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Predictive Analysis of Absenteeism in MNCS Using machine learning Algorithm 2nd

Predictive Analysis of Absenteeism in MNCS Using Machine Lea...

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2nd international conference on Recent Innovations in Computing (ICRIC)

作者： Tewari, Krittika Vandita, Shriya Jain, Shruti Jaypee Univ Informat Technol Dept Elect & Commun Engn Solan Himachal Prades India

ISBN: (纸本)9783030294076;9783030294069

Absenteeism has become a severe problem for many organizations. The problem posed in this paper was to build a predictive model to predict the absenteeism for MNCs by previously recorded data sets. This exercise not only leads to prevent or lower absenteeism but forecast future workforce requirements and suggests ways to meet those demands. For faster processing of massive data set, the data was analyzed efficiently so that we get the minimum response time and turn-around time, which is only possible when we use the right set of algorithms and by hard wiring of the program. Different machine learning algorithms are used in the paper that includes linear regression and support vector regression. By analyzing the results of each technique, we come across that the age parameter mainly affects the absenteeism that is linearly related to absenteeism.

关键词： Absenteeism machine learning Linear regression Support vector regression

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CNN based Design Rule Checker for VLSI Layouts

CNN based Design Rule Checker for VLSI Layouts

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Eshan Saraogi Giriraj Singh Chouhan Dipesh Panchal Manish I. Patel Ruchi Gajjar Institute of Technology Nirma University Ahmedabad India

ISBN: (纸本)9781665443005

Design Rule Checker (DRC) is one of the most important tools of modern VLSI layout design. DRC involves the checking of design rules in a VLSI layout. In recent developments of chips with an even lower channel length of the transistor, the number of DRC violations has increased from a few hundred to thousands, thus checking so many DRC violations has become a critical point in the layout generation of custom circuits. Many industries came up with different approaches to tackle this problem and to make the DRC forming and checking stage faster. Since the evolution of machine learning, researchers from every field of engineering & even from management are relying on various machine learning algorithms to make existing processes faster and more accurate. This paper is aimed in the same direction, which is to implement a machine-learning algorithm to make the DRC checking and execution process faster and more accurate concerning existing processes. This paper is aimed to propose a different approach to implement DRC which is based on Convolutional Neural Network (CNN) model technique. The layout violations in the circuit will be detected by our trained CNN Model, specially trained for some specific layout violations in a circuit. The proposed model is successful in the detection of the design rule constraints in a layout with an accuracy of 98% and can be expanded to larger and more complex layouts. The current scope of this paper includes hundreds of layouts for 3 different logic circuits and the accuracy obtained is quite encouraging.

关键词： Industries Support vector machines machine learning algorithms Layout machine learning Very large scale integration Logic gates

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international conference on Communications, signal processing and VLSI, IC2SV 2019

International Conference on Communications, Signal Processin...

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international conference on Communications, signal processing and VLSI, IC2SV 2019

ISBN: (纸本)9789813340572

The proceedings contain 36 papers. The special focus in this conference is on Communications, signal processing and VLSI. The topics include: Design and Analysis of Wearable Step-Shaped Sierpinski Fractal Antenna for WBAN Applications;Evaluation of Real-Time Embedded Systems in HILS and Delay Issues;acoustic Feedback Cancellation Using Optimal Step-Size Control of the Partition Block Frequency-Domain Adaptive Filter;A Compact IPD Based on-Chip Bandpass Filter for 5G Radio Applications;An 18-Bit Incremental Zoom ADC with Reduced Delay Overhead Data Weighted Averaging for Battery Measurement Systems;a Non-Uniform Digital Filter Bank Hearing Aid for Aged People with Hearing Disability;optimal Resource Allocation Based on Particle Swarm Optimization;Design and Simulation of a W-Band FMCW Radar for Cloud Profiling Application;Single Layer Asymmetric Slit Circularly Polarized Patch Antenna for WLAN Application;recognition of Natural and Computer-Generated Images Using Convolutional Neural Network;contour and Texture-Based Approaches for Dental Radiographic and Photographic Images in Forensic Identification;an Efficient Low Latency Router Architecture for Mesh-Based NoC;high-Performance Image Visual Feature Detection and Matching;systolic-Architecture-Based Matrix Multiplications and Its Realization for Multi-Sensor Bias Estimation Algorithms;fast Encoding Using X-Search Pattern and Coded Block Flag Fast Method;Energy Efficient and Accurate Hybrid CSA-CIA Adders;machine learning Oriented Dynamic Cost Factors-Based Routing in Communication Networks;Design of Look-Up Table with Low Leakage Power Enhanced Stability 10 T SRAM Cell;Conformal Omni Directional Antenna for GPS Applications;total Internal Reflection Quasi-Phase-Matching-Based Broadband Second Harmonic Generation in Isotropic Semiconductor: A Comparative Analysis;automated Bayesian Drowsiness Detection System Using Recurrent Convolutional Neural Networks.

