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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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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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Skin Lesion Detection and Classification from Dermoscopic Images Using a Hybrid Network 15th

Skin Lesion Detection and Classification from Dermoscopic Im...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Murmu, Anita Dash, Yajnaseni Abraham, Ajith School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

Classification of skin abnormalities in dermatology plays an important role in the detection and diagnosis of various skin diseases such as melanoma. However, accurately classifying skin cancer remains difficult due to imbalanced classes, variances within classes, lack of training data, and failure to focus on semantically significant lesion parts. To address these issues, the proposed novel Deep-Convolutional Network (DCN) with a machine-Learning (ML)-based SVM technique has been used to categorize skin cancer. To prevent overfitting, the DCN-SVM method is used as a classifier. Furthermore, the classification model applies the expert framework for learning based on domain functions. The effectiveness of test results using a UNet-MobileNet with 10,000 training images (HAM10000) dataset in seven distinct human-machine classes. The results demonstrate the effectiveness of this method in classifying dermatological diseases, demonstrating its potential to improve dermatological care and diagnostic accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Cloud-Enhanced machine Learning: Exploring the Synergy for Intelligent Automation and Optimization 15th

Cloud-Enhanced Machine Learning: Exploring the Synergy for I...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Choudhary, Rakesh Kumar Pandey, Shriyansh School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810855

The convergence of cloud computing and machine learning (ML) is evolving in the new era of technological innovation. This paper elaborates on the symbiotic relationship between these two fields, highlighting how cloud infrastructure enables scalable, flexible, and cost-efficient ML development and deployment. At the same time, ML enhances cloud services through intelligent automation, optimization, and data-driven insights. This study of synergy presents transformative and revolutionizing opportunities in the industry and opens doors to evolved ML models with the help of cloud environments. Additionally, the paper mentions best practices, challenges, and future trends in integrating ML and cloud technologies, providing a comprehensive guide for students, professionals, and researchers. Also, the pre-existing research on the subject has been reviewed thoroughly and summarized, making this study a guide for researchers. Through performance analyses, we illustrate the practical benefits and certain pitfalls of this integration, highlighting valuable insights for leveraging the full potential of ML and cloud synergy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intelligent computing

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Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial intelligence 24

Research on Federated Learning's Contribution to Trustworthy...

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3rd international Symposium on Robotics, Artificial intelligence and Information Engineering, RAIIE 2024

作者： Huang, Shijia Liang, Yaxin Shen, Fangzhou Gao, Fanru Fu Foundation School of Engineering and Applied Science Columbia University New York10027 United States Viterbi School of Engineering Electrical and Electronic Engineering University of Southern California Los Angeles90007 United States Department of Mathematics and Statistics San Jose State University San Jose95129 United States Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9798400718311

As an emerging machine learning paradigm, federated learning allows multiple partners to work together to complete specific machine learning tasks while ensuring that the original data of participants is not leaked. This method effectively solves the problem of"data islands" in traditional centralized machine learning, and has been widely used in many fields such as smart healthcare, smart cities, and finance. Federated learning (FL), as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism for sharing learning experiences without directly exchanging data. Federated learning, as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism to share learning experiences without the need to directly exchange data. This study explores the contribution of federated learning in building trustworthy AI systems, reducing the risk of data leakage by designing privacy protection mechanisms and enhancing data security measures, and enhancing the generalizability and robustness of models by using multi-party participation in the updating and validation of models. In addition, we have introduced a new way to assess the fairness of the model, aiming to achieve more fair and unbiased decision-making in a variety of use cases. In experimental analysis, our results show that by implementing these strategies, federated learning can significantly improve the transparency and fairness of AI systems, while achieving high model performance and low privacy risk on multiple cross-domain datasets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Parallel O(log(n) ) Computation of the Adjacency of Connected Components 3rd

Parallel O(log(n) ) Computation of the Adjacency of Conne...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Banaeyan, Majid Kropatsch, Walter G. TU Wien Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9783031092817

