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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20250 条记录，以下是271-280 订阅

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A Hybrid Decision-Tree and ANN learning Model for Air Contamination Forecasting 3

A Hybrid Decision-Tree and ANN Learning Model for Air Contam...

引用

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

作者： Gunasekar, S. Pius Agbulu, G. Joselin Retna Kumar, G. Raghavendra Reddy, N.V. Dileep Kumar, Y. Ramesh Prasad, R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Chinhoyi University of Technology Department of Mechatronic Engineering School of Engineering Sciences & Technology Private Bag 7724 Chinhoyi Zimbabwe SRM Institute of Science and Technology Department of Electronics and Instrumentation Engineering Chennai India RV Institute of Technology and Management Department of Mechanical Engineering Karnataka India Mohan Babu University Department of Electronics & Communication Engineering School of Engineering Tirupati India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350391565

Development of urban intelligent cities introduces a severe rise in environmental pollution, ranging from air and water pollution to noise pollution. Explicitly, the upsurge in deforestation, industrialization, production, and automobile activities presents increased air pollution. To recommend a suitable preventive method, a proper forecasting model is required. Presently, (ML) machine learning methods aid intelligent data evaluation and are employed in many domains. This study examines the forecasting of air contamination employing distinct ML methods. A hybrid decision-tree and (ANN) artificial neural network model is recommended for proper gauging. The analyses reports reveal that the suggested model outclasses other classical algorithms in terms of accuracy. Precisely, the presented model realized 99% and 99.88% accuracy and precision, with 99% and 99.88% sensitivity and specificity. © 2024 IEEE.

关键词： Adversarial machine learning

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Application and Optimization of intelligent Teaching Assistance System in Physical Experiment Teaching 24

Application and Optimization of Intelligent Teaching Assista...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Ma, Jinbei Shandong University of Engineering and Vocational Technology Shandong Jinan China

ISBN: (纸本)9798400712692

This paper designs an intelligent teaching assistance system to improve the effect of physical experiment teaching based on artificial intelligence technology. The system integrates intelligent algorithm modules for real-time data processing and analysis to ensure accurate monitoring of the experimental process. The system can automatically collect, process and analyze experimental data, reduce the burden of teachers in data management, and improve the efficiency of experimental teaching. The system was experimentally verified in multiple teaching scenarios, and the results showed that it can effectively assist students to complete experimental operations and improve data accuracy and experimental efficiency. The simulation results include specific and accurate data analysis, which verifies the stability and practicality of the system. The intelligent teaching assistance system proposed in this paper can optimize the experimental teaching process and improve students' learning effects. At the same time, it has good scalability and can be applied to other scientific teaching fields. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： data reduction

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EchoNet Dynamic: Deep learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

Advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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A No-Code Approach to Classifying Neurological Disorders Using Orange data Mining 3

A No-Code Approach to Classifying Neurological Disorders Usi...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Davidraj, Yabez Elavarasan, S. Vivek, T. Munirathinam, Ramesh Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798331523893

Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machine learning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer's disease, Huntington's disease, motor neuron disease, and Pick's disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector Machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Next-Generation Intrusion Detection Systems with Machine learning and Deep learning-Based Firewalls 3

Next-Generation Intrusion Detection Systems with Machine Lea...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

The growing complexity of cyber-attacks has underlined the requirement of strong Intrusion Detection Systems (IDS) able to instantly recognize a variety of hazards. This work offers a strategy for improving Next-Generation Intrusion Detection Systems (NGIDs) combined with firewalls by use of machine learning (ML) and deep learning (DL) approaches. Using the NSL-KDD dataset - which includes both normal and attack network traffic - the work creates a system able to classify traffic patterns into "Normal"and "Attack"categories. The proposed method consists in four phases: data collecting, data preprocessing, feature selection and extraction, and classifier application. In the preprocessing stage, the dataset is cleaned and normalized;hence, suitable feature selection follows to increase classification accuracy. Trained and tested for classification using performance criteria including accuracy, precision, recall, and F1-score, naïve bayes and support vector machine (SVM) models are then compared in efficacy. The results reveal that the SVM classifier has low misclassification and great accuracy (97.29%), therefore proving its prospective use in advanced IDS. The article underlines how several models could be merged inside a firewall system to offer real-time network protection. Future studies will look at using deep learning techniques and ensemble methods to raise detection accuracy even more, therefore allowing NGIDS to be more adaptable and efficient in countering evolving cyber threats. © 2025 IEEE.

关键词： Network intrusion

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An Enhanced Puma Optimizer for Cloud Computing Task Scheduling 3

An Enhanced Puma Optimizer for Cloud Computing Task Scheduli...

