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A circular shifting binary descriptor for efficient rotation invariant image matching 26

A circular shifting binary descriptor for efficient rotation...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Soleimani, Parastoo Li, Kin Fun Capson, David W. Univ Victoria Dept Elect & Comp Engn Victoria BC Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Image matching is a key component in computer vision and has many applications. In this work, we propose a circular shifting binary descriptor to increase the speed of rotation invariant image matching algorithms. We can compute the descriptors of the rotated image patches without rotating either the sample points or the image patch by circularly shifting the binary descriptor. Thus, operations such as multiplications and divisions from the orientation estimation step are eliminated from the image matching process which significantly reduces the number of operations for computing the descriptor. In addition, our experiments illustrate that the circular shifting binary descriptor shows limited rotation error in comparison with other descriptors such as ORB while attaining comparable mean average precision.

关键词： Computer vision Image matching Estimation Computer architecture Hardware pattern recognition Tuning

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A Modified Sugeno Integral based Similarity Measure for Intuitionistic Fuzzy soft Set and its Application

A Modified Sugeno Integral based Similarity Measure for Intu...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Das, Niher Ranjan Lohani, Q. M. Danish South Asian Univ Dept Math New Delhi 110068 India

ISBN: (纸本)9798350332285

In the paper, intuitionistic fuzzy soft set (IFSS) is considered for the mathematical modeling of real-world issues. The IFSS involves an extra parameter to extrapolate the traditional notion of intuitionistic fuzzy set, and hence it is highly utilized for real-life applications. The similarity measures for IFSSs are defined to deal with the problems of pattern recognition, machine learning, and decision-making. This paper introduces a Sugeno integral based similarity measure for IFSSs in which a novel concept of (alpha, beta) - cut is applied for the first time. Sugeno integral is a measure to calculate the total expected value of similarity between two IFSSs. Since IFSS consists of two independent components, (alpha, beta) - cut is used to ensure the operationalization of the measure. Here, some of the mathematical properties related to the proposed measure are also studied. Moreover, we compare the performance of the proposed measure with some well-known similarity measures over a benchmark example. We have further validated the efficiency of the proposed measure on the basis of accuracy, sensitivity, F-measure, and error rate over UCI Machine Learning Repository data set.

关键词： soft sets Intuitionistic fuzzy soft sets Sugeno integral (alpha, beta) - cut Similarity measure Performance Analysis

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pattern recognition and Prediction in Time Series Data Through Retrieval-Augmented Techniques

Pattern Recognition and Prediction in Time Series Data Throu...

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Singh, Nitya Swetapadma, Aleena Deemed to be University School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350378092

Time series forecasting is an essential technique used in various domains, with finance being one of the primary users. There are several techniques for time series forecasting, with machine learning being one of the most common practices. Machine learning models rely on numerical input with time encoded in the input for prediction. However, visual pattern-based time series forecasting is also a popular method practiced by professionals in various fields. Traditional numerical input-based models succeed in recognizing patterns but often fail when the patterns are skewed or stretched compared to those in the training data. The aim of the work is to address this issue using a visual approach. In this work, OpenAI's CLIP model has been used to perform a visual similarity search among visual time series patterns to achieve forecasting using the top k most similar patterns. The performance of the model shows it can be used effectively for time series analysis. © 2024 IEEE.

关键词： Prediction models

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A Dual-Model Anomaly Detection Algorithm for non-linear stream data in Smart City Environments 20

A Dual-Model Anomaly Detection Algorithm for non-linear stre...

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20th Annual international conference on Distributed computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Bustamante, Anthony J. Asad, Sarah Nicklas, Daniela Lagesse, Brent Univ Washington Bothell Bothell WA USA Otto Friedrich Univ Bamberg Bamberg Germany

ISBN: (纸本)9798350369458;9798350369441

In this paper, we introduce a complementary and straightforward mechanism for anomaly detection tailored for smart city infrastructures, utilizing a combination of regression algorithms. Our methodology employs two distinct regression models to generate future predictions from a given dataset. The primary model is crafted to yield high-fidelity predictions, while the secondary model is purposefully designed to introduce a degree of noise. Both models work together as a defense against Flooding attacks through the detection of abnormal levels of data inflow (detection of outliers). We calculate the alignment cost, or Euclidean distance, between the predictions from these two models, establishing a threshold against which real future traffic can be evaluated. The alignment cost or euclidean distance of the actual traffic is computed in relation to the high-quality predictions and then compared with the established threshold to pinpoint anomalies. Through experimentation with various regression algorithms, including linear regression, support vector regression, decision trees, etc., we identified an optimal combination for peak performance. Our assessments, grounded in comprehensive smart city datasets, center on the process of transforming complex non-linear data into a more appropriate form to detect anomalous data points. Conclusively, the dualmodel anomaly detection framework we propose stands out as an invaluable tool in defending smart city infrastructures from data irregularities and potential threats, highlighting the criticality of bespoke solutions in contemporary urban digital environments.

