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检索条件"任意字段=3rd International Conference on Advanced Algorithms and Neural Networks, AANN 2023"

共 553 条记录，以下是541-550 订阅

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Empowering Textile Manufacturing: TensorFlow-Powered Fabric Anomaly Detection for Enhanced Quality Assurance

Empowering Textile Manufacturing: TensorFlow-Powered Fabric ...

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Advancement in Technology (ICONAT), international conference for

作者： R. Murugeswari D. Balakrishnan Umasree Mariappan B. SandeepNarayana D. Karishma G. Akhila Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Information Technology P.S.R. Engineering College Sivakasi Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The textile sector depends heavily on the ability to identify fabric flaws. Problems can be fixed before the fabric clients if they are discovered early. Currently, the appears assure in detecting these flaws. However, our current methods lack accuracy and are slow. Consequently, this study proposes to leverage the software TensorFlow to develop an improved fabric flaw detection method. The paper proposes the minor and major detections of different types fabrics using TensorFlow single shot detectors (SSD) and CNN. The slow and imprecise systems currently in use in the textile industry are supposed to be fixed by this new approach. CNN and SSD consistently achieve higher accuracy compared to KNN in various tasks because of their sophisticated architectures and advanced algorithms.

关键词： Accuracy Quality assurance Fault detection System performance Process control Quality control Fabrics Software Convolutional neural networks Textile industry

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Research on Bad Data Identification Method for Power Information Communication Process

Research on Bad Data Identification Method for Power Informa...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Fuxiang Zhang Zheng Li Meng Li Chong Li Haibo Sun State Grid Liaoning Information and Communication Company Shenyang China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

In traditional electrical power communication processes, the denoising ability of the existing methods for identifying faulty data is inadequate, which results in a diminished identification efficiency. To address this, the present study devises a novel method for identifying faulty data in electrical power information communication processes, grounded in wavelet analysis. Leveraging the singularity characteristic of the wavelet transformation, this method conducts localized singularity detection on the power information communication process. Following the detection, it discriminates between normal and faulty data utilizing neural network algorithms. Subsequently, to eliminate noise-containing signals within the faulty data, both soft and hard threshold denoising techniques are employed. After calculating the noise intensity, the wavelet transformation coefficients within a certain scale are arranged in ascending order based on their squares, and the likelihood estimation vector is computed. The method then proceeds to remove the noise segments within the signal, guided by the minimum and maximum values in the vector. Empirical results illustrate that this approach significantly enhances the denoising capability and bolsters identification efficiency, showcasing its potential as a robust solution in improving the quality and reliability of electrical power communication processes.

关键词： Wavelet transforms Fault diagnosis Noise reduction Noise neural networks Process control Estimation

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Enhancing Fingerprint Security Using CNN for Robust Biometric Authentication and Spoof Detection

Enhancing Fingerprint Security Using CNN for Robust Biometri...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Munish Kumar Ankita Tiwari Shilpa Choudhary Monali Gulhane Bhawna Kaliraman Rashmi Verma Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering (AI & ML) Neil Gogte Institute of Technology Hyderabad India Department of CSE Symbiosis Institute of Technology (SIT) Nagpur Symbiosis International (Deemed University) (SIU) Pune India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

Fake fingerprint recognition needs to pose a significant problem in the quickly developing field of biometric security. The creation of creative and effective spoof detection systems has become crucial as weaknesses in traditional authentication methods have been revealed. To solve this problem, this research employs Convolutional neural networks (CNNs) for next-generation biometric security, a ground-breaking approach. The proposed CNN-based fingerprint spoof detection approach utilizes deep learning inherent capabilities to extract complex patterns and information from fingerprint photos. The CNN model trains on a broad dataset containing multiple spoofing attempts to distinguish precisely between authentic and fake fingerprints. There are two significant contributions from this study. First, in order to fully train and assess the CNN model, we provide two datasets covering a wide range of fingerprint spoofing scenarios. Second, by successfully adjusting to the always-changing environment of spoofing tactics, our custom-built architecture outperforms traditional approaches and establishes new standards in detection accuracy. These results highlight CNNs' better capacity to discriminate between authentic and false fingerprints, placing them as a key component of future biometric security systems. This study highlights the value of utilizing cutting-edge technologies, like CNNs, to strengthen biometric security against new threats. By combining cutting-edge deep learning algorithms with the complexities of fingerprint spoof detection, we enter a new age of next-generation biometric security that promises enhanced accuracy and resilience in the face of shifting challenges. Comparing the suggested system to earlier methods, accuracy is 98.99%.

