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Analysis of engineering structures and components created using the generative design

Analysis of engineering structures and components created us...

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2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023

作者： Tkachenko, Arseny Wang, Chang Jiang Department of Engineering and Design School of Engineering and Informatics University of Sussex BrightonBN1 9RH United Kingdom

Generative design is an AI-driven process to explore all the possible design variants, it mimics nature's evolutionary approach to create an optimised final design. Generative design and additive manufacturing are interlinked because additive manufacturing is able to fabricate complex geometries. Generative algorithms have demonstrated their effectiveness in reducing costs, weight and development time across various industries including automotive and aerospace. This paper analysed components and structures to show the advantages of using the generative design method, problems encountered and issues that could be addressed to improve the final designs. Loading conditions, stress concentrations and manufacturability of the generative-designed components were discussed in the paper. © Published under licence by IOP Publishing Ltd.

关键词： 3D printing

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Glaucoma Classification Using Improved Pretrained Model 12th

Glaucoma Classification Using Improved Pretrained Model

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819731794

The most common cause of blindness in the world is glaucoma. It can lead to a decline in eyesight and quality of life if not addressed within the stipulated time. In computer vision, convolutional neural networks (CNNs) have achieved a viable solution. The most standard glaucoma screening method is fundus imaging due to the compromises it makes between mobility, size, and price. In this paper, we utilized the RIM-1 DL dataset in two different versions: RIM-1 Hospital and RIM-1 Randomly. To enhance the image quality and increase accuracy, we used median filtering pre-processing techniques in this work. We also used fundus images to automatically diagnose glaucoma using six different Image-net-trained models (DenseNet121, DenseNet169, VGG19, InceptionV3, ResNet50, and MobileNetV2). The six CNN architectures mentioned above are modified using normalization, inception, and global pooling layers in the model with the pre-trained classification models. Results indicate that the DenseNet-121 is the most accurate model in this work, improving accuracy in the RIM-1 Hospital dataset from 70.68 to 80.13 and in the RIM-1 randomly generated dataset from 79.31 to 87.67 after the models were fine-tuned. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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Enhancing Supply Chain Efficiency with IoT and Data Analytics

Enhancing Supply Chain Efficiency with IoT and Data Analytic...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Jayalakshmi, D. Vimal, V.R. Loganayagi, S. Narayanan, Lakshmi Kanthan Hemavathi, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Thandalam Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

This study aims at exploring the use of internet of things technology and data analytics in the field of supply chain management. Using a range of software tools, including Apache Kafka, Apache NiFi, Python libraries such as Pandas, and Scikit-learn, the study accumulated, assimilated, and quantified data from IoT sensors, supply chain systems, and qualitative interviews. The primary data sources are primary supply chain data history and IoT sensor data while secondary data includes academic and industrial journals, and reports. This paper presents a number of basic quantitative measures showing the general performance indicators: the average percentage of order fulfilment as 92.5% and the inventory turnover ratio equalling 6.2. When using linear regression, random forest, and LSTM neural network, the models reveal the mean absolute errors of demand forecasting between 80 and 120 units. The results suggest that IoT and data analysis offer significant opportunities to create value through the integration of SC, cost cutting, and higher customer satisfaction. It has now become essential for organizations to effectively apply data trends to enable competitive gains within the complex business setting to foster operational efficacy and stability. © 2024 IEEE.

关键词： Customer satisfaction

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A Review of Spectrum Sharing in 6G for IoT Environment 2

A Review of Spectrum Sharing in 6G for IoT Environment

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Alazzawi, Esraa Mohammed Jamil, Akhtar Sharda School of Engineering & Technology Department of Computer Science & Engineering UP Greater Noida India University of Baghdad Al-Jadriya Department of Computer Engineering Baghdad10071 Iraq National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798331529529

With increase in demand the resources have proven to be limited. Spectrum sharing is a way to use the limited resources to their full potential. These can exceptionally improve the way IoT devices work. The ever-increasing demand in communication can be revolutionized by 6G network as it proves to be superior in terms of latency, reliability, data rates, connectivity and coverage. Hence the concept of spectrum sharing in 6G holds a great prospect for the future. This paper reviews the recent advances in the field of spectrum sharing for 6G in the IoT environment. It lays down the important techniques and the security and privacy concerns in IoT environments. It further lays down the important future prospects in terms of mm Wave, THz and optical spectrum and their most important use cases. © 2024 IEEE.

