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Smartphone-Based Human Activity and Fall recognition Using Deep Feature Extraction and machine-learning Classifiers 17

Smartphone-Based Human Activity and Fall Recognition Using D...

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17th international Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) / 3rd international conference on Artificial Intelligence and Internet of Things (AIoT)

作者： Nooyimsai, Laksamee Pakdeepong, Onnicha Chatchawalvoradech, Supajitra Phiakhan, Tipkasem Laitrakun, Seksan Thammasat Univ Sirindhorn Int Inst Technol Pathum Thani Thailand

ISBN: (数字)9781665457279

ISBN: (纸本)9781665457279

Human activity recognition (HAR) and fall detection using smartphone sensors are currently popular because they can be extended to many useful applications especially when a person needs an urgent treatment such as a fall. Several methods based on machine learning (ML) and deep learning (DL) have been proposed to improve classification performances. In this work, we propose hybrid models of convolutional neural network (CNN) models and ML algorithms to classify human activities and falls using smartphone-sensor data. The CNN model will be used as feature extraction to extract a set of features. Thereafter, the ML algorithm will apply this set of features to predict the corresponding activity and fall. Several combinations of CNN models and ML algorithms are investigated on two public datasets: UniMiB SHAR and UMAFall. Their accuracy scores are compared in order to determine the best hybrid model. On the UniMiB SHAR dataset, the hybrid model based on the AlexNet model and the extra trees algorithm achieves the highest accuracy score of 95.27%. On the UMAFall dataset, the hybrid model based on the Xception model and the support vector machine/k-nearest neighbors/extra trees algorithms offer the highest accuracy score of 82.24%.

关键词： convolutional neural network (CNN) deep learning fall detection human activity recognition (HAR) machine learning

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Mechanical Fault Diagnosis of Transformer Core Based on VMD and GWO-HKELM 3

Mechanical Fault Diagnosis of Transformer Core Based on VMD ...

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3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Cheng, Long Ma, Hongzhong Xiao, Yusong Chen, Yuelin College of Energy and Electrical Engineering Hohai University Nanjing211100 China

ISBN: (纸本)9798350382426

Aiming at the problem that it is difficult to accurately identify the mechanical fault of the transformer core, a method of transformer core mechanical fault diagnosis based on variational mode decomposition (VMD)-multi-scale permutation entropy (MPE) and a gray wolf optimization algorithm to optimize the parameters of hybrid kernel extreme learning machine (GWO-HKELM) is proposed. Firstly, VMD is used to decompose the transformer vibration signal and select multiple IMF components that are highly correlated with the original vibration signal. Secondly, we calculate the MPE values of the IMF components and use them to construct a feature dataset. Furthermore, GWO is used to optimize the key kernel parameters of HKELM and establish a GWO-HKELM diagnostic model. Then, the feature dataset is imported into the GWO-HKELM diagnostic model for recognition. Finally, the effectiveness and superiority of the suggested method are verified through experimental data and comparison with other methods. The experimental results show that the suggested method can effectively identify different types and degrees of mechanical faults caused by transformer core looseness, with a diagnostic accuracy of over 98.0%. It can provide theoretical guidance for the formulation of on-site maintenance strategies for transformers. © 2023 IEEE.

关键词： Fault diagnosis Grey Wolf Optimization Algorithm Hybrid kernel Extreme learning machine Iron core Power transformer

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Improved Model Accuracy Based on a Simple Frame of Temporal-Correlation Representation Method in Sleep Staging 3

Improved Model Accuracy Based on a Simple Frame of Temporal-...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Zhao, Liang Liu, Guanzhou Tang, Xianwen Bai, Yang Li, Yuzhi Wang, Xingjun Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China South China University of Technology School of Electronic and Information Engineering Shenzhen China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

We proposes a new kind of contrast learning method based on Temporal-Correlation characters of electroencephalogram (EEG) used in sleep staging. This method, called simple framework of temporal-correlation representation(simTCR), was studied and tested on Open EEG dataset, sleep-EDF and clinical EEG dataset from polysomnography (PSG). Deep learning used in clinical diagnosis is often faced with problems of deficiency of well labeled labels. And physiological signal, like EEG signal, often shows temporal correlation character, which means the signal maintains one state in a period of time before it transforms into a new state. Therefore, there exist high probability that the nearby frames of EEG signal belong to same categories and shows similar characters. This character can be considered as self-supervised features and a loss function was raised here to pretrain the model. Our result shows that, (1) simTCR can improve the sleep staging accuracy based on EEG signal to 1%-17% in both clinical dataset and Open dataset, sleep-EDF;(2) The improved effect is more obvious when the training data in fine-tuning process is more less;(3) the hyper-parameter batch size plays limited roles in the accuracy improvement;(4) simTCR costs about three magnitudes of more time than the traditional supervised deep learning method. © 2022 IEEE.

关键词： Electroencephalography

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Precision Monitoring of Health-Care Using Big data and Java from Social Networking and Wearable Devices 3

Precision Monitoring of Health-Care Using Big Data and Java ...

