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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

1st International Conference on Computational Intelligence i...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

ISBN: (纸本)9789811390418

The proceedings contain 88 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Classification of Speech and Song Using Co-occurrence-Based Approach;Intelligent Analysis for Personality Detection on Various Indicators by Clinical Reliable Psychological TTH and stress Surveys;automatic Multilingual System from Speech;an Improved Data Hiding Scheme Through image Interpolation;generalization of Multi-bit Encoding Function Based Data Hiding Scheme;a machine learning Approach to Comment Toxicity Classification;a Minutia Detection Approach from Direct Gray-Scale Fingerprint image Using Hit-or-Miss Transformation;face Identification via strategic Combination of Local Features;a study on Reversible image Watermarking Using Xilinx System Generator;a Novel Enhancement and Segmentation of Color Retinal image Based on Fuzzy Measure and Fuzzy Integral;SVM and MLP Based Segmentation and recognition of Text from Scene images Through an Effective Binarization Scheme;user Identification and Authentication Through Voice Samples;template Matching for Kinship Verification in the Wild;Attack Prevention Scheme for Privacy Preservation (APSP) Using K Anonymity in Location Based Services for IoT;statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection;Singer Identification Using MFCC and CRP Features with Support Vector machines;object Proposals Based on Variance Measure;deep learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen;Robust Non-blind Video Watermarking Using DWT and QR Decomposition;detection and Classification of Earthquake images from Online Social Media;On the Cryptanalysis of S-DES Using Binary Cuckoo Search Algorithm;numerical Integration Based Contrast Enhancement Using Simpson’s Method;self Driving Car.

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1st international conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2020

1st International Conference on Electronics, Biomedical Engi...

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1st international conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2020

ISBN: (纸本)9789813369252

The proceedings contain 63 papers. The special focus in this conference is on Electronics, Biomedical Engineering, and Health Informatics. The topics include: Design of Electrical Energy storage System Produced by Thermoelectric Generator;performance Comparison of Three Thermoelectric Generator Types for Waste Heat Recovery;cross-Gender and Age Speech Conversion Using Hidden Markov Model Based on Cepstral Coefficients Conversion;design of Electric Wheelchair with Joystick Controller as Personal Mobility for Disabled Person;comparison of the Effects of Feature Selection and Tree-Based Ensemble machine learning for Sentiment Analysis on Indonesian YouTube Comments;assessing Beads Generation in Fabricating Nanofiber Bioactive Material-Based Associated with Its Fluid Factors;Intelligent System of Handling In Vitro Fertilization (IVF) Patients Post Embryo Transfer to Reduce the Level of Patient Anxiety and Help Fertility Doctors Quickly Answer Patient Questions;Object Detection for Using Mask in COVID-19 Pandemic with Faster R_CNN Inception V2 Algorithm;long Range Ultrasonic Testing System Based on Lamb Wave Method for Validation an Optimized Piezoelectric Sensor Gap Array;Design and Implementation of a Registration System with Mobile Application at Public Health Center Based on IoT Using a REstful API;fuzzy Logic Error Compensation Scheme for Eddy Current Testing Measurement on Mild steel Superficial Crack;Development of Cutting Force Measurement Instrument for Turning Tool Post Using Arduino UNO;spatial Mapping and Prediction of Diphtheria Risk in Surabaya, Indonesia, Using the Hierarchical Clustering Algorithm;internet of Things in Flood Warning System: An Overview on the Hardware Implementation;Auto Tracking Mobile Robot Navigation Based on HUE Color of image pattern.

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1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

1st International Conference on Emerging Technology in Model...

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1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

ISBN: (纸本)9789811374029

The proceedings contain 70 papers. The special focus in this conference is on Emerging Technology in Modelling and Graphics. The topics include: An Efficient Descriptor for Gait recognition Using Spatio-Temporal Cues;supervised Classification Algorithms in machine learning: A Survey and Review;breast Cancer Diagnosis Using image processing and machine learning;Using Convolutions and image processing Techniques to Segment LungsÂ from CT Data;analyzing Code-Switching Rules for Englishâ€"Hindi Code-Mixed Text;detection of Pulsars Using an Artificial Neural Network;modification of Existing Face images Based on Textual Description Through Local Geometrical Transformation;a Neural Network Framework to Generate Caption from images;remote Sensing and Advanced Encryption standard Using 256-Bit Key;grasp-Pose Prediction for Hand-Held Objects;an Automated Segmentation Approach from Colored Retinal images for Feature Extraction;a Multi-level Polygonal Approximation-Based Shape Encoding Framework for Automated Shape Retrieval;a Hand Gesture recognition Model Using Fuzzy Directional Encoding ofÂ Polygonal Approximation;signature-Based Data Reporting in Wireless Sensor Networks;wine Quality Analysis Using machine learning;generalized Smart Traffic Regulation Framework with Dynamic Adaptation and Prediction Logic Using Computer Vision;a Short Review on Applications of Big Data Analytics;a Survey of Music Recommendation Systems with a Proposed Music Recommendation System;interactive Systems for Fashion Clothing Recommendation;embedded Implementation of Early started Hybrid Denoising Technique for Medical images with Optimized Loop;stress Profile Analysis in n-FinFET Devices;epileptic Seizure recognition Using Deep Neural Network;an Annotation System to Annotate Healthcare Information from Tweets;broad Neural Network for Change Detection in Aerial images;topic Modeling for Text Classification.

