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Strategic facility & space planning utilising Design for Lean Six Sigma

International Journal of Sustainable engineering 2023年第1期16卷 1-13页

作者： Ó Longaigh, Brían Noonan, John Trubetskaya, Anna McDermott, Olivia Dept of Engineering University of Limerick Limerick Ireland Department of Computer Science and Information Systems University of Limerick Limerick Ireland College of Science & Engineering University of Galway Galway Ireland

This study aims to develop and implement a Strategic Facility Planning process at a highly regulated manufacturing site to optimise manufacturing space and capacity in a facility running out of space. The project demonstrates the application of Design for Lean Six Sigma and a structured Define, Measure, Analyse, Design, and Verify methodology in designing and implementing a process that enables the case study manufacturing site to improve its space utilisation and free up space. The project resulted in increased space usage of approx. 38000 sq. ft. equating to a hard cost saving of over $13 million for the organisation and a cost avoidance of $22 million. The study’s contribution is that it highlights for the first time that the Design for Lean Six Sigma methodology can be utilised for space and facility utilisation and can be leveraged by other manufacturers. This study has academic and managerial implications for identifying best practices for Design for Lean Six Sigma methodology application in Strategic facility planning. This study contributes to the few academic published works to utilise Design for Lean Six Sigma methodology for space utilisation in a highly regulated environment. This study will contribute to managerial practice in aiding other manufacturing organisations with recommendations on utilising Design for Lean Six Sigma and design for improved space utilisation to reduce costs. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Design

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Attention-Based AdaptSepCX Network for Effective Student Action Recognition in Online Learning 5

Attention-Based AdaptSepCX Network for Effective Student Act...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Dey, Arnab Anand, Anubhav Samanta, Subhajit Sah, Bijay Kumar Biswas, Samit Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

In the realm of online learning and distance education, the issue of inadequate supervision looms large, posing a significant obstacle. This paper delves into the challenges posed by the lack of supervision in online learning environments and proposes an innovative solution to understand and recognize students' behaviors. This study's primary objective is to detect and recognize students' actions in images captured through webcam. This task distinguishes itself from the well-established video-based student action recognition domain, which relies on temporal cues. Recognizing student actions from images intensifies the complexity of the problem. To meet this challenge, a novel deep learning model named AdaptSepCX Attention, specifically designed for student action recognition in online learning environments, is introduced. The proposed method exhibits exceptional performance with 92.73% validation accuracy on the Student Online Action Image dataset (SOAId), a carefully curated collection comprising 2029 student-centric images. The proposed model outperforms well-established models such as DenseNet121, NASNet Mobile, ConvXNet, DELVS1 and MobileNetV2 in student action recognition. Action recognition for students has broader implications beyond the online classroom. It has the potential to revolutionize educational technology, making online learning more interactive and engaging. Enabling machines to understand and respond to student actions enhances education, personalizes learning, and supports students' academic success and well-being. This research enhances the understanding of student involvement in online learning and offers an effective solution for recognizing actions from images. © 2024 The Authors. Published by Elsevier B.V.

关键词： Students

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Adversarial Machine Learning Attack on Machine Learning-based Controller for Solar Inverters

Adversarial Machine Learning Attack on Machine Learning-base...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Massa, Joaquin Kim, Kyoung-Tak Kim, Taesic Ahn, BoHyun Venayagamoorthy, Kumar Zeng, Jianwu Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science Kingsville United States Soongsil University Dept. of Electrical Engineering Seoul Korea Republic of University of Missour-Columbia Dept. of Electrical Engineering and Computer Science Columbia United States Clemson University Dept. of Electrical and Computer Engineering Clemson United States Minnesota State University Mankato Dept. of Electrical and Computer Engineering and Technology Mankato United States

ISBN: (纸本)9798350376067

Adversarial machine learning (ML) attacks are stealthy attacks designed to mislead the ML model results. This paper explores adversarial ML attacks that generate adversarial noisy input data in an ML-based controller in a solar inverter. Three types of ML models, long short-term memory (LSTM), gated recurrent unit (GRU)), and bidirectional-LSTM (Bi-LSTM), are designed to replace proportional-integral (PI) controller-based vector control for a solar inverter and two white-box adversarial ML attacks (Basic Iterative Method (BIM) attack and Fast Sign-Gradient Method (FGSM)) are applied to the ML controllers. It is observed that the adversary ML attacks designed in stealthy way do not affect the PI controller, while significantly degrading performance of the ML-based controllers. Moreover, the BIM attack is more effective than FGSM and Bi-LSTM-based controller is relatively robust to the attacks compared to peer. © 2024 IEEE.

