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15th International Conference on Machine Learning and computing, ICMLC 2023

作者： Xuan, Pengyu Chen, Aiguo Sha, Zexin University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781450398411

Federated reinforcement learning (FRL) uses data from multiple partners interacting with the environment to train a global decision model while maintaining data privacy. In specific situations, it is necessary to protect not only data privacy but also model privacy. That is, our objective is to collaboratively train a global model that can be used privately and independently by the initiator. We refer to this need for model privacy preservation as model monopolization. It allows the task initiator to ensure the privacy of the model during the training process and to own the trained model alone. To the best of our knowledge, model monopolization protection in Federated Reinforcement Learning has not been studied. We propose monopolized federated reinforcement learning in complementary environments via function transformation. We also provide contribution metrics to ensure the sustainability of monopolized federated reinforcement learning and to provide a quantitative basis for task initiators to give back to task participants. The experimental results show that our method achieved model monopolization by training agents in the Grid-world. © 2023 ACM.

关键词： Reinforcement learning

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Revolutionizing lemon grading: an automated CNN-based approach for enhanced quality assessment

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4155-4166页

作者： Sanyal, Samriddha Adhikary, Rahul Choudhury, Suvra Jyoti School of Computing and Data Science FLAME University Maharashtra Pune 412115 India Centurion University of Technology and Management Odisha Parlakhemundi 761211 India Indian Institute of Information Technology Maharashtra Nagpur 441108 India

Lemons, renowned for their versatile applications in the culinary world and various industries, have become a staple commodity worldwide. The demand for high-quality lemons continues to soar, underscoring the need for reliable and efficient grading methods. Traditional lemon grading, which is largely reliant on human expertise, often falls prey to subjectivity, human errors, and time-consuming processes. In this paper, we propose a CNN-based lemon grading model to automate and improve the accuracy of lemon quality assessment. The proposed model utilizes CNNs in a modular and sequential fashion for extracting pertinent features from lemon images and categorizing them into distinct quality grades. A comprehensive dataset of lemon images with corresponding quality labels is used to train and validate the model. The experimental results demonstrate the effectiveness of the CNN-based approach in accurately grading lemons, offering a promising solution for enhancing efficiency and objectivity in the lemon grading process. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： CNN modular organization Deep learning Incremental learning Lemon health detection

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Application of Distributed Systems and Artificial Intelligence in Addressing Security Challenges in Smart Transportation

Application of Distributed Systems and Artificial Intelligen...

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Computer science, Engineering, and Education (CSEE), International Conference on

作者： Yuxi Jiang Burra Venkata Durga Kumar Jia Yew Teh Department of Software Engineering School of Computing & Data Science Xiamen University Malaysia (XMUM) Department of Information & Communications Technology (ICT) School of Computing & Data Science Xiamen University Malaysia (XMUM)

ISBN: (数字)9798331505165

ISBN: (纸本)9798331505172

As the concept and practical application of smart cities become more and more popular, the technology becomes more and more advanced, and the complex network structure and the large amount of data lead to some security-related problems. Therefore, this study aims to propose an integrated security framework based on artificial intelligence and distributed systems to address the challenges of smart city security. This research focuses on how is AI combined with the current security framework to improve the security of smart city, especially smart transportation. The framework will merge the analytical capabilities of AI for data encryption and decryption, identity authorization and access control, as well as data masking and desensitization, with the characteristics of distributed systems to protect the security of smart transportation. Through the review of previous journals, it can be concluded that the improved accuracy and efficiency brought by AI and distributed systems, as well as the application of the framework in five aspects of identity, protection, detection, response. and recovery. The innovation and practicality of the framework can promote further development and research in the field of smart transportation in smart city security, and promote the development of smart cities in a safer and smarter direction.

关键词： Authorization Technological innovation Accuracy Smart cities Distributed databases Smart transportation Encryption Security Artificial intelligence Sustainable development

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Speech Emotion Recognition Using GRU and LSTM

Speech Emotion Recognition Using GRU and LSTM

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Sharma, Vriti Raushan, Ritik Verma, Ruchi Gupta, P.K. School of Computing and Electrical Engineering Iit Mandi HP India Jaypee University of Information Technology Department of Cse HP Solan173 234 India School of Computing Mohan Babu University Department of Data Science AP Tirupati517 102 India

ISBN: (纸本)9798350364866

Speech emotion recognition (SER) is a hot topic in research, with applications in human-computer interaction, mental health screening, and customer service. In this research, our objective is to develop an accurate and reliable SER system capable of emotion recognition in speech data. We address important issues such as feature extraction, model selection, data imbalance, robustness, and real-time inference using a labeled dataset of various emotional expressions in voice. To extract complicated shapes from auditory characteristics, our method combines deep learning techniques that include long-short-term memory (LSTM) and gated recurrent units (GRU). We carefully manage the data imbalance to ensure that emotions are fairly represented in the training set, and with the implementation of our functional LSTM and GRU models in the training set, we were able to achieve the precision of 62 % and 90 %, respectively. © 2024 IEEE.

关键词： Long short-term memory

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Variational Encoder Based Synthetic Alzheimer's data Generation for Deep Learning, XGBoost and Statistical Survival Analysis 23

Variational Encoder Based Synthetic Alzheimer's Data Generat...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Musto, Henry Stamate, Daniel Stahl, Daniel University of London Data Science and Soft Computing Lab Department of Computing Goldsmiths London United Kingdom School of Health Sciences The University of Manchester United Kingdom Institute of Psychiatry Psychology and Neuroscience Kings College London Department of Biostatistics London United Kingdom

ISBN: (纸本)9798350374889

Alzheimer's Disease (AD) poses significant challenges in research due to limited access to longitudinal patient data caused by privacy constraints. This study uses deep learning, specifically Variational Autoencoders (VAEs), to generate synthetic datasets that replicate real-world AD data while preserving privacy. These datasets were used to train and compare survival analysis models, including Survival XGBoost, Survival Transformers, and the Cox Proportional Hazards model. Results show that deep learning and boosting models outperform the standard Cox model in predicting the progression of AD, achieving a higher degree of accuracy. Our findings suggest that combining synthetic data with advanced machine learning models can improve predictive capabilities in healthcare research, particularly when real patient data is restricted. © 2024 IEEE.

关键词： Adversarial machine learning

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Showcasing Native Wildflowers in Virtual Reality

Showcasing Native Wildflowers in Virtual Reality

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Wagner, Andy Deligiannidis, Leonidas School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350320282

The goal of this project is to develop a virtual reality program on the Oculus Quest 2 to showcase and educate users on native wildflowers that can be found in North America, specifically in the United States. We have created a virtual environment with framed images of some of America's native flowers and, using interactive menus, we provide educational information on each plant. In addition to the 2D images, a 360-image viewer was implemented to show some of the native habitats of these plants to create a more immersive learning experience. Using the Oculus Quest's experimental hand tracking feature, users can switch between using the Oculus controller and using their actual hands as the controller, allowing the users to choose how they interact with the virtual environment. The United States is home to many beautiful native flowers and by showcasing them in virtual reality anyone can experience these flowers and learn about them. © 2022 IEEE.

关键词： Virtual reality

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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Diabetic Retinopathy Detection

Diabetic Retinopathy Detection

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data Engineering and Communication Systems (ICDECS), International Conference on

作者： S Sharanya Arnav Vinod Rai Sai Vedanth Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need.

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