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10th IEEE Smart World Congress, SWC 2024

作者： You, Feiran Du, Hongyang Kang, Jiawen Ni, Wei Niyato, Dusit Jamalipour, Abbas The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia SydneyNSW2122 Australia University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331520861

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users' privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users' privacy within the PPO centralized management framework. © 2024 IEEE.

关键词： Virtual environments

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Vector Quantized Compressed Sensing for Communication-Efficient Federated Learning

Vector Quantized Compressed Sensing for Communication-Effici...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Oh, Yongjeong Jeon, Yo-Seb Chen, Mingzhe Saad, Walid POSTECH Department of Electrical Engineering Gyeongbuk Pohang37673 Korea Republic of Institute for Data Science and Computing University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States

ISBN: (纸本)9781665459754

In this paper, a communication-efficient federated learning (FL) framework is proposed, which leverages ideas from vector quantized compressed sensing, for the first time, to compress the local model updates at wireless devices in FL. For the compression, each local model update is projected onto a lower dimensional space;then, the projected local model update is quantized by using a vector quantizer. The global model update at a parameter server is reconstructed by using a sparse signal recovery algorithm on the aggregation of the compressed local model updates. A key feature of our compression strategy is that the local model update after the projection is effectively modeled as a Gaussian random vector by the central limit theorem. Inspired by this feature, the optimal vector quantizer is derived for minimizing the compression error of the local model update. Simulation results on the MNIST dataset demonstrate that the proposed framework that uses 0.5 bit to represent each local model update entry shows less than a 1% decrease in classification accuracy compared to FL without local update compression. © 2022 IEEE.

关键词： Compressed sensing

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Diagnosis of Diabetic Retinopathy Using Hierarchical Feature Learning and Modified CNN with ACAF 4

Diagnosis of Diabetic Retinopathy Using Hierarchical Feature...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sankarananth, S. Uma Maheswari, G. Arun Kumar, B. Nagasuresh, M. Arun Kumar, R. Karthiga, M. Bamnari Amman Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sathyamangalam India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Karpagam Academy of Higher Education Department of Artificial intelligence and Data Science Tamilnadu Coimbatore India Karpagam Institute of Technology Department of Information Technology Tamilnadu Coimbatore India V.S.B Engineering College Department of Electrical and Electronics Engineering Tamilnadu Karur India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Diabetic retinopathy (DR) remains one of the leading causes of vision loss and blindness today, making the need for accurate and reliable detection techniques paramount. This work presents a new method for feature extraction through combining two methods namely;vSLM and Wavelet CNN. The approach is complemented by a modified CNN model that is equipped with an AdaptiveConvolutional ActivationFunction (ACAF) for classification. As it was mentioned before, the vSLM technique creates detailed retinal maps, and thus can be effective for the detection of critical DR indicators such as microaneurysms, haemorrhages, and exudates. Wavelet CNN enhances the feature extraction procedure, taking into consideration both spatial and spectral features of the retinal images. The resolution provided by the proposed CNN model with ACAF demonstrates outstanding classification results with accuracy rates of 97.12% on DiaretDB0 dataset, 99. 12% on the public EyePACS Kaggle dataset and 99.15% on the DRIVE dataset and 98.12% on the CHASE dataset. These findings highlight the importance of using the feature extraction and classification approach for improving the accuracy of DR detection and classification. The findings point to severe advancements in the diagnostic tools of DR and the effect of these enhancements in the pragmatic aspect of identify and handling of DR. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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AIGC-Driven Real-Time Interactive 4D Traffic Scene Generation in Vehicular Networks

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IEEE Network 2025年

作者： Li, Xiaolong Deng, Ruiting Wei, Jianhao Wu, Xin Chen, Jiayuan Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan University of Technology and Business School of Computer Science China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Concordia University Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Real-time, interactive 4D traffic scene generation enables rapid digital twinning of traffic scenarios, improving management and decision-making in intelligent transportation systems. However, current text-to-video models, such as Sora, struggle to maintain the temporal coherence of traffic elements and interact with dynamic environments and users when generating 4D scenes. This article introduces a novel cloud-edge-terminal collaborative framework that leverages Artificial Intelligence-Generated Content (AIGC) in vehicular networks to tackle these challenges, ensuring long-term coherence and improved interactivity. The framework presents a comprehensive architecture for real-time interactive 4D scene generation, encompassing data collection, management, model pre-training, fine-tuning, and inference. We examine key design requirements and challenges, demonstrating that our microservice-based framework enables the system to generate and update 4D traffic scenes in real time, effectively responding to traffic data and user inputs. To the best of our knowledge, this is the first successful implementation of real-time, interactive 4D traffic scene generation. Performance evaluations show the superiority of our framework, powered by microservice-based code fine-tuning, over traditional frameworks. Finally, we discuss future research directions to enhance AIGC-driven 4D traffic scene generation. © 1986-2012 IEEE.