关键词： Terahertz waves

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Exploring the Usability of Adaptive Weighting Factors in Transformer Health Diagnostic Practices 14

Exploring the Usability of Adaptive Weighting Factors in Tra...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Prasojo, Rahman Azis Maharani, Devira Anggi Safarina, Liska Suwarno Putra, Muhammad Akmal A. Ekojono Politeknik Negeri Malang Department of Electrical Engineering Malang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350374605

This paper examines the potential of using adaptive weighting factors in the Health Index (HI) method for transformer health diagnostics. Typically, HI methods rely on weighting factors that are set by experts. While effective, this approach often static and may not always incorporate the latest insights or data. This study aims to enhance the HI method's accuracy and reliability by integrating adaptive, data-driven weighting factors. To achieve this, the usability of deep neural network algorithms to derive these weighting factors from actual transformer oil data is investigated. The dataset includes 500 high-voltage transformer oil samples, each with five oil testing features and known oil quality conditions. This data is used to develop and evaluate the approach. The dataset is analyzed at different scales (5, 25, 50, 100, 250, and 500 randomized samples) to ensure a comprehensive and repeatable evaluation. The main procedure involves feeding the dataset into a neural network, which then extracts the optimal weighting factors. These factors are subsequently applied in the conventional HI formula to assess their accuracy in classifying transformer oil quality. The study focuses on three key areas: firstly, the influence of the dataset size on the precision of the adaptive weighting factors, secondly, the effect of varying the neural network's structure, and third, the choice of optimizers. The aim of this study is to shift from a predominantly expertbased approach to a more data-driven methodology for reliable transformer health diagnostics framework. © 2024 IEEE.

关键词： Adaptive Weighting Factors Health Index Methodology Neural Network Algorithms Transformer Health Diagnostics Transformer Oil Data Analysis

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AUTE: Peer-Alignment and Self-Unlearning Boost Adversarial Robustness for Training Ensemble Models 39

AUTE: Peer-Alignment and Self-Unlearning Boost Adversarial R...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Lifeng Su, Tian Gao, Chengying Liu, Ning Huang, Qiong College of Mathematics and Informatics South China Agricultural University China School of Computer Science and Engineering Sun Yat-sen University China

ISBN: (纸本)157735897X

Adversarial attacks poses a significant threat to the security of AI-based systems. To counteract these attacks, adversarial training (AT) and ensemble learning (EL) have emerged as widely adopted methods for enhancing model robustness. However, a counter-intuitive phenomenon arises where the simple combination of these approaches may potentially compromising adversarial robustness of ensemble models. In this paper, we propose a novel method called Alignment and Unlearning for Training Ensembles (AUTE), aiming to effectively integrate AT and EL to maximize their benefits. Specifically, AUTE incorporates two key components. Firstly, AUTE divides the ensemble into a big peer model and a single member in a loop manner, aligning their outputs for boosting robustness of each member. Secondly, AUTE introduces the concept of unlearning, actively forgetting specific data with over-confident properties to preserve model capacity to learn more robust features. Extensive experiments across various datasets and networks illustrate that AUTE achieves superior performance compared to baselines. For instance, a 5-member AUTE with ResNet-20 networks outperforms state-of-the-art method by 2.1% and 3.2% in classifying clean and adversarial data. Additionally, AUTE can easily extend to non-adversarial training paradigm, surpassing current standard ensemble learning methods by a large margin. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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An AutoML Approach Integrated with Live Weather Data in Rain Forecasting System (RFS) 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Mazahir Mehdi Zaidi, Syed Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

