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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是11-20 订阅

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machine learning and data mining in pattern recognition - 10th international conference, MLDM 2014, Proceedings

Machine Learning and Data Mining in Pattern Recognition - 10...

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10th international conference on machine learning and data mining in pattern recognition, MLDM 2014

ISBN: (纸本)9783319089782

the proceedings contain 40 papers. the topics discussed include: efficient error setting for subspace miners;towards the efficient recovery of general multi-dimensional Bayesian network classifier;a cost-sensitive based approach for improving associative classification on imbalanced datasets;multiple regression method based on unexpandable and irreducible convex combinations;a novel approach for identifying banded patterns in zero-one data using column and row banding scores;fast pattern recognition and deep learning using multi-rooted binary decision diagrams;integrating weight with ensemble to handle changes in class distribution;a robust one-class classification model with interval-valued data based on belief functions and Minimax strategy;and monitoring distributed data streams through node clustering.

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12th international conference on machine learning and data mining in pattern recognition, MLDM 2016

12th International Conference on Machine Learning and Data M...

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12th international conference on machine learning and data mining in pattern recognition, MLDM 2016

ISBN: (纸本)9783319419190

the proceedings contain 60 papers. the special focus in this conference is on machine learning and data mining in pattern recognition. the topics include: Evolving a low price recovery strategy for distressed securities;a spectral clustering based outlier detection technique;a learning framework to improve unsupervised gene network inference;a closed frequent subgraph mining algorithm in unique edge label graphs;using glocal event alignment for comparing sequences of significantly different lengths;using support vector machines for intelligent service agents decision making;list price optimization using customized decision trees;adaptive mountain silhouette extraction from images;k-means over incomplete datasets using mean Euclidean distance;a time series model of the writing process;semantic aware Bayesian network model for actionable knowledge discovery in linked data;driving style identification with unsupervised learning;fast and memory efficient discovery of closed high-utility itemsets;fast detection of block boundaries in block-wise constant matrices;inferring censored geo-information with non-representative data;efficient mining of weighted frequent itemsets in uncertain databases;automatic detection of latent common clusters of groups in multigroup regression;content based identification of talk show videos using audio visual features;an algorithm to the incremental extraction of significant sequences from environmental sensor data;a language modeling approach for time series classification;a new strategy for case-based reasoning retrieval using classification based on association and a review on artificial intelligence based parameter forecasting for soil-water content.

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machine learning and data mining in pattern recognition - 7th international conference, MLDM 2011, Proceedings

Machine Learning and Data Mining in Pattern Recognition - 7t...

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7th international conference on machine learning and data mining, MLDM 2011

ISBN: (纸本)9783642231988

the proceedings contain 44 papers. the topics discussed include: quadratically constrained maximum a posteriori estimation for binary classifier;hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification;decisions: algebra and implementation;ACE-cost: acquisition cost efficient classifier by hybrid decision tree with local SVM leaves;informative variables selection for multi-relational supervised learning;separability of split value criterion with weighted separation gains;granular instances selection for fuzzy modeling;parameter-free anomaly detection for categorical data;GENCCS: a correlated group difference approach to contrast set mining;collective classification using heterogeneous classifiers;spherical nearest neighbor classification: application to hyperspectral data;adaptive kernel diverse density estimate for multiple instance learning;and boosting inspired process for improving AUC.

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Multi Dimensional Deep Encoding for Categorical Feature Space 24

Multi Dimensional Deep Encoding for Categorical Feature Spac...

引用

13th international conference on Computing and pattern recognition, ICCPR 2024

作者： Alamuri, Madhavi Surampudi, Bapiraju Negi, Atul School of Computer and Information Sciences University of Hyderabad Telangana State Hyderabad India Cognitive Science Lab International Institute of Information Technology Telangana State Hyderabad India

ISBN: (纸本)9798400717482

Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machine learning. It is crucial to understand how to provide categorical input to machine learning algorithms because the majority of them are developed for numerical inputs. Any machine learning algorithm's performance depends not only on the model and hyper parameters but also on the way the data is prepared for and fed into the model. With high cardinality features and categorical predictor variables, machine learning approaches that handle categorical data frequently encounter challenges. In this scenario, we investigate categorical encoding techniques, develop a taxonomy of encoding techniques, and use the machine learning algorithms KNN, MLP, SVM, and ELM and make a comparative study. We suggest a method of hybrid encoding using CNN that gives better performance results. In this paper, we propose the machine learning strategy employing multi-dimensional encodings with CNN, which outperforms other machine learning approaches on majority datasets. It is clear that CNN's performance in learning categorical features has been much enhanced by the use of multi-dimensional encoded input vectors. In our suggested method for supervised and unsupervised encoding, we have used four distinct encodings. these results encourage us to further study the outcomes of different hybrid combinations of encodings for categorical feature classification. © 2024 Copyright held by the owner/author(s).