关键词： Adversarial machine learning

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data 4

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Fernandis, Rasio Swanjaya, Daniel Ramadhani, Risky Aswi Kasih, Patmi Sahertian, Julian Universitas Nusantara Pgri Kediri Faculty of Engineering and Computer Science Dept. Informatics Engineering Kediri Indonesia

ISBN: (纸本)9798350376111

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children's development at school through the results of each semester's assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children's anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children's anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children's anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children's anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes. © 2024 IEEE.

关键词： anecdotal record coherence score kindergarten Latent Dirichlet Allocation topic modeling

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Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing 5

Detection of Bullying Text: A Multi-faceted Approach using M...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumar, Kotyada Mohan Kiran Ullas, Karthik Reddy, Vivek Srikar Snehitha, Munaga Sai Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Computer Engineering Bengaluru India

ISBN: (纸本)9798331504403

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message's context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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A Critical Analysis and Classification of Breast Cancer Using Histopathology Images

A Critical Analysis and Classification of Breast Cancer Usin...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Singh, Pritpal Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Cse Punjab 140413 India National University of Science and Technology Faculty of Computer Science and Mathematics College of Technical Engineering Dept of Computer Science Dhi Qar Iraq

ISBN: (纸本)9798350359688

In Breast Cancer ML is instrumental in early detection through the analysis of mammographic and histopathological images, assessing individual risk factors, personalizing treatment plans based on genomic data, and providing decision support for healthcare professionals. ML also contributes to post-treatment monitoring, integrates diverse datasets for comprehensive insights, and accelerates research and drug discovery efforts. Overall, it enhances the accuracy, efficiency, and personalized nature of breast cancer care across the entire healthcare continuum. The paper presents a comparative review of recent techniques that use histopathological images for breast cancer classification. The study synthesizes findings from a range of research efforts employing diverse methodologies, including deep learning, ensemble techniques, and innovative model architectures. Researchers have explored the application of artificial neural networks, convolutional neural networks, and transfer learning in increasing the accuracy and objectivity of breast cancer classification. Additionally, novel approaches such as ViT-AMCNet, Ensembled Transfer Learning (ETL), and Curriculum Feature Alignment Network (CFAN) have been introduced, each contributing to the evolving landscape of histopathology image analysis. This review also includes a case study based on the EfficientNetB3 architecture that achieved a 98% accuracy in the diagnosis of breast cancer, with remarkable recall values of 0.95 and 0.97 for positive and negative cases, respectively, along with precision rates of 0.96 for positive cases and 0.95 for negative cases. The review highlights the significance of these approaches in addressing challenges related to feature extraction, nuclei segmentation, and classification accuracy, ultimately advancing our understanding of breast cancer pathology through the lens of computational methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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An Optimized Grid Computing based Feature Selection approaches from Brain Image dataset using Pretrained models 1

An Optimized Grid Computing based Feature Selection approach...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Nayak, Sradhanjali Biswal, Pradyut Kumar Mishra, Pravakar Pradhan, Sateesh Kumar Niis Institute of Business Administration Dept. of Computer Science Bhubaneswar India Iiit Bhubaneswar Dept. of Etc Bhubaneswar India Niis Institute of Business Administration Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Dept. of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9798350355093