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3rd international conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Goel, Rishabh Satish, C.J. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665453578

Wearable sensors and social networking sites help gather participant information for healthcare monitoring. Wearable sensors generate a lot of healthcare data for continuous patient monitoring. Social networking sites' user-generated healthcare data is huge and unstructured. Existing healthcare monitoring systems have trouble gathering and evaluating sensor and social network data, and traditional machine learning methods are insufficient to anticipate abnormalities in big healthcare data. A novel cloud-based healthcare monitoring architecture is presented to properly save and evaluate healthcare data and increase classification results. A Wireless Sensor Network and Big data Analytics based Intelligent Health Monitoring System (IHMS) is suggested in this paper. The suggested large data analytics engine uses data mining, ontologies, and Bidirectional Long Short-Term Memory (Bi-LSTM). data processing approaches preprocess information and minimize dimensionality. Proposed ontologies give semantic information about diabetes and blood pressure entities, aspects, and relationships (BP). Bi-LSTM categorizes information properly to forecast medication side effects and patient abnormalities. The suggested approach defines patients' health using diabetes, BP, mental health, and medicine reviews, and this model uses Java and Protégé Web Ontology Language. The findings demonstrate that the suggested system accurately manages healthcare information and predicts pharmacological side effects. © 2023 IEEE.

关键词： data Analytics

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Detecting Criminal Activities from CCTV by using Object Detection and machine learning Algorithms 3

Detecting Criminal Activities from CCTV by using Object Dete...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Singla, Surbhi Chadha, Raman CSE AIML University Institute of Engineering Chandigarh University Mohali India CSE University Institute of Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350338607

Now, a day's Crime in every country is increasing day by day. Generally, Every day we listen to the news of different crimes of different categories like rape, assault, Kidnapping ,Robbery ,ATM Theft, Murders etc happening in different states, cities , countries. Almost all the newspapers, TV channels', social media are filled with the news of Criminal activities happening all around the Whole World. In earlier times there is no method to detect Crime. After That the CCTV cameras were used to detect Crimes. But Watching these Videos manually by humans for detecting crimes is a very time Consuming process especially in today's world of Artificial Intelligence and machine *** this crime detection in CCTV surveillance becomes an important area of research in the field of machine learning. So, there is a very urgent need of the intelligent system which will detect the crimes from the real time CCTV Feed and classify them and provides an alert system to the nearest police stations and ambulances etc. So, that system will help in reducing the crime rate in any country. This paper reviews all prior research in this area, including approaches for object recognition and finding priority frames, techniques and algorithms like Yolo used to detect crimes , various datasets used and algorithms used to analyze crime data and train the *** covers the various recent trends in researches in this field and analyzing the challenges faced and various research gaps and this paper also discuss how we can overcome these gaps in research so as to develop a better intelligence surveillance system in ml field. © 2023 IEEE.

关键词： Object detection

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Federated machine learning Architecture for Heterogeneous Quantum Devices 1

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3rd international conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Singh, Nagendra Tiwari, Abhishek Khaneja, Vivek Embedded Systems Division Centre for Development of Advanced Computing Noida India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

Quantum computing shows immense potential for solving traditional, near to impossible, complex problems. It also shows how the performance and speed can be improved for the solution of existing classical problems by using quantum mechanics. However, the computation power of current computers, servers, and supercomputers is well enough to speed up the existing problem solutions. But as data generated from various resources increases rapidly, in current scenarios more than 2.5 quintillion bytes of data is generated daily and is expected to be 200+ zettabytes on the cloud by 2025. Handling and storing such a massive amount of data is a significant issue for current computing and storage devices. Quantum computing has the capability to resolve this issue with quantum rules. In this paper, the task of pattern finding and recognizing in data (i.e., machine learning) is explored with the help of quantum computing and introducing a collaborative learning architecture. It enables collaborative computation from heterogeneous quantum computing devices for machine learning tasks. Also, it can be adapted to any quantum machine learning approach and speed up the learning phase with better data privacy. The proposed scheme is implemented with the MNIST dataset to compare with current existing research work. Results show that the proposed architecture matches existing research work’s performance with the additional features of privacy and heterogeneity of quantum devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Sentiment Analysis of Handwritten and Text Statement for Emotion Classification using Intelligent Techniques: A Novel Approach 3

Sentiment Analysis of Handwritten and Text Statement for Emo...