The increasing climate unpredictability highlights the critical need for accurate rain prediction methods. Traditional approaches often struggle to adapt and accurately forecast rain across varied climatic conditions. This research work investigates the fusion of automated machine learning (AutoML) with real-time weather data to tackle these challenges. Previous methodologies have relied on manual algorithm selection and optimization, leading to inefficiencies and suboptimal outcomes. In contrast, the proposed AutoML-based approach automates algorithm selection and optimization, resulting in heightened accuracy and efficiency. Integration of diverse meteorological data streams and advanced preprocessing techniques enhances data quality for model training. Evaluation against traditional methods showcases significant enhancements in rain prediction accuracy, with scores ranging from 80.60 to 86.09% across distinct algorithms. Specifically, the Random Forest Classifier achieved an accuracy score of 85.43% and an AUC score of 0.88, Gaussian NB achieved 80.60% accuracy and 0.82 AUC, K Neighbors Classifier achieved 82.89% accuracy and 0.82 AUC, and XGBoost achieved 86.09% accuracy and 0.89 AUC. The developed approach not only advances rain forecasting but also offers potential applications in agricultural planning, urban water management, and disaster preparedness. Future research avenues may explore further enhancements to the AutoML framework and its extension to other environmental prediction domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rain

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique 3

An Efficient Approach of Brain Tumor Detection & Extraction ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Bahadure, Nilesh Bhaskarrao Gonge, Sudhanshu Patni, Jagdish Chandra Shah, Bhoomi Raju, Nagrajan Patil, Prasenjeet D. Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India Maharashtra Pune India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Tamil Nadu Thanjavur India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Image segmentation

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Supply Chain Management Using Optimization and Machine Learning Techniques 7th

Supply Chain Management Using Optimization and Machine Learn...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Pandey, Honey Neelima, N. Nagaraja, K.V. Department of Computer Science Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031699856

This study focuses on developing a predictive model for late deliveries in the supply chain, using the Data Co Supply Chain dataset. It involves data cleaning, visualization, and training various machine learning algorithms, selecting the most effective model based on accuracy and recall values. The research explores challenges like transportation delays, production issues, natural disasters, and supplier disruptions. Businesses are adopting proactive supply chain and risk management strategies to enhance resilience. The paper discusses how machine learning techniques can optimize supply chain management, offering insights into mitigating risks associated with late deliveries. By identifying challenges in advance, businesses can improve operational efficiency, minimize financial setbacks, and maintain customer trust. In a dynamic business environment, this research provides valuable solutions for supply chain professionals, integrating big data and machine learning to forecast and address late deliveries. It not only aids in predicting delays but also contributes to proactive decision-making, improving overall supply chain performance amidst uncertainties. © IFIP International Federation for Information Processing 2024

关键词： Adversarial machine learning

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Advanced U-Net++ Architecture for Precise Brain Tumor Segmentation in MRI Images: A Robust Solution for Medical Image Analysis

Advanced U-Net++ Architecture for Precise Brain Tumor Segmen...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham Department of Computer Science and Engineering India University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350385199

Tumor segmentation in MRI images is a crucial process in medical imaging, aimed at accurately identifying and delineating tumor regions within the brain or other tissues. Hence proposed a modified U-Net++ based segmentation with predicted mask. To further improve the performance of the proposed method, incorporated additional layers into the U Net++ architecture. U-Net++ consists of Encoder Decoder and the encoder captures image content, while the decoder delineates object boundaries. U-Net++ excels in capturing both high-level and intricate details, which significantly boosts segmentation performance. This helps in accurate tumor segmentation of complex MRI images. To measure the effectiveness of the proposed model for segmentation and detection, Accuracy, Intersection over Union(IOU), Dice Coefficient and Area under the ROC Curve(AUC) scores are used. The proposed U-Net++ brought accuracy to 99 percent, which could significantly help in the process of accurate segmentation of human tissues. Accuracy of 39 % for IOU of 80, Dice Coefficient of 89%, and 33% for the test set. This is equivalent to 8% sensitivity and accuracy and AUC of 78. The proposed method can be used as an additional diagnosis aid to help doctors determine the suitability of the treatment and the changes occurring in the body during the development of the disease © 2024 IEEE.

关键词： Magnetic resonance imaging

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