关键词： Decision making

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DETECTION OF AUTHENTICITY – OF CONTENT FOR FORENSICS USING FORENSHIELD 11

DETECTION OF AUTHENTICITY – OF CONTENT FOR FORENSICS USING ...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Surendran, Aswin P. Kumar, Sanjeev S. Tesfahun, Abebe Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

The rampant spread of deepfake videos, a significant threat to media integrity, occurs widely on social media and news platforms. Detecting Deepfakes is a formidable challenge. The proposed study involves a novel algorithm to detect the deepfake. The model comprises of various pre-existing classifiers and a novel algorithm named ForenShield – which detects the deepfakes with an accuracy of 97%. The proposed novel technique works on voting strategy and it outperforms all the pre-existing classifiers, exhibiting the models excellence through the same. ©2023 IEEE.

关键词： Classifiers Deepfake ForenShield Voting Strategy

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Yoga in digital Age: A Hybrid Approach using ML and computer vision 11

Yoga in digital Age: A Hybrid Approach using ML and Computer...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Achaiah, K.K. Pramod, Arjun S. Tesfahun, Abebe Department of Computer Science Amrita School Of Computing Amrita Vishwa Vidyapeetham Mysuru campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

—In today’s fast-paced world, stress and pressure affect people of all ages, both physically and mentally. Yoga has emerged as a highly sought-after solution due to its numerous benefits. In the present technology-dependent era of digital age, it’s only natural that we turn to technology for assistance in their yoga journey. This is where the proposed article comes into play, serving as one’s digital Yoga Guru. In this paper, seven classifiers, out of which six are Machine Learning classifiers namely Support Vector Machine, Naïve Bayes, Random Forest, K-Nearest Neighbor, Logistic Regression, and Ensembler are employed to classify the dataset. These classifiers exhibit a promising average accuracy exceeding 90%. Additionally, the proposed method incorporates a Deep Learning model, specifically the Multi-Perceptron Neural Network, which has been trained to predict outputs with an impressive accuracy of 92%. This Paper utilizes the power of MediaPipe, a cutting-edge technology that plays a pivotal role in this innovative approach to yoga. MediaPipe is capable of identifying individuals on the screen and provides 33 key-points, including critical markers like facial key-points, and main keypoints of the body such as shoulders, elbows, knees, feet, wrists, and etc. These key points are essential for pose recognition, and assessing the correctness of yoga postures. What sets the proposed method apart is its real-time, live tracing feature, boasting an impressive accuracy rate of 99%. As one engages in yoga practice, this technology tracks their movements, and compares them to the ideal yoga postures. This immediate feedback ensures that one performs each pose correctly, promoting better results and minimizing the risk of injury. In essence, the proposed method bridges the gap between ancient yoga wisdom and modern technology, offering a digital Yoga Guru that guides one to achieve physical and mental well-being with precision and confidence. ©2023 IEEE.

关键词： Decision trees

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Evaluating importance of AI in Robotics and Automation in Healthcare for Different age Groups 1

Evaluating importance of AI in Robotics and Automation in He...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Pethkar, Rajendra Huda, Bintul Patil, Jaykar Bhaskar Gatla, Ranjith Kumar Kumbhar, Ramchandra Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Mechanical Engineering Karad India Sharda University Department of Computer Science & Technology Srinagar India Jaywant Institute of Management Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Radio-Diagnosis Karad India

ISBN: (纸本)9798350360707

Nowadays robotics is mostly used in the world. There are many industries which used robots for reducing time and to have high precision of work. The use of artificial intelligence is now increasing which helps in automations and processing of machines. Thus, the focus of study is to find the different applications of automation of robotics (i.e. robotic process automation) in the field of healthcare and its impact on the employment of medical field. Four major categories can be used to group AI applications in healthcare: expressive, analytical, prognostic, and prescriptive. The only way to close the skills gap in the healthcare industry is to increase the application of artificial intelligence. Hospitals are developing mental commit robots to treat their patients. Through physical contact, these robots can have an impact on humans' psyche, physiology, and social interactions. Thus, in coming time most of the healthcare industry will be with the robots which will reduces the human interventions with the patients through one of the host servers. © 2024 IEEE.

关键词： Medical robotics

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET 7

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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

作者： Ramya, R. Yazhini, A. Geetha, T. Devi, R. Sahila Kavin, K.S. Dhanalakshmi, S. Saveetha School of Engineering Department of Nano Electronics Materials and Sensors ECE Chennai India Anna University College of Engineering Department of Computer Science & Engineering Chennai600025 India Agni College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Palkulam629401 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350372816

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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