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Feature-aware unsupervised learning with joint variational attention and automatic clustering 25

Feature-aware unsupervised learning with joint variational a...

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25th international conference on pattern recognition (ICPR)

作者： Wang, Ru Li, Lin Wang, Peipei Tao, Xiaohui Liu, Peiyu Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Univ Southern Queensland Sch Sci Toowoomba Qld Australia

ISBN: (纸本)9781728188089

Deep clustering aims to cluster unlabeled real-world samples by mining deep feature representation. Most of existing methods remain challenging when handling high-dimensional data and simultaneously exploring the complementarity of deep feature representation and clustering. In this paper, we propose a novel Deep Variational Attention Encoder-decoder for Clustering (DVAEC). Our DVAEC improves the representation learning ability by fusing variational attention. Specifically, we design a feature-aware automatic clustering module to mitigate the unreliability of similarity calculation and guide network learning. Besides, to further boost the performance of deep clustering from a global perspective, we define a joint optimization objective to promote feature representation learning and automatic clustering synergistically. Extensive experimental results show the promising performance achieved by our DVAEC on six datasets comparing with several popular baseline clustering methods.

关键词： Clustering algorithms

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Improved YOLO algorithm for identifying abnormal states in electric energy metering devices

Improved YOLO algorithm for identifying abnormal states in e...

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Electrical Engineering and Control Science (IC2ECS), international conference on

作者： Xiaoyi Zhang Ye Tang Sicheng Zhou Sitong Dong Hao Zhou Shaonian Hu Marketing Service Center State Grid Liaoning Electric Power Co. Ltd. (Metering Center) Shenyang China Smart Energy Efficiency Division Wasion Group Co. Ltd. Changsha China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

the energy metering device is an important component of the power system, and its normal operation is crucial for ensuring the reliability of power supply. In order to timely identify the abnormal state of the device, the time-series data of the operating state of the electric energy metering device is collected. the Fourier transform algorithm is used to obtain the frequency domain characteristics of the operating voltage data of the intelligent electric energy metering device. In order to address the problem of misjudgment that may occur when analyzing state data with high similarity using the YOLO algorithm, the K-means clustering algorithm is used to improve the YOLO algorithm. By updating the clustering center, the clustering results of the intelligent electric energy metering device data are optimized, and finally the abnormal state recognition results of the electric energy metering device are obtained. the test results verified that the voltage monitoring results of the method are basically consistent with the true values, and the extracted current operation data features are basically consistent with the actual features, with small errors and high recognition accuracy.

关键词： YOLO Time-frequency analysis machine learning algorithms Accuracy Clustering algorithms Voltage Feature extraction Object recognition data mining Monitoring

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4th international conference on IoT and Big data Technologies for Health Care, IotCare 2023

4th International Conference on IoT and Big Data Technologie...

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4th international conference on IoT and Big data Technologies for Health Care, IotCare 2023

ISBN: (纸本)9783031900839

the proceedings contain 29 papers. the special focus in this conference is on IoT and Big data Technologies for Health Care. the topics include: Research on Intelligent Mental Health Diagnosis and Treatment Big data Anomaly Early Warning Algorithm;research on Nursing Service Quality Evaluation Model Based on Internet of things and Big data Technology;research on Artificial Intelligence Medical Rapid Diagnosis Method Based on Sports Injury data;research on Hierarchical Sharing Method of Athletes’ Physical Health data Under the Background of Mobile Medicine;medical and Health Information Retrieval Method Based on Natural Language Processing;secure Storage Method of Students’ Mental Health Privacy data Based on machine learning;Personalized Recommendation of TCM Pharmacy Distance Teaching Resources Based on data mining;research on the Method of Integrating Teaching Resources of Traditional Chinese Medicine Pharmacology Based on Deep learning Algorithm;research on Patients’ Choice Behavior in Online Health Community Based on Multi Source data Analysis;multi Perspective Recommendation Method Based on Clustering Algorithm for Pharmaceutical Specialty Integration of Ideological and Political Teaching Materials;intelligent Evaluation Method of Students’ Mental Health Based on Internet of things Technology;monitoring and Warning Method of Students’ Mental Health data Based on Internet of things;design of Low Delay Communication System for Emergency Medical Rescue Vehicle Based on Narrowband Internet of things;research on Intelligent Monitoring Method of Medical Financial Information Privacy Based on Internet of things;a Method of Classifying and Querying Student Mental Health data Based on Internet of things Technology.

