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Reference set based metric learning method for person reidentification against overfitting problem 3

Reference set based metric learning method for person reiden...

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3rd international conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Si, Chenhao Courant Institute of Mathematical Sciences New York University New York United States

ISBN: (纸本)9781510661240

Similarity distance measurement is an important method in data classification, data recognition and other tasks, and has a very wide range of applications in machine learning, computer vision and other fields. However, there are model overfitting problems in complex data classification identification tasks in existing metric learning model. And those problems will negatively infect the accuracy and stability of the metric models. We study on person re-identification (person re-ID) task to design a robust similarity distance metric learning model based on a novel approach of overcoming over-fitting problem. The proposed method sets up a reference set based on training sample. Using the reference set and test images to form similar sample pairs, we can optimize the distribution information and projection feature. Finally, by testing on benchmark dataset, VIPeR, the experimental results validate the effectiveness of the proposed method. It achieves the best identification rates. © 2022 SPIE.

关键词： Classification (of information)

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A systematic review of techniques, tools and applications of machine learning 3

A systematic review of techniques, tools and applications of...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Abhishek Dhankar, Abhishek Gupta, Neha Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9780738111834

machine learning is a part of Artificial Intelligence. A branch of artificial Intelligence(AI), that offers the capability to the system by learning on their own and work better from experience without human intervention. By using machine learning (ML), software applications become more precise for outcome prediction with no requirement of explicit programmed. machine learning has a fundamental concept i.e. building of algorithms for accepting input data and with the help of statistical analysis they can predict the output. Nowadays, machine learning has gained vast popularity, and its algorithms are used in every field like object detection, pattern recognition, text interpretation, and different research areas. One of the basic goals of machine learning is to teach computers to use the data to solve a particular problem. A fair range of machine-learning applications include e-mail classification training to distinguish spam from non-spam messages, fraud detection, etc. This paper aims at varied machine learning methods, algorithms, and tools required to run the machine learning projects. It also focuses on the advancements that are administered in order so that the current researchers can be benefited out of it. © 2021 IEEE.

关键词： Reinforcement learning

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A Performance Comparison on machine learning for Forecasting Heart Disease

A Performance Comparison on Machine Learning for Forecasting...

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Smart data Intelligence (ICSMDI), international conference on

作者： Kakarla Sai Bharath Anudeep Sanakkayala Abhishek Kadiyam Gudapati Pradeep Chandra Iwin Thanakumar Joseph S K. B. V. Brahma Rao Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

Heart is a vital organ in the human body. It is one of the superior organs, which receives more attention from the internal organs. According to various research studies, heart diseases were considered as the leading cause of death worldwide. The heart disease prediction techniques require more accuracy and precision to identify and forecast various disorders. Any improper disease diagnosis can cause death. Many researchers have been experimenting to develop a software system to predict heart disease using machine learning. The primary goal of this research work is to predict cardiac disease in humans using a machine learning algorithm. This study has reviewed some data mining and machine learning methodologies to perform heart disease prediction.

关键词： Heart Support vector machines Training machine learning algorithms Cardiac disease Biological systems Software systems

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Research on Performance Prediction Model of Metal Structural Materials Based on machine learning Algorithm

Research on Performance Prediction Model of Metal Structural...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Jianning Zhan Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

The traditional performance evaluation of metal materials depends on trial and error, which is expensive and time-consuming. machine learning algorithm, especially deep learning, has brought revolutionary changes to this field because of its powerful ability in data processing and pattern recognition. In this paper, a Deep Random Forest Regression Network (DRFRN) model is proposed, which combines the feature extraction ability of the deep neural network with the high robustness and interpretability of the RF algorithm to realize the accurate prediction of the properties of metal materials. In the data preprocessing stage, abundant data of metal materials are obtained from databases such as Materials Project and NIST Inter-Laboratory Materials database, and cleaned, standardized and principal component analysis are carried out to reduce the data dimension. In the model construction stage, the compound loss function is designed, and the strategy of phased training, early stop and dynamic learning rate adjustment is adopted to optimize the model performance. The experimental analysis shows that DRFRN model shows high prediction accuracy and good data fitting ability when predicting the key performance indexes of stainless steel, aluminum alloy and titanium alloy. Compared with support vector machine (SVM), decision tree (DT) and convolutional neural network (CNN) models, DRFRN model has obvious advantages in prediction accuracy, robustness and generalization ability. This study not only provides a new solution for the performance prediction of metal materials, but also provides a reference for the performance prediction in other fields.

