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Automation 5.0: The Key to Systems intelligence and Industry 5.0

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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data-Driven Exploration and Visualization of Diverse fields of Startup in India

Data-Driven Exploration and Visualization of Diverse fields ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Blessy, Sardar Rechel Supraja, Balerao Rathi, Kushal Prathima, T. Vasanth, K. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798350319200

The objective of this research is to analyze and comprehend the trends in the startup culture of India. Startup insights refer to the knowledge and understanding gleaned from the analysis and visualization of various startup venture information. Startup insights can assist entrepreneurs, investors, and other stakeholders in the startup ecosystem to better understand the factors that come up while building a new business, and to make informed decisions regarding strategy, resource allocation, and risk management. The research involved collection and analysis of data on numerous startup companies. Descriptive statistics were used to explain trends and patterns in the data such as, which sectors are attracting the most startups, which locations are seeing the most startup activity, how many number of startups that have been established yearly and so on. © 2023 IEEE.

关键词： Descriptive statistics Entrepreneurs Stakeholders Startup

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Hybrid Particle Swarm and Gray Wolf optimization for Prediction of Appliances in Low-Energy Houses

Hybrid Particle Swarm and Gray Wolf optimization for Predict...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： El-Kenawy, El-Sayed M. Eid, Marwa M. Abdelhamid, Abdelaziz A. Ibrahim, Abdelhameed Takieldeen, Ali E. Elkhalik, Said H. Abd Delta University Faculty of Artificial Intelligence Department of Artificial Intelligence Mansoura Egypt Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo Egypt Mansoura University Faculty of Engineering Computer Engineering Department Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Egypt

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

In this paper, we propose a new feature optimization prediction framework. This framework consists of two phases. The first phase is the optimization of the feature selection process, in which the dataset is preprocessed by removing non-predictive factors, then the best set of features is selected based on the hybrid modified gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms. Whereas the second phase consists of an ensemble model based on the k-nearest neighbors (KNN) regression model, which is trained on the selected features to generate predictions of the appliances in low energy house. The proposed ensemble model consists of three regression models namely, KNN regression, multi-layer perceptron (MLP), and decision trees (DT). Experimental results show that the proposed model achieved the best accuracy and lowest error. In specific, the achieved Root Mean Square Error (RMSE) is (0.00225) and the achieved Mean Absolute Error (MAE) is (0.0002). These results emphasize the effectiveness of the proposed approach when compared with the separate application of regression models. © 2022 IEEE.

关键词： Forecasting

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Quadratic Funding in Blockchain-Based DAO: Empirical Analysis and Future Prospects 4

Quadratic Funding in Blockchain-Based DAO: Empirical Analysi...

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4th IEEE International Conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Lin, Fei Zhang, Tengchao Ma, Siji Huang, Jun Song, Ping Li, Juanjuan Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

This paper provides an in-depth analysis of the application of the Quadratic Funding (QF) mechanism in blockchain-based Decentralized Autonomous Organizations (DAOs) of the QF mechanism. Then, the empirical analysis on the influence of QF on promoting project funding is examined, taking the first round of BNB funding on the DoraHacks platform as the exemplar. The focus has been put on its performance on community voting and fund allocation as well as its real-world impact. Finally, based on the analysis results, the paper proposes improvements to the QF mechanism and presents forward-looking strategies for applying Large Language Models (LLMs) in decentralized decision-making. These insights provide valuable perspectives for understanding and optimizing Decentralized Finance (DeFi). © 2024 IEEE.

关键词： Decentralized finance

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From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Gantayat, Pradosh Kumar Kaur, Tejinder Majhi, Madhusmita Jena, Uttam Kumar Das, Swatismita Soni Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Department of Mmictbm Mullana Haryana Ambala133207 India Department of Computer Science and Engineering Telangana Hyderabad501203 India Raajdhani Engineering College Biju Patnaik University of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Raajdhani Engineering College Bhubaneswar India

ISBN: (纸本)9798331509828

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence. © 2025 IEEE.