Connected Component Labeling (CCL) is a fundamental task in pattern recognition and image processing algorithms. It groups the pixels into regions, such that adjacent pixels have the same label while pixels belonging to distinct regions have different labels. The common linear-time raster scan CCL techniques have a complexity of O(image- size) in a 2D binary image. To speed up the procedure of the CCL, the paper proposes a new irregular graph pyramid. To construct this pyramid, we use a new formalism [1] that introduces an order of the pixels in the base grid to detect the redundant edges through the hierarchical structure. These redundant edges, unlike the usual methods of constructing the irregular pyramid, are removed before contracting the edges. This not only simplifies the construction processes but may decrease memory consumption by approximately half. To perform the CCL task efficiently the proposed parallel algorithm reduces the complexity to O(log(n) ) where the n is the diameter of the largest connected component in the image. In addition, using an efficient combinatorial structure the topological properties of the connected components including adjacency of CCs, multi-boundaries and inclusions are preserved. Finally, the mathematical proofs provide fully parallel implementations and lead to efficient results in comparison with the state-of-the-art. © 2022, Springer Nature Switzerland AG.

关键词： Pixels

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A Case Study for Removing Cardiac Artifacts from Non-Invasive Electrospinography Signals 3

A Case Study for Removing Cardiac Artifacts from Non-Invasiv...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial intelligence and Neural Engineering, MetroXRAINE 2024

作者： Gracia, Desirée I. Polo-Hortigüela, Cristina Bayón-Calatayud, Manuel Iáñez, Eduardo Ortiz, Mario Azorín, José M. Central University Hospital of Asturias - Huca Oviedo Spain Miguel Hernández University of Elche Brain-Machine Interface Systems Lab Elche Spain Engineering Research Institute of Elche - I3E Miguel Hernández University of Elche Elche Spain Valencian Graduated School and Research Network of Artificial Intelligence - VaIGRAI Valencia Spain

ISBN: (纸本)9798350378009

The recording of spinal cord neuronal activity through high-density surface electromyography is susceptible to electrocardiographic artifacts, needed to be removed before further analysis. In this study, seven algorithms were compared: Template Subtraction, Adaptive Template Subtraction, High-pass filter with 200 Hz cutoff frequency, Adaptive Template Sub-traction and subsequent high-pass filtering, Stationary Wavelet Transform, Second order Extended Kalman Smoother, and Empirical Mode Decomposition. These algorithms were assessed using five performance indicators: Relative Error, Signal-to-Noise Ratio, Cross-Correlation, Spectral Distortion, and Kurtosis Ratio. Among the algorithms, Adaptive Template Subtraction demon-strated superior efficacy in denoising cardiac waves, regardless of the electrocardiographic artifact morphology. Despite potential limitations in fully preserving low-frequency components, its prowess in signal preservation renders it a valuable asset for future research endeavors spanning a wide frequency spectrum. © 2024 IEEE.

关键词： High pass filters

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Optimizing Energy Efficiency and Network Performance in Smart Street Lighting Systems Using LoRaWAN Technology 3

Optimizing Energy Efficiency and Network Performance in Smar...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sivakumar, S. Jayanth, R. Mughesh Kumar, N.R. Srm Institute of Science and Technology Dept. of Comp. Sci. and Engineering Specialization in Artificial Intelligence and Machine Learning Tamil Nadu Tiruchirapalli India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

This paper presents a groundbreaking Smart Street Lighting System utilizing LoRaWAN technology to overcome the challenges of scalability, energy inefficiency, and communication limitations inherent in older smart street technologies. The system employs seamless, long-range communication for real-time monitoring, adapting to dynamic urban environments, and achieving a balance between energy conservation and public safety. Advanced components, including a Passive Infrared (PIR) sensor and microwave motion sensor, enhance object detection and urban safety. A unique feature of the system is its ability to automatically dim the street lights to 1/4th intensity when no object is detected, while instantly adjusting to full brightness upon object detection. Furthermore, the incorporation of a response factor ensures accurate detection and swift response to communication disruptions. Notably, the system achieves an outstanding 70% energy savings through optimized lighting and dynamic remote control, significantly contributing to the evolution of intelligent and energy-efficient street lighting solutions in smart city infrastructures. © 2024 IEEE.

