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Reinforcement Learning Based Collaborative Perception for Vehicular Networks

Reinforcement Learning Based Collaborative Perception for Ve...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Chen, Hongyi Lin, Zhiping Zhu, Yunjun Li, Jieling Xiao, Liang Tang, Yuliang Zhang, Yanyong Institute of Artificial Intelligence Xiamen University China Xiamen University Dept. Information and Communication Engineering China University of Science and Technology of China School of Computer Science and Technology China

ISBN: (纸本)9798350351255

Reinforcement learning (RL)-based collaborative perception in vehicular networks chooses the sub-frame of radio channel resources for connected autonomous vehicles (CAVs) to exchange sensing data to enhance the perception performance, but leads to inaccurate detection in the light detection and ranging (LiDAR)-based object detection due to the asynchronous scan period of the LiDAR point clouds. This paper proposes a RL-based collaborative perception scheme to choose the transmit power and sub-frame to share the feature maps extracted from point clouds. Based on the estimated packet timestamp, the network topology and the channel gains among CAVs, this scheme enhances the perception accuracy and latency against path-loss and interference. The collaborative risk in the policy distribution is formulated as a weighted sum of the perception latency and packet loss rate to avoid the time asynchronization and information loss of the feature map exchange. The performance bound of the perception accuracy and latency is provided based on a Nash equilibrium of the cooperative game among CAVs. Simulation results based on five CAVs show the performance gain of the perception accuracy and latency over the benchmarks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Wireless Information Power Transfer Assessment by Deterministic Radio Propagation Estimations in Smart City Contexts 2

Wireless Information Power Transfer Assessment by Determinis...

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2nd IEEE Virtual Conference on communications, VCC 2024

作者： Picallo, Imanol Lopez-Iturri, Peio Klaina, Hicham Celaya-Echarri, Mikel Rodriguez-Corbo, Fidel Alejandro Azpilicueta, Leyre Falcone, Francisco Institute of Smart Cities Public University of Navarre Statistics Computer Science and Mathematics Dept. Pamplona Spain Institute of Smart Cities Public University of Navarre Electric Electronic and Communication Engineering Dept. Pamplona Spain School of Engineering and Sciences Tecnologico de Monterrey Mexico

ISBN: (纸本)9798331530099

The development of context aware environments, such as those given in the framework of Smart Cities and Smart Regions require the use of ubiquitous connectivity and more recently, energy availability for a wide variety of Internet of Things based applications. In this work, estimations of wireless channel distributions for coverage/capacity as well as for energy availability will be presented, for outdoor as well as indoor scenarios with the aid of deterministic inhouse developed simulation tool. Volumetric assessment can be obtained, aiding in the radio network planning process, as well as in the feasibility of electromagnetic based energy harvesting solutions for Internet of Things based applications. © 2024 IEEE.

关键词： Rectennas

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Building Resilient Rural Communities: Participatory Approaches in Khattukhal Village for Sustainable Development 1

Building Resilient Rural Communities: Participatory Approach...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Makadi, Yila Caiaphas Gadde, Ashok Patel, Aniket Adusumilli, Tarun Paladugu, Suchal Sankranti, Srikar Cornet, Souresh Amrita School for Sustainable Futures Amritaputi Amrita Vishwa Vidyapeetham India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Coimbatore India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electrical and Computer Engineering Coimbatore India

ISBN: (纸本)9798350306637

The work presents a comprehensive study conducted in a village in Uttarakhand, India namely Khattukhal village, concentrating on the identification of sustainable development concerns and the implementation of participatory solutions. Using a combination of Participatory Rural Appraisal (PRA) tools and Human-Centered Design (HCD) methodologies, the very first stage of work focuses mainly on to analysis, which includes surveys, observations, brainstorming sessions, as well as problem tree analysis, which aims to address the community's pressing problems. The study emphasises the serious issues with river and canal systems, the lack of systems for collecting water and effective irrigation techniques, and insufficient waste management practises. This study report presents practical strategies to minimise these problems and encourage sustainable development in Khattukhal village by proactive community engagement and partnership with local stakeholders. © 2023 IEEE.

