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Modifying Lyapunov exponent of chaotic map by self-cascading

science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun School of Electronic and Information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Sign Language Detection to Enhance Online Communication for Deaf and Mute Individuals through Deep Learning Models

Sign Language Detection to Enhance Online Communication for ...

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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Ruslim, Gladys Jessica Salim, Nicholas Matthew Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368802

Communicating online presents significant hurdles for deaf and mute people, who frequently confront challenges due to speech and hearing limitations. These limitations can make their digital interactions difficult and irritating, as many online platforms and communication tools are not designed with their special needs in mind. This study intends to improve these people's online communication experiences by creating an object detection and classification model that detects Sign Language using computer vision. Three deep learning models, VGG16, ResNet50, and MobileNet, are evaluated using the SIBI Sign Language Dataset. The comparison focused on determining the most effective approach for accurate sign language detection. The results show that the VGG16 model outperforms the other models by 0.9680 accuracy and 0.9694 F1 score. By incorporating such technologies, a critical gap in digital accessibility can be closed, allowing these individuals to participate more completely in the digital world. © 2024 IEEE.

关键词： Convolutional neural networks

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Revolutionizing Safety: the Smart Accident Response System 1

Revolutionizing Safety: the Smart Accident Response System

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Nandal, Poonam Jindal, Srishty Singh, Meeta Bura, Deepa School of Engineering & Technology Department of Computer Science & Engineering Faridabad India

ISBN: (纸本)9798350356816

Due to the negligence of driver or to some exterior factors may cause many people lost their lives in road accidents. So, there is a vital requirement to develop an efficient and effective accident detection system which will help in providing early detection of accidents hereby reduces the number of collisions on the road. In this paper, a novel approach is proposed to automatically detect the accidents and providing timely intimation or alert to the authorities to reduce the number of the casualties caused due to the accidents. © 2024 IEEE.

关键词： Highway accidents

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A Multimodel Accurate Classification of Brain Tumor Based on Deep Learning Techniques 2

A Multimodel Accurate Classification of Brain Tumor Based on...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Dino, P. Ashwin Lakshmanan, S. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350365269

A brain tumor is a highly persistent disease, with over 84,000 people expected to receive a primary brain tumor diagnosis in 2023, resulting in an estimated 24,000 deaths from meningioma tumors in the same year. Magnetic resonance imaging (MRI) is the most effective technique for detecting brain tumors. Neural networks have demonstrated remarkable accuracy in solving image classification and segmentation problems. In this paper, we present a multimodal accurate classification based on neural networks to identify and classify brain tumors into glioma, meningioma, pituitary, or no tumors. Using different optimizer models has been integral to achieving higher accuracy, resulting in MobileNetV2 and MobileNetV3 consequently producing improved results for the multi-class tumor classification model. The proposed model exhibits significant performance improvements and excellent validation accuracy. © 2024 IEEE.

关键词： Tumors

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Implementing Domain Adaptation to YOLOv5 Algorithm for Pothole Detection

Implementing Domain Adaptation to YOLOv5 Algorithm for Potho...

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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Meinardy, Darlyn Budianto, Violetta Jeremy, Nicholaus Hendrik Lumentut, Jonathan Samuel School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

Potholes are a major concern for both road safety and vehicle maintenance, as they not only increase the risk of accidents but also lead to high repair costs. Traditional methods of detecting potholes manually are inefficient, time-consuming, and prone to errors, which has led to the adoption of ad-vanced machine-learning techniques. In this study, we utilize the YOLOv5 algorithm, well-known for its real-time object detection abilities, to tackle the pothole detection problem under various environmental conditions. One of the key challenges addressed is the algorithm's performance variability when exposed to different weather and lighting conditions, which can significantly impact the accuracy of detection. To mitigate these challenges, domain adaptation techniques were employed, allowing the model to adapt effectively to diverse environmental scenarios and thereby improving detection accuracy. The methodology involved training the YOLOv5 model on datasets that simulated a wide range of conditions and subsequently testing its performance on different datasets. The results indicate that domain adaptation greatly enhances the model's precision, with the highest precision metric achieved being 0.968. This study demonstrates that the integration of domain adaptation with advanced object detection algorithms like YOLOv5 can significantly improve pothole de-tection, contributing to safer roads as well as reducing vehicle damage. © 2024 IEEE.