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A Generative Adversarial Approach for the Detection of Typical and Drowned Action Potentials 17

A Generative Adversarial Approach for the Detection of Typic...

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17th IEEE international conference on Intelligent Computer Communication and processing, ICCP 2021

作者： Dodon, Alexandru Adrian Calugar, Marius Potolea, Rodica Lemnaru, Camelia Dînşoreanu, Mihaela Moca, Vasile V. Muresan, Raul C. Technical University of Cluj-Napoca Cluj-Napoca Romania Transylvanian Institute of Neuroscience Cluj-Napoca Romania

ISBN: (纸本)9781665409766

Spike sorting methods are central for the interpretation of extracellular recordings and provide insight into more complex brain processes. To be able to apply the sorting methods spikes are detected, conventionally, by using an amplitude threshold that takes into account the features of the signal. This offers a straightforward detection solution that is able to discriminate the background noise, but comes at the cost of ignoring spikes with an amplitude below the threshold and limiting the picture provided by the recording. We propose a machine learning approach that captures spikes overlooked when considering only the amplitude. The proposed method leverages Generative Adversarial Networks (GANs) to learn particularities of the spike and noise components of the recordings and then extract the underlying components of novel samples with the goal of detecting the presence of spikes. We quantify the detection metrics with respect to the signal to noise ratio of the recording and show that the proposed method exhibits significantly higher sensitivity while providing comparable specificity to conventional spike detection methods across all signal environments, especially when spikes are almost drowned in the background noise. © 2021 IEEE.

关键词： signal to noise ratio

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EEG Based Mental Arithmetic Task Classification Using a Stacked Long Short Term Memory Network for Brain-Computer Interfacing 2

EEG Based Mental Arithmetic Task Classification Using a Stac...

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2nd international conference on VLSI Device, Circuit and System (IEEE VLSI DCS)

作者： Ganguly, Biswarup Chatterjee, Arpan Mehdi, Waqar Sharma, Soumyadip Garai, Soumyadeep Jadavpur Univ Elect Engn Dept Kolkata India Meghnad Saha Inst Technol Elect Engn Dept Kolkata India

ISBN: (纸本)9781728119335

This paper proposes an electroencephalogram (EEG) based mental arithmetic task classification using a stacked long short-term memory (LSTM) architecture for brain computer interfacing (BCI). EEG signals from 22 channels are taken from 36 subjects, as mentioned in the Physionet database. A deep learning framework based on LSTM is employed to identify and classify the mental arithmetic task by reducing the number of electrodes. Window segmentation is applied for data augmentation as well as feature extraction from all the recorded EEG signals. Eight features per electrode have been fed into the proposed LSTM architecture. The main aspect of this network along with the dropout layers is to enhance feature learning ability and to avoid over fitting.

关键词： brain computer interface (BCI) electroencephalogram (EEG) LSTM network mental arithmetic task signal processing

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Performance of probabilistic approach and artificial neural network on questionnaire data concerning taiwanese ecotourism 2nd

Performance of probabilistic approach and artificial neural ...

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2nd international conference on Sensor Networks and signal processing, SNSP 2019

作者： Bína, Vladislav Kratochvíl, Václav Váchová, Lucie Jiroušek, Radim Lee, Tzong-Ru Faculty of Management University of Economics Prague Czech Republic Institute of Information Theory and Automation Czech Academy of Sciences Prague Czech Republic Department of Marketing National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789811549168

This paper aims to perform modeling of Taiwanese farm and ecotourism data using compositional models as a probabilistic approach and to compare its results with the performance of an artificial neural network approach. Authors use probabilistic compositional models together with the artificial neural network as a classifier and compare the accuracy of both approaches. The probabilistic model structure is learned using hill climbing algorithm, and the weights of multilayer feedforward artificial neural network are learned using an R implementation of H2O library for deep learning. In case of both approaches, we employ a non-exhaustive cross-validation method and compare the models. The comparison is augmented by the structure of the compositional model and basic characterization of artificial neural network. As expected, the compositional models show significant advantages in interpretability of results and (probabilistic) relations between variables, whereas the artificial neural network provides more accurate yet "black-box" model. © Springer Nature Singapore Pte Ltd 2021.

关键词： Deep learning

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2nd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2020

2nd EAI International Conference on Multimedia Technology an...

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2nd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2020

ISBN: (纸本)9783030510992

The proceedings contain 83 papers. The special focus in this conference is on Multimedia Technology and Enhanced learning. The topics include: Mining and analyzing behavior patterns of smart home users based on cloud computing;research on behavior recognition method based on machine learning and fisher vector coding;data scheduling method of social network resources based on multi-agent technology;a local occlusion face image recognition algorithm based on the recurrent neural network;analysis of the training method for the time-of-time of the movement based on the wireless;moving target location method of video image based on computer vision;design of anti-interference control system for vacuum packaging machine based on wireless network;design of sealing transformer for vacuum packaging machine based on single chip microcomputer control;image segmentation technology of marathon motion video based on machine learning;research on durability test for composite pipe fitting materials of automobile;research on the adaptive tracking method for the tracking of the track of the long-jump athletes;application of big data’s association rules in the analysis of sports competition tactics;identification and analysis of limb swing image in short run training based on visual signal processing;intelligent agricultural information remote data storage method based on block chain;design of multi-parameter monitoring system for intelligent agriculture greenhouse based on artificial intelligence;design of agricultural network information resource sharing system based on internet of things;an evaluation model of the efficiency of agricultural information resources allocation in the big data environment.