关键词： Deep learning Support vector machines Accuracy machine learning algorithms databases Metals Predictive models Robustness Performance analysis Random forests

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3rd international conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

3rd International Conference on Recent Trends in Advanced Co...

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3rd international conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

ISBN: (纸本)9789811664472

The proceedings contain 61 papers. The special focus in this conference is on Recent Trends in Advanced Computing. The topics include: Design of Automatic Credit Card Approval System Using machine learning;Ensemble Methods with Bidirectional Feature Elimination for Prediction and Analysis of Employee Attrition Rate During COVID-19 Pandemic;comparative Investigation on Acoustic Attributes of Healthy Young Adults;predictive Policing—Are Ensemble Methods More Accurate Than Regression Methods?;A Fast Method for Retinal Disease Classification from OCT Images Using Depthwise Separable Convolution;machine learning-Based Smart Surveillance and Intrusion Detection System for National Geographic Borders;evaluation of Propofol General Anesthesia Intravenous Algorithm for Closed-Loop Drug Delivery System;A Study on the Repercussions of the COVID-19 Pandemic in the Mental Health of the Common Public: machine learning Approach;information Retrieval Using n-grams;an Automated Decision Support Systems Miner for Intuitionistic Trapezoidal Fuzzy Multiple Attribute Group Decision-Making Modeling with Constraint Matrix Games;disaster Mitigation Using a Peer-to-Peer Near Sound data Transfer System;ioT-Based Sheep Guarding System in Indian Scenario;comparative Analysis of Wireless Communication Technologies for IoT Applications;ioT-Based Auto-Disinfectant Sprinkler System for Large Enclosed Space;Implementation of Pupil Dilation in AI-Based Emotion recognition;Compassion Detection from Text: A Comparative Analysis Using BERT, ULMFiT and DeepMoji;a Review: Reversible Information Hiding and Bio-Inspired Optimization;Person Re-identification Using Deep learning with Mask-RCNN;hand Signs recognition from Cellphone Camera Captured Images for Deaf-Mute Persons;Efficient Algorithm for CSP Selection Based on Three-Level Architecture;identifying Mood in Music Using Deep learning;human Emotion Detection Through Hybrid Approach.

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EMG-based Gesture recognition using Extreme learning machine

EMG-based Gesture Recognition using Extreme Learning Machine

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international Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Tanishqa Tyagi Anukul Pandey Kaustubh Ranjan Singh Department of Electronics and Communication Engineering Delhi Technological University (formerly Delhi College of Engineering) Delhi India

Electromyography (EMG) signals are crucial data to track muscle activities making them a key point for design of prosthetic devices. In order to classify the finger movements in upper limb prothesis, a recognition method based on an extreme learning machine (ELM) is studied in this paper. The target to be recognized is constructed by grouping two kinds of hand gestures, individual finger movements, and combined finger movements. The recognition feature matrix is established by decomposing the windowed signal using wavelet transform and extracting conventional time-domain features from that. These features are then fed to three different classifiers for recognition of binary class. Currently, the best accuracy achieved using ELM is above 95%, outperforming Support Vector machine (SVM) and Fine Trees in this study

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LFM radar signal recognition with different SNR based on deep learning 23

LFM radar signal recognition with different SNR based on dee...

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Proceedings of the 2023 3rd international conference on Big data, Artificial Intelligence and Risk Management

作者： Hongyan Wang Tangchun Liu Rui Zhou Xue Hao Air Force Aviation University China Unit 95507 of the PLA China

ISBN: (纸本)9798400716478

Radar signal sorting and recognition is the key problem of reconnaissance system. In the field of radar signal modulation pattern recognition, the recognition method based on deep learning is still exploring. In this paper, the problem of radar signal intelligent sorting and recognition under complex electromagnetic environment is studied. Firstly, the signal is simulated and recognized under ideal conditions, then the recognition degree of the signal is analyzed under different SNR, and the recognition rate of different conditions is compared by using the accuracy recognition software. This paper presents a feasible scheme for LFM modulated radar signal recognition by using deep learning in complex environment, and demonstrates it.

