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A Novel Procedure for Recognising Human Activity Using machine learning

A Novel Procedure for Recognising Human Activity Using Machi...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Neelam Singh Mansi Bhatt Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity recognition (HAR) is one of these sensors' applications. Given the dependence on general machine learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. the study focuses on Human Activity recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used.

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Adaptability of Simple Classifier and Active learning in Music Emotion recognition 2021

Adaptability of Simple Classifier and Active Learning in Mus...

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4th international conference on Electronics, Communications and Control Engineering (ICECC)

作者： Yu, Xiaoxiao Beijing Univ Technol Beijing Dublin Int Coll Beijing 100000 Peoples R China

ISBN: (纸本)9781450389129

this article selects five common classifiers in music emotion recognition, namely SVM (kernel function: RBF), SVM (kernel function: LINEAR), kNN (K = 3) and kNN (K = 5), as well as GMM, application active learning performs music emotional classification. the experiment uses 365 music clips in the PMEmo data set, and after manual labeling, they are divided into four groups: HAPPY, SAD, ANGRY and RELAX according to the thayer emotion model and the classification effect is judged by comparing F1-measure. Experiments show that active learning can reduce the need for manual labeling by about 78%, and it can help to achieve better results with fewer labels. At the same time, among the three simple classifiers, the SVM model and active learning have a better degree of adaptation in the field of music emotion recognition.

关键词： Music emotion recognition machine learning Active learning

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Artificial Intelligence and machine learning in Human Resource Management and Market Research for Enhanced Effectiveness and Organizational Benefits 4

Artificial Intelligence and Machine Learning in Human Resour...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Indoria, Devadutta Narendra Kiran, P.B. Kumar, Anuj Goel, Manjusha Shelke, Nitin Arvind Singh, Jagendra Vikram Dev University Department of Commerce Jeypore India Mittal School of Business Lovely Professional University Phagwara India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

this study investigates the substantial implications of cutting-edge technology on forecasting market demand, improving safety, and human resource management (HRM). the study simplifies the candidate selection method by applying Long Short-Term Memory (LSTM) models to automate application screening. the use of Arduino micro controllers in conjunction with temperature, fire, and smoke sensors increases real-time reactions to potential dangers for improving safety in resource management. Artificial neural networks (ANN) are used to forecast market demand based on data from automotive sales from 2016 to 2023. the outcomes demonstrate how effective each technique was. LSTM-driven applicant screening demonstrates the capacity to boost HRM by accelerating the recruiting process. the integration of Arduino controllers demonstrates rapid and precise risk recognition, enhancing industrial safety. When it comes to forecasting automotive sales for 2022, the ANN model, in particular, predicts market demand with astonishing precision, achieving a 100% accuracy rate. this implies that it is capable of strategic planning and resource management. these findings underscore the critical role that AI-driven systems play in improving organisational performance, security, and decision-making, as well as the revolutionary potential of AI -driven systems across a wide range of industries. By acting as a sobering reminder of the dynamic interplay that happens between industry and technology, the research lays the path for a more safe and successful future. © 2023 IEEE.

关键词： Human resource management

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Survey: Emotion recognition from Text Using Different Approaches 4th

Survey: Emotion Recognition from Text Using Different Approa...

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4th international conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

作者： Shah, Aanal Chopade, Madhuri Patel, Parth Patel, Parin Gandhinagar Institute of Technology Gandhinagar India Institute of Technology & Research Gandhinagar India Ahmedabad Institute of Technology Ahmedabad India

ISBN: (纸本)9789811950360

Text Processing is a method for comprehending, analyzing, and cleaning text as well as performing actions on the same data. the technique is used to extract meaningful data from text. It is a written form of communication to express emotions through text. Happy, neutral, fear, sadness, surprise, disgust, and anger are the most common emotional expressions. As a result, in the social media era, identifying emotions from text is especially important. A survey of operational methods and approaches for identifying emotion from textual data is discussed in this paper. this research primarily focuses on existing datasets and methodologies that incorporate a Lexical keyword, machine learning and Hybrid-based approach. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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4th international conference on Computational Intelligence, Communications and Business Analytics, CICBA 2022

4th International Conference on Computational Intelligence, ...