关键词： Domain adaptation Environmental conditions Object detection Potholes YOLOv5

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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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Gait Cycle Analysis Algorithm Based on Depth Camera Measurement 12

Gait Cycle Analysis Algorithm Based on Depth Camera Measurem...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Yu, Zhenfu Guo, Shaoxiang Zhan, Xiaoyu Song, Xinyuan Dong, Junyu Ocean University of China Department of Computer Science and Technology Qingdao China

ISBN: (纸本)9798331510633

Gait analysis is widely used in the evaluate in medical gait diagnosis, patient rehabilitation training, and scientific training guidance. Gait analysis system effectively reconstruct 3D human joints model to obtain related gait information through specialized calculations. In this paper, we present a Real-Time Gait Analysis Algorithm (RTGA) based on RGB-D images input, which replaces traditional methods that capturing human body motion trail from wearable inertial measurement units (IMU) equipment. Combining real-time gait data acquisition and a deep learning-based gait medical feature analysis algorithm, we propose a gait measurement and acquisition system based on reliable 3D human joints distribution. We first take joint sequence smooth algorithm to filter the noise data the original 3D motion curve. Then, we perform joints transformation and position correction for the proposed method, which eliminates the gait cycle detection error caused by random noise interference. Finally, we propose a well-designed evaluation system to identify gait cycle and calculate each medical index (such as step width, speed, frequency). We employ Azure-Kinect DK to acquire 3D human body semantic content as input and validate the effectiveness of our RTGA system in the wild. © 2024 IEEE.

关键词： Patient rehabilitation

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Secure Data Deduplication for Efficient Cloud Storage Using Blockchain Technologies 7

Secure Data Deduplication for Efficient Cloud Storage Using ...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandra Sekar, M. Shanthi, H.J. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Secure data deduplication for efficient cloud storage using blockchain technologies addresses the challenge of reducing storage costs and improving data management in cloud environments. By leveraging blockchain technology, this approach ensures data integrity and security during deduplication processes. The system eliminates redundant data blocks while maintaining a secure authentication mechanism to verify the integrity of stored data. Blockchain's decentralized and tamper-resistant nature enhances the trustworthiness and transparency of the deduplication process. Through the utilization of cryptographic techniques and smart contracts, the system guarantees secure data handling, ensuring that only authorized users can access and modify deduplicated data. By incorporating blockchain technology, the proposed solution enhances the efficiency and security of cloud storage operations, making it a promising approach for organizations seeking to optimize storage resources and protect sensitive information. © 2024 IEEE.

关键词： Data integrity

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Thresholding Techniques Comparsion on Grayscale Image Segmentation 1st

Thresholding Techniques Comparsion on Grayscale Image Segmen...

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1st Analytics Global Conference, AGC 2023

作者： Nassa, Vinay Kumar Wedpathak, Ganesh S. Department of Computer Science and Engineering Rajarambapu Institute of Technology Rajaramnagar India

ISBN: (纸本)9783031508141

Image segmentation is a prime domain of computer vision backed by a huge amount of research involving both image processing-based algorithm and learning-based techniques. Due to this there is an upsurge in different segmentation technique from the research community. Various image segmentation techniques have their strength and weakness and some specific application are more geared up to some segmentation techniques. The automation systems like object detection, robotics and intelligent video analytics, do a lot of segmentation technique and hence there is need to evaluate the performance of these techniques. The paper implements the different types of segmentation techniques. Threshold techniques including like histogram thresholding, mean thresholding, edge thresholding, variable thresholding and percentile (P%-tile) exists. Algorithms are applied using MATLAB coding on the considered images. White Pixel Ratio (WPR) parameter is used for analysis of various methods. Similar to the theoretical concept, practical approach shows that WPR is better for histogram thresholding as compared to other techniques. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

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Robust Hand Gesture Recognition Based On computer Vision

Robust Hand Gesture Recognition Based On Computer Vision

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Tharun, R. Lakshmi, I. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350375237

Research in this field has been provoked by the developing interest in human-computer interaction. Also, normal client interfacing and encounters (NUI/NUX) that make use of a user's discourse and developments have been done. In NUI/NUX, a discourse or signal acknowledgment calculation is required. Tragically, numerous acknowledgment calculations have disadvantages since of their complex engineering and long preparation times. Pre-processing, normalization, and extraction must therefore be carried out. In this thought, we planned and actualized a hand-mouse interface that utilizes Kinect to extract a user's physical highlights in genuine time, showing a special concept known as a 'virtual screen.' The hand mouse may work in a virtual zone, which is much appreciated for this virtual show. It is conceivable to outline the arrangements on the virtual show precisely to those on the genuine screen. Based on the virtual screen concept, a hand-mouse interface jams the exceptionally instinctive nature of the past;consider upgrading the accuracy of mouse operations. We tried the precision and value of theinterface with fifty volunteers, extending in age from 15 to 50. 84D44 of members in this test of instinct were able to utilize them in less than a miniature. The precision try too illustrated the accuracy of the mouse's tap, drag, and double-click capacities. Typically, a good illustration of an interface will be utilized in the future for manual framework control. © 2024 IEEE.

关键词： computer vision

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