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Revisiting the thermal ageing on the metallised polypropylene film capacitor:from device to dielectric film

High Voltage 2023年第2期8卷 305-314页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Kaizheng Wang Reimund Gerhard Ronald Plath Pawel Rozga Pavel Trnka State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina Soft Transducer Materials and Systems Universität PotsdamPotsdamGermany Faculty IV Electrical Engineering and Computer Science Technische Universität BerlinBerlinGermany Institute of Electrical Power Engineering Lodz University of TechnologyLodzPoland Faculty of Electrical Engineering University of West BohemiaPilsenCzech Republic

Metallised polypropylene film capacitors(MPPFCs)are widely used in power electronics and are generally degraded by elevated *** work aims to determine the relationships between the structural changes of MPPFC and the microstructural variations of the PP film during the thermal ageing of MPPFC at 100℃ for 38 *** capacitance of MPPFC has a slight decrease during thermal ***,the breakdown voltage of the MPPFC decreases by 39.4%by the *** partial discharge(PD)number of MPPFC increases linearly with ageing *** tear-down analysis of the MPPFC reveals that the molecular structure of the PP film has not been altered but has led to molecular chain scission and the generation of some polar fragments/***,the relative permittivity of the PP films rises as the ageing time ***,thermal ageing causes the conversion of aluminum to alumina in the metallised electrode,which is hydrophilic for polar groups and leads to an adhesion effect between the metallised electrodes and the PP *** angle measurements prove that the surface hydrophilicity of the PP sample increased after thermal ***,the PD/breakdown voltage in the MPPFC increases/decreases due to the uneven adhesion of the metallised PP film.

关键词： film thermal ageing

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Development of Mobile Application Monitoring for Thai Food and Beverage Based on Portable Near-Infrared Spectroscopy technology 20

Development of Mobile Application Monitoring for Thai Food a...

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20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Pannurat, Natthapon Phothisonothai, Montri Sooknuan, Thanat Kampeephat, Saran Wiboonjaroen, Wichupong Kasetsart University Sriracha Faculty of Engineering at Sriracha Department of Computer Engineering Chonburi Thailand Rajamangala University of Technology Isan Faculty of Engineering and Technology Nakornrachasima Thailand

ISBN: (纸本)9798350310467

The paper aims to propose 'Portable NIRS technology for Thai Food Industry' with applications using non-destructive based technology called Near-Infrared Spectroscopy (NIRS) for determining whether the sampled food is expired or not. This proposed NIRS technology will be applicable especially for the agricultural and food industries. The basic process of the proposed system can be divided into the following two parts, i.e., 1) a sensor device that detects samples and 2) a mobile application developed for connecting to the NIRS device. The binary k-nearest neighbors algorithm (k-NN) is proposed to classify the five common Thai local samples, i.e., soy milk (SM), fresh milk (FM), salad dressing (SD), corn milk (CM), and banana in coconut milk (BM). The output results are classified into two classes, i.e., Unexpired (Un) and expired (Ex), based on the wavelength measuring from the proposed NIRS system. © 2023 IEEE.

关键词： Infrared spectroscopy

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A Review of Machine Learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of Machine Learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations

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computers, Materials & Continua 2023年第3期74卷 5301-5317页

作者： Muhammad Ibrahim Imran Sarwar Bajwa Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain Department of Computer Science The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Bahria University Lahore CampusLahorePakistan Software Engineering Department Faculty of ITUniversity of Central PunjabLahorePakistan Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia

Recommendation services become an essential and hot research topic for researchers *** data such asReviews play an important role in the recommendation of the *** was achieved by deep learning approaches for capturing user and product information from a short ***,such previously used approaches do not fairly and efficiently incorporate users’preferences and product *** proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True *** overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or *** proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed *** results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.

