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Adaptive Sampling for Weighted Log-Rank Survival Trees Boosting 10th

Adaptive Sampling for Weighted Log-Rank Survival Trees Boost...

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11th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Vasilev, Iulii Petrovskiy, Mikhail Mashechkin, Igor Lomonosov Moscow State Univ Comp Sci Dept MSU Moscow 119899 Russia

ISBN: (纸本)9783031245374;9783031245381

the field of survival analysis is devoted to predicting the probability and time of the occurrence of an event. the global problem is to predict the event probability over time. It has applications in healthcare, credit scoring, etc. the most widely used method for assessing the covariate impacts on survival is the Cox proportional hazards approach. However, the assumption of non-overlapping survival functions usually does not hold on real data, and the linear dependence on features limits the quality of the method. there are tree-based machine learning methods to solve these problems. Usually, to evaluate the difference between the samples, it used the log-rank test. Obtained survival decision tree models also have strong interpretability, they can evaluate the importance of predictors, but they demonstrate inferior performance in comparison to Cox proportional hazards models. To overcome these issues, this paper proposes a new boosting of the survival decision tree model that uses adaptive sampling and weighted log-rank split criteria. the model iteratively corrects an error in the ensemble. Each decision tree is trained on a sample, taking into account the weights of observations and subsequently adjusting the probabilities of getting into the next sample. We introduce an experimental comparison of the proposed adaptive boosting method against Cox proportional hazard and widely used survival trees and their ensembles: random forest and gradient boosting. Experiments on healthcare datasets show that our model outperforms the state-of-the-art survival models in terms of the following metrics: the concordance index, the integrated Brier score, and the integrated AUC.

关键词： machine learning Time-to-event analysis Survival analysis Random survival forest Cox proportional hazard Gradient boosting survival analysis Adaptive boosting

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5th international conference on Computational Intelligence, Cyber Security and Computational Models, ICC3 2021

5th International Conference on Computational Intelligence, ...

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5th international conference on Computational Intelligence, Cyber Security and Computational Models, ICC3 2021

ISBN: (纸本)9783031155550

the proceedings contain 14 papers. the special focus in this conference is on Computational Intelligence, Cyber Security and Computational Models. the topics include: Program Architecture for Structural Health Monitoring of Pamban Bridge;masking Based De Trop Noise Exclusion and Image Inpainting Instance Restoration;Extending machine learning Techniques Using Multi-level Approach to Detect and Classify Anomalies in a Network on UNSW-NB15 dataset;evaluation and Recommendation of Fertility Hospitals through Multi Criteria Decision Making and Rank Correlation;up-Link/Down-Link Availability Calculation in Cloud Using Ford Fulkerson Algorithm;machine learning in Cancer Genomics;Privacy-Preserving Blockchain-Based EHR Using ZK-Snarks;offensiveness Detection in Hinglish Code-Switched Language;preface;iris recognition Using Symmetric Graph Structure Based pattern Matching;Modelling RNA Motifs Using Matrix Insertion-Deletion System;pupil Segmentation Using Stirling’s Interpolation Based Fractional Differential Mask.

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A Review - ML and DL Classifiers for Emotion Detection in Audio and Speech data 5

A Review - ML and DL Classifiers for Emotion Detection in Au...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Prajapati, Yogeshkumar J. Gandhi, Priyesh P. Degadwalai, Sheshang Gujarat Technol Univ Ahmadabad Gujarat India Sigma Inst Engn Vadodara India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Emotion is an inherited characteristic in all modes of communication and is a natural part of human behavior. the human brain, can sense others' expressions, feelings, and moods due to their brain knowledge, making us more reasonable and understanding. machines, on the other hand, have a hard time understanding content-based information like text, music, or video. It is a requirement of the period that machines be educated to accurately interpret emotions in order to improve understanding and minimize misunderstanding. Furthermore, audio emotion analysis has several applications in a variety of industries, such healthcare, banking, defense, and information technology. Text emotions, on the other hand, are easier to comprehend because tone and pitch are irrelevant, but in audio emotion analysis, both aspects must be considered for higher accuracy. there are also other elements that degrade accuracy, such as noise, disruption, and various gaps in transmission. Making a machine recognize the respondent's emotion is a difficult task. this review investigates the use of ML and DL algorithms to recognize emotion in speech data.

