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A spatial structural similarity triplet loss for auxiliary vehicle re-identification

science China(Information sciences) 2021年第7期64卷 239-240页

作者： Jianqing ZHU Liu LIU Xiaobin ZHU Huanqiang ZENG College of Engineering Huaqiao University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney Department of Computer Science and Technology University of Science and Technology Beijing College of Information Science and Engineering Huaqiao University

Dear editor,Vehicle re-identification aiming to match vehicle images captured by different cameras plays an essential role in video surveillance systems for public security. However, vehicle re-identification is a challenging computer vision problem because vehicle images usually contain a list of adverse factors, such as viewpoint variations, blurs, and ***, how to design an effective vehicle re-identification method has received increasing attention.

关键词： vehicle re-identification spatial strutural simialrity triplet loss deep learning video surveillance system

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Shubhi Srivastava Bhargav H K Vijayakumar Gurani Arpana Chaturvedi Ankita Gandhi Yogesh Dharangutti Department of Information Science and Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Tumakuru India Department of Computer Science Karnatak University Dharwad India Department of IT/AI/ML New Delhi Institute of Management New Delhi India Department of Computer science and Engineering Parul Institute of Engineering and Technology Gujarat India Faculty of Law Symbiosis Law School Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

This paper presents a novel ai-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios.

关键词： Interpolation Visualization PSNR Superresolution Streaming media Mathematical models Real-time systems Hybrid power systems Kernel Faces

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Control of Two-Wheel Self-Balancing Robot: LQR and MPC Performance Analysis

Control of Two-Wheel Self-Balancing Robot: LQR and MPC Perfo...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Ashutosh Mishra Kritika Bansal Department of Electronics Engineering Madhav Institute of Technolgy and Science Gwalior India Centre for Artificial Intelligence (AI & ML) Madhav Institute of Technolgy and Science Gwalior India

This paper presents the design and comparative analysis of Linear Quadratic Control and Model Predictive Control strategies for a two-wheel self-balancing robot. A detailed mathematical model of the two-wheel self-balancing is provided. After that, the Linear Quadratic Regulator and Model Predictive Control law are designed. In Order to compare the performances of both the controllers, various performance indices such as settling time and peak overshoot value are evaluated. The robustness of these methods is tested against impulse and sinusoidal disturbance. The control signals from linear quadratic regulator and model predictive controllers are also examined. The simulation results are obtained using MATLAB wherein it is observed that model predictive control performed better in both tracing the set points and generating minimal control signal.

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Enhanced accuracy of deep learning method for fruit images classification 25

Enhanced accuracy of deep learning method for fruit images c...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Arabiat, Mohammad Abuowaida, Suhaila Alshdaifat, Nawaf Aburomman, Ahmad Al Henawi, Esraa Dmour, Mohmmad Elrashidi, Ali Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In the last few years, artificial intelligence has shown its importance and ability in computer vision due to its high level of accuracy and wide acceptance. The main goal of the research is to look into a better way to separate different types of fruits. Although deep learning is used, the standard classification methods for different types of fruit don't work very well. The main steps of the recommended better multiple fruit classification method are: 1) Making changes to the filter's size. Selecting the most suitable number of repeats is part of the process of improving the ResNet-101 block. 3) It is proposed that there be one more convolutional layer. Exists support for the suggested method after collecting and labeling 27,000 pictures as part of an actual study. The dataset with many fruits is split into two parts: 80% is used for training and 20% is used for testing. These pictures were used to train an application based on deep learning (DL) to help successfully determine multiple fruit objects in images. Then and then possible to compare the results of this method to the most current methods in the area of object division. The application being studied at does better than expected in terms of accuracy AP in a range of levels. The values always go farther than the equivalent measures of three widely used methods. Compared to the other methods, the suggested one takes less time to train. There are higher numbers for the thresholds than there are for the state of the art method's thresholds © 2024 IEEE.

关键词： Deep reinforcement learning

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Fog enabling technologies in healthcare: A review

Fog enabling technologies in healthcare: A review

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Yadav, Aditya Pradhan, Onesimus Chandra Khanam, Ruqaiya Amrita Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India Department of EECE Center for AI in Medicine Imaging and Forensic Sharda University U.P Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India

ISBN: (纸本)9781032669663

This paper provides an overview of fog computing healthcare technologies that are playing a huge role in the current healthcare Industry. In this paper, we suggest the Data mining is the most effective option for disease prediction. The suggested framework intends to support health care based on disease symptoms, and health-related data. Gathering and analyzing activity-specific data that may be utilized to gauge the health benefits of exercise and its intensity as well as provide suggestions. Health Informatics makes extensive use of wireless sensor networks (WSNs). Patients at risk for chronic illnesses are increasingly being monitored via wireless and wearable sensors. This ensures that patients follow their treatment schedules and protects them from unexpected assaults. Along with its abilities, the fog computing framework also brings over from the cloud computing platform a number of security flaws that must be solved for the benefit of the user. Fog computing is crucial because it aids in the system’s elimination of latency problems, which are crucial in the healthcare sector. © 2024 The Author(s).