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Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images 1

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1st 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with 23rd international conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Chen, Haibin Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Perception Vision Medical Technology Guangzhou China

ISBN: (数字)9783030671945

ISBN: (纸本)9783030671938

The automatic segmentation of head and neck (H&N) tumor from FDG-PET and CT images is urgently needed for radiomics. In this paper, we propose a framework to segment H&N tumor automatically by fusing information of PET and CT. In this framework, multiple 3D-Unets are trained one-by-one. The predictions and features of upstream models will be captured as additional information for the next one to further refine the segmentation. Experiments show that iterative refinements can improve the performance. We evaluated our framework on the dataset of HECKTOR2020 (The challenge of head and neck tumor segmentation) and won the 5th place with average DSC, precision and recall of 0.7241, 0.8479, 0.6701 respectively. © 2021, Springer Nature Switzerland AG.

关键词： Computerized tomography

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Hybrid Texture Features and Support Vector machine for Brain Tumor Cataloguing in MRI images: An Inclusive Investigation

Hybrid Texture Features and Support Vector Machine for Brain...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Neelesh Singh Josphin Selsia A Varun Ojha Shakeela Banu C Santosh Kumar P. K. Miniappan Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka India AIDS Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Worldwide, cancer kills more people than any other disease, and it will overtake all others as the top killer in the coming decades. The most effective ways to reduce death from brain cancer have been shown to be early identification and treatment. Recent advances in soft computing and image processing have substantially improved the understanding of medical pictures, which in turn has helped in early diagnosis. The absence of effective treatments, very high tumor heterogeneity, and low survival rates are hallmarks of GBM. Plus, there aren't a ton of MR pictures. It takes a lot of time and has a number of drawbacks to manually analyze these photographs. Maintaining a laser-like focus during the categorization process, which in turn increases the false hit rate, is also very taxing. For this reason, CAD has great promise as an automated approach for MR image analysis. Improving the accuracy of brain tumor categorization using routinely acquired T1 post-contrast MR imaging is the primary goal of this paper, which aims to develop, deploy, and assess a pattern recognition system for this purpose. Automatic brain tumor identification in MRI scans utilizing texture features extracted from the scans using a HTF and SVM is the primary focus of the first piece of research. Various assessment metrics are used to verify the experimental outcomes, including sensitivity, specificity, accuracy, matching index, overlap fraction, and extra fraction. In terms of classification accuracy, HTF with Achieving the greatest accuracy of 98% with GLCM-based vectors of features and 96%, SVM continuously showed the best performance across both conclusion tables When compared with other classifiers, HTF with SVM has a lower error in classification.

关键词： Support vector machines Location awareness Accuracy Magnetic resonance imaging Transfer learning Brain tumors Feature extraction Vectors Convolutional neural networks Medical diagnostic imaging

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Identifying and Analyzing Data Model Requirements and Technology Potentials of machine learning Systems in the Manufacturing Industry of the Future 61

Identifying and Analyzing Data Model Requirements and Techno...

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61st international Scientific conference on Information Technology and Management Science of Riga-Technical-University (ITMS)

作者： Schuh, Gunter Scholz, Paul Leich, Thomas May, Richard Rhein Westfal TH Aachen Lab Machine Tools & Prod Engn WZL Aachen Germany Rhein Westfal TH Aachen Fraunhofer Inst Prod Technol IPT Aachen Germany Harz Univ Appl Sci Dept Automat & Comp Sci Wernigerode Germany

ISBN: (纸本)9781728191058

Although machine learning (ML) methods have already been well described in science, the transfer into manufacturing business practice is only slowly taking place. One of the reasons is that current research is lacking a comprehensive analysis of working ML methods and their characteristics. Therefore, this paper systematically analyzes successfully implemented ML solutions to facilitate the design process for future machine learning systems (MLS) implementations in manufacturing companies. First, a systematic literature review based on 18 scientific publications is conducted to confirm the lacks assumed. Second, 15 MLS approaches are analyzed based on a technology framework to solve the shortcomings identified and extract further findings. In total, we identified two general MLS design patterns. Furthermore, we extracted seven suitable data and data model requirements as well as technology potentials. The results show that theory-based ML approaches are often based on linear methods requiring low-dimensional data, e.g. in image recognition. This points towards the hypothesis, that the application of non-linear ML methods processing high-dimensional data could increase the number of possible use cases. Thus, further high technology potentials regarding the application of MLS in the manufacturing industry would arise.

