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Employee Turnover Prediction System

Employee Turnover Prediction System

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international conference on Information Systems and computer Networks (ISCON)

作者： Mehul Jhaver Yogesh Gupta Amit Kumar Mishra Manipal University Jaipur Jaipur INDIA DIT University Dehradun

ISBN: (数字)9781728136516

ISBN: (纸本)9781728136523

Employee turnover has been recognized as a main problem for industries due to its contrary influence on work place throughput and long term development tactics. To resolve this issue, officialdoms use machine learning techniques to guess employee turnover. Precise predictions qualify administrations to take action for holding of employees. Nonetheless, the data for this type of issue comes from HR Information Systems. this leads to the supremacy of noise in the data that extracts predictive models disposed to over-fitting and hence imprecise. this is the main problem that is the motivation of this study, and one that has not been handled earlier. the innovative contribution of this study is to reconnoiter the application of Gradient Boosting technique which is more robust because of its regularization formulation. the global retailer data is used to compare Gradient Boosting against three traditionally used supervised classifiers like Logistic Regression, Support Vector machine, Random Forest and reveal its suggestively higher accuracy for predicting employee turnover. In this study I have also implemented Artificial Neural Network to study how to the neural network helps in classification of different classes. this paper is aimed at developing models which can predict employee turnover and it can help the organization to take necessary steps to retain these employees.

关键词： Organizations Remuneration machine learning algorithms Boosting Correlation Logistics

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Facial Recognition using machine learning Algorithms on Raspberry Pi

Facial Recognition using Machine Learning Algorithms on Rasp...

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international conference on Electrical, Electronics, Communication, computer and Optimization Techniques (ICEECCOT)

作者： Seema Singh R. Ramya V. Sushma S.R. Roshini R. Pavithra BMS Institute of Technology and Management Bangalore Kamataka India

ISBN: (数字)9781728132617

ISBN: (纸本)9781728132624

Facial recognition is a non-invasive method of biometric authentication and useful for numerous applications. the real time implementation of the algorithm with adequate accuracy is required, with hardware timing into consideration. this paper deals with the implementation of machine learning algorithm for real time facial image recognition. Two dominant methods out of many facial recognition methods are discussed, simulated and implemented using Raspberry Pi. A rigorous comparative analysis is presented considering various limitations which may be the case required for innumerable application which utilize facial recognition. the drawbacks and different use cases of each method is highlighted. the facial recognition software uses algorithms to compare a digital image captured through a camera, to the stored face print so as to authenticate a person's identity. the Haar-Cascade method was one of the first methods developed for facial recognition. the HOG (Histogram of Oriented Gradients) method has worked very effectively for object recognition and thus suitable for facial recognition also. Both the methods are compared with Eigen feature-based face recognition algorithm. Various important features are experimented like speed of operation, lighting condition, frontal face profile, side profiles, distance of image, size of image etc. the facial recognition model is implemented to detect and recognize faces in real-time by means of Raspberry Pi and Pi camera for the user defined database in addition to the available databases.

关键词： Face recognition Face Histograms Feature extraction machine learning algorithms Databases Lighting

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Variable selection and outlier detection in regularized survival models: application to melanoma gene expression data 4th

Variable selection and outlier detection in regularized surv...

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4th international conference on machine learning, Optimization, and Data Science, LOD 2018

作者： Carrasquinha, Eunice Veríssimo, André Lopes, Marta B. Vinga, Susana IDMEC Instituto Superior Técnico Universidade de Lisboa Avenida Rovisco Pais 1 Lisbon1049-001 Portugal INESC-ID Instituto Superior Técnico Universidade de Lisboa Rua Alves Redol 9 Lisbon1000-029 Portugal

ISBN: (纸本)9783030137083

the importance of gene expression data analysis for oncological diagnosis and treatment has become widely accepted in recent years. One of the main associated challenges is the development of mathematical and statistical methods for data analysis to improve prognosis and guide treatment decisions. One of the difficulties that researchers face when dealing with gene expression datasets concerns their high-dimensionality. In this context, the goal of this work is to reduce the dimensionality of gene expression data using regularization techniques such as Lasso and Elastic net, complemented with DegreeCox, a network-based regularization method for survival analysis recently proposed. Also identification of long or short-term survivors (outliers) may lead to the detection of new prognostic factors, and the Rank Product test is used to identify those observations. An example based on the the Cancer Genome Atlas (TCGA) Melanoma dataset is presented, where the covariates are patients’ gene expression. the application of data reduction techniques to the Melanoma dataset enabled the selection of relevant genes over a range of parameters evaluated, with 5 in common between elastic net regularization and DegreeCox for one of the two models further evaluated. Moreover, a long term survivor was detected as outlier by the Rank Product test, being systematically highly ranked for the martingale residuals of the models evaluated. © Springer Nature Switzerland AG 2019.

