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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是301-310 订阅

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Construction Method of Intelligent Analysis Rule Library for Monitoring Information Under Power Supply Reliability 3

Construction Method of Intelligent Analysis Rule Library for...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Xie, Jinlong Song, Youle He, Bo Yunnan Power Grid Co. Ltd. Yuxi Xinping Power Supply Bureau Yunnan Yuxi China Yunnan Power Grid Co. Ltd. Electric Power Science Research Institute Yunnan Kunming China Yunnan Power Grid Co. Ltd. Production and Operation Monitoring Center Yunnan Kunming China

ISBN: (纸本)9798350380989

In order to build a real-time intelligent analysis rule base of distribution network monitoring information, a construction method of intelligent analysis rule base of distribution network monitoring information based on machine learning is proposed. All the monitoring rule heads in the rule base are sorted and set as the main chain, and the rules are imported into the linked list to generate the rule set, so as to ensure that each monitoring information packet has an analysis rule;the machine learning based fault data classification method of distribution network is used to identify the fault data in the monitoring information of distribution network, and extract the frequent item sets of fault data. The parallel incremental updating algorithm of association rules based on MapReduce is used to update the information intelligent analysis rules in the analysis rule based, so as to ensure the real-time performance of the information intelligent analysis rules in the analysis rule base. The experimental results show that the accuracy and the average detection rate of the proposed method are greater than 0.97, and the false positive rate is 0.01. The proposed method can identify the real-time fault information of the distribution network monitoring system in time, and ensure the real-time updating of information intelligent analysis rules. © 2024 IEEE.

关键词： association rule updating distribution network intelligent analysis machine learning monitoring information rule base

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Applying Artificial Intelligence and Deep learning to Identify Neglected Tropical Skin Disorders 3

Applying Artificial Intelligence and Deep Learning to Identi...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Pattnayak, Parthasarathi Mohanty, Arpeeta Das, Tulip Patnaik, Sanghamitra Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Sri Sri University Faculty of Emerging Technologies Cuttack India Kiit Deemed to Be University School of Law Bhubaneswar India

ISBN: (纸本)9798350381931

Visual inspection plays a significant role in the identification of skin diseases, albeit it is not the only method. Teledermatology techniques are thus particularly suitable for the diagnosis and treatment of these disorders. In vision tasks, deep learning (DL), a subset of machine learning (ML) and/or artificial intelligence (AI), has produced outstanding results. There has been few research in this field, and even fewer that have focused on dark skin, despite the growing interest in using this technology to improve diagnostics for neglected tropical diseases affecting the skin (skin NTDs). In this work, we used clinical photos to create AI models based on deep learning. In order to determine whether or not various models with training patterns can increase diagnosis accuracy. The five skin NTDs in which data were gathered were yaws, scabies, mycetoma, leprosy, and Buruli ulcer. © 2024 IEEE.

关键词： Diagnosis

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Predicting Stroke: Advanced machine learning Ensemble Techniques 3

Predicting Stroke: Advanced Machine Learning Ensemble Techni...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Ankita Mittal, Sonam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354171

Stroke is the main cause of long-term disability and death worldwide;it is a terrible medical condition caused by disrupted blood flow to the brain. This work uses patient health and lifestyle data to explore, using different machine learning (ML) methods, comparing Four different models AdaBoost, Support Vector machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR) alongside an ensemble method known as the Weighted Voting classifier takes front stage. This study uses 4982 records with 12 unique parameters from the dataset: among others, age, hypertension, heart disease, and BMI. To ascertain their ability to forecast brain strokes, the models were assessed on several performance criteria including accuracy, precision, recall, and F1-score. The ensemble approach employing the Weighted Voting classifier surpassed them, attaining a better accuracy rate of 90%. The need for ensemble approaches in improving the predictive accuracy of medical diagnosis instruments is emphasized in this work. Particularly in important uses like stroke classification, where early and precise identification is vital for efficient treatment, the results imply that combining several ML models can greatly increase the dependability and robustness of predictions. © 2024 IEEE.

关键词： Logistic regression

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Exploring Strategies for Privacy-Preserving machine learning in Distributed Environments 3

Exploring Strategies for Privacy-Preserving Machine Learning...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dodda, Suresh Kumar, Anoop Kamuni, Navin Ayyalasomayajula, Madan Mohan Tito IIT Roorkee India AI-ML BITS Pilani WILP United States Aspen University PhoenixAZ United States

ISBN: (纸本)9798350372120

machine learning (ML) with distributed privacy preservation is growing in significance as it focuses on facilitating multi-party learning without requiring actual data sharing. This is especially helpful for companies that want to work together but are unable to do so because of ethical, regulatory, or budgetary constraints on sharing data. In order to address these issues, this study examines three privacy-preserving algorithms: regularized logistic regression with Differential Privacy (DP), stochastic gradient descent (SGD) with differentially private updates, and a distributed Lasso that distributes gradients among data centers. The study emphasizes the relationship between error rate and privacy through these algorithms. In order to improve error rates for large datasets, both DP algorithms modify their sensitivity dependent on the amount of data, highlighting the significance of training data volume in model performance in the study. Results demonstrate that using the SGD;error rate can be reduced by employing random projections in advance. © 2024 IEEE.

