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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37467 条记录，以下是541-550 订阅

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Optimizing Federated Learning Efficiency: Exploring Learning Rate Scheduling in Sparsified Ternary Compression

Optimizing Federated Learning Efficiency: Exploring Learning...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Nithyaniranjana Murthy, C. Manjula, Sh. University of Visvesvaraya College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350359688

Federated Learning facilitates the collaborative training of a deep learning model by leveraging the combined data of multiple entities, with a focus on maintaining the privacy of individual datasets. The Sparse Ternary Compression (STC) method improves gradient sparsification by integrating downstream compression, ternarization, and optimal Golomb encoding of weight updates. Although STC enhances compression techniques in federated learning, the broader scope of Federated Learning lies within the domain of machine learning. Researchers investigating improvements in STC compression techniques must consider neural network concepts and the impact of hyperparameters, such as learning rate, on convergence speed and accuracy. Increased communication across multiple devices poses challenges in achieving desired accuracy levels. Our research underscores the importance of selecting appropriate learning rate schedules when implementing algorithms like STC. Incorrect choice of learning rate schedules can hinder convergence, even if the algorithm functions effectively, emphasizing the crucial role of hyperparameters in reducing communication costs and achieving desired accuracy levels © 2024 IEEE.

关键词： Federated learning

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A Modified Deep Learning Architecture for Detecting MalariaInfected Cells

A Modified Deep Learning Architecture for Detecting MalariaI...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Shahshikiran, S. Srinivas Babu, N. Pramanik, Souvik Sathwik New Horizon College of Engineering Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350359688

Malaria is a major public health problem because it is prevalent in tropical and subtropical regions with inadequate healthcare systems and limited resources. A correct diagnosis is necessary for prompt malaria intervention and treatment. Expert pathologists manually review blood smear images to obtain a microscopic diagnosis. Alternatively, they use a 50% accurate Rapid antigen malaria test. Advancements in Deep Learning(DL) have provided promising features in automating malaria diagnosis. Generating predictions based on patterns seen in large-scaledatasets. the DL approach, which is recommended work, provides a more reliable diagnosis by reducing the cost of diagnosis, especially in areas with limited resources. The approach we propose combines Image transformation, Feature extraction, Feature engineering, and data augmentation using the Convolutional Neural Network(CNN) architecture with an accuracy of 94.44% in classification, and by incorporating a Support Vector Machine (SVM) function as a method of activation in the final layer of the CNN design, it influences the layers. Our objective is to improve deep neural networks' performance in classifying cells that are affected by malaria parasite. © 2024 IEEE.

关键词： Diseases

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Enhancing Waste Management Through Smart Bins 4

Enhancing Waste Management Through Smart Bins

引用

4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Sheela, T. Snegaa, G. Rupa Devi, T. Sri Sai Ram Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Food waste (FW) generation is a major global issue and a top political priority. Lack of precise information regarding the volume, timing, and causes of waste creation is one of the primary causes of it. This article addresses the architecture, implementation, and exploration of an Internet of Things (IoT)-driven food waste (FW) monitoring system that gathers waste data in real-time during manufacturing and disseminates it to all parties involved in the food supply chain (FSC). A case study of a ready-meal factory includes the hardware and software design and architecture for tracking food waste (FW), how it is implemented in the plant, and the successful outcomes derived from data analysis. The case study highlights the advantages of using a digital FW tracking system, such as FW reduction. © 2024 IEEE.

关键词： Smart manufacturing

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Reviewing Machine Learning Techniques in Credit Card Fraud Detection 16

Reviewing Machine Learning Techniques in Credit Card Fraud D...

引用

16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Medarhri, Ibtissam Hosni, Mohamed Ettalhaoui, Mohamed Belhaj, Zakaria Zine, Rabie MMCS Research Team LMAID ENSMR Rabat Morocco MOSI Research Team LM2S3 ENSAM Moulay Ismail University of Meknes Meknes Morocco School of Science and Engineering Al Akhawayn University in Ifrane Ifrane Morocco

ISBN: (纸本)9789897587160

The growing use of credit cards for transactions has increased the risk of fraud, as fraudsters frequently attempt to exploit these transactions. Consequently, credit card companies need decision support systems that can automatically detect and manage fraudulent activities without human intervention, given the vast volume of daily transactions. Machine learning techniques have emerged as a powerful solution to address these challenges. This paper provides a comprehensive overview of the knowledge domain related to the application of machine learning techniques in combating credit card fraud. To achieve this, a review of published work in academic journals from 2018 to 2023 was conducted, encompassing 131 papers. The review classifies the studies based on eight key aspects: publication trends and venues, machine learning approaches and techniques, datasets, evaluation frameworks, balancing techniques, hyperparameter optimization, and tools used. The main findings reveal that the selected studies were published across various journal venues, employing both single and ensemble machine learning approaches. Decision trees were identified as the most frequently used technique. The studies utilized multiple datasets to build models for detecting credit card fraud and explored various preprocessing steps, including feature engineering (such as feature extraction, construction, and selection) and data balancing techniques. Python and its associated libraries were the most commonly used tools for implementing these models. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Towards Interoperable Digital Twins: Integrating SysML into AAS with Higher-Order Transformations 21

Towards Interoperable Digital Twins: Integrating SysML into ...