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advanced AI Techniques for Autonomous Moving Object Detection and Tracking

Advanced AI Techniques for Autonomous Moving Object Detectio...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： G. Meenakshi Sundaram Nalluru Mourya Sai Eatesh Manjiri Ulhas Karande Warish Patel B Kannadasan Harshal Patil Department of Electronics and Communication Engineering Aditya college of Engineering Surampalem Andhra Pradesh India Computer science and business systems R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Padm. Dr. V. B. Kolte College of Engineering Malkapur Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of Civil Architectural Engineering and planning Kannadasan Balakrishnan Research Foundation Kalieswari nagar Peraiyur Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving objects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods based solely on image information face substantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuable resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to object tracking, the study delves into the capabilities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Object Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study’s findings contribute significantly to the advancement of AI techniques, enhancing the capabilities of autonomous systems for object detection and tracking in complex and dynamic real-world environments.

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VGG-16 Technique to Reduce the Global Food Crises and Enhance the Crop Yields: Deep Learning Approaches

VGG-16 Technique to Reduce the Global Food Crises and Enhanc...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Sivakumar Depuru K Amala Pera Supriya A Basi Reddy Ravala.Sai Gireesh Dept of CSE School of Computing Mohan Babu University Tirupati India Dept.of. ECE Sri Venkateswara College of Engineering (Autonomous) Tirupati A.P India Department of Computer Application Mohan babu University Tirupati Andhra Pradesh India Dept. of. Computer Application Mohan babu University Tirupati A.P India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In the digital era, enhancing agriculture plays a critical role in addressing food crises by improving crop yield and ensuring food security. Early detection and management of crop diseases are paramount for agricultural sustainability, significantly affecting food security and farmer livelihoods. Farmers often struggle to identify crop diseases at early stages, leading to reduced yields and potential food shortages. This research aims to assist farmers in early disease identification, thereby increasing crop yield and preventing food crises. This study introduces a machine learning approach using Convolutional neural networks (CNNs), specifically the VGG-16 model, to accurately identify crop diseases. VGG-16’s architecture is particularly effective in recognizing patterns in images, making it a superior choice for diagnosing plant diseases compared to existing models. By leveraging VGG-16, the research demonstrates improved accuracy in detecting crop diseases at early stages, providing a valuable tool for farmers to manage crop health proactively. The findings of this research highlight the potential of advanced machine learning techniques to enhance agricultural practices and contribute to global food security.

关键词： Support vector machines Technological innovation Accuracy Machine learning algorithms Food security Agriculture Convolutional neural networks

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Gesture Recognition Based on Spatial-Temporal Fusion Residual Attention Network

Gesture Recognition Based on Spatial-Temporal Fusion Residua...

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Networking Systems of AI (INSAI), international conference on

作者： Puhao Liu Mengjia Wang Yang Cao School of Cyber Science and Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

In the field of gesture recognition, previous traditional algorithms need to extract and process features by manual means, and when the model encounters different expressions of gestures or different datasets, the recognition effect tends to differ greatly, and there is the problem of a single application scenario and a weak generalization ability. However, current algorithms that utilize complex computer vision and deep learning techniques also suffer from the problems of high model complexity and large consumption of computer resources. To solve the above problems, we have developed a gesture recognition network model (RATS) based on residual network blocks and multiple attention mechanisms, which can greatly reduce the computational cost while improving accuracy. RATS combines residual network blocks of dynamic graphs and a spatial-temporal attention mechanism. The key idea of the network is to build a fully connected graph based on the skeleton of the hand and use residual network blocks to preserve local features and details. Then, we use the multi-head attention mechanism to automatically learn the node features and edge information in the temporal and spatial domains, and then finally feature fusion features extracted by the attention mechanism with the local features preserved by the residual network block. We used the benchmark dataset DHG-14/28 to validate the accuracy of our model. The experimental results show that our model has superior performance compared to current mainstream models. Our study provides a valuable contribution to the development of this field and will serve as a reference for future related research.