关键词： Large datasets

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2024 3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

2024 3rd International Conference on Advancement in Electric...

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

ISBN: (纸本)9798350388282

The proceedings contain 180 papers. The topics discussed include: optimizing diabetes prediction accuracy: a comprehensive approach with advanced preprocessing and diverse machine learning classifiers;designing a graphics processing unit for 2D rendering on FPGA for educational purpose;an ensemble approach of transfer learning and vision transformer to identify COVID-19 from chest X-rays;advancing water vending industry through RFID and IoT empowerment;voltage harmonics mitigation of non-linear loads using model predictive control in a three phase system;design and analysis of a 6G terahertz aeronautical antenna based on graphene;circularly polarized single layer large scale microstrip patch array antenna for wireless communications;and enhancing autism spectrum disorder diagnosis through a novel 1D CNN-based deep learning classifier.

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Enhancing Power Grid Security by Detecting Relay Setting Manipulation Attacks with machine learning 3

Enhancing Power Grid Security by Detecting Relay Setting Man...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Varghese, Samya Rebecca Jiji Venu, Ankita Vijaya Chandrakala, K.R.M. Gireesh Kumar, T. Arun Shankar, V.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Danfoss Industries Private Limited United States

ISBN: (纸本)9798350372120

As power grids undergo extensive digitalization, they become susceptible to diverse cybersecurity threats, including potentially manipulating protective relay settings. Traditional detection methods struggle to identify these attacks effectively, posing a significant risk of widespread disruptions. This research introduces a machine learning-based approach for detecting and categorizing attacks that manipulate relay settings. The experiments evaluate the efficacy of various algorithms, including Random Forest, XGBoost, and Decision Trees, and Random Forest emerged as the best-performing model, showcasing the capabilities of machine learning to enhance the security of power grid operations. © 2024 IEEE.

关键词： Anomaly detection

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Breast Cancer Classification using Metaheuristic Optimization and machine learning 3

Breast Cancer Classification using Metaheuristic Optimizatio...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chandra, Avinash Kadam, Ayush Sangram Jain, Palak Kumawat, Mohit Patil, Hemprasad Yashwant Gawas, Mahadev Anant Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India State Higher Education Council Directorate of Higher Education State Government of Goa Goa India

ISBN: (纸本)9798350372120

This research utilizes advanced machine learning techniques, specifically focusing on Decision Tree, Naive Bayes, Random Forest, and Ada Boost models, to conduct a thorough examination of breast cancer prognosis. Our findings reveal compelling insights into the effectiveness of diverse machine learning models in assessing breast cancer. The Decision Tree model achieves an impressive accuracy of 93.5%, accompanied by a precision rate of 86.73%. The Naive Bayes model demonstrates high accuracy, reaching 95.5%, with a precision of 91.32%. Notably, the Random Forest model stands out with exceptional performance, achieving an impressive accuracy of 98.3% and a precision of 96.5%. The Ada Boost model also proves to be robust, boasting an accuracy of 97.0% and a precision of 93.37%. After performing metahueristic hyperparameter tuning with differential evolution algorithm and derived feature engineering we have achieved accuracy of 98.83% and 98.41% precision. This study serves as a valuable resource for healthcare professionals, researchers, and policymakers, providing essential insights into the strengths and limitations of various machine learning models in the context of breast cancer prognosis. © 2024 IEEE.

关键词： Decision trees

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Proceedings - 2024 3rd international conference on Energy and Power engineering, Control engineering, EPECE 2024

Proceedings - 2024 3rd International Conference on Energy an...

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3rd international conference on Energy and Power engineering, Control engineering, EPECE 2024

ISBN: (纸本)9798350366099

The proceedings contain 45 papers. The topics discussed include: the application of dynamic simulation and PID control in the kinematic analysis of suspended crane;simulation of predictive maintenance model of power enterprise equipment based on deep learning algorithm;user side power supply full path online monitoring method based on machine vision technology;application of artificial intelligence technology in digital transformation of power grid;research on ventilation and noise reduction technology for substations based on deep learning;application of T-S neural network and coupling coordination degree model in the research of development trend of new energy automobile industry;visual damage detection technology for conveyor belts in coal-fired power plants;and incremental cost-driven analysis model of power grid based on multi-dimensional penetration mode of new energy.

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international conference on machine learning and data engineering, icmlde 2024

International Conference on Machine Learning and Data Engine...

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3rd international conference on machine learning and data engineering, icmlde 2024

The proceedings contain 399 papers. The topics discussed include: detection and classification of potato leaves diseases using convolutional neural network and Adam optimizer;explainable ai based lung and colon cancer diagnosis using enhanced feature analysis;wireless sensor network optimization and management with EH and machine learning prognostication algorithm;effective filtering methods for visibility of enhanced mammogram vessels;virtual assistance system for visually impaired people using deep learning;space debris surveillance: insights on trajectory using two-line element sets;a residual network and bi-directional LSTM based hybrid approach to remote sensing image captioning;and electronic nose using quartz tuning fork sensors and machine learning for detection of ketoacidosis.

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