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Practically Adopting Human Activity recognition 23

Practically Adopting Human Activity Recognition

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29th Annual international conference on Mobile computing and Networking, MobiCom 2023

作者： Xu, Huatao Zhou, Pengfei Tan, Rui Li, Mo Nanyang Technological University Singapore60005510 Singapore University of Pittsburgh Pittsburgh60015543 United States The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781450399906

Existing inertial measurement unit (IMU) based human activity recognition (HAR) approaches still face a major challenge when adopted across users in practice. The severe heterogeneity in IMU data significantly undermines model generalizability in wild adoption. This paper presents UniHAR, a universal HAR framework for mobile devices. To address the challenge of data heterogeneity, we thoroughly study augmenting data with the physics of the IMU sensing process and present a novel adoption of data augmentations for exploiting both unlabeled and labeled data. We consider two application scenarios of UniHAR, which can further integrate federated learning and adversarial training for improved generalization. UniHAR is fully prototyped on the mobile platform and introduces low overhead to mobile devices. Extensive experiments demonstrate its superior performance in adapting HAR models across four open datasets. © 2023 ACM.

关键词： pattern recognition

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Human Brain Stroke Prediction using Machine Learning Methods with Synthetic Minority Oversampling Approach 7

Human Brain Stroke Prediction using Machine Learning Methods...

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7th international Women in Data Science conference at Prince Sultan University (WiDS-PSU)

作者： Mujahid, Muhammad Ayesha, Noor Azar, Ahmad Taher Saba, Tanzila Haider, Zeeshan Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou 450001 Henan Peoples R China Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Prince Sultan Univ Automated Syst & Soft Comp Lab ASSCL Riyadh 11586 Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

ISBN: (纸本)9798350395839;9798350395846

The brain is considered to be the principal anatomical component of the human body. A stroke is a medical condition defined by cerebral injury that occurs as a consequence of blood vessel rupture within the brain. Symptoms might become apparent when there is an interruption in the brain circulation of blood and other vital chemicals. Stroke is widely acknowledged as a significant medical disorder because to its elevated mortality rates, enduring cognitive impairments, and concomitant comorbidities. The timely recognition of the numerous prodromal manifestations linked to a stroke has the capacity to attenuate its magnitude. The primary objective of this study is to utilize machine learning methodologies in order to predict the likelihood of an early-stage occurrence of a cerebral stroke. In order to assess the efficacy of the algorithm, a dataset for stroke prediction was acquired via the Kaggle platform. Various preprocessing techniques are applied to normalize and clean the dataset for stroke prediction. This research utilized many classification models including Random Forest, Ada Boost, LightGBM,, Decision Tree (DT), Logistic Regression (LR),and Support vector machine (SVM) to perform classification tasks. The Random Forest classifier has a higher level of classification accuracy in comparison to alternative machine learning classifiers, obtaining an impressive accuracy rate of 99.45%. The results of the investigation revealed that machine learning approaches demonstrated a higher level of performance in comparison to alternative methods.

关键词： Brain stroke Prediction Random Forest Stroke data SMOTE Health Risks

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6th international conference on Smart computing and Informatics, SCI 2024

6th International Conference on Smart Computing and Informat...

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6th international conference on Smart computing and Informatics, SCI 2024

ISBN: (纸本)9789819619801

The proceedings contain 46 papers. The special focus in this conference is on Smart computing and Informatics. The topics include: Resilient Domain Authentication Framework for Enhancing Digital Identity Security;traffic Sign Detection with pattern recognition Techniques Using Image Processing;exploring Advanced Techniques in Natural Language Processing and Machine Learning for In-depth Analysis of Insurance Claims;Network Intrusion Detection with SMOTE-ENN and Deep Learning Techniques;leveraging Transfer Learning to Enhance Location Accuracy in Mapping Services: A Case Study of Google Maps;assessment of Enhanced Email Spam Detection System Through Machine Learning Algorithms;machine Learning Methods for Predicting Traffic Congestion Forecasting;hybridization of Computational Intelligence Algorithm for Scheduling of Tasks and Balancing of Load in Cloud Network;MDSV: Mobs Detection by Enhanced Fused Feature Base Deep Neural Network from Surveillance Camera;ioT-Based Solution for Enhanced Tracking of Individuals Living with Dementia;a Novel Task Scheduling Algorithm in Heterogeneous Multi-cloud Environment;Evaluating the Integration and Usage of AI in Higher Education;evaluating the Connectional Benefits of Artificial Intelligence in the Digital Classroom;Influence of AI as an Aspect of Modern Education Era in Present World;Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection;the Advancement and Utilization of Artificial Intelligence and Machine Learning in the Financial Industry and Its Impact on Macro and Microeconomics;analysis on the Cutting-Edge Approach to Assess Artificial Intelligence’s Educational Consequences in Contemporary Studies;data Analytics in Sales and Marketing: A Comprehensive Methodology for Business Analysts;Wireless Energy Transfer for UAV (Drone) Using Machine Learning.