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Short Speech Key Generation Technology Based on Deep learning 4th

Short Speech Key Generation Technology Based on Deep Learni...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Lv, Zhengyin Wu, Zhendong Chen, Juan Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9783031200984

With the increasing popularity of biometric identity authentication in important key authentication applications, biometric key generation technology has attracted more and more attention. How to extract secure, stable and high-strength keys from biometrics has become a key research direction of biometric key generation technology. However, the proposed biometric key generation technology can not meet the security requirements as a key, that is, the key is easy to be cracked, resulting in unpredictable consequences. this paper presents a framework of voiceprint key generation based on deep neural network, which is mainly composed of voiceprint feature extraction model and fuzzy extraction model. the voiceprint feature extraction model is based on the deep neural network, and the sigmoid layer is added at the end of the neural network. this operation makes the features extracted by the neural network have high accuracy and low error recognition rate after binary quantization. the voiceprint database used in this paper is aishell-wakeup-1 wakeup word database. the voiceprint key generation model proposed in this paper generates the key on the voiceprint database with the generation intensity of more than 1000bit, the accuracy rate of more than 93%, and the error recognition rate of less than 0.001%. these data can prove that the voiceprint key generation model proposed in this paper can meet the needs of users for generation strength and security. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Advanced Seizure Prediction System for Wearable Health Management Devices using KNN Algorithm

Advanced Seizure Prediction System for Wearable Health Manag...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： M. Raja Suguna Nithisha J A. Ramesh Babu M.H Ananda R. Venkatesan N. Malathi RMK College of Engineering and Technology Chennai Tamil Nadu India SRM Institute of Science and Technology Chennai Tamil Nadu India Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India REVA University Bangalore Karnataka India Muthayammal Engineering College Namakkal Tamil Nadu India Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

the K-Nearest Neighbors (KNN) algorithm is used to provide a novel approach to predicting seizures in wearable health monitoring devices. Physiological signals, such as EEG and ECG, are used in the preprocessing and analysis of real-time data performed on a Raspberry Pi platform by the system. After being trained on a large dataset that comprised normal and seizure states, the KNN model demonstrates good classification accuracy. the wearable equipment, intended to be unnoticeable, performs continuous analysis of physiological data and triggers alerts upon recognizing patterns that point to the possibility of an oncoming seizure. this cutting-edge gadget has the ability to identify seizures in their earliest stages, allowing for timely medical attention and intervention to be provided to patients. It is easily incorporated into one’s routine and activities. the research highlights the importance of machine learning in healthcare wearables and the efficacy of the KNN algorithm for predictive analytics in neurological illnesses. It is also shown in the research that machine learning is relevant to healthcare wearables. Because of the study’s emphasis on neurological disorders, this result is significant.

关键词： machine learning algorithms Accuracy Medical services machine learning Prediction algorithms Real-time systems pattern recognition

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Development of an object recognition algorithm based on neural networks With using a hierarchical classifier

Development of an object recognition algorithm based on neur...

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Nguyen, V. T. Pashchenko, F. F. Moscow Inst Phys & Technol Moscow Russia Russian Acad Sci VA Trapeznikov Inst Control Sci Moscow Russia

this paper proposes the architecture of a convolutional neural network that creates a neural network system for recognizing objects in images using our own approach to classification using a hierarchical classifier. the architecture will be assigned to find the optimal solution to the problem for many sets of image data and, unlike existing approaches, will have high performance indicators without losing the number of parameters during recognition, and most importantly, the best value of object recognition accuracy compared to existing models of convolutional neural networks. the main attention is paid to the approach to training such a network and conducting experiments on the generated samples of various datasets using graphic processing units (GPUs). (C) 2021 the Authors. Published by Elsevier B.V.

关键词： neural network machine learning image recognition during shooting optimization algorithm for convolutional neural networks hierarchical classifier

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Supervised ML based Model to Predict Satisfaction of Student Performance During the Pandemic

Supervised ML based Model to Predict Satisfaction of Student...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Prajkta P. Chapke Anjali B. Raut Department of Computer Science and Engineering H.V.P.M’s C.O.E.T. Amravati India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

the coronavirus, COVID-19 has spread around the globe, causing unprecedented changes in major areas including education and the economy. Amid a pandemic, the educational sector is relying heavily on performance prediction models to aid in the study of student status and the subsequent improvement of performance. the term “Educational data mining” describes the practice of using data mining principles and methods in the classroom. the explosion of educational data in recent years has made educational data mining a hot issue. Methods for discovering latent patterns in educational data might be developed within this area. the data can be learned more about student’s, their learning habits, and how well they will do in school by applying the techniques drawn from Educational data mining. this study aims to predict how universities would modify e-learning in the aftermath of the outbreak by investigating student’s actual experiences utilizing e-learning facilities during this time. A formal questionnaire was distributed and an online survey was created for this purpose. Following an exhaustive survey, a good outcome was achieved in terms of the dataset. In addition, the selected supervised machine learning algorithms such as Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector machine (SVM), and XG Boost (XGB) are utilized to train the prediction model to determine how likely students are to adopt the e-learning approach in the future. MLP produced the best results compared to other approaches, with an accuracy of 84.1%. the unexpected emergence of the epidemic caused tremendous disruptions to the academic pursuits of people.

