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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81-90 订阅

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A Quick Overview of Recent Developments in Image Restoration using machine learning 5

A Quick Overview of Recent Developments in Image Restoration...

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5th IEEE India Council international Subsections conference, INDISCON 2024

作者： Kumari, Neelam Kansal, Isha Sharma, Preeti Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350376753

Digital images are gradually getting importance in every industry, including computer science, medical, communications, transport, security, and surveillance. Degraded Images can be restored from a variety of distortions. Distortions occur due to malfunctioning imaging equipment transmission channels, atmospheric and environmental conditions, etc. Image restoration techniques have applications across various domains like image processing, data mining, and predictive analysis. Today machine learning is used in almost every area by taking into consideration its excellence efficiency, decision making, to automate task and to retrieve pertinent data for analysis. In an effort to solve issues, numerous academics, mathematicians, and programmers have conducted research and used a variety of machine learning techniques. this paper centers on a comprehensive overview of machine learning, techniques used for image restoration methodologies. the purpose of the study is to compare evolution of various parameters like Peak Signal to Noise Ratio (PSNR), Structure Similarity Index Matrix (SSIM), Mean Structure Similarity Index Matrix (MSSIM), Mean Square Error (MSE) and Natural Image Quality Evaluator (NIQE) between machine learning (ML) and traditional techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Education 4.0 with Artificial Intelligence 5

Enhancing Education 4.0 with Artificial Intelligence

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5th IEEE international conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Ghosh, Debjyoti Tiwari, Rajesh Kumar Gundalwar, Pravin Ramesh Vinoba Bhave University Department of Computer Applications Jharkhand Hazaribag India RVS College of Engineering and Technology Jharkhand Jamshedpur India Anurag University Department of Information Technology Medchal-Malkajgiri District Venkatpur Ghatkesar Hyderabad India

ISBN: (纸本)9798350351378

this study advocates for self-regulated learning in Higher Education 4.0, integrating smart technologies like sensors, wearables, analytics, and machine learning. It introduces an Early recognition System driven by predictive analytics, inspired by N. A. Crowder's Auto Tutor theory. Personalized assessments and adaptive feedback empower students to steer their learning journey independently. this framework benefits students, professors, and administrators alike, fostering a dynamic educational environment. Enhanced data-driven decision-making leads to optimized resource allocation and program effectiveness, culminating in empowerment, engagement, and success for all stakeholders. © 2024 IEEE.

关键词： machine learning

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Elevated learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance 5

Elevated Learning based Secured Phishing Website Identificat...

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Ramkumar, G. Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798350379945

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Phishing

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Spatiotemporal Visual Tracker Based on Deformable Attention 5

Spatiotemporal Visual Tracker Based on Deformable Attention

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Wu, Zhuo Hamdulla, Askar Huang, Yucheng School of Computer Science and Technology School of Cyberspace Security Xinjiang University Urumqi China School of Intelligence Science and Technology School of Future Technology Xinjiang University Urumqi China

ISBN: (纸本)9798350355925

At present, the visual tracking method based on Transformer has made significant progress, and has shown excellent performance on various data sets. However, the traditional Transformer tracker pays too much attention to global features, and its intensive attention calculation will increase the space-time complexity of the tracking algorithm. And the features of the relevant region will be affected by the features of the non-relevant region, resulting in a decrease in tracking accuracy. In addition, the Transformer tracker lacks the ability to utilize the spatiotemporal information between frames, which further hinders the improvement of its tracking performance. To solve these problems, this paper proposes a new tracker SVT-DA based on deformable attention. the tracker captures local and global important features through CNN and deformable attention. In addition, it uses a cross-attention module nested with self-attention to achieve deep semantic information interaction between template features and search features. In order to verify the effectiveness of the proposed method, we conduct experiments on LaSOT, GOT-10K, OTB100 and VAU123 datasets. the result indicators are 4%5% higher than the baseline method. the experimental results show that our method improves the tracking performance while reducing the number of model parameters, and is highly competitive with other state-of-the-art methods. © 2024 IEEE.

