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

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6th international conference on Recent Innovations in Computing, ICRIC 2023

6th International Conference on Recent Innovations in Comput...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

ISBN: (纸本)9789819778614

the proceedings contain 49 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: Artificial Intelligence (AI)-Powered Intelligent Systems for Disease Prognosis: A Bibliometric Study;unleashing the Potential: Study on Artificial Intelligence Effect in Education Sector;AI-Powered Traffic Orchestration: Reinforcement learning at Traffic Intersections;a Novel Melanoma Diagnosing System Using Multi-directional pattern Extraction-Based Distributed Deep learning Model;a Review of Artificial Intelligence and machine learning for Vaccine Research;machine learning Techniques for Detection and Classification of Crop (Solanum lycopersicum) Plant Diseases Due to Pests: A Review;fake News Detection Using machine learning Classification Algorithms;Deep learning Model-Based Approach for DDoS Detection and Classification;data Analysis on Dynamic Flow Characteristics of Variable Delivery Hydraulic Axial Piston Pump;exploring Explainable machine learning in Healthcare: Closing the Predictive Accuracy and Clinical Interpretability Gap;An Enhanced Neural Network Collaborative Filtering (ENNCF) for Personalized Recommender System;diagnosis of Behavioural Disorders Using Swarm Intelligent Metaheuristic Techniques;leveraging data mining Optimization for Enhancing Safety Management in Public Security Prevention and Control Application;forecasting Criminal Activity: An Empirical Approach for Crime Rate Prediction;early Detection of Crypto-Ransomware Pre-encryption Phases: A Review;are We Ready to Investigate Robots? Issues and Challenges Involved in Robotic Forensics;unmasking the Vulnerabilities: A Deep Dive into the Security threat Landscape of Humanoid Robots;review of Cyber-Physical System-Based Security datasets for learning-Based Intrusion Detection Systems;survey and Analysis of Intrusion Detection Frameworks for Cyber-Physical Systems: A Comprehensive Study;enhancing IoT Security through Deep learning: A Comprehensive Study.

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machine learning in medical sites and healthcare 6

Machine Learning in medical sites and healthcare

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6th international conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Rai, Shreyansh Sehgal, Abhinav Gupta, Danish Jindal, Harsh Upadhyay, Ashish Kumar Mishra, Aditya Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh Grouo of Colleges Landran Mohali India

ISBN: (纸本)9798350374629

machine learning is a technique through which computer learns by using the data provided from the user and getting experience from it and then using that experience to predict new things on its own. machine learning has revolutionized the field of healthcare in recent years, and one area where it has found extensive application is in medical websites. Medical websites are essential resources for healthcare providers and patients, providing up-to-date information on medical conditions, treatments, and healthcare services. By incorporating machine learning algorithms into medical websites, healthcare providers can improve patient outcomes by enhancing the accuracy of diagnosis, optimizing treatment, and reducing the risk of medical errors. machine learning uses features like image processing, text recognition to scan reports and give summary of the report to the people. It also helps in predicting stages of diseases by scanning reports like X-Ray scans etc. and analyzing it. In this paper, we will explore the various ways in which machine learning is being used in medical websites, the benefits of this technology, and the challenges associated with its implementation. ©2024 IEEE.

关键词： Websites

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6th international conference on Recent Innovations in Computing, ICRIC 2023

6th International Conference on Recent Innovations in Comput...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

ISBN: (纸本)9789819734412

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6th international conference on Recent Innovations in Computing, ICRIC 2023

6th International Conference on Recent Innovations in Comput...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

ISBN: (纸本)9789819728381

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Text Categorization using Supervised machine learning Techniques 6

Text Categorization using Supervised Machine Learning Techni...

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6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

作者： Dawar, Ishaan Kumar, Narendra Negi, Sakshi Pathan, Sayeedakhanum Layek, Shirshendu DIT University School of Computing Uttarakhand Dehradun India VNR Vignana Jyothi Institute of Engineering & Technology Department of Computer Science Engineering Hyderabad India Dept. of Mining Engineering Dhanbad India

ISBN: (纸本)9781665477239

Text categorization is a task for text mining that involves pattern classification and is essential for the effective management of textual information systems (TIS). Each document is automatically assigned one or more categories from a set of predetermined categories as part of TIS. there is increased interest in creating tools to aid people in more effectively finding, filtering, and managing existing digital resources as the amount of information is rising. those developed tools are involved in the administrative sectors and social benefits. this paper provides a general overview of text categorization models using various supervised machine learning techniques, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector machine (SVM), and AdaBoost, and compares their performance on various metrics, including accuracy and precision. the comparative analysis shows that the most effective algorithm for text classification is NVM with the highest accuracy of 96.86%, furthermore, AdaBoost is the worst in this case study with its minimum accuracy of 74.49%. We have also shown a comparison of other supervised machine-learning algorithms in this paper. © 2023 IEEE.

关键词： Classification (of information)

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Development of a Mobile App to Detect Counterfeit Currency using machine learning 6

Development of a Mobile App to Detect Counterfeit Currency u...

