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AI-Smart Factory: Design and verification of Korean SME AI smart factory using level diagnosis system 18

AI-Smart Factory: Design and verification of Korean SME AI s...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information technology (SEIT)

作者： Park, Byungjun Kim, Taeyong Jong-pil, Jeong Sungkyunkwan Univ Coll Sport Sci 2066 Seobu Ro Suwon 16419 South Korea Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 South Korea

This paper presents a new AI-based smart factory construction model for SMEs based on the Korean manufacturing industry and proposes an effective construction process that implements the informatization, automation, and intelligentization characteristics of smart factories. The Korean AI-based smart factory construction process starts with the accumulation of digital information by attaching sensors to the manufacturing processs, and then creates a customized AI model for the company through cloud accumulation of sensor data and big data analysis through CNN techniques. The Korean SME Smart Factory is an AI-based smart factory through AHP analysis and smart factory level diagnosis system, which is a construction process, which means a consumer-oriented intelligent factory that combines next-generation digital technology and manufacturing technology beyond the existing level of factory automation (FA). Various products can be produced on one production line, and modularization is expected to change from mass orders to individual flexible production systems. The transition to smart factories is expected to dramatically improve the productivity of manufacturing, as well as realize energy savings and a more human-centered work environment. By monitoring and controlling manufacturing sites in a virtual space, it is expected to facilitate factory management and lead to improved quality and cost competitiveness. (c) 2023 The Authors. Published by Elsevier B.V.

关键词： Wafer stage Semiconductor Ellipsometer Machine Vision Auto-Focusing

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Big Data Insights: Pioneering Changes in FinTech 9

Big Data Insights: Pioneering Changes in FinTech

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Anusha Hegde, H. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science And Engineering Mangalore Surathkal India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

The amount of data generated and stored by finance sector companies is rapidly increasing, allowing corporations to conduct data analytics and enhance their businesses. However, data scientists face immense challenges in efficiently handling massive amounts of data and generating insights with real business value. Big Data Analytics (BDA) tools and methods are required to handle vast data. Financial technology's (FinTech's) growth in mobile Internet, cloud computing, big data, search engines, and blockchain technology has dramatically changed the financial industry. The appropriate application of big data in the management and business innovation of FinTech is therefore a significant concern that confronts the whole finance industry. This paper explores the significance of big data methods in the financial sector and offers insights into the difficulties in applying them as well as future potential for technological advancement. Along with its classifications, the paper examines how FinTech evolved from traditional to modern banking. Corporate banking encompasses several aspects, such as financial markets, corporate credit, and trade, involving substantial transactions and monetary resources. Consequently, this sector has a favorable opportunity to use emerging information technology (IT) advancements. Lastly, the study examines how BDA contributes to FinTech difficulties and projects how FinTech will develop in the future within the context of BDA. © 2024 IEEE.

关键词： Data Analytics

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Multichain Blockchain Solutions for Ensuring Trust and Transparency in IoT Healthcare Environment 7

Multichain Blockchain Solutions for Ensuring Trust and Trans...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Kumar, Atul Guleria, Kalpna Sharma, Ishu Khan, Adil Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Institut Polytechnique de Paris France

ISBN: (纸本)9798350372816

The use of Internet of Things (IoT) technology inside healthcare systems has the potential for significant breakthroughs in the treatment and monitoring of patients. Nevertheless, this convergence also gives rise to noteworthy security apprehensions, namely about safeguarding confidential medical information and ensuring the reliability of networked equipment. This article suggests using Multichain blockchain technology as a strong approach to improve security in IoT healthcare contexts, to address these concerns. Multichain blockchain provides a distributed, unchangeable, and resistant-to-tampering method of storing data, guaranteeing the privacy, accuracy, and accessibility of patient information while reducing the dangers linked to centralized data repositories. Using a Multichain blockchain architecture in IoT healthcare systems enables the establishment of trust among many stakeholders, mitigates the risk of illegal access, and enhances the security of data exchange across diverse devices and platforms. This presents a comprehensive summary of the suggested methodology and its possible ramifications for augmenting security in healthcare environments provided by the Internet of Things. © 2024 IEEE.