关键词： Support vector machines Surveys Radio frequency COVID-19 Electronic learning Pandemics Biological system modeling

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Concept Drift Detection by Tracking Weighted Prediction Confidence of Incremental learning 2022

Concept Drift Detection by Tracking Weighted Prediction Conf...

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4th international conference on Image, Video and Signal Processing, IVSP 2022

作者： Wang, Pingfan Woo, Wailok Jin, Nanlin Davies, Duncan Computer Information and Sciences Northumbria University Newcastle United Kingdom Department of Computing Xi'an Jiaotong-liverpool University China Notify Technology Ltd Newcastle Upon Tyne United Kingdom

ISBN: (纸本)9781450387415

data stream mining is great significant in many real-world scenarios, especially in the big data area. However, conventional machine learning algorithms are incapable to process because of its two characteristics (1) potential unlimited number of data is generated in real-time way, it is impossible to store all the data (2) evolving over time, namely, concept drift, will influence the performance of predictor trained on previous data. Concept drift detection method could detect and locate the concept drift in data stream. However, existing methods only utilize the prediction result as indicator. In this article, we propose a weighted concept drift indicator based on incremental ensemble learning to detect the concept. the indicator not only considers the prediction result, but the change of prediction stability of predictor with occurs of concept drift. Also, an incremental ensemble learning based on vote mechanism is especially used to get constantly updated value of indicator. Based on the experiment result on both benchmark and real-world dataset, our method could effectively detect concept drift and outperform other existing methods. © 2022 ACM.

关键词： Forecasting

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A Novel Approach using machine learning and NLP for Revolutionizing Pali Manuscript Conservation

A Novel Approach using Machine Learning and NLP for Revoluti...

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Sustainable Expert Systems (ICSES), international conference on

作者： Sangita R. Gudadhe Aashish A. Bardekar Amitkumar B. Ranit dept. of Computer Science & Engineering Sipna COET Amravati Amravati India dept.of Civil Engineering Prof. Ram Meghe CEM Badanera Amravati India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

the preservation and analysis of ancient Pali manuscripts are crucial for understanding India's cultural heritage. However, the unique shapes of Pali characters present significant challenges for accurate identification and classification. this research proposes a novel system that utilizes machine learning, image processing, and natural language processing (NLP) to convert Pali characters into understandable formats and decipher the Pali Prakrit language. By combining computer science and linguistics, the proposed model aims to effectively extract data from Pali literature. the proposed model was validated using the Government of India dataset, demonstrating improved character recognition accuracy and reduced memory footprint. this research contributes to the preservation of ancient Pali manuscripts and facilitates the study and understanding of this valuable cultural heritage.

关键词： Training Accuracy Shape Computational modeling machine learning Natural language processing Cultural differences Convolutional neural networks Character recognition Testing

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Pneumonia Image recognition Based On Deep learning

Pneumonia Image Recognition Based On Deep Learning

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Information Technology,Big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Zhengyu Jiang Teoh Teik Toe Glasgow College HN University of Electronic Science and Technology of China China NTU Business AI Lab Nanyang Technological University Singapore

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

this article introduces a comprehensive method for diagnosing pneumonia using chest X-ray (CXR) images. By using deep learning models for feature extraction and machine learning methods for classification, the effectiveness of feature extraction has been evaluated using CNN, ResNet.50, and ViT models. the performance of random forest and logistic regression classifiers based on these models were compared, conducting a comprehensive evaluation of accuracy and F1 score to determine the most effective model. this study addresses the challenges of diagnosing artificial pneumonia and emphasizes the urgency of automated solutions. In the end, the combination of ResNet.50 and logistic regression achieved higher accuracy (78.68%), higher precision (99.99%), and more ideal F1 score (0.6239). these results demonstrate that the application of deep learning in clinical diagnosis can improve the clinical efficiency and accuracy of pneumonia detection, which is beneficial for the subsequent treatment of patients.

关键词： Deep learning Training Logistic regression Pneumonia Accuracy machine learning algorithms Feature extraction data models Stability analysis X-ray imaging

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