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A Fuzzy System to Measure Additive Manufacturing Maturity: A Case Study in the Automotive Industry 6

A Fuzzy System to Measure Additive Manufacturing Maturity: A...

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6th international conference on Industry 4.0 and smart Manufacturing, ISM 2024

作者： Espadinha-Cruz, Pedro Reboredo, Elisa UNIDEMI Department of Mechanical and Industrial Engineering NOVA School of Science and Technology FCT NOVA NOVA University of Lisbon Lisbon Portugal Laboratório Associado de Sistemas Inteligentes LASI Guimaraes4800-058 Portugal NOVA School of Science and Technology | FCT NOVA NOVA University of Lisbon Lisbon Portugal

To survive in an increasingly competitive environment, adopting innovative technologies is crucial, particularly in the 4th industrial revolution. Additive manufacturing (AM), a key technology of Industry 4.0, enables layer-by-layer production and fosters business model innovation. Organizations must integrate AM using maturity models to diagnose, monitor, and guide its implementation effectively. However, no existing AM Maturity Model addresses the imprecision inherent in human judgment or the uncertainty in assessing AM maturity. This article proposes a novel contribution: a fuzzy system designed to diagnose and evaluate AM maturity, incorporating fuzzy set theory to handle imprecision and ambiguity and using knowledge rules to model various maturity perspectives. The system was applied in the automotive industry, diagnosing an intermediate AM maturity level of 3, where AM complements traditional manufacturing. The findings also identified challenges, such as the need to improve employee AM competencies. This system is as a valuable tool for decision-makers, supporting strategic planning and resource alignment with with AM capabilities. © 2024 The Authors.

关键词： smart manufacturing

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

7th International Conference on Wireless Communications, Net...

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7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

ISBN: (纸本)9789819624089

The proceedings contain 38 papers. The special focus in this conference is on Wireless Communications, Networking and Applications. The topics include: EffiNet: An Efficient, Low-Complexity Neural Network for Monocular Depth Estimation on Embedded Devices;adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection;a Cache Scheduling Method Based on Adaptive Expiration for Data Process System;Research on Airport Communication and Navigation Equipment Inspection Assistance System Based on AR technology;application of Quality Function Deployment in Assessing Unmanned Assault Capabilities for Mountain Counter-Terrorism Operations;research on the Application of Internet of Things technology in Power Grid Limited Space Security Monitoring;research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving technology;research on Cybersecurity System of Scientific Research Institutions Based on National Cybersecurity Classified Protection 2.0: Take the Chinese Academy of Agricultural Sciences as an Example;Research on DOA Estimation Method of Underwater Acoustic Signal Based on Machine Learning;Design and Implementation of smart Home Control System Based on STM32;communication Anti-jamming System Based on Deep Reinforcement Learning;fault Diagnosis of Infrared Sensor Based on Convolutional Neural Network;graph Neural Network knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information;an Approach to Microenvironment-Based Particle Swarm Optimization Algorithm;a Verifiable Ciphertext Retrieval Scheme for smart Grids;panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap;An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN;recognition and Calculation of Fish Rafts in Mariculture on the Basis of Artificial Intelligence;digital Twin for Power Load Forecasting.

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Analyzing the Correlation of smart Contract Vulnerability and Readability: A Machine Learning Approach 5th

Analyzing the Correlation of Smart Contract Vulnerability ...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Sameera, K.M. Vinod, P. Rafidha Rehiman, K.A. Kumari, Poonam Raj, Harsh Kumari, Rinki Kumar, Aman Mittal, Ritika Department of Computer Applications Cochin University of Science and Technology Kerala Kochi682022 India Department of Mathematics University of Padua Padua35121 Italy