引用

21st international conference on Software Architecture (ICSA)

作者： Ferko, Enxhi Berardinelli, Luca Bucaioni, Alessio Behnam, Moris Wimmer, Manuel Malardalen Univeristy Vasteras Sweden Johannes Kepler Univ Linz Linz Austria

ISBN: (纸本)9798350366266;9798350366259

The functional suitability of digital twin systems relies on accurately capturing, modelling, and exchanging data from their corresponding assets or processes. Consequently, achieving interoperability among various components and different digital twin systems is crucial. In the current landscape, characterized by various languages supporting diverse semantic models, achieving interoperability is a complex task. Achieving interoperability might involve translating diverse models, either peer-to-peer or through a central pivotal model. In this study, we propose a model-driven engineering approach that leverages higher-order transformations in conjunction with the Asset Administration Shell, acting as the pivotal model to tackle the interoperability challenges associated with digital twin systems. Higher-order transformations are a specific type of model transformation, characterized by their input and/or output being model transformations themselves. Our hypothesis is that such transformations would eliminate the need to manually craft multiple translations toward the Asset Administration Shell. Instead, a single higher-order transformation would automatically generate these translations. We chose the Asset Administration Shell as our pivotal model, because it is widely recognized as a foundational element for application interoperability in Industry 4.0/5.0. We illustrate our approach through a vehicle use case represented using the systems Modeling Language version 2 and consolidating this description into an Asset Administration Shell model. Hence, we showcase the applicability and suitability of our proposed approach. To the best of our knowledge, our work represents the first effort to address the translation of systems Modeling Language version 2 into the Asset Administration Shell.

关键词： Digital twin Asset administration shell SysML v2 interoperability Model-driven engineering

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Unlocking Water Conservation: Synergizing IoT and Machine Learning for Agricultural Irrigation Efficiency

Unlocking Water Conservation: Synergizing IoT and Machine Le...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Praveen Kumar, K. Manjula, S.H. University of Visvesvaraya College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350359688

The traditional irrigation method often results in excessive water usage and wastage, highlighting the urgent need for smarter irrigation solutions. Using the Internet of Things (IoT) and Machine Learning (ML) to their full capacity offers a viable way to automate irrigation procedures with little to no human involvement. This paper suggests an IoT-enabled Machine Learning recommendation system that is taught to maximize water efficiency and minimize farmer intervention. IoT sensors are placed in fields to gather accurate environmental and ground data, which is then processed on a cloud-based server using Machine Learning methods to provide farmers with customized irrigation recommendations. Incorporating a feedback mechanism ensures the system's adaptability and robustness. By embracing IoT and ML technologies, farmers stand to benefit from significant improvements in water conservation, reduced operational costs, and enhanced agricultural productivity. This interdisciplinary approach not only promotes sustainable water management practices but also fosters innovation in precision agriculture, contributing to a more resilient and resource-efficient farming landscape. © 2024 IEEE.

关键词： Smart agriculture

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Improving Healthcare data Security Using Cheon-Kim-Kim-Song (CKKS) Homomorphic Encryption

Improving Healthcare Data Security Using Cheon-Kim-Kim-Song ...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Sathishkumar, P. Pugalarasan, K. Ponnparamaguru, C. Vasanthkumar, M. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350359688

In recent years, the proliferation of mobile devices in healthcare settings has revolutionized patient care delivery and medical data management. However, the increased reliance on mobile platforms for collecting, transmitting, and storing sensitive healthcare data has raised concerns regarding data security and privacy. To address these challenges, this paper proposes a novel approach for healthcare mobile data security leveraging the CKKS (Cheon-Kim-Kim-Song) fully homomorphic encryption scheme. CKKS offers the ability to perform secure computations on encrypted data, enabling healthcare applications to protect patient privacy while preserving the confidentiality of sensitive medical information. This paper presents a comprehensive analysis of the CKKS scheme's applicability to healthcare mobile data security, including parameter selection, encryption, homomorphic operations, and performance optimization techniques tailored to resource-constrained mobile devices. Furthermore, the paper discusses the integration of CKKS encryption into healthcare mobile applications, highlighting its role in safeguarding patient records, sensor data, and diagnostic information. Through experimental evaluations and performance benchmarks, we demonstrate the feasibility and effectiveness of the proposed approach in ensuring healthcare mobile data security without compromising computational efficiency. Overall, this paper contributes to advancing the state-of-the-art in healthcare information security by introducing a practical and scalable solution for protecting sensitive medical data on mobile devices using the CKKS encryption scheme. © 2024 IEEE.

