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

共 7122 条记录，以下是371-380 订阅

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Advancing Temperature Prediction for Pune City: machine Learning-Enabled Temperature Prediction 3

Advancing Temperature Prediction for Pune City: Machine Lear...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sathe, Ganesh Pathak, Anagha M. Jadkar, Sandesh Bhalla, Vishal Dattangire, Rahul Savitribai Phule Pune University Center for Energy Studies M.S. Pune India School of Artificial Intelligence Coimbatore India Rivier University NH United States

ISBN: (纸本)9798350369083

This study investigates the efficacy of machine learning methodologies in enhancing temperature forecasting accuracy for Pune City, a region characterized by diverse geographical and climatic factors. Leveraging historical weather data, a comprehensive dataset is compiled, encompassing various meteorological variables. Various machine learning regression algorithms is used and evaluated to predict future temperature trends. Despite encountering challenges such as data scarcity and model complexity, the study demonstrates promising results. Random Forest Regressor exhibited the highest accuracy with an average mean absolute error of 0.02, followed closely by Gradient Boosting Regressor and Neural Network regression techniques. The implications of this research are significant, as accurate temperature forecasts are crucial for sectors such as Thermal Energy Storage Management, Refrigeration and Air Conditioning Sector (RAC), agriculture, transportation, and urban planning. By innovatively applying machine learning techniques, this study contributes to advancing weather prediction capabilities, providing valuable insights into temperature dynamics and enhancing preparedness for climate-related events. The novelty and significance lie in addressing unique weather forecasting challenges specific to Pune City, ultimately fostering more efficient and reliable weather prediction systems in urban environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Toward an Ontology of pattern Mining over Data Streams 3rd

Toward an Ontology of Pattern Mining over Data Streams

引用

3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Samb, Dame Slimani, Yahya Ndiaye, Samba Management Department UIDT Cite Malick SY N2 BP: A967 Thies Senegal ISAMM Tunis Tunisia Mathematics and Computer Science Department Cheikh Anta Diop University Dakar Senegal

ISBN: (纸本)9783031463341

pattern Mining over Data Stream (PMDS) is part of the most significant task in data mining. A major challenge is to define a representational framework that unifies PMDS algorithms dealing with different pattern types (frequent itemset, high-utility itemset, uncertain frequent itemset), using different methods (test-and-generate, pattern-growth, hybrid) and different window models (landmark, sliding, decay, tilted) in a uniform fashion. This will help standardize the process and create a better understanding of the algorithm design, provide a base for unification and research opportunities. It also facilitates the variability management and allows the derivation of tools for wide experimentation. In this publication, we propose a reference ontology to formalize the domain knowledge around PMDS. The design process of the ontology followed leading practices in ontology engineering. It is aligned to the most popular data mining and machine learning ontologies and thus, represents a major contribution toward PMDS domain ontologies. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge representation

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Integrating IoT and machine Learning for Enhanced Forest Fire Detection and Temperature Monitoring 3

Integrating IoT and Machine Learning for Enhanced Forest Fir...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Varun, M. Kesavraj, K. Suman, S. Raj, X Suman Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350343632

Forest fires have become more frequent and intense in recent years, which has caused serious problems for public safety and environmental preservation. In the situation, combining Internet of Things (IoT) devices with cutting-edge machine learning algorithms offers a viable approach for early temperature and forest fire detection. In order to identify forest fires and track temperature swings in real-time, an intelligent system that integrates IoT sensors and machine learning algorithms is designed, developed, and evaluated in detail in it. The proposed methodology improves the effectiveness of early fire detection while also offering insightful information for efficient wildfire management and prevention methods. The report illustrates the system's efficacy and dependability via thorough experimentation and research, demonstrating its potential for wider use in wooded regions across the world. The paper also analyzes implementation difficulties, such as data privacy and system scalability, and offers suggestions for resolving these problems. The findings in the work add to the body of research on IoT-based environmental monitoring systems and provide new avenues for investigation and advancement in the field of intelligent forest fire detection and prevention. © 2023 IEEE.

关键词： early warning system environmental monitoring forest fire detection Internet of Things (IoT) pattern recognition remote areas

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Enhanced Diagnostic Performance in Ophthalmology: A Comparative Assessment of VGG19 and EfficientNetB3 3

Enhanced Diagnostic Performance in Ophthalmology: A Comparat...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Singh, Preet Hasija, Taniya Ramkumar, Kr Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354171

Global eye diseases, such as diabetic retinopathy, glaucoma, age-related macular degeneration, and cataracts, pose significant public health concerns. Therefore, it is crucial to facilitate early detection and treatment. Recent developments in Deep Learning (DL) and medical imaging permit accurate and quick identification of diseases, which has transformed ophthalmology. This study uses VGG19 architecture and EfficientNetB3 to explore fundus images-based automated classification of eye disorders. Combining two DL methods are VGG19, a deep neural network renowned for depth and accuracy in visual classification these methods followed the EfficientNetB3 transfer learning model, well-known for performance in image recognition applications. After training and evaluation using a 5,000 patient record Ocular Disease intelligence recognition (ODIR) dataset including colour fundus images and diagnostic keywords, VGG19 displayed a 94% accuracy;EfficientNetB3 showed a higher accuracy of 96%, so implying its improved performance in disease categorisation. Over several patient demographics and imaging modalities, EfficientNetB3 beats VGG19 in accuracy and loss by generalising. Most crucially for early intervention and better patient outcomes in clinical environments, our comparative study validates EfficientNetB3's accuracy and durability in automated disease detection. © 2024 IEEE.

