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Automated Machine Learning Evaluation of Squat Movement patterns Using Smartphone and CoreML 12

Automated Machine Learning Evaluation of Squat Movement Patt...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Sokolowska, Marta Podpora, Michal Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole45-758 Poland

ISBN: (纸本)9798350358056

There is a vast diversity of Machine Learning use cases, and both the hardware and the methodology itself are constantly getting better. This article presents a very specific use case - the recognising, classifying, and evaluation of human physical activity by using just a video stream from a smartphone and potentially instructing people how to exercise to get better results. The paper presents two key aspects of the challenge: (1) defining the problem in the physical activity domain and choosing a proper ML approach and tool, and (2) conducting the experiment using consumer-grade hardware (a smartphone) to evaluate if the proposed methodology is applicable using an easy-to-use tool and widely accessible hardware for users with limited (or without) Machine Learning experience. Chapter II presents the process of gathering and preparing the dataset, while the next chapter presents the creation of a Machine Learning model, model training using the acquired dataset, and the model performance. It was possible to obtain the result of 92.4% validation accuracy for the squat movement pattern classification model. The trained model can be used in an ML smartphone application for evaluation of user-provided examples of squats. © 2023 IEEE.

关键词： action classiication Artiicial Intelligence Machine Learning movement patterns pattern recognition video analysis

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pattern recognition for Identifying a String Within a Text File Using Finite Automata

Pattern Recognition for Identifying a String Within a Text F...

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international conference on computing and Networking Technology (ICCNT)

作者： Mamidi Prajana Harsha Rajkumar Akepati Sai Sannidhi Kammari Vidyasri Niharika Panda Dept. of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

pattern recognition is crucial across diverse domains, including retrieval of information, data mining, and bioinformatics. Numerous algorithms exist for string matching, and finite state machines (FSM) provide a robust framework for implementing these pattern recognition algorithms. This study introduces an efficient pattern recognition application utilizing a finite state machine, using the SSFSM library in Python. The SSFSM library facilitates the creation and management of FSMs, offering methods to define states and transitions efficiently. An algorithm is proposed, that identifies strings within a text file with the help of the SSFSM library in Python. It employs deterministic finite automata (DFA) for pattern recognition, scanning text files word by word and then character by character, and recording execution times to evaluate efficiency. The algorithm’s effectiveness is demonstrated through a comparative analysis of its performance for the number of characters and size of the input file. Results demonstrate the algorithm’s effectiveness in accurately identifying and quantifying specific string occurrences within large text files. It also shows that the search time of a string increases with an increase in the frequency of the character, size of the text file, and length of the search key, underscoring its potential for broader applications. Further, our results also show that there is approximately a 20% increase in search time when the search key length increases by 1. Future enhancements may include extended functionalities, support for additional document types, and improved complexity management.

关键词： Time-frequency analysis Automata Approximation algorithms Libraries pattern recognition Complexity theory Recording Data mining Bioinformatics pattern matching

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Alzheimer's Diseases Detection by Using Xception Deep Neural Network with Data Augmentation

Alzheimer's Diseases Detection by Using Xception Deep Neural...

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2024 international conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sivakumar, Soubraylu Bharadwaj, R. Abhichandra, S. SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

In our modern age, amidst the whirlwind of progress and change, there exists a silent epidemic that transcends time and technology, affecting individuals and families in profound ways: Alzheimer's disease. This illness, which slowly causes memory loss and difficulty thinking, reminds us how delicate our minds are and how complex brain health can be. This work focuses on leveraging deep learning algorithms with data augmentation to enhance the accuracy of AD detection. The study examines four cutting-edge deep learning models: Xception, InceptionV3, EfficientNetB3, and ResNet50. Robust feature extraction from MRI images is accomplished by using ResNet50, which is renowned for its deep design with skip connections. EfficientNetB3, with its efficient design and high accuracy, contributes to effective classification of AD stages. InceptionV3's multi-level feature extraction capabilities are utilized to capture intricate patterns indicative of AD progression. Xception's depth wise separable convolutions enhance pattern recognition, aiding in precise disease detection. Utilizing deep learning algorithms, notably Xception, and employing data augmentation techniques, our study attained an exceptional accuracy of 97.18% in detecting Alzheimer's Disease, utilizing the "Alzheimer's Diseases Detection"dataset. In order to improve early diagnosis and treatment planning for Alzheimer's patients, the research intends to give a thorough examination of these deep learning methods in AD detection. © 2024 IEEE.