关键词： Hands Deep learning Attention mechanisms Accuracy Computational modeling Heuristic algorithms Gesture recognition Feature extraction Skeleton Residual neural networks

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Utilizing Generative Adversarial networks Compensator in 4X4 MIMO 5G Backhaul Convergence with Radio-over Fiber (RoF) System

Utilizing Generative Adversarial Networks Compensator in 4X4...

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Advances in Engineering Science and Technology (AEST), international conference on

作者： Sura Mousa Ali Ehab Abdulrazaaq Hussein Department of Electrical Engineering College of Engineering University of Babylon Baghdad Iraq Department of Electrical Engineering College of Engineering University of Babylon Babylon Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

The utilization of fiber systems for transmitting Millimeter-Wave (MMW) signals has gained significant traction in recent years, particularly for advanced wireless communication applications such as 5G and beyond. However, wirelessly transmitting MM-Wave radio signals via the atmosphere experiences significant signal deterioration, known as attenuation. To address this constraint, it is beneficial to utilize Radio Over Fiber (RoF) technology wherever possible. This study investigates developments in the combination of wireless and optical networks RoF and compensates for the error by Generative Adversarial networks (GAN). The proposed system is constructed by utilizing the simulation programs VPIphotonics and Python. VPIphotonics (Ver.11.1) and Python (Ver. 3.7.6) are programming platforms designed to develop algorithms, analyze data and applications, and design models in real-time to compensate for the errors that occur in the proposed system. GANs used to compensate for errors in the RoF system shows a sophisticated application of machine learning techniques to improve signal integrity and reduce distortions. The 4X4 MIMO proposed system has a bandwidth of 17 GHz and reached a data rate of 133.33 Gb/s; the centre frequency is 160 GHz. This signal is transmitted over fiber lengths (50 km) with EVM ≈ 4.5 at the 50 km distance and 4.5 % with data rate Gb/s for the 4X4 MIMO proposed system.

关键词： 5G mobile communication neural networks Millimeter wave technology Optical fiber networks Programming Generative adversarial networks Real-time systems Millimeter wave communication Signal integrity Python

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Age and Gender Classification Using Convolution neural Network

Age and Gender Classification Using Convolution Neural Netwo...

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Delhi Section conference (DELCON), IEEE

作者： Thonduru Anjum Javeriya Sumit Kumar Banshal Gnanaprakasam Thangavel Raka Moni Department of Computer Science and Engineering Alliance University Bangalore India Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This work aims to improve the accuracy of computer vision and deep learning methods for age and gender prediction in real time from individual photos or video frames. Automated techniques to recognize these characteristics have grown in significance with the emergence of social media platforms. Still, there are issues with the current approaches, especially with regard to extraneous elements that might skew forecasts, such as cosmetics, lighting, and facial expressions. Our work aims to overcome these difficulties by applying advanced algorithms that accurately assess an individual's gender and estimate their age within a given range. The goal of the project is to analyze visual input in real-time for image and video analysis by combining convolutional neural network (CNN) with open Cv. Because the model is taught to recognize and adjust to real-world situations, it is more resilient to change in environmental variables and picture quality. Our investigation shows that while determining an individual's precise age is still challenging, accuracy is much increased when estimating age ranges. Additionally, across a range of circumstances, the model produces accurate gender predictions. These results demonstrate how age and gender estimates may be enhanced by using deep learning and real-time processing techniques. Finally, this work shows that more robust age and gender prediction algorithms may be developed, which can find applications in social media and security, among other domains, by tackling the problems posed by the intrinsic heterogeneity of real-world images. Keywords-Automatic Age and Gender Identification, Real-time Processing, Deep Learning, Image and Video Analysis, Age Range Classification, Facial Feature Extraction.

关键词： Biometrics Deep learning Ethics Visualization Accuracy Social networking (online) Streaming media Real-time systems Convolutional neural networks Standards

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Research on Noise Reduction Method for Broad-Spectrum Electromagnetic Radiation Detection Data Based on Improved Wavenet Model

Research on Noise Reduction Method for Broad-Spectrum Electr...