关键词： Categorical data

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

ISBN: (纸本)9789819780891

the proceedings contain 45 papers. the special focus in this conference is on Computational Intelligence in pattern recognition. the topics include: Vietnamese Text Summarization Using Transformer Approach;DQN Trader: Reinforcement learning for Automated Trading;indirect Feature Extraction for Efficient Fingerprint recognition;A Novel and Intelligent Neuroimage Analysis for Tumor Classification with thermo-Color Segmentation Using VGG-16;a Deep Feature Ensemble Methodology for 2D Biomedical Image Classification;detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit;convolutional Neural Network Segmentation for Satellite Imagery data to Identify Landforms Using U-Net Architecture;performance Comparison of Different machine learning Classifiers for Diabetes Prediction;Intelligent Anti-tamper Systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT;contrasting Various Cryptographic Algorithms for Storage and Cloud Computing Services;A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits;CrimeScene2Graph: Generating Scene Graphs from Crime Scene Descriptions Using BERT NER;a Squeeze and Excitation Framework Utilizing ResNet-152 for Alzheimer’s Disease Dementia Classification;Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs);efficient Detection of Fake Twitter Profiles on Social Media through Hybrid Deep learning Model;religious Sentiment Analysis and Detection on Social Media;Visualizing NASDAQ Composite Index Historical data Using Tableau;typeface Identification for English Using Convolutional Neural Networks;Adaptive Ensemble of ML Regressors and LSTM for Stock Price Prediction;PolyNMT: Neural machine Translation Model with Seq2Seq Encoder-Decoder System;a Rectangular Slotted Sierpinski Fractal Bowtie Antenna for Multiband Wireless Communications;enhancing Cardiac Health: machine learning in Coronary Artery Disease Predi

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Secured Challan Filling Automation using OCR Technology and machine learning 6

Secured Challan Filling Automation using OCR Technology and ...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sivakumar, Adhithya Mohan, Karthick Elango, Gunasekar Jayakumar, Nithya Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331522667

the Automated Challan Filling System integrates several advanced technologies, such as Optical Character recognition (OCR), machine learning (ML), and voice assistance to modernize the bank challan process. Such technologies reduce manual entries and errors in the physical forms by automatically extracting key data from *** helps validate the accuracy of data in addition to detecting false cases for enhanced security. the option of multiple authentication methods such as PIN, OTP, and biometric avenues ensures secure, personalized transactions. With a voice assistant that guides users step-by-step, it makes the system user-friendly, especially to literate and non-literate and low-tech users. Multilingual and with a user-friendly interface, the system enhances accessibility, efficiency, and transaction speed in addressing the disadvantaged users’ challenges in accessing traditional banking processes. © 2025 IEEE.

关键词： Optical character recognition

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Alzheimer’s Disease Detection using Various machine learning Algorithms 6

Alzheimer’s Disease Detection using Various Machine Learnin...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Patel, Parthvi thakkar, Krishi Valani, Prince Patel, Krishna Goel, Parth Vadhavana, Vaishali Anand Changa India

ISBN: (纸本)9798331522667

Alzheimer’s disease (AD) is a brain-degenerative mental illness defined by growing forgetfulness. It also includes the loss of cognitive and behavioural characteristics. this research aims at developing a machine learning based tool that can work on early recognition of AD. this paper intends to develop an accurate, non-invasive machine learning model in detecting AD. Classification for AD has been done using behavioural, clinical, and lifestyle characteristics. the idea is to get such patterns from the data in a model, which will help in early detection of AD. For the detection of the AD dataset has been taken from Kaggle. this paper delivers an in-depth comparison of various algorithms, including Support Vector machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Artificial Neural Network (ANN), with the goal of improving the detection of Alzheimer's disease (AD). the algorithms were evaluated using various performance metrics, such as F1 score, Precision, Accuracy, and Recall. the results indicate that the Random Forest algorithm attained the highest Accuracy, reaching 95%. © 2025 IEEE.

关键词： Adversarial machine learning

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Boosting Software Quality: A machine learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. this work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. the objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. these indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. these algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. this approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： data reliability

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A Survey on Exploring Advances and Challenges in Social Spam Detection through machine learning Approaches 6th

A Survey on Exploring Advances and Challenges in Social Spam...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Reddy, M. Sivapratap Reddy, P. Phanindra Kumar Research Scholar JNTUA Anantapur India Department of Artificial Intelligence & Data Science Annamacharya Institute of Technology and Sciences AP Rajampet India

ISBN: (纸本)9789819780891

As online social platforms continue to proliferate, the menace of social spam has become a pressing concern, necessitating effective detection mechanisms. this chapter provides a comprehensive review of the current landscape of Social Spam Detection (SSD), focusing on the pivotal role played by machine learning approaches. We delve into the advancements and challenges associated with leveraging machine learning algorithms for identifying and mitigating social spam. the review encompasses a thorough examination of various techniques, methodologies, and datasets employed in recent studies. Additionally, we highlight emerging trends, gaps in existing research, and potential avenues for future exploration in the dynamic realm of SSD. this review serves as a valuable resource for researchers, practitioners, and industry stakeholders seeking insights into the evolving field of machine (M) learning-driven SSD. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings

6th International Conference on Mobile Computing and Sustain...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

ISBN: (纸本)9798331522667

the proceedings contain 289 papers. the topics discussed include: a comparative analysis of machine learning techniques for the detection and classification of lung cancer;empirical examination of mobile ad hoc routing protocol on wireless sensor network;optimizing performance and cost efficiency in AI-driven cloud infrastructures: a multi-objective approach;healthcare data protection and biometric access on cloud platforms;network anomaly detection using similarity-aware ensemble learning;a review of advancements in NLP and speech recognition for enhanced operating systems;comparative analysis of edge-based and cloud-based intrusion detection systems: a systematic literature review;utilization of virtual reality technology for enhancement of e-commerce experience;and breast cancer detection using ultrasound images based and convolutional neural network.

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