ISBN: (纸本)9789819745395

Blockchain’s rapid growth is revolutionizing sectors like finance and healthcare, providing universal computational access for diverse, decentralized applications (DApps). It also facilitates the creation of DApps, which integrates smart contracts on the blockchain with a user-friendly front-end interface. This research aims to assist researchers and developers in analyzing the smart contract’s code readability level and vulnerability types. In this work, we collect and analyze smart contracts from the "Etherscan" platform to demonstrate the tool’s effectiveness. To the author’s knowledge, this is the first work examining the association between the readability and vulnerability of solidity smart contracts. To examine the degree of readability of smart contracts, we employ a set of readability matrices that the previous research demonstrated related to code readability. Our suggested method evaluates the smart contract automatically and calculates a set of metrics for the level of code readability. Furthermore, We propose a machine learning model to create an automated system that discerns between readable and non-readable smart contracts. The result shows that XGBoost outperformed other classifiers with an impressive 98.856% F-Measure and 98.858% accuracy. Additionally, we utilize the Slither to analyze security issues in smart contracts. Afterward, we examined the most prevalent and critical vulnerability types discovered in each smart contract. Also, it is observed that there is a correlation between non-readability and vulnerability. Our analysis revealed that 72.6% of non-readable files exhibit vulnerabilities, showing that files with high vulnerability are characterized by low readability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Code readability smart contracts Vulnerability

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Deep Learning Applications for Detecting Crop Diseases from Image Data

Deep Learning Applications for Detecting Crop Diseases from ...

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2025 IEEE international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Praveen, R.V.S. Mittal, Mudit Parida, Prasanta Begum, Rowsonara Kumar, Yogendra Nijhawan, Ginni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India School of Rural Management KIIT University Odisha Bhubaneswar India MLR Institute of Technology Department of Comp. Sc. & Engg. Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Dept. of Comp. Sc. & Engineering Phagwara India

ISBN: (纸本)9798331512965

In order to optimize the productivity of crops and ensure food security the crop diseases should be identified as soon as possible. In this study, we study the image based crop disease identification using Deep Learning Methods. However, the traditional illnesses detection techniques are typically based on specialist knowledge and human inspection, and the latter also has the disadvantages of being very time consuming and error prone. Use of convolutional neural networks (CNNs), in particular, have made deep learning powerful for many imaging problems such as precise and automated identification of disease. The ultimate goal of this research is to build a CNN based model which, through the analysis of photos of a damaged leaf, can identify and categorize various crop diseases. With this large data set with excellent images of both healthy and sick crops, we take advantage of. The model is trained and tested based on F1 scores, accuracy, precision and recall. The findings indicate that the model proposed in this research outperforms generic machine learning techniques in diagnosing diseases such as rust, mildew, and blight at high accuracy. The real time implementation of the system is also scalable enough to monitor big areas in the smart farming set up. I show in this study that deep learning has the potential to significantly enhance agricultural disease management and to pave the way to new and more effective and sustainable ways to grow crops. © 2025 IEEE.

关键词： smart agriculture

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Untangling the Complexities of the Uptake of smart Construction Sites in South Africa 20th

Untangling the Complexities of the Uptake of Smart Construct...

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20th international conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Ikuabe, Matthew Aigbavboa, Clinton Ngcobo, Ntebo Aghimien, Lerato cidb Centre of Excellence Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Civil Engineering Technology Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Construction Management and Quantity Surveying Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa School of Construction Economics and Management Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

ISBN: (纸本)9783031842078

Using technological applications, smart construction aims to improve the planning, management, and execution of construction project delivery. This has the potential to forestall several of the challenges associated with project execution and delivery using conventional methods. Therefore, this study evaluates the challenges of implementing smart construction sites in the South African construction industry. Employing a quantitative technique, data was elicited from the target respondents who were sampled using convenience and snowball sampling techniques. Retrieved data was analysed using exploratory factor analysis using the principal component analysis extraction technique. The study’s findings showed that the significant challenges faced by implementing smart construction sites in South Africa are institutional drawbacks, financial impediments, and lack of technical expertise. The study contributes theoretically and practically to the growing call for construction digitalisation as it fills the gap in knowledge by unravelling the encumbrances of the use of smart construction sites. Furthermore, the study’s outcome is a sound theoretical base for future studies geared towards construction digitalisation in developing countries. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Discover and Explore Weak Causality and Causal Disposition in Images for smart Manufacturing Tasks 6

Discover and Explore Weak Causality and Causal Disposition i...