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Energy, Power and Electrical Technology (ICEPET), international conference on

作者： Jianxin Li Zongchang Luo Jinlin Lin Jiqi Pan Mengzhu Hu Yarong Wei Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. LTD. Nanning China Guangxi Power Grid Co. LTD. Nanning China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

In the realm of electromagnetic radiation (EMR) data collection and simulation, where noise is an ever-present challenge, this study introduces a novel approach for noise reduction employing an enhanced WaveNet model. EMR is deeply intertwined with modern life, finding applications in everything from household Wi-Fi networks to advanced mobile communications and industrial instrumentation. Nevertheless, the potential health and environmental ramifications of EMR necessitate the precise monitoring and assessment of its impact. The central issue in EMR monitoring lies in effectively distinguishing the valuable signal from intrusive background noise. Our method combines the capabilities of WaveNet, a potent deep learning architecture, with a Fast Fourier Transform (FFT) module and an atention mechanism. This enhancement aims to harness WaveNet's prowess in extracting meaningful features and its innate self-atention mechanisms to elevate the quality of EMR data by reducing noise. In our comprehensive experimental investigations, we conducted a comparative analysis, piting classical filtering techniques, including median and Gaussian filters, against neural network algorithms such as LSTM, GRU, and TCN. The outcomes undeniably underscore the superior performance of our proposed method in denoising electromagnetic radiation data. This advancement not only bolsters the quality of the data but also provides substantial support for the broader endeavor of EMR monitoring and assessment. It is noteworthy that both traditional methods and advanced neural network techniques have demonstrated their effectiveness in handling EMR data denoising. However, our proposed approach, while building upon these foundations, modestly presents itself as a notable enhancement. It is this humble enhancement that we believe contributes to the ongoing pursuit of refining EMR monitoring and assessment. In summation, our research humbly advances the field of noise reduction in EMR data through a no

关键词： Adaptation models Fast Fourier transforms Computational modeling Noise reduction neural networks Noise Electromagnetic radiation

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3rd international conference on Ubiquitous Security, UbiSec 2023

3rd International Conference on Ubiquitous Security, UbiSec ...

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3rd international conference on Ubiquitous Security, UbiSec 2023

ISBN: (纸本)9789819712731

The proceedings contain 29 papers. The special focus in this conference is on Ubiquitous Security. The topics include: Impact of Library Code in Binary Similarity Systems;how Does Post-quantum Cryptography Affect Central Bank Digital Currency?;FRAD: Front-Running Attacks Detection on Ethereum Using Ternary Classification Model;a Comprehensive Survey of Attack Techniques, Implementation, and Mitigation Strategies in Large Language Models;process Mining with Programmable Logic Controller Memory States;honey-Gauge: Enabling User-Centric Honeypot Classification;Improving DNS Data Exfiltration Detection Through Temporal Analysis;deploying Post-quantum algorithms in Existing Applications and Embedded Devices;SCOrd: Shuffling Column-Oriented Relational Database to Enhance Security;A SLAHP in the Face of DLL Search Order Hijacking;poison Egg: Scrambling Federated Learning with Delayed Backdoor Attack;channel Spatio-Temporal Convolutional Network for Trajectory Prediction;multi-NetDroid: Multi-layer Perceptron neural Network for Android Malware Detection;SmartBuoy: A Machine Learning-Based Detection Method for Interest Flooding Attacks in VNDN;loft: An Architecture for Lifetime Management of Privacy Data in Service Cooperation;privacy-Preserving Blockchain-Based Traceability System with Decentralized Ciphertext-Policy Attribute-Based Encryption;a Probability Mapping-Based Privacy Preservation Method for Social networks;truFaaS - Trust Verification Framework for FaaS;detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence;privacy Preserving Elder Fall Detection Using Deep Learning;research on Authorization Model of Attribute Access Control Based on Knowledge Graph;simulation of Mixmining Reward Parameters for the Nym Mixnet;blockchain-Based Privacy-Preservation Platform for Data Storage and Query Processing;is It Really You Who Forgot the Password? When Account Recovery Meets Risk-Based Authentication;a Unified Knowledge Graph to Permit Inte

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