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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3137 条记录，以下是211-220 订阅

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Application of Chord Length Histogram to Enhance the Accuracy of Pulmonary Nodule recognition on Computed Tomograms 5

Application of Chord Length Histogram to Enhance the Accurac...

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5th international conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Smirnova, Daria S. Pchitskaya, Ekaterina I. Chukanov, Vyacheslav S. Institute of Physics and Mechanics Peter the Great St. Petersburg Polytechnic University Laboratory of Biomedical Imaging and Data Analysis St.Petersburg Russia Institute of Biomedical Systems and Biotechnology Peter the Great St. Petersburg Polytechnic University Laboratory of Biomedical Imaging and Data Analysis St.Petersburg Russia

ISBN: (纸本)9798350363739

the advancement of computed tomography (CT) technologies in pulmonary nodule medical diagnostics necessitates the development of efficient classification and segmentation methods. this study assesses the utility of the Chord Length Distribution Histogram (CLDH) in conjunction with machine learning techniques, including SVR, random forest, and gradient boosting, for comprehensive analysis of the three-dimensional (3D) geometry of pulmonary nodules. the results indicate that CLDH serves as a promising shape descriptor, providing high accuracy in classifying pulmonary nodules. Compared to neural network approaches, the CLDH-based method maintains effectiveness even when confronted with limited training data, thus optimizing computational resource utilization. the proposed methodology for characterizing pulmonary nodule morphology showcases robust data separability and achieves a classification accuracy rate of 0.8 on the LUNA16 dataset. these results emphasize the importance of integrating classical machine learning methodologies into the analysis of three-dimensional geometric structures to refine lung cancer diagnosis accuracy. © 2024 IEEE.

关键词： Computerized tomography

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Process mining Techniques for Collusion Detection in Online Exams 5th

Process Mining Techniques for Collusion Detection in Online ...

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5th international conference on Process mining (ICPM)

作者： Maldonado, Andrea Zellner, Ludwig Strickroth, Sven Seidl, thomas Ludwig Maximilians Univ Munchen Munich Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9783031561061;9783031561078

Honesty and fairness are essential. As many skills, practicing those values starts in the classroom. Whether students are examined online or on-site, only testing their knowledge righteously, educators can assess their skills and room for improvement. As online exams increase, we are provided with more suitable data for analysis. Process mining methods as anomaly detection and trace clustering techniques have been used to identify dishonest behavior in other fields, as e.g. fraud detection. In this paper, we investigate collusion detection in online exams as a process mining task. We explore trace ordering for anomaly detection (TOAD) as well as hierarchical agglomerative trace clustering (HATC). Promising preliminary results exemplify, how process mining techniques empower teachers in their decision making, while via flexible configuration of parameters, leaves the last word to them.

关键词： Collusion Detection Hierarchical Agglomerative Clustering Positive Unlabeled data Teacher in the Loop

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5th international conference on Cognitive Computing and Information Processing, CCIP 2023

5th International Conference on Cognitive Computing and Info...

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5th international conference on Cognitive Computing and Information Processing, CCIP 2023

ISBN: (纸本)9783031607240

the proceedings contain 14 papers. the special focus in this conference is on Cognitive Computing and Information Processing. the topics include: MVR Delay: Cooperative Light Weight Detection and Prevention of False Emergency Message Dissemination in VANET;Antarctic Ice Sheet Surface Mass Balance Using UAV-Based Digital Elevation Model;estimation of Glacier Velocity of Ny-Ålesund, Arctic Using Sentinel-1 Remote Sensing data;Implementation of ECPM for High-Speed Area-Efficient Processor on FPGA;comparative Analysis of machine learning Approaches to Assess Stability in Micro Grid;cloud-Based Real-Time Sign Language Detection and Voice Synthesis for Impaired Individuals Using machine learning;machine learning Approaches for Analysing Sentiment in Reviews on Massive Open Online Courses;Deep Belief Network Optimization Using PSOGAA Algorithm for Efficient Crop Recommendation;a Practical Solution Towards Development of Real-Time Face Attendance System;Ancient Kannada Handwritten Character recognition from Palm Leaf Manuscripts Using PyTesseract-OCR Technique;Anisotropic Guided Filtering and Multi-level Disintegration Method for NIR and Visible Image Fusion;an Approach for Detecting and Restoring Tampering in Digital Image Watermarking.

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Ensemble Models for Multi-class Classification of Diabetic Retinopathy 9th

Ensemble Models for Multi-class Classification of Diabetic R...

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9th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Sahayam, Subin Manasa, Tutturu Lakshmi Jayaraman, Umarani Indian Inst Informat Technol Design & Mfg Chennai 600127 Tamil Nadu India

ISBN: (纸本)9783031126994;9783031127007

Artificial intelligence-based methods help in the automatic detection and diagnosis of various diseases in the medical field. Diabetes affects 5 in 10 people in the world. It can cause diabetic retinopathy(DR) that damages the retina, leading to irreversible vision loss. DR is generally diagnosed by an Ophthalmologist using a patient's fundus image. the Ophthalmologist grades the disease based on severity. Early intervention can delay the progression of the disease. Automatic disease detection can help large-scale patient screening, early detection of diabetic retinopathy, and reduce human error. the work aims to study various ensemble learning models and their ensemble voting methods for the DR classification task. the study also focuses on the effects of data augmentation along with preprocessing. the performance of each of the models has been studied using the Aptos-blindness detection 2019 dataset.

