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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Research on a data Risk Identification Technology Based on machine learning 5

Research on a Data Risk Identification Technology Based on M...

引用

5th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Shen, Wuqiang Wang, Yechao Yao, Chaosheng Zhou, Zeyuan Jin, Ge Guangdong Power Grid Corporation Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China Guizhou Power Grid Corporation Guizhou China Nari Group Corporation Nanjing China

ISBN: (纸本)9798350366174

With the development of power digitization, network transmission business traffic is increasing day by day. Various attacks and network anomalies have brought new challenges to power network space security. Aiming at the problem of low accuracy in power network traffic data risk identification, this paper studies feature selection and data risk identification, and proposes a new method for power data risk identification. In terms of feature extraction, the feature selection method is used to effectively extract the best subset;In terms of data risk identification, Smote-PSO-XGBoost model is established to efficiently and accurately identify network traffic data risk under unbalanced data sets. Experimental results show that this method has obvious advantages in recognition accuracy compared with KNN, decision tree, etc. © 2024 IEEE.

关键词： cyberspace security data risk identification Electric power digitization feature selection

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Knowledge Distillation Scheme for Named Entity recognition Model Based on BERT 5

Knowledge Distillation Scheme for Named Entity Recognition M...

引用

5th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2023

作者： Ye, Chengqiong Hernandez, Alexander A. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

In order to improve the applicability of named entity recognition model in industrial application scenarios, this paper studies the BERT named entity recognition task. From the perspective of lightweight model, an adaptive weight distillation method is designed to compress the BERT named entity recognition model. By adjusting the weight hyperparameter of distillation loss to an adaptive function, this method enables the model to automatically adjust the weight value according to the training process, avoiding the influence of manual parameter adjustment. Furthermore, by decreasing the middle hidden layer's dimension, this method compresses the teacher model from the width and incorporates the distillation of the middle Transformer layer of the model into the distillation process, allowing it to fully utilize the rich semantic information in the deep structure of the model. Ultimately, a light-weight, well-functioning model with a straightforward structure and few parameters is trained. By employing the suggested technique, the model's inference speed can be increased by seven times while reducing the number of model parameters to 15% of the original model with little loss of accuracy. © 2023 IEEE.

关键词： Distillation

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A Deep learning-Based Regression Scheme for Angle Estimation in Image dataset 5th

A Deep Learning-Based Regression Scheme for Angle Estimation...

引用

5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Rane, Tejal Bhatt, Abhishek Coll Engn Pune Pune Maharashtra India

ISBN: (纸本)9783031235986;9783031235993

A machine needs to recognize orientation in an image to address various rotation related problems. To calculate this rotation, one must require the information about different objects that present into the image. Hence this becomes a pattern recognition task. By using Deep learning this issue of calculation of image rotation can be addressed as deep learning possess excellent ability of feature extraction. this paper proposes a novel deep learning-based approach to estimate the angle of rotation very efficiently. Kaggle dataset (Rotated Coins) and Caltech256 has been used for this research, but the data available was limited hence this research utilize data augmentation by rotating the given dataset at random angles. Initially the unlabeled image has been rotated at different angles and store the values to be used as training dataset. Finally at the output a regression layer has been used to identify the angle of rotation for input image. the proposed deep learning approach provides a better result in terms of validation parameters like R-square, MSE, MAE. With proposed approach the value of R-square, MSE, and MAE for Kaggle dataset (Rotated Coins) obtained is 0.9846, 0.0013 and 0.0127 respectively. While forCaltech-256 dataset proposed approach reportedR-square, MSE, and MAE of 0.9503, 0.0039 and 0.0240 respectively. the proposed approach also helps in finding the position of an object by calculating the angle of rotation in an image.

关键词： Convolutional neural networks Deep learning Image orientation estimation Linear regression Mean squared error (MSE) Mean absolute error (MAE)

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

引用

5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

ISBN: (纸本)9789819718405

the proceedings contain 35 papers. the special focus in this conference is on Advances in Distributed Computing and machine learning. the topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled Systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing data Annotation with Convergence of Deep learning and Active learning to Enhance Credibility on Twitter datasets;statistical and Deep-learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and recognition in Drone Imagery Using FaceNet;valluvan: Processing Name Board Images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep learning-Based Hybrid Algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.

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SKTR: Trace Recovery from Stochastically Known Logs 5

SKTR: Trace Recovery from Stochastically Known Logs

引用

5th international conference on Process mining, ICPM 2023

作者： Bogdanov, Eli Cohen, Izack Gal, Avigdor Technion-Israel Institute of Technology Data & Decision Sciences Haifa3200003 Israel Bar-Ilan University Faculty of Engineering Ramat Gan5290002 Israel

ISBN: (纸本)9798350358391

Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs. In this work we formulate trace recovery, the task of generating a deterministic log from stochastically known logs that is as faithful to reality as possible. An effective trace recovery algorithm would be a powerful aid for maintaining credible process mining tools for uncertain settings. We propose an algorithmic framework for this task that recovers the best alignment between a stochastically known log and a process model, with three innovative features. Our algorithm, SKT R, 1) handles both Markovian and non-Markovian processes;2) offers a quality-based balance between a process model and a log, depending on the available process information, sensor quality, and machine learning predictiveness power;and 3) offers a novel use of a synchronous product multigraph to create the log. An empirical analysis using five publicly available datasets, three of which use predictive models over standard video capturing benchmarks, shows an average relative accuracy improvement of more than 10% over a common baseline. © 2023 IEEE.