关键词： Deep learning Accuracy Data augmentation Feature extraction Robustness Classification algorithms Planning pattern recognition Alzheimer's disease Residual neural networks

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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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On-Chip Learning with a 15-neuron Digital Oscillatory Neural Network Implemented on ZYNQ Processor 22

On-Chip Learning with a 15-neuron Digital Oscillatory Neural...

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international conference on Neuromorphic Systems (ICONS)

作者： Abernot, Madeleine Gil, Thierry Todri-Sanial, Aida Univ Montpellier LIRMM CNRS Montpellier France

ISBN: (纸本)9781450397896

Real-time on-chip learning is an important feature for current neuromorphic computing to enable smart embedded systems capable of learning. Neuromorphic computing based on Oscillatory Neural Networks (ONNs) are networks of coupled oscillators computing with phase information. ONNs with fully-connected connections can perform auto-associative memory applications when trained with unsupervised learning rules. In this paper, we propose for the first time an architecture to perform on-chip learning with a digitally implemented ONN. We implement the digital ONN with programmable logic of a ZYNQ processor and we perform learning on the processing system of the same chip. We validate our solution on a 15-neuron ONN trained with either Hebbian or Storkey learning rules up to three patterns. We report a stable resource utilization for both learning rules and timing from 119 mu s (Hebbian) to 163 mu s (Storkey). Additionally, accuracy is equal to the off-chip learning implementation.

关键词： Oscillatory Neural Networks Auto-associative Memory On-chip Learning pattern recognition

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CNN-based Model for Gender and Age Classification based on Palm Vein Images 12

CNN-based Model for Gender and Age Classification based on P...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Hernandez-Garcia, Ruber Feng, Zheng Barrientos, Ricardo J. Manuel Castro, Francisco Ramos-Cozar, Julian Guil, Nicolas Univ Catolica Maule CIEAM Res Ctr Talca Chile Univ Sydney Sch Elect & Informat Engn Sydney Australia Univ Catolica Maule Fac Engn Sci Talca Chile Univ Malaga Dept Comp Architecture Malaga Spain

ISBN: (纸本)9781665466943

Automatically predicting gender and age group from biometrics traits is an essential and challenging task in many real-world applications. There are several works about using machine learning methods to identify human gender or age through the face, iris, or fingerprint, but only limited research about using palm vein patterns. Considering the powerful feature representation ability of Convolutional Neural Networks (CNN) and the advantages of palm vein biometrics, this paper introduces a new CNN-based method for gender and age classification based on palm vein images. Experimental results show that the proposed model is able to learn discriminative features from palm vein images for these tasks, achieving state-of-the-art results on the VERA database by using a shallow CNN architecture. Besides, the obtained results suggest the feasibility of further studies on multi-task identification approaches and the reduction of the penetration rate in massive databases.

关键词： Palm vein images Gender classification Age estimation Convolutional neural networks soft biometrics

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

2nd International Conference on Emerging Trends in Expert Ap...