关键词： Deep learning Deformable attention Feature fusion Transformer Visual object tracking

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Identification of Java lock contention anti-patterns based on runtime performance data 5

Identification of Java lock contention anti-patterns based o...

引用

5th IEEE/ACM international conference on Automation of Software Test (AST)

作者： Ahmed, Aritra Liscano, Ramiro Azim, Akramul Chang, Yee-Kang Sundaresan, Vijay Ontario Tech Univ Oshawa ON Canada IBM Canada Toronto ON Canada

ISBN: (纸本)9798400705885

Locks play a crucial role in multi-threaded applications, offering an effective solution for synchronizing shared resources. Yet, mishandling locks and threads can result in contention, leading to performance deterioration and compromising the scalability of software applications. In this study, several machine learning models were evaluated on how well they could detect the Java lock contention anti-pattern that caused the lock contention fault based on run time performance data. We trained the machine learning models with performance data generated from the execution of eight Java lock contention anti-patterns and tested the prediction of the models against 30% of the training data as well as performance data from six applications in the Dacappo benchmark that exhibit lock contention. Our results show that we can accurately identify the lock contention anti-pattern based on runtime performance data with an accuracy close to 90%.

关键词： Lock Contention Faults Lock Contention Anti-patterns Supervised machine learning

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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...

引用

2024 IEEE international conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

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

ISBN: (纸本)9798350376135

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. © 2024 IEEE.

关键词： Adversarial machine learning

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Harnessing IoT and Robotics for Comprehensive Human Activity recognition in Smart Buildings 5th

Harnessing IoT and Robotics for Comprehensive Human Activity...

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5th 5th Olympus - international conference on Supply Chain

作者： Toupas, Petros Tsamis, Georgios Zotou, Dimitra Giakoumis, Dimitrios Votis, Konstantinos Tzovaras, Dimitrios Ctr Res & Technol Hellas Informat Technol Inst Thessaloniki Greece

ISBN: (纸本)9783031693434;9783031693441

the incorporation of Internet of things (IoT) devices and robotics into smart building environments has introduced new opportunities for human activity recognition (HAR). the paper introduces an innovative approach that combines IoT sensors and robotic data to create a complete dataset for Human Activity recognition (HAR) in smart building settings. the system utilises a synchronised data collection technique to gather information from fixed IoT sensors, wearables, and smart devices, as well as from mobile robots. the dataset obtained includes a wide variety of sensor modalities, such as accelerometers, gyroscopes, microphones, motion sensors, smart plugs, and cameras (thermal, RGB-D). A proposed strategy is presented to tackle the difficulties of multimodal data fusion and classification by fusing all sensor data into a single machine learning model. this study lays the groundwork for future works into Human Activity recognition (HAR) systems, which involve the integration of multi-sensor settings, sophisticated data fusion algorithms, context-sensitive identification, and tailored support for individuals working or residing in smart environments. the setup of the sensors and use cases of this study were conducted in a smart home environment. Nevertheless, the integrated system can be tailored to suit the requirements of any smart building.

关键词： Multi-Sensor Fusion data Collection Robotics

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Hybrid Quantum Computing and Deep learning Approaches for Enhancing Wireless Communication Security 5

Hybrid Quantum Computing and Deep Learning Approaches for En...

引用

5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Ramu, V. Barla, Kalyan Kavitha, V. Aluvala, Srinivas Chandrasekhar, Sankepally Athiraja, A. Yerragattugutta Hasanparthy Telangana Hanamkonda506015 India Bannari Amman Institute of Technology Department of It Sathyamangalam Erode India Sr University School of Computer Science & Artificial Intelligence Warangal India Department of Mechanical Engineering Medchal-Malkajigiri District Telangana Maisammaguda India Medchal-Malkajigiri District Telangana Maisammaguda India

ISBN: (纸本)9798331522100

A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of quantum algorithms with the pattern recognition powers of deep learning, this article tackles the crucial problems of protecting wireless networks, which are vulnerable to eavesdropping, spoofing, and data breaches. the research uses a hybrid framework in which deep learning models like convolutional neural networks (CNNs) identify and stop possible invasions in real time, while quantum key distribution (QKD) guarantees safe data transfer. Simulations and experimental validation in a 5G network environment show that the proposed system has a 40% reduction in latency and a 35% increase in intrusion detection accuracy compared to traditional security methods, like standard encryption techniques and rule-based intrusion detection systems that lack quantum cryptographic mechanisms and advanced AI-driven pattern recognition. the findings demonstrate that integrating quantum and AI-driven methods to build a more robust wireless communication infrastructure is feasible. the study concludes by highlighting the potential of hybrid quantum-AI systems as a critical first step in protecting next-generation networks. © 2025 IEEE.