关键词： Gene expression

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A Survey on Data Stream Mining Towards the Internet of things application

A Survey on Data Stream Mining Towards the Internet of Thing...

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Technology Innovation Management and Engineering Science international conference (TIMES-iCON)

作者： Saifun Nahar Ting Zhong Happy N. Monday Michael O. Mills Grace U. Nneji Hassan S. Abubakar School of Information and Software Engineering School of Computer Science and Engineering School of Information and Communication Engineering University of Electronic Science and Technology of China ChengduSichuan P.R China

ISBN: (数字)9781728137551

ISBN: (纸本)9781728137568

In the era of the Internet of things, a widespread of applications depend on time with the various number of different data generated and collected from different devices available. these devices depend on the type of application. these fast stream data are real-time and large in dimension for the purpose of making decision as well as predicting future occurrence and analytics. Datastream analytics of internet technology for both businesses and everyday life is very valuable in terms of developing good quality of life. In this study, first of all, we focus on the concept of Internet of things and its relationship with its architecture, large and flowing data. In addition, the approach of Internet of things applied knowledge discovery process and deep learning frameworks are presented in this paper. Finally, the Internet of things and its features are introduced in this work as well as the commonly used tools.

关键词： Internet of things machine learning Big Data Data mining Data analysis Sensors Python

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4th international conference of the Virtual and Augmented Reality in Education, VARE 2018

4th International Conference of the Virtual and Augmented Re...

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4th international conference of the Virtual and Augmented Reality in Education, VARE 2018

ISBN: (纸本)9788885741218

the proceedings contain 29 papers. the topics discussed include: motostudent and the reality-virtuality continuum;augmented reality: modernizing rural education in India;VR-technology for risk assessment of and accident prevention at machine tools;educational augmented reality systems: benefits of implementation and government support;development and evaluation of work support system by AR using HMD;development and evaluation of the museum support system using an AR technology;alternatives generation via data analytics for decision making using VR;the use of virtual reality training application to increase the effectiveness of workshops in the field of lean manufacturing;knowledge exchange using holograms in the teaching factory concept;the use of augmented reality to promote tourism in thailand;decision support method for using virtual reality in education based on a cost-benefit- analysis;virtual spaces for collaboration and learning constructing multi user virtual environments for learning;juxtaposing visual layouts – an approach for solving analytical and exploratory tasks through arranging visual interfaces;visualizing law - a norm-graph visualization approach based on semantic legal data;exploring dimensionality reduction effects in mixed reality for analyzing tinnitus patient data;and virtual reality and augmented reality low cost: an experience of heritage education in primary school.

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38th SGAI international conference on Innovative Techniques and applications of Artificial Intelligence, AI 2018

38th SGAI International Conference on Innovative Techniques ...

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38th SGAI international conference on Innovative Techniques and applications of Artificial Intelligence, AI 2018

ISBN: (纸本)9783030041908

the proceedings contain 37 papers. the special focus in this conference is on Innovative Techniques and applications of Artificial Intelligence. the topics include: the dreaming variational autoencoder for reinforcement learning environments;abnormality detection in the cloud using correlated performance metrics;Directed recursion search: A directed DFS for online pathfinding in random grid-based environments;Modelling trust between users and AI;multi-criteria decision making with existential rules using repair techniques;gramError: A quality metric for machine generated songs;computational complexity analysis of decision tree algorithms;forecasting student’s preference in E-learning systems;human motion recognition using 3D-Skeleton-data and neural networks;implementing rules with artificial neurons;autonomous swarm agents using case-based reasoning;beat the bookmaker – winning football bets with machine learning (best application paper);rule-mining and clustering in business process analysis;machine learning in control systems: An overview of the state of the art;Predicting fluid work demand in service organizations using AI techniques;workforce rostering via metaheuristics;incorporating risk in field services operational planning process;risk information recommendation for engineering workers;generalised decision level ensemble method for classifying multi-media data;spotting earnings manipulation: Using machine learning for financial fraud detection;informed pair selection for self-paced metric learning in siamese neural networks;context extraction for aspect-based sentiment analytics: Combining syntactic, lexical and sentiment knowledge;confidence in random forest for performance optimization;designing a website using a genetic algorithm;regulated information sharing and pattern recognition for smart cities;a middleware to link lego mindstorms robots with 4th generation language software NetLogo.

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EPSCICON 2018 - 4th international conference on Power, Signals, Control and Computation

EPSCICON 2018 - 4th International Conference on Power, Signa...