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

ISBN: (纸本)9789819919086

The proceedings contain 48 papers. The special focus in this conference is on Emerging Trends in Expert Applications and Security. The topics include: Securing Account Hijacking Security Threats in Cloud Environment Using Artificial Neural Networks;ioT-Based Smart Pill Box and Voice Alert System;early Identification of Plant Diseases by Image Processing Using Integrated Development Environment;usability Attributes and Their Mapping in Various Phases of software Development Life Cycle;controlling Devices from Anywhere Using IoT Including Voice Commands;ioT-Based Air Quality Monitoring System;modeling of Order Quantity Prediction using soft computing Technique: A Fuzzy Logic Approach;development of Classification Framework Using Machine Learning and pattern recognition System;Human Part Semantic Segmentation Using Custom-CDGNet Network;current Web Development Technologies: A Comparative Review;effect on Compressive Strength of Portland Pozzolana Cement on Adding Admixtures Using Machine Learning Technique;tech Track for Visually Impaired People;intelligent Compression of Data on Cloud Storage;diagnosis of Diabetic Retinopathy Using Deep Neural Network;multi-parameter Sensor-Based Automation Farming;comparing Ensemble Techniques for Bilingual Multiclass Classification of Online Reviews;detection of Disease in Liver Image Using Deep Learning Technique;how to Quantify software Quality Factors for Mobile Applications?: Proposed Criteria;dysgraphia Detection Using Machine Learning-Based Techniques: A Survey;Designing AI for Investment Banking Risk Management a Review, Evaluation and Strategy;R-Peak-Based Arrhythmia Detection as an Impact of COVID-19;a Neutrosophic Cognitive Maps Approach for Pestle Analysis in Food Industry;assistive Agricultural Technology—Soil Health and Suitable Crop Prediction;smart Chatbot for Guidance About Children’s Legal Rights.

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Using LUPI to Improve Complex Activity recognition 1

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3rd international conference on Activity and Behavior computing (ABC)

作者： Adachi, Kohei Lago, Paula Hattori, Yuichi Inoue, Sozo Kyushu Inst Technol Kitakyushu Fukuoka Japan Univ Nacl Abierta & Distancia Bogota Colombia

ISBN: (数字)9789811903618

ISBN: (纸本)9789811903618;9789811903601

Sensor-based activity recognition can recognize simple activities such as walking and running with high accuracy, but it is difficult to recognize complex activities such as nursing care activities and cooking activities. One solution is to use multiple sensors, which is unrealistic in real life. Recently, learning using privileged information (LUPI) has been proposed, which enables training using additional information only in the training phase. In this paper, we used LUPI for improving the accuracy of complex activity recognition. In short, training is performed with multiple sensors during the training phase, and a single sensor is used during testing. We used four published datasets for evaluating our proposed method. As a result, our proposed method improves by up to 16% in F1-Score to 67% compared to the baseline method when we used random-split cross-validation of each subject.

关键词： pattern recognition

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Enhancing Foundation Model Robustness for Multi-center Real-World Medical Image Analysis 1st

Enhancing Foundation Model Robustness for Multi-center Real...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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9th international conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Ajay, D. Pon Hidaya David, P. Tirupattur635 601 India

ISBN: (纸本)9789811666155

The aim of this paper is to introduce an alternative pattern Recognizing technique using similarity measure in the spherical fuzzy set environment. In many applications of pattern recognition, the structural data is intrinsically ambiguous. It is essential to boost the descriptive power of pattern recognition in such applications, and the fuzzy technique is often used. So, a fuzzy logic (FL)-based similarity measure (SM) called Exponential similarity measure for Spherical fuzzy sets (SFSs) is applied in the field of pattern Recognizing. In this paper similarity measures known as Exponential Similarity Measure (ESM), Weighted Exponential Similarity Measure (W-ESM), Weighted Average Exponential Similarity Measure (WA-ESM), Weighted Generalized Average Exponential Similarity Measure (WGA-ESM) are developed. Spherical fuzzy set is extended from fuzzy sets to cope with uncertain situations more accurately. Further, a problem on Intrusion Detection and Risk evaluation is chosen as an application of pattern recognition and is solved using similarity measures proposed in this paper. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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