关键词： Data integrity Interconnected devices IoT Healthcare Multichain Blockchain Patient privacy Security Techniques Transparency Trust

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A Multi-Task Optimization-based Heterogeneous Expensive Multi-Objective Evolutionary Algorithm 3

A Multi-Task Optimization-based Heterogeneous Expensive Mult...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing technology, AIoTC 2024

作者： He, Chunlin Zhang, Yong School of Information and Control Engineering China University of Mining and Technology Xu Zhou China

ISBN: (纸本)9798331530280

Multi-objective optimization problems with different evaluation costs for performance metrics or objective values are commonly seen in the real world. Such problems are known as heterogeneous expensive multi-objective optimization problems (HE-MOPs). Although there are some researches available, the heterogeneity among objectives can cause search bias, which affects the performance of these methods on HEMOPs. To address this, we propose a Multi-Task Optimizationbased Heterogeneous Expensive Multi-Objective Evolutionary Algorithm (MT-HEMOEA). This algorithm firstly uses a multilayer sampling method based on objective evaluation costs to obtain a uniformly distributed sample set on objectives space. Then we employ multi-task exploitation strategy focused on convergence and multi-task exploration strategy focused on diversity to reduce search bias and improve sample diversity. Experimental results on WFG test problems show that the proposed algorithm achieves higher-quality optimal solution sets compared to four existing heterogeneous multi-objective optimization algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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A Smart Contract-Based Decentralised Crowdfunding Platform for Increased Security and Transparency 1

A Smart Contract-Based Decentralised Crowdfunding Platform f...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Antad, Sonali Vidhale, Arpit Vikhar, Shivani Thite, Sanket Warke, Aditya Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350361780

In the contemporary landscape of financial systems and digital innovations, the convergence of blockchain technology and crowdfunding models has unveiled a promising avenue for revolutionizing traditional fundraising platforms. This research paper delves into the intricate fabric of designing and implementing a robust crowdfunding platform underpinned by the decentralized principles of blockchain technology integrated with Artificial Intelligence. The research embarks on an in-depth exploration of the underlying mechanics of blockchain, elucidating its distributed consensus, immutability, and transparency features. A comprehensive survey of existing literature pertaining to crowdfunding and blockchain integration is presented, establishing a solid foundation for the proposed platform's development. The conceptual framework delineates the architectural blueprint of the decentralized crowdfunding platform, intricately woven with smart contracts, artificial intelligence, cryptographic protocols, and a personalized recommendation system. The amalgamation of these components engenders an environment that ensures trust, security, and automation throughout the crowdfunding lifecycle. Moreover, this paper delves into the pivotal aspect of tokenization within the blockchain-based crowdfunding ecosystem. By implementing these cases against theoretical frameworks, the paper elucidates a comprehensive set of best practices and potential pitfalls that can guide the development and deployment of a successful blockchain-based crowdfunding platform. © 2024 IEEE.

关键词： Smart contract

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Disentangling Client Contributions: Improving Federated Learning Accuracy in the Presence of Heterogeneous Data 21

Disentangling Client Contributions: Improving Federated Lear...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liu, Chunming Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Zeng, Cheng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

Federated Learning (FL) is a promising paradigm that leverages distributed data sources to train machine learning models, thereby offering significant privacy advantages. However, the inherent statistical heterogeneity among clients, characterized by distinct data distributions and varying model performance, poses a substantial challenge. Such heterogeneity adversely affects both the convergence and overall performance of the global model within the FL framework. To address this issue, we introduce a novel FL algorithm, FedVa, which considers both the local data volume and model accuracy to determine client contributions, assigning respective weights. This consideration facilitates greater engagement from key clients during the aggregation process, thereby enhancing their influence on the global model refinement. Experimental results indicate that FedVa surpasses prevailing methods in managing statistical heterogeneity and enhancing global model accuracy, without incurring additional communication costs. Specifically, FedVa achieves a convergence speed 3.9 times faster than FedAvg on the MNIST dataset, and on the CIFAR-10 dataset, it results in a 2.3% increase in final model accuracy compared to FedAvg. Our code is publicly available at https://***/ChunmingLiu23/FedVa. © 2023 IEEE.