关键词： Qubits

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AutoML Applied to Time Series Analysis Tasks in Production Engineering 5

AutoML Applied to Time Series Analysis Tasks in Production E...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Conrad, Felix Maelzer, Mauritz Lange, Felix Wiemer, Hajo Ihlenfeldt, Steffen TUD Dresden Univ Technol D-01062 Dresden Germany Fraunhofer Inst Proc Engn & Packaging D-01189 Dresden Germany

this paper demonstrates the application of a fully automated machine learning (ML) pipeline on time series data from the domain of production engineering. the workflow aims to streamline the machine learning (ML) process and reduce manual effort by incorporating automated machine learning (AutoML) and automated featurization techniques. A comparative study is created to evaluate the proposed workflow against existing state-of-the-art methods using six datasets from production engineering, five widely used ones and a newly presented one. the study compares the performance of the automated workflow with the manual data mining process described in the literature. the results lead to two main conclusions: first, an automated featurization process cannot match the performance of manual feature generation, but still delivers good results without requiring manual work. Second, if the manually generated features are used, AutoML can outperform the manual process in solving the combined problem of algorithm selection and hyperparameter optimization, leading to better prediction results. the presented approach serves as a useful reference for other methods, showing the practicality of baseline workflow without human bias. Overall, the proposed data processing pipeline demonstrates the potential of AutoML in production engineering and provides valuable insights for practitioners in this domain. (C) 2024 the Authors. Published by Elsevier B.V.

关键词： Automated machine learning AutoML Time Series Analysis Usable Artificial Intelligence UAI Production Engineering

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5th international conference on Applied Technologies, ICAT 2023

5th International Conference on Applied Technologies, ICAT 2...

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5th international conference on Applied Technologies, ICAT 2023

ISBN: (纸本)9783031589522

the proceedings contain 40 papers. the special focus in this conference is on Applied Technologies. the topics include: Unsupervized Techniques to Identify patterns in Gynecologic Information;web Application for Early Cataract Detection Using a Deep learning Cloud Service;Analyze and Implement a Reinforced AI Chatbot in Guayaquil to Improve Mental Health in Adolescents with the Use of the Neural Generative Models;detection of Ovarian Cancer Using Improved Deep learning Model;Navigating the Chatbot Terrain: AI-Driven Conversational Interfaces;intelligent Virtual Assistant for Elevators Powered by Facial recognition and Voice Commands;otsu Segmentation and Deep learning Models for the Detection of Melanoma;Hybrid Residual Network and XGBoost Method for the Accurate Diagnosis of Lung Cancer;generic Framework of New Era Artificial Intelligence and Its Applications;Exploring the Molecular Diversity of SARS, Ebola, MERS, and SAR-COV-2 Viruses Using Genomics Virus Classification Algorithm: ViroGen;improving Dynamic Behavior of Vehicular Ad Hoc Networks by Integrating Game theory Technique;machine learning to Investigate Determinants of Intention to Purchase Organic Food;Influence Analysis of Driving Style on the Energy Consumption of an Electric Vehicle through PID Signals Study;prediction of Academic Outcomes Using machine learning Techniques: A Survey of Findings on Higher Education;identification of Factors and Teacher Profile Associated with Student Performance Using Fuzzy Techniques and data mining;predictive Model for Accurate Horticultural Product Pricing Using machine learning;analysis of Driving Style and Its Influence on Fuel Consumption for the City of Quito, Ecuador: A data-Driven Study;preventing Drug Interactions in Diabetic Patients: the Role of a Mobile Conversational Agent.

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