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6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Kadu, Rakesh K. Gupta, Praneet Jain, Pranay Nakade, Saurabh Shri Ramdeobaba College of Engineering and Management Information Technology Dept. Nagpur India

ISBN: (纸本)9798350394702

Counterfeit poses a serious threat to financial institutions and the global economy. this work introduces machine learning techniques that use image processing, pattern recognition, and classification algorithms to accurately identify false positives. these studies are based on analysis of real banknotes, their security features and counterfeiting techniques. Images of real and fake invoices are carefully recorded to create robust data for training and testing learning models such as neural networks (CNN). Image processing techniques such as edge detection and feature extraction improve the model's detection capabilities. the development model, evaluated in terms of efficiency, is integrated into a user-friendly interface and accepts cash register images as input. this solution aims to contribute to continuous efforts to combat fraud, providing a clear and effective way to protect financial institutions and increase business security. © 2023 IEEE.

关键词： Cash registers

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Viability of Using data Augmentation with a Small Sample of Demographical Information to Predict Student Specialization in Master of Applied Computing Course 6th

Viability of Using Data Augmentation with a Small Sample of ...

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

作者： Kiat, Lau Noel Kuan Ashraf, Humaira Khan, Navid Ali Taylor’s University Selangor Subang Jaya Malaysia

ISBN: (纸本)9789819780891

this research studies the viability of small samples of basic student demographical information as training data for machine learning models to perform multiclass classification of the specializations/majors of students enrolled in the Master of Applied Computing course at Taylor’s University. If successful, this approach can be used by educational institutions with sparse student data to recommend university majors/specializations. this research uses SMOTE-NC to augment the training dataset from 25 rows to 100 rows and trained three machine learning models on the augmented dataset—logistic regression, random forest, and support vector machine. the support vector machine classifier performed the best in terms of accuracy at 86%, but all three classifiers showed highly inconsistent performance when predicting different specializations. Further research is required to understand this outcome, but potential causes include weak correlations between the input features and the target feature and small sample size. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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A Multielement recognition Algorithm Based on Deep learning 6

A Multielement Recognition Algorithm Based on Deep Learning

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Zhang, Haiyan Guo, Yanting Qi, Feng Lv, Tonglin Wang, Huaixi National University of Defense Technology College of Electronic Countermeasures Hefei China

ISBN: (纸本)9798350325485

With the development of deep learning, the classification and recognition of wasps has become one of the hot issues in research. the paper based on the text and images in the provided data set. Firstly, input the image data of the wasp into the deep residual network to obtain its confidence coefficient, with an accuracy of 75%;then, the obtained text reports are generated into training sets, test sets, and validation sets, and the Skip gram method is used to supervise and train the text recognition model. thirdly, using support vector machines for classification has an accuracy of 82.73%. Finally, the recognition results of the image and text are weighted to obtain the final classification result. Examples have shown that this method has good recognition performance. © 2023 IEEE.

关键词： Support vector machines

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Forecasting Health Indicators through a data-Driven machine learning Analysis of Exercise, Sleep, and Water Intake patterns 6th

Forecasting Health Indicators Through a Data-Driven Machine ...

引用

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

作者： Yicheng, Cai Ashraf, Humaira Jhanjhi, N.Z. School of Computer Science SCS Taylor’s University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

this chapter uses machine learning algorithms to build health status prediction models by analyzing multidimensional health behavior data such as exercise frequency, type, sleep duration, and water intake. We collected datasets on health indicators and further explored how these lifestyle factors collectively influence health indicator values. the health data collected are used to calculate the target variable "health_value," which is the column that distinguishes whether an individual is healthy or not, based on comparing the mean value of the column, with 1 and 0 indicating healthy or unhealthy. By analyzing the data and machine learning algorithms, we discovered the essential and inseparable relationship between these health indicators and an individual’s health status. We optimized the model to improve the accuracy of the prediction. We also confirmed the generalization ability and performance of the model by giving the optimized model new health data and obtaining health values, as well as whether it is healthy. the results of this chapter highlight the importance of integrating data on lifestyle habits for health prediction to give data support to the development of a scientific basis for personalized health management strategies and health promotion interventions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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6th international conference on machine learning for Networking, MLN 2023

6th International Conference on Machine Learning for Network...

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6th international conference on machine learning for Networking, MLN 2023

ISBN: (纸本)9783031599323

the proceedings contain 18 papers. the special focus in this conference is on machine learning for Networking. the topics include: Attentive, Permutation Invariant, One-Shot Node-Conditioned Graph Generation for Wireless Networks Topology Optimization;enhancing Social Media Profile Authenticity Detection: A Bio-Inspired Algorithm Approach;a Deep learning Based Automatic Outdoor Home Video Surveillance Approach;detecting Abnormal Authentication Delays In Identity And Access Management Using machine learning;SIP-DDoS: SIP Framework for DDoS Intrusion Detection Based on Recurrent Neural Networks;deep Reinforcement learning for Multiobjective Scheduling in Industry 5.0 Reconfigurable Manufacturing Systems;Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications;data Summarization for Federated learning;ML Comparison: Countermeasure Prediction Using Radio Internal Metrics for BLE Radio;towards Road Profiling with Cooperative Intelligent Transport Systems;Study of Masquerade Attack in VANETs with machine learning;detecting Virtual Harassment in Social Media Using machine learning;Leverage data Security Policies Complexity for Users: An End-to-End Storage Service Management in the Cloud Based on ABAC Attributes;machine learning to Model the Risk of Alteration of Historical Buildings;a Novel Image Encryption Technique Using Modified Grain-128;transformation Network Model for Ear recognition.

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