilization of the specific strategy is as yet an open research issue. It is likewise worth to think about the idea of imbalanced information, qualities of the minority class dissemination and their impact on arrangement execution. In any case, current investigations on imbalanced information trouble factors have been predominantly finished with manufactured datasets and their decisions are not effortlessly material to this present reality issues, likewise on the grounds that the techniques for their distinguishing proof are not adequately created. In this paper, we recommended a novel approach Under Sampling Utilizing Diversified Distribution (USDD) for explaining the issues of class lopsidedness in genuine datasets by thinking about the systems of recognizable pieces of proof and expulsion of marginal, uncommon and anomalies sub groups utilizing k-implies. USDD utilizes exceptional procedure for recognizable proof of these kinds of cases, which depends on breaking down a class dissemination in a nearby neighborhood of the considered case utilizing k-closest approach. The exploratory outcomes recommend that the proposed USDD approach performs superior to the looked at approach as far as AUC, accuracy, review and f-measure.

scholarly journals Fuzzy Win-Win: A Novel Approach to Quantify Win-Win Using Fuzzy Logic

2021 ◽  
Author(s):  
Ahmad B. Hassanat ◽  
Ghada A. Altarawneh ◽  
Ahmad S. Tarawneh
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Iron Ore ◽  
Real Life ◽  
The Other ◽  
Uranium Enrichment ◽  
Single Product ◽  
Novel Approach

Abstract The classic win-win has a key flaw in that it cannot offer the parties with right amounts of winning because each party believes they are winners. In reality, one party may win more than the other. This strategy is not limited to a single product or negotiation; it may be applied to a variety of situations in life. We present a novel way to measure the win-win situation in this paper. The proposed method employs the Fuzzy logic to create a mathematical model that aids negotiators in quantifying their winning percentages. The model is put to the test on real-life negotiation scenarios such as the Iranian uranium enrichment negotiations, the Iraqi-Jordanian oil deal, and the iron ore negotiation (2005-2009). The presented model has shown to be a useful tool in practice and can be easily generalized to be utilized in other domains as well.

Feature Subspace Estimation for Resolving Dimensionality Mismatch in Practical Color-Based Face Recognition Systems

2016 ◽  
Vol 30 (04) ◽  
pp. 1656004 ◽  
Author(s):  
Jae Young Choi
Keyword(s):  
Face Recognition ◽  
Color Image ◽  
Recognition Performance ◽  
Real Life ◽  
Practical Applications ◽  
Novel Approach ◽  
Feature Subspace ◽  
New Feature ◽  
Different Color ◽  
Input Level

Recently, considerable research efforts have been devoted to effective utilization of facial color information for improved recognition performance. Of all color-based face recognition (FR) methods, the most widely used approach is a color FR method using input-level fusion. In this method, augmented input vectors of the color images are first generated by concatenating different color components (including both luminance and chrominance information) by column order at the input level and feature subspace is then trained with a set of augmented input vectors. However, in practical applications, a testing image could be captured as a grayscale image, rather than as a color image, mainly caused by different, heterogeneous image acquisition environment. A grayscale testing image causes so-called dimensionality mismatch between the trained feature subspace and testing input vector. Disparity in dimensionality negatively impacts the reliable FR performance and even imposes a significant restriction on carrying out FR operations in practical color FR systems. To resolve the dimensionality mismatch, we propose a novel approach to estimate new feature subspace, suitable for recognizing a grayscale testing image. In particular, new feature subspace is estimated from a given feature subspace created using color training images. The effectiveness of proposed solution has been successfully tested on four public face databases (DBs) such as CMU, FERET, XM2VTSDB, and ORL DBs. Extensive and comparative experiments showed that the proposed solution works well for resolving dimensionality mismatch of importance in real-life color FR systems.

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