关键词： Ophthalmology

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Semi-supervised Video Object Segmentation Using Parallel Coattention Network 10th

Semi-supervised Video Object Segmentation Using Parallel Coa...

引用

10th Biennial international conference on pattern recognition and machine intelligence (PReMI)

作者： Chakraborty, Sangramjit Mahapatra, Monalisha Nandy, Anup Natl Inst Technol Machine Intelligence & Biomot Lab Dept Comp Sci & Engn Rourkela Odisha India

ISBN: (纸本)9783031451690;9783031451706

In this work, a novel method is proposed for video object segmentation. The temporal correlation is measured between subsequent frames in a video sequence to propagate a reference segmentation mask through the video sequence. The aim is to develop a novel deep learning based holistic method for video object segmentation. For this purpose, a parallel coattention module is used to capture the rich temporal information between two subsequent frames and mask. An autoencoder architecture with a siamese encoder module is applied with three inputs, and skipped connections are utilized between the encoder and decoder networks. No auxiliary information such as optical flows are used in this network. The method is validated on DAVIS16 and FBMS datasets with mean region similarity (J(mean)) of 91.2 and 83.2 respectively.

关键词： Coattention Deep learning Autoencoder Video Object Segmentation

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Enhancing Device Authentication in IoT Security Application using Artificial intelligence: A Survey 3

Enhancing Device Authentication in IoT Security Application ...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Brogya, Maame Serwah Dumfeh Ayuso, Enrique Shukla, Aasheesh Goyal, Amit Kumar Sain, Mangal Dongseo University Dept. of Computer Engineering Busan Korea Republic of Gla University Department of Electronics and Communication Mathura India Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798331528201

Technological advancement over the last decade has seen significant technological development, particularly in IoT, which is the most used and commercialized. This is due to its convenience, as it gives users remote control of their appliances via portable devices like smartphones and tablets, without needing to be physically present or close to those devices. As IoT technologies continue to grow and new innovative solutions for everyday applications are being developed there are more interconnected devices and large volumes of data being generated every second. However, the rapid growth of IoT has heightened security concerns, especially around device authentication. This paper reviews IoT authentication complexities and explores machine Learning solutions under AI, comparing various algorithms and, providing a foundation for researchers and developers in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Threat intelligence Forecasting using machine Learning Algorithms 3

Advanced Threat Intelligence Forecasting using Machine Learn...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kumhar, Bhupchand Saxena, Antima Nagar, Mayank Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

This section introduces an innovative approach for advanced threat intelligence forecasting employing machine learning algorithms. The proposed method integrates three powerful algorithms - Random Forest, Support Vector machine (SVM), and Long Short-Term Memory (LSTM), and elucidates the foundational mathematical concepts. The process commences with data preprocessing involving cleaning, normalization, and feature engineering to prepare the raw data for machine learning. Subsequently, Principal Component Analysis (PCA) is employed for optimal feature selection. The integration of Random Forest, SVM, and LSTM algorithms forms a robust predictive framework. Random Forest, known for its accuracy and versatility, constructs an ensemble of decision trees, ensuring reliable classification. SVM seeks the optimal hyperplane to effectively separate classes in feature space, proving valuable in complex, non-linearly separable data scenarios. LSTM, a type of recurrent neural network, excels in capturing long-term dependencies in sequential data, making it indispensable in threat intelligence. © 2023 IEEE.

关键词： Support vector machines

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Smart Patrolling System Using machine Learning 3

Smart Patrolling System Using Machine Learning

引用

3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Megala, P. Prabhu, R. Devi, V. Yamini, S. Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore641202 India Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi642003 India Hindustan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore641032 India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore641021 India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The implementation of Smart Patrolling System using IoT is to provide a safety to the people those who are present in the area by the help of fixing a Sound Sensor, Ultrasonic sensor, Raspberry pi, Node MCU, Night vision camera, GPS, GSM in street lights. The Smart Patrolling System using IoT continuously monitor the place and it gives the signal to the people those who are living in the place first then the control room. The proposed system can be used in Residential areas for giving better safety to the people. The proposed system gives a very most importance to Women and children safety. By the use of our proposed system several crime attacks can be found immediately and it can be avoided from crucial situations. © 2024 IEEE.

关键词： Sensor nodes

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Predictive Analysis for Defector in Banking System 3

Predictive Analysis for Defector in Banking System

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Aruna, S. Bhandari, Utkarsh Nautiyal, Ayush Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350318609

An essential component of risk management for financial organizations is predictive analysis for defectors in the banking system. The amount of credit transactions is growing, and conventional techniques frequently fail to reliably identify possible defaulters. In this paper, suggesting machine learning algorithms which helps to anticipate defaulters more effectively and accurately. Making use of an extensive dataset that includes credit history, transactional data, and demographic data, we apply ANN, random forests, and K-means among other machine learning methods. By applying feature engineering approaches, the raw data is transformed into relevant insights that improve the models' predictive capacity. On both training and test datasets, the suggested models are assessed using common performance metrics like accuracy, precision, recall, and F1-score. The outcomes of the experiments show how well the suggested method works to correctly identify possible defaulters, giving financial institutions the ability to proactively manage risks and streamline decision-making procedures. Detecting Defaulters with machine learning algorithms which produces 83% Accuracy,71% F-Score in Random Forest, 71% F-Score in Random Forest when compared to other Algorithms. © 2024 IEEE.

关键词： Predictive analytics

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