The outcome of simulated experiments expressed the efficiency of grid-based algorithm in the domain of image processing in the context of Brain MRI Images for Tumor Detection. It provides a faster approach in detecting object and achieving higher rate of detection in comparison with existing algorithms for the similar purpose. The foremost goal of this work is improving the rate of detection by using grid based feature fusion approach with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Scalp Swarm Optimization Algorithm. This technique produces efficient result due to hybrid fusion of gridded Images and optimization for exhaustive and precise image analysis. In the initial phase Each image is segmented into 4 grids. Pretrained models like Inception-V2, Resnet 50, Mobnetv2, VGG-16 are used to extract features before and after the images are gridded. The extracted features are analysed with classifiers such as Decision tree, Support vector Machine, Multiplayer and Self Attention Transformer that not only chooses the best set of features as well as best feature extractor. Among all these models the VGG-16 gives best result in terms of Accuracy, sensitivity, specificity and F-score. Hence VGG-16 is utilized as evaluation functions in the optimization Algorithms. The performance of the approach is measured by the values produced against the terms as precision, Recall, F-score, and accuracy rate. In the first phase of iteration, features extracted from each grid are assigned with initial weight. Optimization algorithms are used for optimizing the weight values in the combined features to bring best accuracy ratio. Grid based segmentation of Image stabilize the computation time thereby reduces the time complexity. Weighted sum Scalp Swarm Optimisation assigns best weighted values to segmentation parameters to get maximum performance. The experimental outcomes proves the efficiency of the proposed strategy. © 2024 IEEE.

关键词： Genetic algorithms

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Fake News Detection Using Hybrid Deep Learning Method

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SN computer science 2023年第6期4卷 1-15页

作者： Yadav, Arun Kumar Kumar, Suraj Kumar, Dipesh Kumar, Lalit Kumar, Kapil Maurya, Sandeep Kumar Kumar, Mohit Yadav, Divakar Dept of Computer Science & Engineering NIT Hamirpur HP Hamirpur India School of Computer & Information Sciences IGNOU New Delhi India

The growth of online social networks platforms in recent years has resulted in the widespread dissemination of social news such as commercial adverts, political news, celebrity gossip, and more. This proliferation has resulted in distribution of fake news, that is many times disguised as, genuine real news. This paper evaluates several machine learning and deep learning approaches to automatically identify fake news from headlines and news descriptions. As there is no single large, standard dataset available for fake news detection, this study derives a large dataset by integrating two publicly available datasets—Fake and real news and allData. There are 64,934 labeled news articles in the dataset. For classification, deep learning models such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional Neural Network-LSTM (CNN-LSTM), and CNN-BiLSTM were employed. On the consolidated dataset, the Word2Vec word-embedding technique yielded higher performance when combined with CNN-BiLSTM model, with accuracy, precision, recall, F1 measure, and AUC-ROC values of 0.975, 0.984, 0.970, 0.977, and 0.992, respectively. The proposed methodology outperforms recent state-of-the-art methods in recognizing fake news. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BiLSTM CNN Deep learning Fake news FastText GloVe Machine learning Word2Vec

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Merging Realms: Classical and Quantum-Enhanced Deep Learning Techniques for CIFAR-10 Classification

Merging Realms: Classical and Quantum-Enhanced Deep Learning...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Bobbili, Sai Jayanth Chandrappa, S. Reddy, V. Keshava Guru Prasad, M.S. Deepa, K. Sharma, Mansi Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engineering Bengaluru India VTU Department of ECE India NMAMIT Department of ECE Nitte India

ISBN: (纸本)9798331517892

Classical and quantum-enhanced deep learning techniques are revolutionizing areas of artificial intelligence. This leads to substantial innovations in various domains, including the image processing. The CIFAR-10 dataset is a widely used and benchmarked dataset for assessing the performance of deep learning models. In this proposed method, the CIFAR-10 dataset is used for image classification in an effort to improve model performance. The modern and strong models are employed, such as CNN, InceptionV3, DenseNet, and a Hybrid Quantum-DenseNet. The precision, recall, accuracy, loss, and F1-score metrics are used in this analysis to assess these models. From the analysis, it is observed that CNN and Hybrid Quantum-DenseNet attained 77%-78% accuracy. The InceptionV3 reached the maximum accuracy of 90.15%. The performance of the developed hybrid model was exceptionally well across a range of parameters. DenseNet achieved an accuracy of up to 84%. © 2024 IEEE.

关键词： Adversarial machine learning

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