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2nd international conference on Innovative Computing and Communication, ICICC 2019

2nd International Conference on Innovative Computing and Com...

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2nd international conference on Innovative Computing and Communication, ICICC 2019

ISBN: (纸本)9789811512858

The proceedings contain 50 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: Recommendation System Using Community Identification;comparison of Deep learning and Random Forest for Rumor Identification in Social Networks;ontological Approach to Analyze Traveler’s Interest Towards Adventures Club;performance Analysis of Off-Line Signature Verification;fibroid Detection in Ultrasound Uterus Images Using Image processing;progressive Generative Adversarial Binary Networks for Music Generation;machine learning Approach for Diagnosis of Autism Spectrum Disorders;methodologies for Epilepsy Detection: Survey and Review;scene Understanding Using Deep Neural Networks—Objects, Actions, and Events: A Review;Exploring the Effect of Tasks Difficulty on Usability Scores of Academic Websites Computed Using SUS;scene Text Recognition: A Preliminary Investigation on Various Techniques and Implementation Using Deep learning Classifiers;computer-Aided Diagnosis System for Investigation and Detection of Epilepsy Using machine learning Techniques;moments-Based Feature Vector Extraction for Iris Recognition;a Novel Approach to Improve Website Ranking Using Digital Marketing;a Comparative Study on Different Skull Stripping Techniques from Brain Magnetic Resonance Imaging;Predicting Academic Performance of international Students Using machine learning Techniques and Human Interpretable Explanations Using LIME—Case Study of an Indian University;improved Feature Matching Approach for Detecting Copy-Move Forgery and Localization of Digital Images;sentiment Analysis Using Gini Index Feature Selection, N-Gram and Ensemble Learners;text Summarization by Hybridization of Hypergraphs and Hill Climbing Technique;comparing machine learning Algorithms to Predict Diabetes in Women and Visualize Factors Affecting It the Most—A Step Toward Better Health Care for Women.

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A Comparative Analysis of Ensemble Based machine learning Techniques for Diabetes Identification

A Comparative Analysis of Ensemble Based Machine Learning Te...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Nahid Hossain Taz Abrar Islam Ishrak Mahmud Islamic University of Technology Gazipur Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

This manuscript develops a framework to deliver prediction of diabetes using the Ensemble-based machine learning techniques on Pima Indian Diabetes Dataset (PIDD). We investigate the performance measures of four distinct machine learning classifiers separately following by a soft voting classifier in order to enhance the performance metrics of Diabetes Identification. The classifications are estimated according to average classification accuracy, precision, recall, f1 score and ROC AUC of individual classifier models. LightGBM, XGBoost, AdaBoost and Random Forest classifiers are employed as distinct classifiers, and a Soft Voting classifier is further introduced to improve the robustness of performance metrics. Analyzed performance metrics results confirm that LightGBM has produced the optimum result as an individual classifier with accuracy score 94% and ROC AUC 95%, and Soft Voting classifier has additionally improved the complete structure to accuracy score 95% and ROC AUC 96%.

关键词： Measurement Data preprocessing signal processing Robustness Diabetes Robots Random forests

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GCN2-NAA: Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction 2021

GCN2-NAA: Two-stage Graph Convolutional Networks with Node-A...

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2021 13th international conference on machine learning and Computing, ICMLC 2021

作者： Niu, WeiCai Chen, Quan Zhang, Weiwen Ma, Jianwen Hu, Zhongqiang School of Computers Guangdong University of Technology China

ISBN: (纸本)9781450389310

Joint extraction of entities and relations is critical for many tasks of Natural Language processing (NLP), which aims to extract all triplets in the text. However, the huge challenge is that a sentence usually contains overlapping triplets. In this paper, we propose a joint extraction framework named GCN2-NAA based on a two-stage Graph Convolutional Neural networks (GCN) and Node-Aware Attention mechanism. We obtain multi-granularity representations and regional features of words by stacking multiple feature encoders and 1st-phase GCN. Besides, the node-aware attention mechanism and 2nd-phase GCN to capture the soft attention correlation matrix between all words in each relation type. Based on the constructed soft attention correlation matrix, we utilize GCN to further obtain the interaction between entities, relations, and triplets. Experiment results show that GCN2-NAA outperforms baseline models by 6.5% and 11.4% in terms of F1 score on NYT and WebNLG datasets, respectively. © 2021 ACM.

关键词： Convolutional neural networks

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