关键词： Audio emotion recognition machine learning Deep learning Neural Network Audio classification Artificial Neural Network

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Classification Rules based Breast Cancer Detection using machine learning Approach 5

Classification Rules based Breast Cancer Detection using Mac...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Mitra, Dipra Sharma, Neha Rashid, Mamoon Singh, Rajesh Amity University Computer Science & Engineering Jharkhand Ranchi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (纸本)9798350398267

One of the medical field's most researched issues is cancer diagnosis. Many researchers have concentrated on performance enhancement and achieving successful outcomes. One of the most lethal forms of cancer is breast cancer. A significant issue in cancer diagnosis research is the diagnosis of this cancer. A kind of artificial intelligence called machine learning allows a machine to grow over time. In bio informatics, machine learning is frequently employed, notably in the detection of breast cancer. supervised learning method known as K-nearest neighbors' approach, is one well-liked techniques. It's really intriguing to use the K-NN in medical diagnostics. the value of parameter 'k' & distance have a significant impact on the findings' quality. this indicates how many neighbors are in proximity. In this paper, we assess the performance of various K-NN algorithmic distances. Additionally, we investigate this distance using various 'k' parameter values and classification algorithms (the formula used to determine a sample's classification). © 2022 IEEE.

关键词： data mining

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EEG-Based Reaction Time Prediction Using Covariance Augmented 2D Convolutional Neural Network 27th

EEG-Based Reaction Time Prediction Using Covariance Augmente...

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27th international conference on pattern recognition, ICPR 2024

作者： Parekkattil, Adarsh V. Varun, Sanjeev Kumar Bollu, tharun Kumar Reddy Electronics and Communication Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India

ISBN: (纸本)9783031783975

Drowsy driving emerges as a major factor contributing to traffic accidents. Drowsiness is characterized by a feeling of tiredness and a compelling desire to sleep. It is evident through a gradual decrease in Reaction Time (RT) of the driver. Reaction Time (RT) refers to the duration taken for a person or system to respond to a given sudden unexpected stimuli or event. Accurate prediction of Reaction Time (RT) to unexpected events is crucial for enhancing safety and performance. Electroencephalogram (EEG), capturing the brain’s electrical signals, demonstrates the most substantial correlation with drowsiness. Consequently, EEG is broadly recognized as a trustworthy tool for assessing drowsiness, fatigue, and overall performance. While many studies have utilized traditional machine learning and deep learning techniques to detect drowsiness or alertness from EEG data, there has been limited research focused on accurately predicting Reaction Time (RT). In this study, we aim to forecast drivers’ reaction times using EEG data through a novel Covariance 2D CNN-LSTM framework. Here, the objective is to forecast Reaction Time (RT) based on a 5-second EEG trial that precedes it. the superiority of the proposed method was validated through regression metrics, specifically Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), and was compared against current state-of-the-art methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Deep learning in Automatic recognition of Mongolian Compound Nouns

Deep Learning in Automatic Recognition of Mongolian Compound...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ha Si Du La Yang Ding College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China School of Software and Big Data Inner Mongolia Electronic Information Vocational Technical College Hohhot China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Mongolian compound nouns often become the vehicle for the creation of new concepts and terms due to their diverse combinations and flexible expressive power. In the field of natural language shallow syntactic analysis, the recognition of Mongolian compound nouns plays an important role, and its performance will directly affect the processing effect of related research. In the current academic research, the research on automatic recognition of Mongolian compound nouns is still insufficient, especially in the automatic recognition of Mongolian compound nouns using deep learning technology, which is almost in a blank state. In this study, we firstly construct a Mongolian compound noun annotated corpus, and on the basis of the constructed annotated corpus, we propose a Mongolian compound noun automatic recognition model based on deep learning for the fact that there is no deep learning method for recognising Mongolian compound nouns at present. the model takes Conditional Random Field (CRF) as the basic framework to make full use of contextual information by considering the joint probability distribution of the whole sequence. During the model construction process, the TextCNN model is incorporated and by designing a multi-scale convolutional kernel, the model is able to effectively capture the local features of Mongolian compound nouns. the experimental results show that the automatic Mongolian compound noun recognition model based on deep learning proposed in this study performs well on the Mongolian compound noun recognition task, with an F1 value of 93.40%.

关键词： Deep learning Natural languages Syntactics Conditional random fields Probability distribution pattern recognition Compounds Kernel Context modeling

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Enhancing Security in Cloud Registration with Multi Dimensional Features Fusion 5

Enhancing Security in Cloud Registration with Multi Dimensio...