关键词： machine learning machine learning systems design pattern data model requirements technology potentials manufacturing technology framework

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Analysis of Accuracy and Timing in Decision-Making Tasks 1st

Analysis of Accuracy and Timing in Decision-Making Tasks

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1st international conference on Bioengineering and Biomedical Signal and image processing, BIOMESIP 2021

作者： Georgieva, Olga Bocheva, Nadejda stefanova, Miroslava Genova, Bilyana Sofia University "St. Kliment Ohridski" Sofia1164 Bulgaria Institute of Neurobiology Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783030881627

The present study investigates the separation abilities by age and gender based on raw data of two-alternative force choice decision-making task in visuo-motor experiment. The applied methodology is based on machine learning procedure for finding, assessing, and interpreting existing dependencies in interested data spaces. The procedure applies fuzzy cluster analysis to discrimate the biosignal data of the visual task where the location of the pattern center is determined by form cues, motion cues, or by their combination. The obtained grouping results are assessed according to the participants’ age and gender. Further, these results are compared against the results obtained of statistical parameters data of a hierarchical drift-diffusion model (HDDM) processed by the same machine learning methodology. Differences in the subjects’ capabilities to perform the visuo-motor task are summarized. It was found that age groups could be recognized with similar success by both raw and HDDM data clustering analyses. Between factors analysis strongly underlines the informativity of the reaction time. Dynamic conditions are better performed for age distinction in both cases. However, the gender is better recognizable in HDDM data space. The group of young people is characterized by low reaction time and middle value of accuracy in their responce, whereas the reverse is valid for the middle-aged participants. © 2021, Springer Nature Switzerland AG.

关键词： Decision making

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Audio Cochleogram with Analysis and Synthesis Banks Using 1D Convolutional Networks

Audio Cochleogram with Analysis and Synthesis Banks Using 1D...

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Artificial Intelligence and Data Analytics (CAIDA), international conference on

作者： Elias Nemer Cirrus Logic Irvine CA USA

The following topics are dealt with: learning (artificial intelligence); image classification; feature extraction; neural nets; diseases; pattern classification; medical image processing; patient diagnosis; traffic en... 详细信息

ISBN: (纸本)9781665430418

关键词： Training Time-frequency analysis Data analysis Convolution Neural networks Loading Buildings

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Efficient statistical learning Framework with Applications to Human Activity and Facial Expression recognition 5

Efficient Statistical Learning Framework with Applications t...

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5th international conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Najar, Fatma Bourouis, Sami Alshar'e, Marwan Alroobaea, Roobaea Bouguila, Nizar Al Badi, Ali H. Channoufi, Ines Univ Tunis El Manar ENIT Lab RISC Robot Informat & Syst Complexes Tunis 1002 Tunisia Gulf Coll Fac Comp Sci Muscat Oman Gulf Coll Ctr Postgrad Studies & Res Muscat Oman Univ Tunis El Manar ENIT Lab LR SITI Signal Image & Technol Informat Tunis 1002 Tunisia Taif Univ At Taif Saudi Arabia Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ H3G 1T7 Canada

ISBN: (纸本)9781728175133

In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. The multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model's parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.

关键词： machine learning Multivariate Generalized Gaussian mixture models Bayesian learning Face expression recognition Human action recognition MCMC

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A 3D U-Net-Based Approach for Intracranial Aneurysm Detection 22

A 3D U-Net-Based Approach for Intracranial Aneurysm Detectio...

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Proceedings of the 2022 11th international conference on Computing and pattern recognition

作者： Tianyu Zhu Xinfeng Zhang Xiaomin Liu Xiangsheng Li Maoshen Jia Xiaoxia Chang Yuan Meng School of Information and Communication Engineering Beijing University of Technology China Medical Imaging Department Air Force Medical Center PLA China Graduate School Hebei North University China

ISBN: (纸本)9781450397056

Intracranial aneurysm refers to a neoplastic protrusion of the arterial wall caused by a localized abnormal enlargement of the cerebral artery lumen. In clinical practice, patients in the early stage of onset generally have no obvious symptoms, which is very easy to miss diagnosis. In medicine, methods such as MRA, CTA and DSA can be used to display the images of blood vessels. Among them, magnetic resonance angiography (MRA) has the advantages of low cost and small damage to the human body. Which can display the images of blood vessels in the brain. The data set used herein was based on images provided by a three-dimensional time-of-flight magnetic resonance angiography system. The main contributions of this paper are as follows: (1) We improved a classic 3D U-Net model with the combination of attention gate, residual connection, and the changes of size. Which achieved automatic segmentation of aneurysms in MRA. In the detection of aneurysms with mean diameters of 6.10mm and 7.69mm, the sensitivity was 83.4% and 86.4% respectively. (2) On the basis of this sensitivity, we achieved a low false positive rate which was 0.36 FPs/case and 0.34 FPs/case *** CONCEPTS • Computing methodologies∼Computer graphics∼image manipulation∼image processing

关键词： Intracranial aneurysm detection 3D U-Net machine learning Neural networks

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