关键词： Fog computing

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Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks 5

Endoscopic Image Processing: Evaluating the Robustness of De...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gireesh Babu, C.N. Babu, Kaparapu Satish Suneel, Sajja Rao, K.Srinivasa Muralitharan, Divya Aarthi, R. Bms Institute of Technology and Management Dept.of CSE/ISE Karnataka Bengaluru India Geetanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Hyderabad Telangana Dundigal500043 India K.S.R.M. College of Engineering Department of Ai&ml Andhra Pradesh Kadapa516003 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798331504403

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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Novel design of recurrent neural network for the dynamical of nonlinear piezoelectric cantilever mass–beam model

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European Physical Journal Plus 2024年第1期139卷 1-34页

作者： Kausar, Aneela Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Zameer, Aneela Shoaib, Muhammad Graduate School of Engineering Science and Technology (Computer Science and Information Engineering) National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Nilore 45650 Pakistan AI Center Yuan Ze University Taoyüan 320 Taiwan

Piezoelectricity spans a wide range of fields, showcasing its practical significance. In the realm of consumer electronics, piezoelectric materials are utilized in buzzers, sensors, and actuators. These components find applications in everyday devices such as mobile phones, wearable gadgets, and home appliances. The aim of study is to present a novel design of layered recurrent neural network (LRNNs) for solving nonlinear piezoelectric cantilever mass–beam (PE-CMB) model representing the excitation frequency dynamics. The proposed PE-CMB model is mathematically represented with highly nonlinear second-order differential system. The synthetic dataset is created using Adams numerical scheme for piezoelectric cantilever beam system, and the data are randomly segmented for training samples to formulate the network and testing samples to record performance on unbiased data and validation samples for controllability of adaptive procedure of training the weights to approximate the solutions of PE-CMB with LRNNs. The efficiency and consistent performance of the LRNNs design are validated through an evaluation process encompassing measures such as achieved accuracy using mean squared error-derived cost functions, plots illustrating learning curves, adaptively controlled parameters, solution dynamics along with associated error analysis, and a statistical analysis involving distribution of error for each instances via histograms and regression metric. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Real-Time IoT Cybersecurity using Machine Learning-based ai Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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Vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023 7

Vision-Based Object Sorting in Dynamic Environments using YO...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Sahu, Yash Kumar Mittal, Radhika Patel, Deep Maiti, Chayan Sreekumar, M. Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Kancheepuram Chennai India Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Computer Science Kancheepuram Chennai India

ISBN: (纸本)9798350305173

In the intersection of Artificial Intelligence (ai) and Robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. This paper addresses the gap by applying YOLOv4, a state-of-the-art object detection ai algorithm, within the context of the RoboCup Autonomous Robot Manipulation (ARM) Challenge - a competitive platform designed to simulate real-world complexities in autonomous systems. The research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm's high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems. © 2023 IEEE.

关键词： Object detection

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Fault Detection and Classification in Power Transmission Lines Using Discrete Wavelet Transform-Based Swish Recurrent Neural Network 3

Fault Detection and Classification in Power Transmission Lin...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Narasimhulu, N. Irfan, Md. Mujahid Sundaram, S Meenakshi Habelalmateen, Mohammed I. Gurumoorthy, Sasikumar Eee Department Ananthapuramu Gooty India Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

Transmission lines are an essential power grid component that is responsible for managing electricity from a production station to consumers and substations. The power network's transmission lines are primarily met with various faults. However, to maintain normal operation and minimize damage because of transmission line faults, it requires detecting and classifying faults. In this research, the Discrete Wavelet Transform-based Swish Recurrent Neural Network (DWT-SRNN) is proposed to detect and classify faults in power transmission lines using Deep Learning (DL). Initially, the data is acquired from the transmission line workspace by monitoring devices and sensors. Then, the Butterworth filtering is employed to increase the data quality. The DWT is performed to extract the features that transfer the time domain signal to the time-frequency signal during faulty and non-faulty conditions by decomposition. Finally, the SRNN is established to detect and classify faults in power transmission lines effectively. The existing techniques like Graph Convolutional Neural Network (GCNN), Machine Learning (ml), and Long Short-Term Memory (LSTM) are compared with DWT-SRNN. The proposed DWT-SRNN achieves a better accuracy of 99.89% compared to GCNN, ml, and LSTM respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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