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4th international conference on Power, Signals, Control and Computation, EPSCICON 2018

ISBN: (纸本)9781538642085

the proceedings contain 29 papers. the topics discussed include: machine learning for electroencephalography decoding and robotics dextrous hands movement;detection of unhealthy plant leaves using image processing and genetic algorithm with Arduino;review on virtual synchronous generator (VSG) for enhancing performance of microgrid;nuisance attribute projection for improving the performance of obstructive sleep apnea detection;a comparative study of field computation methods: charge simulation method and method of moments;a high frequency resonant EF2 converter for electric vehicle charging;design of microcontroller based PID algorithm for current control and automating the capacity test of class 1 power supplies used in nuclear applications;a three-stage buck in buck, boost in boost grid-tied inverter;a review on active & reactive power control strategy for a standalone hybrid renewable energy system based on droop control;motor contribution to three phase fault currents in a power intensive industry with CPP: a case study;four quadrant operation of BLDC motor suitable for DC micro grid for elevator application;optical based non invasive glucometer with IoT;and detection of malicious node in mobile ad-hoc network.

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Health State of Google's PlayStore Finding Malware in Large Sets of applications from the Android Market 4

Health State of Google's PlayStore <i>Finding Malware in Lar...

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4th international conference on Information Systems Security and Privacy (ICISSP)

作者： Dey, Alexandre Beheshti, Loic Sido, Marie-Kerguelen ESIEA Operat Cryptol & Virol Res Lab CV 38 Rue Docteurs Calmette & Guerin Laval France

ISBN: (纸本)9789897582820

Android has, to this day, more than 80% of the mobile OS market share. Android users also have access to more than 2 million applications via the Google Playstore. the Playstore being an official market, users tend to trust the applications they find in it, and therefore, the store is an interesting platform to spread malware. We want to provide a health state of this store by finding the proportion of malware that managed to get published in it. In this paper, we explain how we developed the crawler that massively downloads the application directly from the Playstore. then we describe what features we extract from the applications and how we classified them with the help of an Artfificial Neural Network. Our study confirms that there are malicious applications on the Playstore. the proportion of them is around 2%, which corresponds to about 40,000 officially downloadable malware.

关键词： Android Malware machine learning Crawler Neural Network Static Analysis

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proceedings - 2018 international conference on Big Data Innovations and applications, Innovate-Data 2018

Proceedings - 2018 International Conference on Big Data Inno...

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4th international conference on Big Data Innovations and applications, Innovate-Data 2018

ISBN: (纸本)9781538677933

the proceedings contain 11 papers. the topics discussed include: semantic data ingestion for intelligent, value-driven big data analytics;towards making distributed RDF processing FLINKer;a cognitive agent for mining bugs reports, feature suggestions and sentiment in a mobile application store;a microservices-based platform for efficiently managing oceanographic data;a comparative evaluation of big data frameworks for graph processing;application of data mining techniques to predict the length of stay of hospitalized patients with diabetes;towards hybrid energy consumption prediction in smart grids with machine learning;survival analysis within stack overflow: python and R;secure indoor location for airport environments;fuzzy logic system for identity theft detection in social networks;and authentication and encryption for a robotic ad hoc network using identity-based cryptography.

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AECID: A Self-learning Anomaly Detection Approach based on Light-weight Log Parser Models 4

AECID: A Self-learning Anomaly Detection Approach based on L...

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4th international conference on Information Systems Security and Privacy (ICISSP)

作者： Wurzenberger, Markus Skopik, Florian Settanni, Giuseppe Fiedler, Roman AIT Austrian Instritute Technol Ctr Digital Safety & Secur Vienna Austria

ISBN: (纸本)9789897582820

In recent years, new forms of cyber attacks with an unprecedented sophistication level have emerged. Additionally, systems have grown to a size and complexity so that their mode of operation is barely understandable any more, especially for chronically understaffed security teams. the combination of ever increasing exploitation of zero day vulnerabilities, malware auto-generated from tool kits with varying signatures, and the still problematic lack of user awareness is alarming. As a consequence signature-based intrusion detection systems, which look for signatures of known malware or malicious behavior studied in labs, do not seem fit for future challenges. New, flexibly adaptable forms of intrusion detection systems (IDS), which require just minimal maintenance and human intervention, and rather learn themselves what is considered normal in an infrastructure, are a promising means to tackle today's serious security situation. this paper introduces AECID, a new anomaly-based IDS approach, that incorporates many features motivated by recent research results, including the automatic classification of events in a network, their correlation, evaluation, and interpretation up to a dynamically-configurable alerting system. Eventually, we foresee AECID to be a smart sensor for established SIEM solutions. Parts of AECID are open source and already included in Debian Linux and Ubuntu. this paper provides vital information on its basic design, deployment scenarios and application cases to support the research community as well as early adopters of the software package.

关键词： Anomaly Detection Intrusion Detection System machine learning Log Analysis

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