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DSWHAR: A Dynamic Sliding Window Based Human Activity recognition Method 19

DSWHAR: A Dynamic Sliding Window Based Human Activity Recogn...

引用

2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Sun, Li Yang, Xiaodong Hu, Chunyu School of Computer Science and Technology Jinan250353 China Shandong Academy of Intelligent Computing Technology Jinan250101 China Institute of Computing Technology Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China

ISBN: (纸本)9798350346558

Sensory data has been widely used for human activity recognition (HAR), where sliding window (SW) is one of the typical methods to segment continuous signals. Most existing HAR methods select fixed-length sliding windows for different activities. However, with complex and diverse human activities, a single fixed sliding window is not suitable for acquiring features of different activities. Some studies have dynamically adjusted the window size based on the activity of feature difference segmentation, but the continuous occurrence of the activity can lead to an oversized window for segmentation and easily introduce additional noise leading to degradation of classification performance. To tackle the above problem, we propose a novel dynamic sliding window based human activity recognition method, called DSWHAR. In the training phase, DSWHAR firstly explores the relationship between the optimal sliding window length and the signal spectrum of each target activity, then a candidate window set is built for the target activity and multiple classifiers are trained using different windows. During the recognition phase, the latest prediction probability is used to dynamically select a suitable window for the next prediction, and the corresponding classifier is used for the final recognition. We compare DSWHAR with the method with fixed sliding windows and dynamic sliding window algorithm on the MHEALTH dataset, respectively, and the experimental results show that DSWHAR not only achieves better overall HAR accuracy but also has unbiased performance on different activities. © 2022 IEEE.

关键词： pattern recognition

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Solving Numerical Puzzles and Sudoku using pattern recognition and Image Processing 22

Solving Numerical Puzzles and Sudoku using Pattern Recogniti...

引用

2nd international conference on computing Advancements: Age of computing and Augmented Life, ICCA 2022

作者： Akhter, Shanjidah Sazzad, Mehedi Hasan Asif, Samir Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9781450397346

Puzzles are a useful means to test an individual's critical reasoning and ingenuity. In a puzzle, to arrive at the solution one must follow a precise logical way abiding by certain pre-defined rules. Puzzles are not only a mere form of entertainment, but it also contributes significantly to advanced areas of computational research and intelligent system design and testing. Solving puzzles often require pattern recognition in a logical sequence. Puzzles based on numerical computations may provide a platform to evaluate an algorithm's performance. Among various types of numerical puzzles, Sudoku is a popular variant that is used and analyzed a lot in mathematical and computational research and algorithm designing. In this work, a 9×9 puzzle grid is studied, and its solution is generated using backtracking. Then the patterns and line segments of the puzzle grid are recognized, and all the intersection points are detected. Finally, the row images are extracted and the digits from the images are detected by comparing them with template data using pattern recognition and image processing techniques using MATLAB. © 2022 ACM.

关键词： Image processing

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A Systematic Review on the Identification and Classification of patterns in Microservices 4

A Systematic Review on the Identification and Classification...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Norton Stanley, S.A. Sebastian, Shoney Department of Computer Science Bengaluru India

ISBN: (纸本)9798350335750

Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices. © 2023 IEEE.

关键词： pattern recognition

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FoCLBP+2DGF: Fusion of Complete Local Binary pattern with 2D Geometry Features for Facial Emotion recognition in Videos 1

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19th international conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Singh, Lovejit Singh, Sarbjeet Aggarwal, Naveen Singh, Ranjit Department of Computer Science and Engineering Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering Panjab University Chandigarh160014 India

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

People have different facial attributes in terms of their skin color, shape, and size. This diversity makes a challenge in the development of generalized automatic intelligent system for human’s emotions recognition through their facial expressions. Over the decades, researchers have significantly contributed to geometry and texture-based methods to act for facial contents in image/video in the form of feature vector. This paper presents a fusion of texture based complete local binary pattern (CLBP) method with two-dimensional (2D) geometry features (FoCLBP+2DGF) method to represent the facial expressions for emotion recognition in videos. The proposed method better describes the contrast and structural behavior of facial contents in spatial domain with fusion of CLBP and 2D geometry feature computing. It aggregates the facial expression’s behavior in temporal domain by sum normalization method to capture ratio among the features. The constructed final feature vector with proposed method is independent from the duration of video. Further, Bayesian optimization method is used to identify the best hyperparameters of multiclass support vector machines (SVMs) with proposed FoCLBP+2DGF method. Experimental results show that the proposed FoCLBP+2DGF method attained ≈ 88.64 ± 0.38% emotion recognition accuracy with SVMs using Gaussian kernel function in videos of crowd-sourced emotional multimodal actors’ dataset (CREMA-D). The proposed method significantly improved accuracy of intelligent system for human emotion recognition in videos over the human accuracy and existing work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intelligent systems

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A Brief Survey on Segmentation and Classification Techniques for Face recognition 1

A Brief Survey on Segmentation and Classification Techniques...