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4th international conference on Computational Intelligence, Communications and Business Analytics, CICBA 2022

ISBN: (纸本)9783031107658

the proceedings contain 34 papers. the special focus in this conference is on Computational Intelligence, Communications and Business Analytics. the topics include: PriHealth: A Fingerprint-Based Mobile Primary Healthcare Management System;eXtreme Gradient Boosting Scheme for Fundus Vessels Separation;deep-learning Based Autoencoder Model for Label Distribution learning;a Secure Image Watermarking Technique Using Chinese Remainder theorem in Integer Wavelet Transformation Domain;a Dual Image Based data Hiding Scheme Based on Difference of Pixel Values in Integer Wavelet Transform Domain;An IWT Based Reversible Watermarking Scheme Using Lagrange Interpolation Polynomial;a New Image Encryption Based on Two Chaotic Maps and Affine Transform;miRNA and mRNA Expression Analysis of Human Breast Cancer Subtypes to Identify New Markers;a Survey on Computational Intelligence Techniques in learning and Memory;GSA Based FOPID Controller Tuning in NSOF Domain for a Class of Nonlinear Systems;artificial Neural Network for Fault Diagnosis in Active Distribution Network;A Secure Communication Gateway with Parity Generator Implementation in QCA Platform;VCI Construction and Shifting Strategy Based Steganography for 3D Images;clustering-Based Semi-supervised Technique for Credit Card Fraud Detection;fake News Identification through Natural Language Processing and machine learning Approach;Facial Emotion recognition in Static and Live Streaming Image dataset Using CNN;An UAV Assisted Need Assessment and Prediction System for COVID-19 Vaccination;Video-Based Depression Detection Using Support Vector machine (SVM);real-Time Piracy Detection Based on thermogram Analysis and machine learning Techniques;understanding the Voice of Customers Using Artificial Neural Networks: A Study of Food Aggregators in the Cachar District of Assam;personalized Healthcare Chatbot: dataset and Prototype System;telegram Bot for Emotion recognition Using Acoustic Cues and Prosody.

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A machine learning-Based Method for Assisted Analysis and Decision Making of Wafer Yield

A Machine Learning-Based Method for Assisted Analysis and De...

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machine learning and Intelligent Systems Engineering (MLISE), international conference on

作者： Jieli Qin The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fu jian Province (Xiamen Institute of Technology) Xiamen China School of Mechanical Electrical and Information Engineering Xiamen Institute of Technology Xiamen Fujian P. R. China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

With the rapid development of the semiconductor industry, the process technology has become more complex, and it is increasingly important to maximize the control of defects in production and improve wafer yield. Defects shown in Wafer Bin Maps (WBMS) are often strongly correlated with local system failures in production. In view of this, this paper proposes the convolutional neural networks (CNNs) architecture based on machine learning, and realizes the classification of hybrid defects by designing a separate classification model of minimum defects and using the probability analysis of recognition results. the purpose is to classify and statistics the faults of the wafer diagram fault diagram, perform yield analysis and calculation, identify defect types according to the wafer (fault pattern recognition), and take corresponding decisions to adjust the process in view of the systematic fault chip. the effectiveness of the method is proved by experiments, and the identification accuracy of single defects is higher, and the detection performance of mixed defects is also improved, and the overall performance is better.

关键词： Semiconductor device modeling Analytical models Accuracy Systematics Scalability Decision making Production

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A Novel Federated learning Framework for Healthcare Applications Using Wearable Devices

A Novel Federated Learning Framework for Healthcare Applicat...

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AI in Cybersecurity (ICAIC), international conference on

作者： Rajesh Natarajan Sujatha Krishna Christodoss Prasanna Ranjith Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Al-Aqr 324 Shinas Oman Computer Science Department Messiah University Mechanicsburg PA USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

A key necessity in managing signals for processing and generating useful information in the healthcare domain is the use of many algorithms. Traditional computing methods have been successful but may not be effective in the future as secure privacy matters. this has led to the increasing popularity of machine learning technologies. However, we present a novel device for devices and sensors for federated learning (FL), which is aimed at establishing a new, safer framework. this pattern uses machine learning with a lot of emphasis on the availability of the devices. In such a case, models for the detection, monitoring, and management of diseases can be created. the FL approach is also believed to be applicable to several healthcare contexts.