关键词： Sustainable development

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An Improved Recommendation system based on Neural Matrix Factorization 4

An Improved Recommendation System based on Neural Matrix Fac...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Maji, Soumadeep Maity, Souraja Das, Joydeep Majumder, Subhashis Heritage Institute of Technology Dept. of Electronics and Communication Engineering WB Kolkata India Heritage Institute of Technology Dept. of Applied Electronics and Instrumentation Engineering WB Kolkata India The Heritage Academy Kolkata WB Kolkata India Heritage Institute of Technology Kolkata Dept. of Computer Sc. & Engg. WB Kolkata India

ISBN: (纸本)9798350349900

This paper presents a comprehensive framework of the Neural Collaborative Filtering (NCF) approach used in the deployment of personalized recommendation systems. Collaborative Filtering (CF) techniques have resulted in the most impressive results in terms of predicting rating and providing good recommendations. However, with the advent of Artificial Intelligence and Deep Learning, the traditional CF has been replaced with NCF combined with the Matrix Factorization (MF) based models called NeuMF. Further, with the growth of the Web and its number of users and items, instead of only one criteria determining the recommendation, multi criteria recommender systems have been developed to provide more personalized recommendation. In this work, we propose a novel approach to NeuMF for multi-criteria sentiment based recommender systems which reduces computation complexity and also improves prediction accuracy. The proposed Dual NeuMF (DNeuMF) approach includes simultaneous training of two NeuMF models that eliminates the need of an aggregate function which is commonly used in multi criteria recommender systems. The entire architecture is fine-tuned through Bayesian-Lipschitz Optimization, enabling the selection of optimal hyperparameters and ultimately enhancing the model's performance. The outcome of our model tested on Amazon Fine Foods dataset, Yelp dataset, Amazon Product dataset in terms of different evaluation parameters provides encouraging results. © 2024 IEEE.

关键词： Matrix factorization

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Obmaaq: Ontology-Based Model for Automated Assessment of Short-Answer Questions 1

Obmaaq: Ontology-Based Model for Automated Assessment of Sho...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Ramnarain-Seetohul, Vidasha Bassoo, Vandana Rosunally, Yasmine University of Mauritius Dept. of Information and Communication Technologies Reduit Mauritius University of Mauritius Dept. of Electrical and Electronic Engineering Reduit Mauritius University of the West of England Dept. of Computer Science and Creative Technology London United Kingdom

ISBN: (纸本)9798350342796

Automated scoring of short answers is becoming increasingly important nowadays, as more classes are taking place online due to COVID-19 outbreaks. Over the past 50 years, different techniques have been applied to develop ways of assessing short answers automatically. The automated assessment of short answer questions has been used with varying kinds of questions in different domains. However, no interoperable tools have been developed to facilitate the automated assessment of short answers for common modules across educational institutions. In this paper, we present a novel system called OBMAAQ, which grades short answers for questions designed according to Bloom's revised taxonomy remember category. The proposed system uses publicly available domain ontologies to grade short answers in different subject areas. This method is unique as it does not use model answers since it relies solely on the domain ontologies by utilizing an ontology-based pruning technique to grade the students' responses. The purpose of the system is to allow teachers from different educational institutions to use a similar approach when assessing the short answers for common modules automatically. Two publicly available domain ontologies in the field of security and computer networks have been used to validate students' answers. Experimental results based on both datasets indicate some promising results even though it is less than the set thresholds. This paper discusses the lessons learned and future recommendations based on the OBMAAQ model. © 2023 IEEE.

关键词： Assessment Automated marking Ontology Short answer Text mining

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Machine Learning Methods Analysis for Indian Currency Counterfeiting 14

Machine Learning Methods Analysis for Indian Currency Counte...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Prathibha, T. Prakash, H.N. Padmajadevi Rajeev Institute of Technology Department of Computer Science and Engineering Hassan India Dept. of CSE Rajeev Institute of Technology Department of Computer Science and Engineering Hassan India Malnad College of Engineering Department of Electronics and Communication and Engineering Hassan India

Imitation of currency made with the intention of misleading people is referred to as counterfeit money. One of the risks that harms a country's economy and so slows global growth is counterfeit money. Producing counterfeit money or creating false features for currency is illegal activity, which needs to be properly analysed. Demonetization resulted in an unexpected inflow of fake currency into banks, which in turn lead an increase in doubtful activities. The review focused on the methodology used to detect counterfeit currency, as well as the success rate of each method. Numerous fake currency detection strategies offered by different researchers are presented in the study. This paper discusses the challenge of determining whether or not a particular sample of cash is fake or real. There are various conventional strategies and methods to detect illegal currency based on the basic features like colors, width, and serial numbers specified on the currency. As per the comparative survey analysis KNN and regression methods will give good accuracy for identifying the fake currency. © Grenze Scientific Society, 2023.