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Research on Traffic Classification Algorithm of Power Communication Network based on Cluster Analysis 3

Research on Traffic Classification Algorithm of Power Commun...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Wang, Lijun Lu, Yi Zhu, Guopeng Wang, Qi Ma, Kai Information Telecommunications Company State Grid Shandong Electric Power Company Jinan250001 China

ISBN: (纸本)9798350319125

Power communication network is an important infrastructure in power system, and its security and stability are very important to ensure the normal operation of power system. With the development and application of power system, the traffic of power communication network is increasing, and the classification of traffic is of great significance for network management and optimization. In this paper, a classification algorithm based on clustering analysis is proposed to solve the problem of traffic classification in power communication network. The algorithm can effectively classify the traffic data and provide powerful support for the management and optimization of power communication network © 2023 IEEE.

关键词： clustering algorithms pattern recognition theory power communication networks Traffic classification

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Quantum Machine Learning Algorithms for Big Data Analytics in Cyber Security 1

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1st international conference on Intelligent computing and Big Data Analytics

作者： Das, Surajit Vishwakarma, Santosh Rao, S. Ashish Reddy, N. Darshini B V Raju Inst Technol Dept AI&DS Narsapur Telangana India

ISBN: (数字)9783031746826

ISBN: (纸本)9783031746819;9783031746826

The article, entitled "Quantum Machine Learning Algorithms for Big Data Analytics in Cyber Security," offers a pioneering investigation into the convergence of quantum computing, machine learning, and cyber security. With the increasing volume and complexity of data in the field of cyber security, traditional computing methods are struggling to efficiently and effectively handle and analyze large datasets. This study explores the revolutionary capacity of quantum machine learning algorithms to change big data analytics in the field of cyber security. Quantum machine learning algorithms utilize the distinct characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, to provide exceptional skills in identifying patterns, detecting anomalies, and making predictions in the field of cyber security. This paper provides a thorough examination of quantum machine learning methods, such as quantum neural networks, quantum support vector machines, and quantum clustering algorithms. It aims to clarify the theoretical principles and real-world applications of quantum-enhanced algorithms for big data analytics. Through the utilization of quantum computers' computing capabilities, researchers and practitioners may get access to novel insights, detect emerging threats, and effectively reduce cyber risks with unmatched speed and accuracy. The abstract closes by emphasizing the revolutionary capacity of quantum machine learning algorithms to tackle the changing obstacles of cyber security in a progressively networked and data-driven society.

关键词： Quantum computing Machine Learning Big Data Analytics Cyber Security Quantum Machine Learning Algorithms pattern recognition

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Hyperdimensional Consumer pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 5

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Goyal, Shivam Singh, Sunil K. Kumar, Sudhakar Sarin, Saket Gupta, Brij B. Arya, Varsha Chui, Kwok Tai Chandigarh College of Engineering and Technology Chandigarh Chandigarh India Asia University Taichung Taiwan Hong Kong Metropolitan University Hong Kong Hong Kong

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. This research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs' unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. This approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. This multidisciplinary effort represents a paradigm shift in quantum technology's application across consumer domains. This paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. The fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential. © 2023 ACM.

关键词： Large datasets

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A pattern recognition Model: Hand Gestures recognition using Convolutional Neural Networks

A Pattern Recognition Model: Hand Gestures Recognition using...

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2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Ayyappa Reddy, D. Esther Jyothi, V. Kasi Viswanath, J. Sampreeth Chowdary, N. Swapna, D. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350333602

Human Beings have the natural ability to recognize body and sign language easily. This is possible because of vision and synaptic interactions formed during brain development. Humans also can pick up contextual information to understand body and sign language. In order to replicate this ability in computers, it is highly required to solve several issues-the ability to differentiate between objects of interest and the background. Choosing image capture technology and image classification algorithms etc. So, Hand gesture recognition is quickly becoming an important and relevant technology. Given the recent growth of AR and VR technologies, Hand gesture recognition has become an exciting new technology field. But currently, hand gestures are very specialized and haven't acquired mainstream adoption. This study intends to show that gesture recognition can enhance many aspects of computer work and teaching. In our predicted model, the system works by the user starting the program, which would start providing the live webcam feed. The user then makes the hand gesture inside the frame of detection shown on the screen. When the hand gesture is made, the gesture is segmented and isolated. Some possible applications are to reduce the use of a physical keyboard and mouse by using hand gestures recognition to control computer operations like Pause/Play, close and open windows, and manipulate media controls. Another application is sign language. Hand gesture recognition can be used for communication and teaching purposes. © 2023 IEEE.

关键词： Gesture recognition

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Effective Early Hypothyroid Disease Prediction Using Traditional and Ensemble Machine Learning Algorithms 15th

Effective Early Hypothyroid Disease Prediction Using Traditi...