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3rd IEEE international conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Ahamad, Rayees Mishra, Kamta Nath Birla Institute of Technology Department of Computer Science and Engineering Mesra India

ISBN: (纸本)9798350338263

Traditionally, many people still wish to write on pen and paper. However, it has some drawbacks, like accessing and storing physical documents efficiently, searching through them, and sharing them efficiently. Handwriting to Text Conversion (HTC) classifies and converts an individual's handwriting into digital form. However, HTC removes all the mentioned problems as storing, retrieving, and using the text as and when required is easier. Emotions are a basic and particularly important aspect of one's life. To understand this important aspect of an individual's life, we must detect emotions using affect data like text, voice, and image. We have used text as the effect data for this work. We can find a person's emotions behind his text by sentiment analysis. Sentiment recognition and analysis is a topic with wide research as many brands, companies, and even famous personalities are very much interested in getting feedback and thus do the evaluation of their performance and knowing what people think about them around the world. Authors have proposed a model where they collected data from social media reviews and classified it into three broad categories, which are positive, negative, and neutral. Find the emotions category viz. happiness, sadness, shame, anger, disgust, fear, surprise, or neutral from three types of classified sentiments. The proposed model combined machine learning, deep learning, and natural language processing techniques to achieve the best outcome. © 2023 IEEE.

关键词： Deep learning

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Design and Application of Human resource management system Based on data mining Technology 3

Design and Application of Human resource management system B...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Jingjiang School of Business Administration Nanchang Institute of Technology Nanchang330099 China

In order to solve the problem of low efficiency of human resource management in the traditional mode, the author proposes a research on data mining technology. The author applies the technology of data mining to human resource management, and designs the employee performance appraisal algorithm based on the analytic hierarchy process, human resource flow algorithm based on Markov model, human resource management system based on ID3 decision tree employee satisfaction algorithm, and use actual data to test the system. The result shows, in the personnel composition prediction experiment, the system prediction error is less than 0.031, and the prediction accuracy rate is over 95%. The system can predict the future personnel flow of the enterprise;In the actual use experience, the satisfaction with the system is above 75%. data mining methods are all based on examples, so each enterprise needs to use different methods to analyze the data, and then compare the analysis results, according to the analysis results, find out the method suitable for your enterprise in the follow-up human resource management work. The method adopted by the author integrates data mining into human resource management, it provides a reasonable reference for the changing trend of enterprise personnel structure, but there are systematic and complex problems in actual use, which need further improvement. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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3rd international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2022

3rd International Conference on Innovations in Computational...

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3rd international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2022

ISBN: (纸本)9789819926015

The proceedings contain 57 papers. The special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. The topics include: Attention Deficit Hyperactivity Disorder Prediction Using Resting-State Networks;Neuroinformatics Deep learning Synthesizer Based on Impulse Control Disorder Using LSTM Cells;Wasserstein GANs-Enabled Spectral Normalization on Credit Card Fraud Detection;Classification of Bipolar Disorder Using Deep learning Models on fMRI data;Predicting Schizophrenia from fMRI Using Deep learning;patrolling Robot with Facial Detection;a Graph-Based Relook Beyond Metadata for Music Recommendation;an Interpretability Assisted Empirical Study of Affective Traits in Visual Content of Disinformation;multi-scale Fusion-Based Object Detection Network for Advance Driver Assistance Systems;secured Face recognition System Based on Blockchain with machine learning;Vectorization of Python Programs Using Recursive LSTM Autoencoders;phenology Detection for Croplands Using Sentinel-2 and Computer Vision Techniques;deep learning-Based Safety Assurance of Construction Workers: Real-Time Safety Kit Detection;Automatic Detection and Classification of Melanoma Using the Combination of CNN and SVM;CyINSAT: Cyclone dataset from Indian National Satellite for Forecasting;classification of Ocular Diseases: A Vision Transformer-Based Approach;Analyzing Performance of Masked R-CNN Under the Influence of Distortions;COVID-19 Detection in Chest X-Ray Images Using Non-iterative Deterministic learning Classifier;multimodal Classification via Visual and Lingual Feature Layer Fusion;a Review on Rural Women’s Entrepreneurship Using machine learning Models;classifying Paintings/Artworks Using Deep learning Techniques;handling Class Imbalance Problem Using Feature Selection Techniques: A Review;an Intelligent Human Pose Recommendation System Using Feature Fusion Technique;ensemble machine learning Algorithms for Predicting Cardiovascular Disease;AI-Based Open

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Research on the Design of Enterprise Cost Warning System Based on data mining 3

Research on the Design of Enterprise Cost Warning System Bas...

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3rd international conference on Computer Science and Management Technology, ICCSMT 2022

作者： Jiao, Li Li, Hui Shandong Institute of Commerce & Technology Shandong Jinan250103 China

ISBN: (纸本)9798350320404

Crisis prediction for enterprises has been generated in the last century, providing an important reference for investors and managers. Meanwhile the recent emergence of machine learning, deep learning and other data mining techniques are slowly applied to the corporate crisis problem. It is important research significance to provide a strong noise-resistant, less disturbing and high-performance early warning model for enterprises through more extensive data research. In this paper, we introduce an optimized convolutional neural network model based on introducing an attention mechanism in the early warning system and compare the effect with other models. The experimental results show that the traditional Logistic model is less effective in predicting the early stage of the financial crisis, and the gap between the integrated learning and deep learning decreases in the approaching years. The performance of the convolutional neural network can surpass other models after adding the attention mechanism, and the most prominent discriminative effect is obtained in this paper. © 2022 IEEE.

关键词： Costs

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