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

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Studying the Fundamentals of Internet of Things (IoT) in the Healthcare Applications 1

Studying the Fundamentals of Internet of Things (IoT) in the...

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

作者： Abu Eid, Aws Ismail Kadam Rangrao, Shrikant Patil Abhijit, Seema Sharma, Lakshay Rani, D Shobha Ukirade, Chaitali Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Indraprastha Institute of Information Technology Delhi India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Obstetrics and Gynecology Karad India

ISBN: (纸本)9798350360707

The Internet of Things (IoT) has brought smart healthcare systems to the medical industry. These systems are typically made up of a network, a remote server, and sensors with smart capabilities. The primary goals of this system are to offer multidimensional feature monitoring and fundamental therapy recommendations. The primary goals of Internet of Things (IoT)-based healthcare services are to enhance quality of life and offer a great user experience at an affordable price. The primary purpose of IoT is to give elderly patients with chronic illnesses access to trustworthy, efficient, and intelligent healthcare services as well as connectivity to the medical resources that are readily available. Thus, many studies are developed on the different applications which worked on the IoT for the healthcare which includes wearable as well as non-wearable devices or machines. Thus, it makes the users to obtain the data from anywhere and wherever required for the study or analysis of the patients. In the meantime, the Internet of Things is expanding to include even the tiniest sensors and the fundamentals of automation and machine-to-machine communication. © 2024 IEEE.

关键词： healthcare internet of things (IoT) network service

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Habib, Md Selim Department of Data Science and Business Analytics Florida Polytechnic University LakelandFL33805 United States Department of Aerospace and Geodesy Technical University of Munich Munich80333 Germany Department of Electrical Engineering and Computer Science Florida Institute of Technology FL32901 United States

A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other m... 详细信息

ISBN: (纸本)9781957171395

关键词： Prediction models

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Adversarial machine learning

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Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

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computers, Materials & Continua 2022年第5期71卷 3375-3392页

作者： AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Rajarajeswari College of EngineeringBengaluru560074India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Thiagarajar CollegeMadurai625019India Department of Applied Data Science Noroff University CollegeKristiansandNorway Al-Mustaqbal University College HillahIraq Department of Information Technology College of Computing and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking *** Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based *** methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor *** and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such *** current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT *** presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and *** first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is ***,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the ***,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the *** order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct *** experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

关键词： Wireless networks internet of things energy efficiency clustering multi-hop routing metaheuristics

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Generative AI-aided Reinforcement Learning for Computation Offloading and Privacy Protection in VR-based Multi-Access Edge computing 10

Generative AI-aided Reinforcement Learning for Computation O...

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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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