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6th international conference on Industry 4.0 and smart Manufacturing, ISM 2024

作者： Streltsov, Oleksii Terziyan, Vagan Vitko, Oleksandra Department of Artificial Intelligence Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Faculty of Information Technology University of Jyväskylä Jyväskylä40014 Finland

In the context of Industry 4.0 and smart manufacturing, effective machine learning models must not only predict outcomes but also understand the underlying causal relationships for tasks such as predictive and prescriptive maintenance. However, traditional Convolutional Neural Networks (CNNs) are often limited by their inability to explicitly capture and utilize these causal relationships within image data, which can lead to suboptimal performance and limited interpretability in industrial applications. This paper addresses these limitations by advancing our prior work on Causality-Aware Convolutional Neural Networks (CA-CNNs), which are designed to identify and utilize hidden causal factors within images. Through a series of empirical validations, we demonstrate that: CA-CNNs outperform conventional CNNs in classification tasks by employing causality maps;pre-trained CNNs enhance the computation of these maps, while weighting features based on causality maps leads to superior performance;the "Lehmer"method improves noise tolerance and adversarial robustness, and the use of "causality shadows"derived from saliency maps increases classification accuracy. We further explore integration strategies, including knowledge-Informed Machine Learning schemas, to merge the discovered features effectively with their causal relationships. Our findings highlight the significant potential of CA-CNNs and their modifications in industrial applications, offering improved predictive capabilities, and explainability in smart manufacturing. © 2025 The Authors.

关键词： smart manufacturing

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Ensuring Trust in Blockchain-Based E-commerce Using smart Contracts 5th

Ensuring Trust in Blockchain-Based E-commerce Using Smart Co...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Surendar, P.D. Kumar, Adhithi Satish Pavani, Venkata Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (纸本)9789819773701

In the ever-changing realm of e-commerce, building trust is crucial for both customers and sellers. Reputation systems play a vital role in enabling informed decision-making by providing vendor ratings and reviews. Conventional reputation systems are typically inadequate in dealing with contemporary issues including privacy, identity theft, and feedback fraud. In order to address these challenges, we suggest implementing a decentralized reputation system that places a high value on privacy and effectively counters existing risks. Our architecture utilizes two permissioned blockchains, Hyperledger Indy for decentralized identity management through verifiable credentials and Zero knowledge Proof (ZKP) technology, guaranteeing anonymity and authenticity, and Hyperledger Fabric for executing smart contracts, facilitating intricate business logic. The system incorporates verified vendor identities, transaction feedback tokens, and discount tokens to encourage genuine evaluations. smart contracts implemented on the Hyperledger Fabric platform provide the security and transparency of the feedback process, hence reducing the occurrence of biased feedback and identity assaults. This technique provides a strong and reliable answer to the issues faced in e-commerce. It ensures the protection of private information and enables the verification of digital identities while maintaining privacy. The implementation of feedback and discount tokens promotes the provision of precise input, mitigating unjust practices and bolstering the dependability and confidence in the system. We demonstrate the effectiveness of our methodology using Zero knowledge Proofs (ZKP), highlighting its importance in establishing confidence within the context of blockchain. Our system represents a notable advancement in creating a safe, transparent, and reliable e-commerce environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Unsupervised Partial Domain Adaptation for Occupants Behavior Modeling in smart Buildings

Unsupervised Partial Domain Adaptation for Occupants Behavio...