关键词： Recovery

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New Energy Power data Prediction Research based on Cross Validation Method 5

New Energy Power Data Prediction Research based on Cross Val...

引用

5th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

作者： Sun, Lijuan Li, Lan Zhang, Xujuan Lanzhou Institute of Technology School of Computer Science & Artificial Intelligence Lanzhou China

ISBN: (纸本)9798350355253

In order to improve the prediction accuracy of photovoltaic power generation and reduce the impact of grid-connected operation of photovoltaic power station on the security, stability and economic operation of power system, a power prediction method combining data mining and machine learning algorithm was proposed. the statistical analysis technology in data mining is used to describe the real historical data of the output of a distributed photovoltaic power plant. Combined with correlation analysis, the main parameters affecting the generation power prediction are found. three power prediction models are designed based on machine learning, including linear regression, gradient boosting tree and random forest. the experimental results show that the gradient boosting tree regression prediction model has the smallest prediction error and the highest generalization ability when the full sample data set is used as the training set to train the model. this method improves the prediction accuracy of photovoltaic power generation, and plays an important role in the optimal operation of photovoltaic power station, the scheduling of photovoltaic power system and the security and stability of power grid. © 2024 IEEE.

关键词： Prediction models

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

ISBN: (纸本)9789819735228

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IoT Based Intruder Detection System Integrated with Chatbot for Authentication 5

IoT Based Intruder Detection System Integrated with Chatbot ...

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Edward Kennady, L. Bethanny Janney, J. Bernatin, T. Sathyabama Institute of Science and Technology Department of Electronics and Communication Chennai India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350321425

Internet of things (IoT) enables connectivity and interoperability between users' devices, systems, services, networks, and especially control systems. Smart video surveillance is an IOT application as it uses the internet for a variety of functions. IOT-based Intruder Detection System (IDS) is the latest technology used in a wide range of places. It serves as a remote eye for the security force. In the existing technique, the AdaBoost algorithm needs a quality data set. And also, noisy data and outliershave to be avoided before adopting this algorithm. So, in this study, machine learning techniques such as the Haar-Cascade and local binary pattern histogram algorithms are used to overcome the above disadvantage to clearly detect the face. the camera module captures an image of a person as soon as the smart home switch is pressed, sends the still image to the Android device through the Telegram app, and alerts the owner about the person who is trying to enter through the door. the entire systems programming has been established in Python and Embedded C for a controller. © 2023 IEEE.

关键词： machine learning

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Handwriting recognition for Predicting Gender and Handedness Using Deep learning 1

引用

5th international conference on Applied machine learning and data Analytics, AMLDA 2022

作者： Saraswat, Mala Agarwal, Ayushi School of Computer Science Engineering and Technology Bennett University UP Greater Noida India ABES Engineering College UP Ghaziabad India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

this research focuses on predicting the gender and handedness and also combined prediction through Handwriting recognition. In this paper we have analyzed various Deep learning approaches using different CNN models. Our proposed approach uses two data sets IAM English dataset and Real-Time dataset that was collected by us for recognizing the handwriting pattern to perform the defined task. Using different Deep learning approaches, experiments were conducted to analyze the performance on the given two datasets for predicting. Gender and handedness. Experiments showed Google Net using CNN is the most efficient for the prediction and recognition with the handwriting samples. For combined prediction the accuracy rate is around 94% with Real-Time dataset and 95% with the IAM dataset. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Analyzing Clinical data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care 5th

Analyzing Clinical Data for Improved Diagnosis Using AI/ML A...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Lakshmisha, S.K. Alam, Intekhab Bhuvana, J. Bhatt, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

this study analyzes the capability for using AI/ML algorithms to improve diagnostic accuracy in smart health care. AI/ML strategies have revolutionized clinical analysis by way of offering state-of-the-art and dependable techniques for studying clinical facts. Via using superior techniques such as machine learning, deep gaining knowledge of, reinforcement studying, temporal pattern recognition, and superior decision tree algorithms, AI/ML algorithms provide plenty extra accurate predictions of diseases and conditions from affected person records. Further, AI/ML algorithms can discover relationships between different clinical elements that aren't visible to medical doctors and clinicians. through the use of AI/ML algorithms, doctors will be able to make greater correct diagnoses and provide higher remedies to patients. the consequences of this take a look at imply that the AI/ML algorithms tested own the ability to enhance prognosis in clever healthcare in phrases of accuracy, precision, do not forget, and cost-effectiveness. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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