关键词： Smart City Security Flooding attacks Denial of Service Attacks(DoS) Anomaly Detection pattern recognition Outlier Detection

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Research on Artificial Intelligence (AI) recognition Automated soft-Shell Crab Breeding System under 'Internet +' Technology 3

Research on Artificial Intelligence (AI) Recognition Automat...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Zheng, Lei Gong, Mingmin Li, Zhenyang Yang, Mingming School of Business Shenzhen Institute of Technology Shenzhen China School of Information Engineering Wuhan College Wuhan China School of Information and Communication Shenzhen Institute of Technology Shenzhen China

ISBN: (纸本)9798350380989

This project aims to use artificial intelligence technology to raise crabs in land equipment, so as to easily obtain soft-shell crabs, to increase the supply of new aquatic products in the market, to meet the needs of market consumption upgrading, and finally to achieve the goal of economic benefits. This project is based on artificial intelligence to identify automatic soft-shell crab cultivation technology, combined with scientific breeding management methods, the general crab to soft-shell crab cultivation. The equipment uses the Internet of Things, big data, cloud computing, computer vision and other technologies to design five systems, including soft shell crab mounting identification system, water quality monitoring system, three-dimensional breeding system, automatic feeding system, integrated control system, to realize the local and remote all-round intelligent monitoring of soft-shell crab breeding base. In this paper, STC15F2K60S2 as the core of the design of MCU based hardware platform is presented. RS485 communication mode is used to collect data of each sensor in real time, and multiple environmental parameters can be monitored in real time. Using video surveillance technology, the pond can be secured, and the pond can be photographed on the surface and underwater. The feeding device is controlled by PLC and the feeding machine and aerator are controlled intelligential. The network uses ESP8266 wireless network to realize network connection to AP. Through the establishment of the 'server' management software, we enable users to remote query through the computer browser, mobile APP and other ways in the case of network. © 2024 IEEE.

关键词： Automatic recognition monitoring system sensor soft shell crab breeding system water quality monitoring

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Fake Currency Detection Using pattern recognition Algorithm 2

Fake Currency Detection Using Pattern Recognition Algorithm

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Ananth, N.S Yoga Suba Lakshmi, S. Narmatha, B. Sundari, G. P.S.R Engineering College Electronics and Communication Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798350384277

The creation of numerous counterfeit banknotes in recent years has had a detrimental effect on society and resulted in significant losses. Thus, it is now imperative to establish a technique for identifying fake cash. To combat this problem, the article uses a pattern recognition algorithm to provide a strong solution for fake currency identification. The aim is to create a precise and effective system that can differentiate between real currency and fake notes. In order to examine and distinguish between the complex characteristics and security elements incorporated in real money notes, The method retrieves important visual properties, including watermarks, security threads, holograms, and other security aspects, by using sophisticated image processing algorithms. After that, a machine-learning model for categorization is trained using these features. The suggested solution incorporates a broad dataset of real and fake banknotes. It uses a diverse dataset of authentic and counterfeit notes to train a machine-learning model. The system's accuracy and efficiency are evaluated against real-world counterfeit samples, resulting in a high detection rate and a low false-positive rate. This technology enhances financial system security and integrity. The system's effectiveness, reliability, and adaptability make it a crucial tool in combating counterfeit currency, safeguarding economic stability, and promoting trust in financial transactions © 2024 IEEE.

关键词： Machine learning

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Application of Wave Cluster Algorithm Based on Kernel Optimization in pattern recognition of High-Frequency Financial Data 3

Application of Wave Cluster Algorithm Based on Kernel Optimi...

引用

3rd international conference on Cloud computing, Big Data Application and software Engineering, CBASE 2024

作者： Yue, Dazhi Business School Xiamen Institute of Technology Xiamen China

ISBN: (纸本)9798331506582

High-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy. © 2024 IEEE.

关键词： Multiresolution analysis

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Differential Privacy with Weighted for Privacy-Preservation in Human Activity recognition

Differential Privacy with Weighted for Privacy-Preservation ...

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2023 IEEE international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Fujimoto, Ryusei Nakamura, Yugo Arakawa, Yutaka Kyushu University Eecs Fukuoka Japan Kyushu University Isee Fukuoka Japan

ISBN: (纸本)9781665453813

Many services based on human activity recognition (HAR) have been developed;however, user activity data include a large amount of private information. Although privacy protection is important in activity recognition, it has not been sufficiently explored. Therefore, we propose a privacy-preserving mechanism for HAR services that uses differential privacy. The proposed method reduces the user recognition accuracy to a level that satisfies the privacy requirements by adding weighted noise to the features in the learning model construction and then improves the activity recognition accuracy (service usefulness). The results indicate that when the privacy requirement is defined as less than the probability of a user being identified by chance, the proposed method improves the activity recognition accuracy by approximately 10 % compared to the conventional method. © 2023 IEEE.

关键词： pattern recognition

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Hybrid soft computing based Approach for Ageing in Face recognition 5

Hybrid Soft Computing based Approach for Ageing in Face Reco...

引用

5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Chaudhary, Jitendra Kumar Nazeer, M. Sameena Singh, Rajesh Verma, Devvret Kasar, Amit B. Dhotay, Megha Graphic Era Hill University School of Computing Bhimtal Campus Uttrakhand Bhowali India Ashoka Women's Engineering College Affiliated to JNTUA Ananthapuramu Department of ECE Andhra Pradesh Kurnool India Uttaranchal Institute of Technology Uttaranchal University Arcadia Grant Chandanwari Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India Savitribai Phule Pune University Hinjawadi International Institute of Information Technology Department of Engineering Sciences Maharashtra Pune India Computer Engineering MIT School of Polytechnic Maharashtra Pune India

ISBN: (纸本)9798350398267

Proposed Approach is a collection of methods for handling a certain class of complicated issues that call for the usage of intelligent software computers. For computational complexity issues, the merger of pattern recognition with soft Context of online like Rnns has demonstrated a bright future, giving birth to strategies and systems with great performance and speed. Utilization of this fusion approach greatly improved the quality of detecting faces in a face - recognition workflow. DCNs are used in our hybrid algorithm, which combines CNN and LSTM, and they focus on the idea of optimizing the face's deep features. Results from our suggested system's testing upon that CACD datasets are quite encouraging and supportive © 2022 IEEE.

关键词： Face recognition

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pattern recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 IEEE.

关键词： CNNs Deep learning Early detection Feature extraction GLCM Local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

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