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14th international conference on smart Cities and Green ICT Systems, smartGREENS 2025

作者： Dridi, Jawher Amayri, Manar Bouguila, Nizar Concordia University Canada

ISBN: (纸本)9789897587511

smart buildings rely on activity recognition (AR) and occupancy estimation (OE) tasks to provide residents with several services such as optimal energy management, HVAC (Heating, ventilation, and air conditioning) systems optimization, and security. Estimating the number of occupants and recognizing their activities is performed using sensor data which is scarce. The collection and labeling of smart building data are tedious, costly, and time-consuming, pushing researchers to consider solutions based on domain adaptation (DA) to transfer knowledge from source domains where data is abundant to target domains where data is scarce. In particular, unsupervised domain adaptation (UDA) has been considered to solve the unavailability of labeled data in target domains. Previous research has focused on standard UDA methods where label space is identical between source and target domains which is not the case for real-world datasets. This work considers unsupervised partial domain adaptation (UPDA) methods where target classes are a subset of source classes. We adapt and evaluate two UPDA techniques called Adversarial Re-weighting for Partial Domain Adaptation (ARPDA) and Selective Adversarial Networks for Partial Domain Adaptation (SAN w PDA). We have compared their performance to Adversarial Re-weighting for Standard Domain Adaptation (ARSDA) and Selective Adversarial Networks for Standard Domain Adaptation (SAN w SDA) as well as several previous UDA methods. The impressive results with scores up to 98% prove the efficiency of the adapted UPDA techniques. We provide the code in the following repository: https://***/JawDri/***. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Unsupervised learning

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Enhancing Troubleshooting Task-Oriented Dialog Systems with Large Language Models 1

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17th international conference on Intelligent Robotics and Applications

作者： Zhou, Jiahao Zhang, Qiang Zhang, Fengda Yuan, Caixia Beijing Univ Posts & Telecommun Beijing Peoples R China State Grid Smart Grid Res Inst CoLtd Beijing Peoples R China State Grid Shandong Elect Power Res Inst Jinan Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

Task-oriented dialog (TOD) systems use external knowledge sources to help users accomplish specific tasks. While most current TOD research focuses on simple information-collecting tasks in a slot-filling framework, multi-step reasoning tasks like troubleshooting remain under-explored. Leveraging the advancements of large language models (LLMs), we propose a novel LLM-based multi-agent learning framework to build troubleshooting dialogue systems and evaluate the effectiveness of various multi-agent learning settings in a TOD system. Our results show that LLMs designed for open-domain dialog face challenges when directly applied to TOD systems, but with multi-agent cooperative enhancements, LLMs can achieve commendable performance.

关键词： Large Language Model Task Oriented Dialog System Multi-agent

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Securing the smart revolution: Navigating security and privacy concerns in Industry 4.0 vs. Industry 5.0 1st

Securing the smart revolution: Navigating security and priva...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Uprety, Deepak Chandra Rastogi, Ritesh Jain, Garima Ahamad, Shakeel Yadav, Chandra Shekhar Banarjee, Dyuti Dwivedi, Rashmi Department of CS NIET Engineering College Knowledge Park-II Gr UP Noida India Department of CSBS NIET Engineering College Knowledge Park-II Gr UP Noida India Department of DS NIET Engineering College Knowledge Park-II Gr UP Noida India Department of MCA NIET Engineering College Knowledge Park-II Gr UP Noida India India Department of Economics NIMS University Rajasthan Jaipur India

ISBN: (纸本)9781032874036

Industry 5.0 is defined by the manufacturing sector’s adoption of cutting-edge technology, including robotics, AI, and the IOT. Industry 5.0 substantially improves productivity, efficiency, and customization, but it also presents new security and privacy issues that must be addressed for its successful adoption. This study compares Industry 4.0 with Industry 5.0 on the basis of security and privacy concerns and suggests ways to reduce the risks. One major concern is the increased connectivity of industrial systems and the corresponding rise in the attack surface. This article proposes a complete approach that stresses the integration of security measures from the design phase and the application of privacy-by-design principles for security and privacy issues in Industry 4.0 vs. Industry 5.0. By proactively addressing these challenges, Industry 5.0 can unlock its full potential while fostering trust among stakeholders, enabling sustainable growth, and safeguarding societal well-being. © 2025 the Author(s).

关键词： Differential privacy

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