关键词： Smart city

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machine Learning-Based Fault Detection in VLSI Circuits 3

Machine Learning-Based Fault Detection in VLSI Circuits

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3rd IEEE international conference on Device intelligence, Computing and Communication Technologies, DICCT 2025

作者： Saha, Chandrama Siddiqui, Bushra Bhoi, Harsh Sudhanya, P. SRM Insttitute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543358

This paper addresses the growing complexity of electronic devices and the need for efficient fault detection in VLSI circuits. Traditional fault detection methods are often inaccurate and time-consuming, motivating the use of artificial intelligence techniques. The paper proposes a deep learning-based model for detecting stuck-at faults in digital VLSI circuits. Our model leverages an Autoencoder algorithm to compress features and mitigate the search space explosion problem, achieving a range of accuracies of 75.3% to 99.9% for various ISCAS'85 benchmark circuits. The model incorporates ATPG tools such as the ATALANTA tool for generating test patterns and corresponding fault-free responses using the FAN algorithm. These test patterns are preprocessed to be used as a dataset where the Autoencoder algorithm classifies faulty and non-faulty responses based on their reconstruction error. These approaches demonstrate the potential of deep learning in improving the efficiency and accuracy of fault detection in digital VLSI circuits. © 2025 IEEE.

关键词： Automatic test pattern generation

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Advanced Notebook: A tool for enhanced Management of machine Learning models and procedures in the Healthcare Domain 3

Advanced Notebook: A tool for enhanced Management of Machine...

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3rd international conference on Applied Mathematics and Computer Science, ICAMCS 2023

作者： Danciu, Gabriel Nicolae, Irina E. Ilie, Iulia Septimiu Nechifor, C. Data Analytics and Artificial Intelligence Siemens Technology Configuration Technologies Braov Romania

ISBN: (纸本)9798350324266

The significant improvements in machine learning today necessitate procedures and frameworks that are simple to configure by data scientists, ML researchers, and non-DevOps engineers, allowing them to rapidly develop ML models and address real-world challenges. This article provides an in-depth analysis of the architectural design of a holistic system that addresses the management of machine learning processes and deployment procedures. The proposed system has been designed to provide seamless configuration options and smooth integration with other solutions by enabling the rapid development of ML models. Its versatility makes it applicable in various scenarios, enhancing its usability across different domains. The effectiveness of the proposed solution has been validated through practical implementation in two distinct machine ML scenarios, both of which were integrated into European Union (EU) projects centered around the healthcare domain. The findings from these specific use cases have been documented and are presented as part of the empirical evidence supporting the viability and success of the solution. © 2023 IEEE.

关键词： machine learning

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Capturing the Essence: Exploring Cutting-Edge Feature Extraction Methods in Digital Image Processing

Capturing the Essence: Exploring Cutting-Edge Feature Extrac...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Prabu S Natarajan B Roshan George S. Sujitha P Divya Bharathi S. Rajeswari School of Computer Science and Engineering VIT University Chennai India Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Roever Engineering College Tamilnadu India Department of Artificial intelligence & Data science Panimalar Engineering College Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Tiruchirappalli Tamilnadu

Feature Extraction (FE) plays a vital role in the fields of image processing, machine learning and pattern recognition. Binarization, resizing, thresholding and normalization are the initial steps applied before extracting features from an image. FE methods are applied to images to extract the most essential features that help in detection, recognition and classification tasks. FE techniques are used in numerous applications, such as object recognition, character recognition and pattern recognition. This research study presents a comprehensive research study of different image FE techniques, including SURF, BRISK, PCA and SIFT, which have been employed for various applications in recent times. This research study explains each method's fundamental concepts and pros and cons and demonstrates in what scenario which feature extraction technique will be better. This research work also presents a summary table of FE techniques used in various applications with their accuracy percentage.

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