关键词： Multi-class Classification Microaneurysm Transfer learning Ensemble learning

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505706

the proceedings contain 121 papers. the special focus in this conference is on Multimedia Technology and Enhanced learning. the topics include: Security Awareness Method of Opportunistic Network Routing Protocol Based on Deep learning and Knowledge Graph;research on Pedestrian Intrusion Detection Method in Coal Mine Based on Deep learning;personalized Recommendation Method of College Art Education Resources Based on Deep learning;global Planning Method of Village Public Space Based on Deep Neural Network;a Multi Stage data Attack Traceability Method Based on Convolutional Neural Network for Industrial Internet;machine learning Based Method for mining Anomaly Features of Network Multi Source data;data Anti-jamming Method for Ad Hoc Networks Based on machine learning Algorithm;a data Fusion Method of Information Teaching Feedback Based on Heuristic Firefly Algorithm;research on Pain Information Management System Based on Deep learning;research on database Language Query Method Based on Cloud Computing Platform;reliability Evaluation Method of Intelligent Transportation System Based on Deep learning;forecasting Method of Power Consumption Information for Power Users Based on Cloud Computing;power Consumption Behavior Analysis Method Based on Improved Clustering Algorithm of Big data Technology;anomaly Detection of Big data Based on Improved Fast Density Peak Clustering Algorithm;data Privacy Access Control Method Based on Ciphertext Policy Attribute-Based Encryption Algorithm;Evaluation Method of Enterprise Circular Economy Development Level Based on AHP Fuzzy Inference;research on Performance Evaluation of Industrial Economic Management Based on Improved machine learning;application of Big data Processing Technology in Power Consumption Information Acquisition.

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505799

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Analysis on Modulation and Demodulation Techniques for Visible Light Communication 5

Analysis on Modulation and Demodulation Techniques for Visib...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Dharshini, K. R. Priya Kalaivani, R. Sampoornam, K. P. Logamurthy, P. Ragulgandhi, S. Vishal, S. V. Anna Univ Affiliated Erode Sengunthar Engn Coll Autonomous Elect & Commun Engn Erode India Bannari Amman Inst Technol Elect & Commun Engn Sathyamangalam India Anna Univ Affiliated Nandha Engn Coll Autonomous Elect & Commun Engn Erode India

ISBN: (纸本)9798350386813;9798350386820

Visible Light Communications (VLC) is a wireless communication system for enhanced 5G and 6G high speed wireless networks. VLC is one of the most accepted varieties of short-range wireless communication. With a large number of mobile devices, high-speed data transmission is an essential one. VLC uses an intensity modulation as well as direct recognition for illumination and data transmit. VLC is a disruptive form of wireless communication that supplements radio frequency communication. Demodulation of radio signals plays an important role in VLC systems. Artificial intelligence (AI) addressed the distinctive challenges in VLC. AI techniques are explored to overcome the unique challenges in VLC, whereas significant barriers are found in practical VLC systems applied with intelligent learning approaches. machine learning (ML) illustrates potential challenges in non-linear extenuating for their natural capability to fit every sort of transfer function in VLC. Different modulation methods are studied for performing efficient visible light communication. However, the bit error rate was not minimized as well as accuracy level was not enhanced. To address these issues, different modulation and demodulation methods were reviewed.

关键词： Visible light communication intensity modulation data transmission radio frequency communication

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An Novel Approach in Malware Detection with Cryptography Based Approach Using machine learning 5

An Novel Approach in Malware Detection with Cryptography Bas...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kaur, Ishmeet Aanjankumar, S. School of Computing Science and Artificial Intelligence VIT Bhopal University Madhya Pradesh Sehore466114 India VIT Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331530013

A critical concern that arises these days is identifying and controlling the advancement of malware threats. Recognizing malware through conventional anti-malware tools has become challenging due to its constant development. the constant emergence of malware renders common malware-identifying methods less effective, necessitating the use of advanced artificial intelligence in the form of machine learning models to identify and mitigate the spread of malware. the field of artificial intelligence (AI) has resulted in major developments in technology. these advancements have had an essential influence on cryptography, where AI methodologies exhibit outstanding potential for improvising the accuracy and stability of the cryptographic system. Malware frequently uses cryptographic methods to conceal its malicious activities and hidden features from antivirus engines, making it easier for security professionals to identify and revert it. recognition of cryptographic functions in malware becomes crucial in detecting and precisely examining malicious content. Numerous efforts have been taken for tackling the existing problem, but the methodologies currently in use have major disadvantages, including cost prohibitive restrictions due to prior knowledge and failure in the achievement of results with high accuracy. this paper provides a comprehensive overview of malware detection using AI and a cryptographic approach. We utilized machine learning models like random forest and blowfish cryptography to identify malware data, achieving an accuracy of 88.24%. this model outperformed traditional machine learning models like KNN 54.13, ANN 56.82, and Gradient Boosting 68.21 in terms of accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. this paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). the employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. the research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. the model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

5th EAI International Conference on Multimedia Technology an...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

ISBN: (纸本)9783031505768

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