关键词： Differential privacy

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AI-Enhanced Pedagogy in Higher Education: Redefining Teaching-Learning Paradigms

AI-Enhanced Pedagogy in Higher Education: Redefining Teachin...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Kakhkharova, Matlyuba Tuychieva, Sayyora International Islamic Academy of Uzbekistan Department of International Relations and Social Sciences Tashkent Uzbekistan

ISBN: (纸本)9798350359688

This paper delves into the transformative landscape of higher education through the lens of AI, introducing a paradigm shift in teaching and learning methodologies. The exploration of AI-enhanced pedagogy emerges as a pivotal response to the dynamic challenges faced by educational institutions, aiming to redefine conventional approaches. The core objective is to harness the potential of artificial intelligence in order to individualize and optimize the learning experience. By synthesizing advanced machine learning algorithms and intelligent systems, the proposed framework creates an adaptive learning environment tailored to the unique needs and preferences of each student. The paper conducts a thorough literature survey, unveiling gaps in current research, and positions the proposed work within the context of existing knowledge. The envisioned methodology involves the strategic integration of data collection, preprocessing, and algorithm selection, ensuring the efficacy of the AI-enhanced pedagogical model. The implementation model, discussed in detail, incorporates natural language processing, recommendation systems, and learning analytics to construct a scalable and customizable educational framework. The results, drawn from the implementation, showcase improvements in student performance, engagement, and satisfaction. In conclusion, this paper advocates for the widespread adoption of AI-enhanced pedagogy, emphasizing its potential to revolutionize higher education by providing a personalized and effective learning experience for students and educators alike. Future research directions and challenges are also contemplated, contributing to the ongoing discourse on the transformative role of AI in education. © 2024 IEEE.

关键词： Adversarial machine learning

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Supply Chain Resilience and Artificial Intelligence-based Techniques: A Survey Study 7

Supply Chain Resilience and Artificial Intelligence-based Te...

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7th international conference on Mechanical manufacturing and Industrial engineering, MMIE 2024

作者： Karot, Thanathorn Pornsing, Choosak Department of Industrial Engineering and Management Faculty of Engineering and Industrial Technology Silpakorn University Nakhon Pathom73000 Thailand

ISBN: (数字)9781510683297

ISBN: (纸本)9781510683280

This study examined the capabilities and barriers of artificial intelligence in supply chain resilience in the Thai industry. Artificial Intelligence (AI) can analyze and assess different options in complex scenarios, including supply chain disruptions involving multiple data dimensions. The research started with the literature examined. Then, semi-structured interview questions were formulated. The reliability was tested using stability, consistency, and equivalence concepts. The validity was tested using the triangulation technique. Thirty-five managers who have experience with the COVID-19 pandemic were asked. The thematic analysis identified deficiencies in conventional systems and explored how artificial intelligence may contribute to developing robust system architectures. Moreover, we have recognized the obstacles to attaining artificial intelligence systems. Our report not only explores the capabilities and barriers of artificial intelligence-based techniques in supply chain resilience management but also recommends the study directions for applying artificial intelligence in other functions such as finance, marketing, and production. © 2024 SPIE.

关键词： COVID-19

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The Attitude of engineering Students Towards Robotization 1

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26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Nagy, Katalin Kanczne Toth, Peter Budapest Univ Technol & Econ H-1111 Budapest Hungary

ISBN: (数字)9783031533822

ISBN: (纸本)9783031533815;9783031533822

The investigation of the attitudes of members of society towards robotization has been the focus of research for years. The studies, in addition to being task and development oriented, also focus significantly on the social effects of robotization. Opinions about the human-likeness and communication abilities of robots are measured, as well as the emotional factors formed by joint tasks with robots. This research was conducted among engineering students studying at the Budapest University of Technology and Economics (Hungary), in which 320 students participated. The main goal of the research is to map the attitude of university students towards robotization, as a result of which we want to formulate a proposal for the further development of the curricula. In relation to robotics, artificial intelligence, cloud-based IT and Big data education are considered the most important. As the studies progress, these areas of knowledge are considered more and more important, and this is especially the case for women and those participating in part-time training (that is, those with work experience). Those students who have already learned about robotics or who can be considered professionals (mechanical engineer, electrical engineer, IT engineer, mechatronic engineer) attach more importance to the education of these topics. The topics to be taught were combined into three factors: (1) robotics in production, (2) virtual robotics, (3) integrated network communication.

关键词： Robotization Attitude engineering Students Empirical Research

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