关键词： Feature extraction

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Data Protection Methods and Intrusion Detection systems Employing Machine Learning and Deep Learning in the IoT: A Comparative Review 2

Data Protection Methods and Intrusion Detection Systems Empl...

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2nd International Conference on computer Science, engineering and Applications, ICCSEA 2022

作者： Al-Khuzaie, Zahraa Majeed Albermany, Salah A. K. Abdlnibe, Mohammed Ahmed University of Kufa Dept. of Computer Science Kufa Iraq University of Kufa Dept. of Electronics and Communication Engineering Kufa Iraq

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Scarcely any Intrusion detection systems (IDS) are based on the principle of Machine Learning (ML). Such systems are the potential next-generation tool for enhancing cyber security in networks. These systems can detect zero-day attacks. These attacks are anonymous and unorthodox for researchers and users. Recently, various advanced attacks have been occurring simultaneously with the unprecedented development of the Internet. To detect such attacks, both Machine Learning (ML) and Deep Learning (DL) have been introduced. Therefore, researchers have proposed several methods for detecting any advanced attacks. In this paper, a thorough classification is introduced for algorithms of Machine Learning (ML) and Deep Learning (DL). In addition, the detailed survey will cast light on various methods adopted in detection attacks. All of these methods are based on the principles of DL and ML. Further, many platforms and devices will be introduced both of which are used in executing the methods of the DL and ML and in suggesting security solutions that could be utilized within the field of the Internet of Things (IoT). © 2022 IEEE.

关键词： Internet of things

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Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Media

Unconventional Computing based on Four Wave Mixing in Highly...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Sozos, Kostas Deligiannidis, Stavros Mesaritakis, Charis Bogris, Adonis University of West Attica Dept. of Informatics and Computer Engineering. Aghiou Spiridonos Egaleo Athens12243 Greece University of the Aegean Dept. of Information and Communication Systems Engineering Palama 2 Karlovassi Samos83200 Greece

ISBN: (纸本)9798350345995

Nowadays, photonic analog computing in the form of neuromorphic photonics or photonic neural networks has resulted in powerful hardware platforms that accelerate difficult tasks with marginal power consumption. Although linear transformations are efficiently carried out in the optical domain, nonlinear transformations are also of paramount importance. Here, we propose four wave mixing (FWM) process as an elegant solution for the generation of diverse nonlinear higher order products of an original signal. On one hand, these products can act as activation functions in different types of neural networks, either recurrent neural networks (RNNs) and reservoir computers or in any other type of brain-inspired frameworks. On the other hand, the same products can form the basis for polynomial regression which is a very powerful technique used in machine learning in different tasks i.e. for the prediction of COVID-19 transmission [1]. © 2023 IEEE.

关键词： Recurrent neural networks

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Blockchain-Based Scalability Solutions for IoT: A Decentralized Design to Enhance Performance and Security

Blockchain-Based Scalability Solutions for IoT: A Decentrali...

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IEEE International Conference on Electronics, Computing and communication Technologies (CONECCT)

作者： Shweta Babu Prasad Ashok Kumar A. R Rajini V Honnungar Dept. of Computer Science and Engineering RV College of Engineering Dept. of Electronics and Communication Engineering RNS Institute of Technology

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The Internet-of-Things facilitates the interlinking of an extensive number of devices, each capable of executing independent functions and acquiring diverse forms of information. These entities, in conjunction with the copious quantities of data they generate, necessitate scalable, proficient and safe-guarded management. Centralized solutions are not desired due to security concerns and scalability issue. Alternative to the centralized system, Blockchain – a decentralized systems that is proven to be platform to support both integrity and security is a choice. But, as the IoT devices are resource constrained devices, deploying a blockchain for the data management is a challenging task. In this manuscript, we put forth a multi-layer blockchain framework that accomplishes the goal of achieving scalability and access control in Internet of Things (IoT), gadgets including data. This framework is comprised of numerous permissioned blockchains that come together to configure a multi-layer architecture. As a result, it also attains high scalability using parallel processing, specifically through the utilization of blockchain sharding schemes.

关键词： Access control Sharding Scalability computer architecture Consensus algorithm Parallel processing communications technology

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Advanced Machine Learning Techniques for Enhancing Data Security in Cloud Computing systems 5

Advanced Machine Learning Techniques for Enhancing Data Secu...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Dhinakaran, M. Sundhari, M. Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Galgotias College of Engineering & Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN's ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model's 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model's performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change. © 2024 IEEE.

关键词： Cloud Computing Data Security Machine learning Q-Learning

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