引用

1st international conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Rangayya Virupakshappa Uplaonkar, Deepak S Patil, Nagabhushan Sharnbasva University Kalaburagi India International Institute of Information Technology Pune India P D A College of Engineering Kalaburagi India

ISBN: (纸本)9781665497107

Face detection, registration, and identification has become a fascinating subject for researchers. The huge interest in the subject originates from the need to improve accuracy of real-time applications. Numerous techniques have been recognised and presented in recent years. Acquiring data (database), pre-processing, segmentation, feature extraction, and classification are the five stages of the whole system. This survey's goal is to provide an overview of facial recognition techniques. The survey needs to take into account research papers published between 2018 and 2021 from a number of different databases. The paper provides an overview of the work performed so far to increase the efficiency of face recognition methods using hybrid methods depend on level-set based segmentation methods, support vector machine (SVM), and random forest methods. © 2022 IEEE.

关键词： Local binary pattern

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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7 9

Research on the Detection of Abnormal Cervical Cells Based o...

引用

9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Wang, Xuewei Yuan, Shuang Yang, Lidong Niu, Dawei Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia China

ISBN: (纸本)9798350376548

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. The algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. The experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively. © 2024 IEEE.

关键词： Convolution

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Deep Learning Based Image Analysis for Classification of Foot and Mouth Disease in Cattle 5

Deep Learning Based Image Analysis for Classification of Foo...

引用

5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Shebiah, R. Newlin Arivazhagan, S. Mepco Schlenk Engineering College Centre for Image Processing and Pattern Recognition Department of Electronics and Communication Engineering Tamilnadu Sivakasi626005 India

ISBN: (纸本)9798350321425

Foot and mouth disease (FMD) poses a substantial threat to the global cattle industry, as it is a highly contagious viral infection. Timely detection and precise classification of FMD in cattle are essential for effective disease management and control. The proposed method harnesses the power of convolutional neural networks (CNNs) to automatically extract distinguishing features from cattle images and classify whether the cattle is diseased or not. Furthermore, a pre-trained architecture such as MobileNetV2 is employed and fine-tuned for FMD classification. Experiments were performed to assess the effectiveness of the proposed approach, taking into account metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate the efficacy of proposed deep learning-based classification approach, achieving impressive accuracy rates in differentiating between healthy and FMD-infected cattle. © 2023 IEEE.

关键词： Convolutional neural networks

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General pattern recognition using machine learning in the cloud 7

General pattern recognition using machine learning in the cl...

引用

7th international conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Salim, Albert Juliandry Raymond, Louis Moniaga, Jurike V Computer Science Depertment Bina Nusantara University Jakarta11530 Indonesia

Machine learning (ML) and cloud computing are two subjects that mix very well. The existence of cloud computing enables data scientists to create their machine learning models with the benefits of cloud computing which are very low cost, high performance, and high availability. This opens a new opportunity of allowing students and other people to utilize powerful machines for daily use with the help of appropriate cloud service. On the other hand, machine learning is usually perfect for recognizing patterns in our daily life. This article discusses the possibility and the benefit of creating a user-friendly general-purpose pattern recognition that can be played around by anyone using software as a service. © 2022 Elsevier B.V.. All rights reserved.

关键词： pattern recognition

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IEEE international conference on Advances in Electronics, Communication, computing and Intelligent Information Systems, ICAECIS 2023 - Proceedings

IEEE International Conference on Advances in Electronics, Co...

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1st IEEE international conference on Advances in Electronics, Communication, computing and Intelligent Information Systems, ICAECIS 2023

ISBN: (纸本)9798350348057

The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction of rheumatoid arthritis susceptibility using gene mutation rate;a survey paper on emerging techniques used to translate audio or text to sign language;a novel development of blockchain based messaging application;deep machine learning based usage pattern and application classifier in network traffic for anomaly detection;analyzing paralinguistic information from human speech and its applications in medicine;strategic placement of electric vehicle charging stations using grading algorithm;malware detection in android applications using machine learning;the impact of online reviews on product perception and purchase intention;design and implementation of smart classroom using cisco packet tracer;intrusion detection in networks using gradient boosting;real-time lane detection and departure caution gadget for automobiles based on Raspberry pi;development of a Bengali speech-based emotion analysis system;large vocabulary continuous speech recognition system for Marathi;and low-cost smart glasses for people with visual impairments.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819603121

The proceedings contain 158 papers. The special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. The topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-Thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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