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15th international conference on soft computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Mridul, Aunik Hasan Ahsan, Nowreen Saleh, Md. Abu Asif, Asifuzzaman Afrose, Sonia Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Science Clarkson University New York United States Computer Science and Engineering Lovely Professional University Punjab India Nutrition and Food Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031810794

In recent years, there has been a noticeable rise in the prevalence of physical ailments, most notably hypothyroidism, a condition that has garnered substantial attention due to its substantial impact on a significant portion of the population. To gain a comprehensive understanding of the disease’s severity, distinguishing between standard and affected diagnostic reports is imperative. In this work, we suggest using algorithmic models to facilitate early identification and increase knowledge of possible health hazards linked to hypothyroidism. Our approach is straightforward and well-suited for the prediction of uncomplicated cases of hypothyroid illness in real-world scenarios. Our dataset, sourced from Kaggle, served as the foundation for our research, which involved the utilization of a variety of classifiers, including RF, LR, GB, KN, ABC, DT, GS, and ensemble techniques. The DT classifier proved to be the most accurate, with an astounding accuracy rate of 98.278%. The findings were quite successful. In addition, the accuracy of the Voting Classifier RDGL was 97.218%, while the Stacking Classifier RDAS demonstrated an accuracy of 97.483%. Our optimization efforts, which included hyperparameter tuning, further enhanced the performance of each classifier. Based on extensive experimentation and a review of contemporary research, our findings unequivocally endorse the Decision Tree (DT) boosting classifier as exceptionally proficient, demonstrating a remarkable accuracy rate of 98.278% in the precise prediction of hypothyroid disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bagging Boosting Ensemble Hypothyroidism

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Gesture recognition of the Kazakh alphabet based on machine and deep learning models 19

Gesture recognition of the Kazakh alphabet based on machine ...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Mukhanov, Samat Uskenbayeva, Raissa Rakhim, Abd A. Akim, Akbota Mamanova, Symbat International Information Technology University Manas St. 34/1 Almaty050043 Kazakhstan Satpayev University Satpayev St. 22 Almaty050000 Kazakhstan Universiti Tenaga Nasional Kajang50000 Malaysia

Currently, a growing body of research focuses on addressing problems using computer vision libraries and artificial intelligence tools. The predominant approaches involve employing machine and deep learning models of artificial neural networks to recognize gestures in the Kazakh Sign Alphabet (KSA) via supervised and deep learning techniques for sequential data processing. pattern recognition in this context involves identifying an object within an image, where the object can be abstract and vary in shape. We have chosen to investigate the field of gesture recognition, specifically. For recognizing Kazakh Sign Language (KSL), the initial step involves mastering the KSA. Training a neural network to recognize KSL necessitates the collection of datasets in the form of images depicting hand gestures. In this research, prominent hand gesture recognition models such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were analyzed. These models differ in their methodologies, processing times, and training data requirements. A significant aspect of this study is the application of unsupervised and supervised learning techniques including CNN, LSTM, and SVM. The experiments yielded diverse results when training neural networks for recognizing gestures in Kazakh sign language based on the dactyl alphabet. This article provides a comprehensive overview of each method, their specific purposes, and their effectiveness in terms of performance and training. Numerous experimental outcomes were documented in a table, showcasing the accuracy of recognizing each gesture. Additionally, specific hand gestures were tested in front of a camera to identify the gesture and display the result on the screen. A notable feature was the use of mathematical formulas and functions to elucidate the operating principles of the machine learning methods, as well as the logical structure and design of the LSTM model. © 2024 The Authors. Published

关键词： Convolutional neural networks

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

5th International Conference on Soft Computing and Signal Pr...

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

ISBN: (纸本)9789811986680

The proceedings contain 60 papers. The special focus in this conference is on soft computing and Signal Processing. The topics include: A Hybrid Approach on Lexical Indian Sign Language recognition;defining the Convergence of Artificial Intelligence and Cyber Security with a Case Study of Email Spam Detection;determining the Attribute Priority Among the Maintenance and Support Phases in software Development Phases Using Intuitionistic Fuzzy Analytical Hierarchy Process;Impact of COVID-19 on Energy Generation in India;decentralized Payment Architecture for E-Commerce and Utility Transactions with Government Verified Identities;a Machine Learning-Based Approach for Enhancing Student Learning Experiences;book Genre Classification System Using Machine Learning Approach: A Survey;a New Method for Imbalanced Data Reduction Using Data Based Under Sampling;Resource-Based Prediction in Cloud computing Using LSTM with Autoencoders;Arduino UNO-Based COVID Smart Parking System;low-Cost Smart Plant Irrigation Control System Using Temperature and Distance Sensors;a Novel Approach to Universal Egg Incubator Using Proteus Design Tool and Application of IoT;dynamic Resource Allocation Framework in Cloud computing;Region-Wise COVID-19 Vaccination Distribution Modelling in Tamil Nadu Using Machine Learning;detection of Phishing Websites Using Machine Learning;detection of Phishing Website Using Intelligent Machine Learning Classifiers;computational Learning Model for Prediction of Parkinson’s Disease Using Machine Learning;eliminating Environmental Context for Fall Detection Based on Movement Traces;performance Analysis of Regression Models in Stock Price Prediction;review on the Image Encryption with Hyper-Chaotic Systems;Object Detection Using Mask R-CNN on a Custom Dataset of Tumbling Satellite;decentralized Blockchain-Based Infrastructure for Numerous IoT Setup;Sentiment Analysis Toward COVID-19 Vaccination Based on Twitter Posts.

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