关键词： Learning systems

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An Efficient Attention-Based Deep Reinforcement Learning Model for Traffic Signal Control 7

An Efficient Attention-Based Deep Reinforcement Learning Mod...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Lin, Aodi Chen, Feng University of Science and Technology of China Department of Automation Hefei China

ISBN: (纸本)9798331529314

Traffic Signal Control (TSC) is a significant challenge within intelligent transportation systems. As Vehicle-to-Everything (V2X) technology advances, TSC systems are increasingly able to utilize extensive vehicle driving data to enhance decision-making. This paper proposes a reinforcement learning-based model for TSC at urban intersections. We design a vehicle driving information matrix that integrates vehicle position and speed information. The state space includes vehicle driving information and the current traffic signal phase. The action space includes various traffic signal phases. To efficiently extract traffic features from the large state space, the model incorporates an attention mechanism within the neural network. The simulation results on the Simulation of Urban Mobility (SUMO) demonstrate the convergence and generalization capabilities of our model, showing significant advantages in trip duration compared to several benchmark methods. Ablation studies further validate the effectiveness of our proposed position-speed fusion matrix and attention mechanism in extracting traffic feature representations. © 2024 IEEE.

关键词： Deep reinforcement learning

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An Algorithm with Edge Assembly Crossover Based on Neighborhood Measure for TSP Tours 3

An Algorithm with Edge Assembly Crossover Based on Neighborh...

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3rd international conference on computing, Communication, Perception and Quantum technology, CCPQT 2024

作者： Wu, Jiake Fu, Xiang Xie, Yanjing Chen, Kewei Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China Ningbo Guangqiang Robotics Technology Co. Ltd. No. 36 Moganshan Road Ningbo City Ningbo China

ISBN: (纸本)9798331528386

Genetic algorithm has significant implications for solving the famous NP-hard optimization problem, traveling salesman problem (TSP). The purpose of this study is to combine the edge assembly crossover (EAX), one of the extensions of genetic algorithms, with the concept of neighborhood degree, aiming to test the effectiveness of a new evaluation method for solving TSP problems. The cross in EAX only combines the two recurrent edges of both parents while using the minimum global tree to properly limit the search scope. The neighborhood degree has been used to evaluate routes on the actual truck delivery problems. It is believed that the dynamic evaluation of neighborhood measure can reduce the cost of time. The proposed algorithm was evaluated on 9 TSPs where the number of cities varies from 52 to 1979. The experiment has focused on the accuracy of the convergence result and the time required to converge to the optimal result. Experimental results demonstrate that it helps to reduce the time by 0.634% averagely, but the relative drop in the accuracy of large problems is 0.006% to 5.731%. © 2024 IEEE.

关键词： Combinatorial optimization

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The Intelligent Generation of College Schedule Based on Machine Learning 6

The Intelligent Generation of College Schedule Based on Mach...

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6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Zhang, Chunling Heilongjiang University of Technology Heilongjiang Jixi158100 China

ISBN: (纸本)9781837242672

In order to solve the problems of heavy workload and low efficiency of college resources scheduling. This article adopts reinforcement learning technology to schedule courses on a weekly week. The core of this method is the reward mechanism. By exploring the combination of hard conflict rewards and soft conflict rewards, we can find out the best action at which the average of the cumulative rewards reach maximum with each step, so as to generate the optimal schedule. In terms of computing power, the hybrid and parallel strategy is adopted. The process of learning and convergence is more stable and faster. The generated schedule performs well in many indicators, such as classroom utilization rate, the extent of the difference between the daily teaching time and the average. It has certain application value. © The Institution of Engineering & technology 2024.

关键词： Contrastive Learning

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Chaotic Hippopotamus based Energy Efficient Routing for Wireless Sensor Networks 4

Chaotic Hippopotamus based Energy Efficient Routing for Wire...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Vedanarayanan, V. Jayaraman, Venkatesh Lakshmi, R.T.Thivya Surendran, R. Sathyabama Institute of science and technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

The energy efficient routing in wireless sensor network (WSN) is crucial for enhancing the network longevity. For this, energy efficient routing protocol is introduced with cluster based routing. Initially, the sensor nodes are deployed randomly in the network area and are clustered together using the Chaotic Hippopotamus Optimization (ChHipO) Algorithm. The algorithm considers remaining energy, delay and distance as the multi-objective fitness factor for choosing the cluster head (CH) using the ChHipO Algorithm. The proposed ChHipO Algorithm integrates iterative chaotic mapping in the hippopotamus optimization algorithm for improving the rate of convergence. The analysis of ChHipO Algorithm based on residual energy, network lifetime, throughput and end-to-end delay acquired the values of 0.87, 99.728, 99.589 and 3.01 respectively. © 2024 IEEE.

关键词： Sensor nodes

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