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3rd international conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Wu, Libang Wu, Fengbin Li, Shaobo Xie, Rongxiang Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang Guizhou Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang Guizhou Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang Guizhou Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Aiming at the lack of accuracy of puma optimizer in cloud task scheduling, an improved task scheduling method based on puma optimizer is proposed by combining the intermediate point sample learning strategy and S-shaped transfer function (MSPO). Among them, the middle point sample learning strategy improves the searching ability of the algorithm, while the S-shaped transfer function reduces the response time of the system, thus speeding up the execution speed of the algorithm. By testing different scale problems, it is proved that MSPO can effectively reduce the cost of consumption.

关键词： Task scheduling Puma optimizer S-shaped transfer function

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Medical Prescription Generator using Natural Language Processing 3

Medical Prescription Generator using Natural Language Proces...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paithankar, Siddhesh Patil, Snehal Shivgan, Ashlesha Mahabudhe, Mandar Dharmadhikari, Pramod MIT Academy of Engineering School of Computer Engineering Pune India MIT Academy of Engineering School of Electrical Engineering Pune India

ISBN: (纸本)9798331523893

With the increasing population in India and the limited availability of medical professionals, time efficiency in diagnosis and prescription generation has become crucial. This paper proposes an automated system leveraging Natural Language Processing (NLP) and Natural Language Generation (NLG) to assist doctors in generating patient history and prescription suggestions. The system utilizes previously typed words to predict the next word, drawing on a medical notes dataset tailored to the doctor's specialization. By giving keywords or short descriptions about a patient's condition, the system processes the input using NLP for contextual under-standing and employs NLG to generate detailed histories and prescriptions in a standardized format. This dual approach ensures that the output aligns with medical conventions while maintaining accuracy and efficiency. The system is trained on extensive medical datasets, optimizing its predictions over time through machine learning techniques. Our solution not only enhances accuracy but also reduces the time doctors spend on manual documentation, improving overall efficiency and patient care. © 2025 IEEE.

关键词： Diagnosis

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12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on Machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on Machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired data-Sampling Prototype for Time-Series Analysis;membership-Mappings for data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the Machine learning Algorithm for Gene Expression data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving Machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with rdF2Vec in Knowledge Graphs;bridging Semantic Web and Machine learning: First Results of a Systematic Mapping Study.

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Real-Time Global Parcel Tracking System in the Supply Chain Using RFID and GPS 3

Real-Time Global Parcel Tracking System in the Supply Chain ...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vyshak, T.M. Varun, S.N. Unnathi, G.D. Sujaya, B.L. Munavalli, Jyoti R. Bnm Institute of Technology Affiliated to Vtu Dept. of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331515911

This paper presents an in-depth exploration of a Real-Time Global Parcel Tracking System in the Supply Chain Using RFID and GPS, designed to revolutionize logistics and parcel management. By integrating RFID technology for automated identification, GPS for precise real-time tracking, and data analytics for intelligent routing, the system addresses inefficiencies in traditional supply chain operations. A comprehensive review of literature on RFID-based tracking, GPS integration, clustering techniques, and IoT applications in logistics is presented, highlighting their capabilities, limitations, and future potential. As part of the project's initial implementation, a prototype was developed using an Arduino-based RFID system that links unique order IDs to RFID tags. This low-cost and scalable solution demonstrates how emerging technologies can be harnessed to automate parcel identification and streamline tracking processes. The study underscores the transformative potential of such a system to reduce delays, enhance resource optimization, and provide unparalleled real-time visibility, setting the stage for a further efficient and reliable global supply chain. © 2025 IEEE.

关键词： Global positioning system

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Research on Anomaly Detection in Time-Series Streaming data 3

Research on Anomaly Detection in Time-Series Streaming Data

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3rd international conference on Computer Science, Electronic Information engineering and intelligent Control Technology, CEI 2023

作者： Gong, Luyang School of Software Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9798350372052

The detection of anomalies in streaming data is crucial in enterprise operations, employing statistical and machine learning methods to identify irregularities. This enhances service stability and reduces operational complexity. However, existing monitoring platforms face challenges such as complex rule configurations and a lack of unified standards. To address these issues, this paper proposes a container performance anomaly detection module that combines statistical and machine learning approaches. This module, through steps including streaming data collection, data preprocessing, anomaly detection models, and monitoring alerts, achieves rapid detection and accurate identification of container performance anomalies. In comparison to traditional monitoring platforms, this module not only saves manpower but also demonstrates higher accuracy. © 2023 IEEE.

关键词： Time series

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