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5th international conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Mutha, Rakhi Reddy Gantla, Harish Pandey, Sunil Kr Sayeed Ahmad, Sayed Ahuja, Vandana Bhattacherjee, Abhishek Mamodiya, Udit Dept. of Information Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Information Technology Institute of Technology & Science Uttar Pradesh Ghaziabad India School of Engineering and Computing De Montfort University Dubai United Arab Emirates Dept. of CSE Swami Vivekanand Institute of Engineering and Technology Punjab Ramnagar India Department of Computer Science Engineering Lovely Professional University Punjab India Poornima University Jaipur India

ISBN: (纸本)9798400709418

data mining uses artificial intelligence and other methods to extract hidden potential information from large data sets, which provides an effective way to obtain valuable knowledge from a large amount of information. data mining is also ubiquitous in the process of using deep learning to solve point cloud registration tasks. Global feature extraction and rigid body transformation estimation are two critical stages of uncorrelated point cloud registration. mining the rich information hidden in the two stages is one of the essential tasks of point cloud registration. However, the recently proposed methods tend to ignore low-dimensional local features when extracting global features, resulting in the loss of a large amount of point cloud information, which makes the accuracy of solving transformation parameters in the subsequent rigid body transformation estimation stage unsatisfactory. In this paper, a feature mining network based on multi-dimensional information fusion is proposed, which thoroughly mines the high-dimensional global and low-dimensional local information in the point cloud, and effectively makes up for the lack of local features in the worldwide feature extraction stage of point cloud registration. Dual quaternions are used to estimate pose in the rigid body transformation estimation stage, which can simultaneously represent rotation and translation in a standard frame, providing a compact and accurate representation for pose estimation. Finally, extensive experiments on the ModelNet40 dataset show that compared with the existing state-of-the-art point cloud registration methods without corresponding points, the proposed method can achieve higher accuracy and is more robust to noise. © 2023 ACM.

关键词： data mining

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Unified Smoke and Fire Detection in An Evolutionary Framework with Self-Supervised Progressive data Augment 5

Unified Smoke and Fire Detection in An Evolutionary Framewor...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Zhang, Hang Yang, Su Wang, Hongyong Li, Jie Liu, Hui Fudan University School of Computer Science and Technology Shanghai China State Grid Beijing Electric Power Company Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Few researches have studied simultaneous detection of smoke and flame accompanying fires due to their different physical natures that lead to uncertain fluid patterns. In this study, we collect a large image data set to re-label them as a multi-label image classification problem so as to identify smoke and flame simultaneously. In order to solve the generalization ability of the detection model on account of the movable fluid objects with uncertain shapes like fire and smoke, and it not compactible natures as well as the complex backgrounds with high variations, we propose a data augment method by random image stitch to deploy resizing, deforming, position variation, and background altering so as to enlarge the view of the learner. Moreover, we propose a self-learning data augment method by using the class activation map to extract the highly trustable region as new data source of positive examples to further enhance the data augment. By the mutual reinforcement between the data augment and the detection model that are performed iteratively, both modules make progress in an evolutionary manner. Experiments show that the proposed method can effectively improve the generalization performance of the model for concurrent smoke and fire detection. © 2022 IEEE.

关键词： Smoke

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Automated Detection of Poultry Diseases using Implicit pattern mining over Explicit Image Analysis

Automated Detection of Poultry Diseases using Implicit Patte...

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IEEE international conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Khushi Donthi Kuval Kiran Gowri Srinivasa Dept. of CSE PES Center for Pattern Recognition PES University Bangalore India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Early detection of poultry diseases is crucial for poultry farmers, but traditional identification methods are often slow and tedious due to limited facilities. We present a comprehensive evaluation of implicit pattern mining techniques using lightweight, CNN-based deep learning models such as, MobileNetV2, InceptionV3, and EfficientNetB5 that are amenable for implementation on edge devices. We contrast them with traditional machine learning approaches applied to analyse chicken fecal image data to classify images of healthy samples from unhealthy ones. We use model interpretation to gain insight to the patterns in the input image that the deep learning models use to make predictions. this facilitates corroborating domain understanding with model performance. this study demonstrates the viability of deploying deep learning based methods for poultry disease detection on handheld devices, highlighting its potential for deployment in the field.

关键词： Deep learning Support vector machines Accuracy machine learning Predictive models Feature extraction data mining Random forests Diseases Testing

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Research on Machining Product Quality Prediction Based on machine learning 5

Research on Machining Product Quality Prediction Based on Ma...

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2022 5th international conference on Advanced Algorithms and Control Engineering, ICAACE 2022

作者： Lan, Chen Fei, Wan School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201499 China

data empowerment has attracted widespread attention from manufacturing companies. Enterprises control the production process by mining the data generated in the processing process, so as to achieve the purpose of improving product quality. this paper mainly uses the classification algorithm in data mining to model the data in the processing process and predict the product quality. Using the accuracy of the model prediction as the evaluation standard, the ID3, CART, SVM, KNN and Adaboost algorithms are compared. the experimental results show that the Adaboost classifier is significantly better than other algorithms in the evaluation index of accuracy, so the Adaboost classifier is selected as the final classifier. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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