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Deep learning approach for MI detection using SVM-based classifier

Deep learning approach for MI detection using SVM-based clas...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Priyanka Bera Dept. of Electronics & Communication Engg GLA University Mathura India

Incorporation of automated electrocardiogram (ECG) analysis techniques in home monitoring applications can ensure early detection of myocardial infarction (MI), thus reducing the risk of mortality. The computerized identification and tracking of irregularities from multi-lead electrocardiograms (MECGs) is a common pattern recognition issue that has been studied in recent years. Ubiquity and feature minimization are the key challenges. The main objective of the proposed study is to demonstrate increased classification precision for myocardial infarction (MI) identification from MECG by fusing a support vector machine (SVM) with a deep autoencoder (DAE). The validation of the suggested method was conducted with 100 numbers of ECG signal/data from Physionet, including three major classes (anterior, inferior and posterior) of myocardial infarction (MI). Both the normal and MI ECG beats were fed to feature extraction tool as input and a minimized set of 60 output attributes (/features) was fed to the binary classifier. This proposed classification method used ten-fold cross validation. The proposed classification technique achieved 97.3% and 97.12% of average sensitivity and positive predictivity respectively. The result of the proposed method is compared with other existing classification technique in terms of features and the sensitivity and data length.

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A Brief Review of Unsupervised learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems

A Brief Review of Unsupervised Learning Algorithms for Zero-...

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Computing and machine Intelligence (ICMI), international conference on

作者： Sunkanmi Oluwadare Zag ElSayed Oluwaseun Adekoya Department of Information Technology University of Cincinnati Ohio United States of America Department of Mechanical Engineering University of Cincinnati Ohio United States of America

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This research paper presents a brief review of ten popular unsupervised algorithms widely utilized in pattern recognition publications. The algorithms are assessed based on their popularity, strengths, limitations, and resource require-ments. Considering these factors, we propose two most-preferred algorithms suitable for adoption in IDS (Intrusion Detection Systems) to address the problems associated with Zero Day exploits or attacks. Our review of the surveyed algorithms facilitated the recommendation of specific algorithms that can enhance IDS capabilities in detecting and mitigating Zero-Day attacks and anomalous intrusion attempts. These algorithms leverage unsupervised learning techniques to overcome the limitations of traditional signature-based approaches. By incorporating these algorithms, IDS can better handle sophisticated and evolving attacks that often evade detection. In conclusion, this research provides valuable insights into the strengths, limitations, and resource requirements of popular unsupervised algorithms used in pattern recognition. It highlights the potential of adopting these algorithms in IDS systems to bolster their ability to detect and respond to Zero-Day attacks. By recommending the integration of these algorithms, we contribute to the development of intelligent IDS solutions that can adapt to dynamic threat landscapes.

关键词： Surveys Performance evaluation Reviews Heuristic algorithms Intrusion detection Training data Real-time systems

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Analysis of User's learning Styles and Academic Emotions through Web Usage mining

Analysis of User's Learning Styles and Academic Emotions thr...

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3rd IEEE international conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Rathi, Snehal Deshpande, Yogesh Nagaral, Shashidhar Narkhede, Ankita Sajwani, Radhika Takalikar, Varad Vishwakarma Univ Pune Maharashtra India Vishwakarma Inst Informat Technol Pune Maharashtra India

ISBN: (纸本)9781728185194

Digital learning environment has seen a considerate amount of growth as opposed to the traditional learning environment with a massive shift towards digitalization in day-to-day life. When teaching in person, one can get a deep understanding of how students are grasping information, and their performance can be monitored. On the other hand, this task becomes challenging when in digital learning process. Several techniques have been studied for identification of learning styles and academic emotions such as questionnaires, facial expression recognition, biometrics data, etc. This paper proposes a system which aims at identification and analysis of learning styles and emotional behaviour of users based on the techniques of web usage mining where web server logs are used for data capturing as they have many advantages over other techniques. The logs are further pre-processed and used for extraction of desired results using different machine learning methodologies like data clustering, classification to name a few. This will help to identify hidden patterns of users over the period of completion of course and ultimately improve the teaching learning efficiency.

关键词： machine learning learning styles academic emotions web server logs web usage mining

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