关键词： Human-AI Collaboration Industrial Efficiency Industry 5.0 Innovation LLM 5.0

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

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Innovations and Applications of Generative Adversarial Networks (GANs)

Innovations and Applications of Generative Adversarial Netwo...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Meher Langote Nikita Zade Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

This paper presents a topical understanding of Generative Adversarial Networks (GANs) and their relevance to artificial intelligence and data science. Following a brief description of what GANs are, their background, and their importance in today's AI, the paper provides the following goals, which are designed to clarify the difficulties, progress, and potential prospects in GAN technology. The paper presents an overview of GAN's basics, elements, and training procedures and discusses the issues, including mode collapse, training fluctuation, data demands, and the ethics of elaborating further on this model. In addition, it discusses promising research directions that concern GANs: new architectures, better algorithms, and combining GANs with other AI approaches. The paper also outlines the development of other GAN applications in areas like climate and material sciences, as well as the transformative role that GANs can play in solving the issues affecting the world today. Therefore, it is expected that the outcomes of the current research proposal and future possibilities can offer insights for researchers, practitioners, and enthusiasts in achieving artificial intelligence and innovative applications in GANs.

关键词： Training Ethics Technological innovation Materials science and technology data science Generative adversarial networks Market research Proposals artificial intelligence Meteorology

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A Multi-modal Approach for the Molecular Subtype Classification of Breast Cancer by using Vision Transformer and Novel SVM Polyvariant Kernel

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IEEE Access 2025年 13卷 97545-97558页

作者： Shukla, Vinayak Kaarthika Mathur, Archana Narayan, Piyush Kishor, Kunal Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore560064 India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India

Accurate classification of breast cancer into distinct molecular subtypes is crucial for personalized treatment and improved patient outcomes. Despite advancements in machine learning, predicting these subtypes remains challenging due to tumor heterogeneity and complex data characteristics. This study introduces the Polyvariant kernel, a novel SVM kernel that leverages the Vision Transformer (ViT) for multimodal data integration from the TCGA dataset of 1,081 breast cancer patients. The ViT extracts features from Whole Slide Imaging (WSI), which are fused with gene expression profiles through an Optimization-Based Search mechanism. These fused features are then classified using the Polyvariant kernel. The proposed approach significantly enhances breast cancer subtype classification, outperforming classical RBF, Polynomial, and Utility kernels, achieving 87% accuracy and 87% recall. The Polyvariant kernel is designed with distinct hyperparameters, enabling effective integration of diverse data sources such as gene expression profiles and WSI. We theoretically validated the proposed Polyvariant kernel by proving the Mercer conditions and establishing it as both a universal approximator and a universal kernel. Furthermore, we empirically demonstrated its superiority by benchmarking it against deep learning models (ResNet-50, VGG16, GoogLeNet) and classical ML datasets. Statistical significance tests confirmed its robustness, reinforcing its effectiveness for complex biomedical data integration. The proposed research demonstrates the efficacy of multimodal data fusion in improving breast cancer subtype classification and establishes the Polyvariant kernel as a powerful tool for complex biomedical data analysis. © 2013 IEEE.

关键词： Taxonomies

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Supply Chain Management Using Blockchain Security Enhancement 1

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4th International Conference on Advances in Information Communication technology and Computing, AICTC 2022

作者： Sharma, Aditi Goar, Vishal Kuri, Manoj Chowdhary, Chiranji Lal School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Department of Artificial Intelligence and Data Science Engineering College Bikaner Bikaner India School of Information Technology and Engineering VIT University Vellore India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

The cybersecurity of contemporary systems has drawn much more focus from both academic and industrial viewpoints. The blockchain-based approach has recently become more popular due to how straightforward it is, especially in the management of supply chains. This highlights how crucial the quality factors are from a supply chain management standpoint. Numerous sectors have come to realize how crucial it is to have reliable supply chain and logistics solutions. The introduction of blockchain technology has given rise to several possible breakthroughs in the management and monitoring of corporate operations, specifically supply chain procedures. The blockchain and, more precisely, a smart contract technology that was utilized to manage the process of creating, verifying, and inspecting data over the supply chain management process were both discussed in this study. Next, discuss blockchain cybersecurity in the context of supply chains. Security protection needs to be powerful sufficient to shield the assets and data from dangers, as the smart contract increasingly controls the movement of data over many places. The paper then examines the primary security breaches that have an impact on the data on the blockchain and offers a fix. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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