关键词： Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence

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Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

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computers, Materials & Continua 2024年第8期80卷 2005-2030页

作者： Tareq Hamadneh Khalid Kaabneh Omar Alssayed Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Zoubida Benmamoun Zeinab Monrazeri Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Faculty of Engineering Liwa CollegeAbu Dhabi41009United Arab Emirates Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making *** this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in *** theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy *** tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) *** function served as input for the SOA ***,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and *** extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the *** study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain *** simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

关键词： Optimization supply chain management sustainable lot size optimization bio-inspired metaheuristic stork

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Multi-class Classification Approach for Smart Intrusion Attack Detection Using AI and ML Classifiers 8th

Multi-class Classification Approach for Smart Intrusion Atta...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Prajapati, Ramesh Rathod, Ghanshyam Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE Faculty of Engineering and Technology GLS University Gujarat Ahmedabad India Computer Engineering Department Shree Swaminarayan Institute of Technology Gujarat Gandhinagar India Department of Computer Application Parul Institute of Computer Application Baroda India

ISBN: (纸本)9789819766772

Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion categories and demand a high processing power usage. Notwithstanding the fact that there is a wealth of literature on IDS issues, our objective is to provide a more thorough review. We offer the taxonomy to differentiate between modern IDSs based on the extensive survey and intricate organization. In addition, the architecture we provided in the content aids the reader in grasping the whole idea of IDSs with ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Development of a Nail-based Application for the Detection of Disease using Image Processing 2

Development of a Nail-based Application for the Detection of...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gote, Pradnyawant M. Kumar, Praveen Dhale, Trishul Mishra, Gaurav Vedprakash Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Jawaharlal Nehru Medical College Department of Radiodiagnosis Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Disease diagnosis using non-invasive techniques is a key determinant in the improvement of outcomes in patients and the enhancement of precautionary health measures. The study focuses on the design and development of a nail-based detection application employing image processing techniques to detect diseases such as systemic disease, fungal disease, Leukonychia (White spots on nails) and Paronychia (Nail bed infection) through analysis of the patterns of the fingernails and other related symptoms. The developed system integrates several techniques, comprising a customized imaging application, a user-friendly interface, and software for real-time image processing and disease classification. A variety of algorithms proposed for image processing, such as Median filtering, Sobel Mask and HOG features, are designed to extract important features from nail images and then use machine learning models that match these features against a database of established markers of various diseases. Comprehensive testing utilizing an annotated dataset of nail images demonstrated the system's accuracy in classifying certain diseases, further showcasing its potential for possible usage as a proficient tool for early disease diagnosis. Thus, this application is easy to use, user-friendly interface, and provides advancements in diagnosing diseases both in clinical settings and in the comfort of one's home. The incorporation of this technology into routine clinical practice might facilitate the possibility of early diagnosis and early intervention, which could improve patient outcomes and reduce healthcare system burdens. © 2024 IEEE.

关键词： Median filters

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IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

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computers, Materials & Continua 2023年第3期74卷 6867-6878页

作者： Waleed T.Al-Sit Nidal A.Al-Dmour Taher M.Ghazal Ghassan F.Issa Department of Computer Engineering Mutah UniversityAl-KarakJordanHigher Colleges of TechnologyDubaiUAE Department of Computer Engineering College of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKM43600BangiSelangorMalaysia

In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective and smarter healthcare *** has the potential to shape the future of clinical research in the healthcare *** sensors,patients,healthcare providers,and caregivers can connect through an IoMT network using software,information,and communication *** assisted living(AAL)allows the incorporation of emerging innovations into the routine life events of *** learning(ML)teaches machines to learn from human experiences and to use computer algorithms to“learn”information directly instead of relying on a *** the sample size accessible for learning increases,the performance of the algorithms *** paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network(CNN)algorithm for fast analysis,improved decision-making,and enhanced treatment *** simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

关键词： Smart healthcare system neural network machine learning

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Effect of Graphite Addition on Microstructure, Mechanical Properties and Thermal Properties of Injection Molded AZ91D Alloy+1

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Materials Transactions 2024年第4期65卷 374-380页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Course of Advanced Systems Engineering Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Corp. Suwa399-0211 Japan Department of Systems Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan

Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphite exhibited a dispersion of needle-like graphite particles. No significant voids were observed at the interfaces between the graphite and the matrix. The addition of more than 0.5 mass% graphite decreased the proof stress and tensile strength of the injection-molded products. The Young’s modulus of the graphite-added products tended to decrease with an increase in the graphite content, which is consistent with the lower limit of the rule of mixtures. The thermal conductivity of the 6.9 mass% graphite-added product increased compared with that of the AZ91D magnesium alloy and the coefficient of linear thermal expansion decreased. Both values are within a range that satisfied the rules of mixtures. © 2024 The Japan Institute of Metals and Materials.

关键词： Thermal expansion

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