关键词： data privacy Accuracy Federated learning Biological system modeling Transfer learning Medical services Organizations data models Sensors Biosensors

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ResNet Optimization Model for High-resolution Range Profile Target recognition

ResNet Optimization Model for High-resolution Range Profile ...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Zhansheng Zhang Yongjie Zhang Hongli Li Zhengkun Liu Institute of Beijing Remote Sensing Device Beijing China China Academy of Aerospace Science and Innovation Beijing China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

this paper proposes a radar high-resolution range profile (HRRP) recognition algorithm based on ResNet, combined with the SE (Squeeze-and-Excitation) channel attention mechanism. In most current HRRP target recognition methods, feature extraction largely relies on manual extraction, followed by recognition through machine learning techniques. With the emergence of intelligent System-on-Chip (SoC) devices, which offer high-performance computing capabilities and low power consumption, it has become feasible to deploy deep learning algorithms on smaller radar platforms. By introducing the channel attention mechanism, the sensitivity and discriminative ability of the model to key features are enhanced, enabling ResNet to achieve high-precision recognition of different vehicle targets and corner reflector arrays. Validation using real-world data demonstrates that the proposed recognition algorithm yields superior results.

关键词： Training Attention mechanisms machine learning algorithms Accuracy Sensitivity Target recognition Computational modeling Radar Feature extraction Arrays

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Research on Face recognition System Based on Intelligent machine learning Algorithm

Research on Face Recognition System Based on Intelligent Mac...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Xiaosong Zou School of Design and Art Jingdezhen Ceramic University Jingdezhen China

this paper introduces a real-time face detection technology based on TMS320C6201. through the confidential communication between each subsystem, the synchronization of each subsystem is completed, and the real-time face recognition, feature code extraction, and the closest face matching are carried out. Firstly, Grabcut foreground extraction method is used for pre-background segmentation of recognized images to reduce external interference, and then face detection and identification are carried out according to the segmentation effect. A parallel MPI program is developed by transforming the traditional serialization-based face information updating method into a parallel method. this paper applies existing MPI-based methods and existing web-based facial information acquisition methods to improve the efficiency of existing face recognition technologies. It realizes the distributed processing of the update algorithm in the original face recognition system and enhances the ability of the system to process a large amount of data to achieve the purpose of improving the system performance. the experimental results show that the system combining grab cut and Adaboost algorithm can improve the recognition rate and detection rate, and the recognition speed is faster.

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Predictive Flood Management Using Environmental and Infrastructural data: A machine learning Approach

Predictive Flood Management Using Environmental and Infrastr...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Ezhilarasan D N P G Bhavani Saveetha School of Engineering Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Flood management has more recently become highly essential in countering the destructive impact of climatic changes, especially in vulnerable regions. this research focuses on developing a predictive framework for flood management through environmental and infrastructural data by applying machine learning methods. the proposed system will be based on multiple data sources, including meteorological data, river water levels, terrain features, land use patterns, and urban infrastructure, to enhance the accuracy and timeliness of flood warning predictions. With this paper, the author expects to develop a model that precisely predicts flood events, so that actionable insights are offered to support proactive disaster management strategies. High-dimensional data processing and pattern recognition are conducted using advanced machine learning algorithms, such as neural networks and ensemble models, that optimize the accuracy of the predictions. Real-time data, along with historical flood records, is used for training and validation. Reliability of the model is tested for various flood-prone areas through the metrics of evaluation applied: prediction accuracy, response time, and false alarm rates ensuring robustness and feasibility in operations. the expected outcomes will depict precisely how data-driven approaches might be incorporated into flood management systems that can contribute effectively to the development of resilient infrastructure and community preparation. this research underscores a scalable approach well-suited to different kinds of geographic contexts, especially the potential of machine learning toward environmental resilience.

关键词： Wireless communication Training Accuracy Soft sensors Urban areas Predictive models Mathematical models Floods Time factors Water resources

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