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Effects of nanoscale Sn segregation on corrosion behavior of laser powder bed fusion Cu-15Ni-8Sn alloy

Journal of Materials science & technology 2023年第27期158卷 96-110页

作者： Pengcheng Zuo Zequn Zhang Xiaohong Qi Zhuangzhuang Liu Jiuyang Xia Junshen Wu Xiaogang Li Bowei Zhang Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

Corrosion resistance of laser powder bed fusion(LPBF)Cu-15Ni-8Sn alloys is crucial towards its practical application in marine *** this work,corrosion behavior of LPBF Cu-15Ni-8Sn alloy was com-prehensively *** results suggest that LPBF Cu-15Ni-8Sn alloy exhibits superior corrosion re-sistance than the conventional casting counterpart and their corrosion behavior is highly associated with Sn ***,a triple-layer film will be formed on the surface of LPBF Cu-15Ni-8Sn alloy when being exposed to 3.5 wt%NaCl *** be more detailed,the abundance of nanoscale Sn-rich precipitates at the molten pool boundaries promotes the initial formation of a thick inner layer,where Ni and Sn tend to be distributed at inner and outer positions of the layer,*** contrast,the inner layer on molten pools is much thinner ascribed to a lower Sn content,facilitating the earlier nucleation and growth of a compact middle layer that is mainly composed of numerous Cu-rich *** the outmost position,CuO,Cu(OH)_(2) and Ni(OH)_(2) constitute the major composition of the loose *** results of this study could contribute to the optimal design and processing of additively manufactured Cu-Ni-Sn alloys.

关键词： Laser Powder Bed Fusion(LPBF) Cu-15Ni-8Sn alloy Sn segregation Localized corrosion Oxide film

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Feature Selection in Learning to Rank for Listwise Approach using Multiobjective Evolutionary Algorithm

Feature Selection in Learning to Rank for Listwise Approach ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Information retrieval systems commonly depend on ranking models to prioritize search results, which requires efficient feature selection to improve accuracy. Feature selection improves the model's query relevance ranking by selecting the most relevant features. This unique method avoids diluting the ranking model with unnecessary variables, resulting in more accurate ranks. The proposed approach consists of three separate stages: multi-objective instance selection, feature selection, and ensemble method. By utilizing a listwise methodology, the algorithm directly enhances the optimization of ranking criteria resulting in enhanced performance. The effectiveness of FS-listwise was evaluated using standardized datasets. After conducting experiments, results shown that the proposed method have peak performance than MOFSRank and classical ranking algorithms, such as ListNet and LambdaMART, in terms of Normalized Discounted Cumulative Gain and consistently selecting a minimal yet highly effective feature set. © 2024 IEEE.

关键词： Contrastive Learning

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Fossil-Net Lung Cancer Prediction and Classification from CT Images Using Convolution Neural Networks 2

Fossil-Net Lung Cancer Prediction and Classification from CT...

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2nd International Conference on Computing and data Analytics, ICCDA 2024

作者： Deepa, V. Merlin Christo, F. Rajalakshmi Institute of Science and Technology Department of Artificial Intelligence and Data Science TamilNadu Chennai600124 India

ISBN: (纸本)9798331542573

Lung cancer is the leading cause of death worldwide for both men and women. The most well-known disease is the heart disease followed by lung cancer. Early discovery of lung cancer can effectively treat lung cancer at an early stage. The potential for an early diagnosis of lung cancer to increase a patient's chance of survival. goal of this work was to identify lung malignancies in their early stages using supervised Learning. The Random forest model outperforms all other supervised machine learning methods for the Prediction of Lung cancer. The Objective of this study is to compare the different types of the machine Learning algorithms and provide the better Accuracy. © 2024 IEEE.

关键词： Lung cancer

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Unveiling Hidden Air Quality Patterns: A data-Driven Predictive Regression Analysis for Sustainable Air Management 2

Unveiling Hidden Air Quality Patterns: A Data-Driven Predict...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sanjit Cyrus, R. Suchitra, S. SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The importance of Machine Learning in predicting Air Quality Index (AQI) is increasing day by day. This aids in informed health protection decisions amid air pollution's impacts. The factors that affect air quality and the machine learning model parameters that yield good prediction accuracy are not well covered in the existing literature. This study provides a comprehensive comparison analysis that estimates air quality using multiple regression models. To forecast the Air Quality Index, we utilize data from two distinct places in Chennai City. Manali, an industrial area, and Velachery, a residential suburb, are the two. For the purpose of assisting us or the government authorities in making decisions, the study also focuses on the factors that influence air quality. The main goal is to determine which model performs best by evaluating a variety of factors, including R2 measurements and Mean Absolute Error. © 2024 IEEE.

关键词： Air quality

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Development of the NIST X-ray Photoelectron Spectroscopy (XPS) database, Version 5

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data science Journal 2024年第1期23卷 1-9页

作者： Lee, Angela Y. Powell, Cedric J. Gorham, Justin M. Morey, Adam Scott, John Henry J. Hanisch, Robert J. Office of Data and Informatics National Institute of Standards and Technology Gaithersburg United States Nano Materials Research Group National Institute of Standards and Technology Gaithersburg United States Materials Measurement Science Division National Institute of Standards and Technology Gaithersburg United States

It has been over 20 years since the National institute of Standards and technology (NIST) launched the first web version of its X-ray Photoelectron Spectroscopy (XPS) database (Lee et al., 2002;Wagner, 1991) which has approximately 1000 active users every day. This database was recently redesigned to meet NIST security requirements and to include new features and enhancements. The new application is built upon a relational database using a cross-platform and open-source framework (Henning et al., 2002). In addition to a modern design interface, the new features include custom-built components for displaying formatted molecular formulas and spectral lines, for sorting spectral lines, and for graphical display of chemical shifts of binding energies, Auger-electron kinetic energies, and Auger parameters for elements in different compounds. © 2024 The Author(s).

关键词： Relational database systems

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A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm 4th

A Real-Time Framework for Automatic Sarcasm Detection Using ...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

Social media platforms such as Twitter, Facebook, and Instagram are vast repositories of trending global news. They generate an enormous amount of data, offering a valuable resource for both academic researchers and IT marketing teams. These data can be harnessed for various insights and applications. A prominent and pressing research area within this data is the detection of sarcasm and fake news. The internet is rife with misleading and false information, often causing widespread confusion. Current research primarily categorizes news as either real or fake, using machine learning algorithms. However, this binary approach struggles with news that is partially true yet contains elements that could be deemed fake. To address this issue, our work introduces a real-time framework for sarcasm detection, utilizing an innovative deep learning technique called Tensor-DNN-50 for multiclass classification. The TDNN-50 model not only classifies news articles based on authenticity but also quantifies the level of sarcasm within them. Comparative analysis against leading techniques like SVM, att-RNN, and EANN demonstrates the superiority of our proposed algorithm. It achieves an impressive accuracy of 96.4% for two-class classification, 94.32% for four-class classification, and an astonishing 99.1% for seven-class classification. These results underscore the potential of our Tensor-DNN-50 Model in effectively addressing the intricate challenges of sarcasm detection and news classification with unparalleled accuracy across multiple classes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Breast Cancer Classification using Machine Learning Algorithm

Breast Cancer Classification using Machine Learning Algorith...

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

作者： Anu, D. Vidhya, K. Nitte Meenakshi Institute of Technology Artificial Intelligence & Data Science Bangalore India East West Institute of Technology Information Science & Engg Bangalore India

ISBN: (纸本)9798350359688

Around the globe in this era breast cancer is the most common disease among women, previously detection of breast cancer was a long and tedious process. In the proposed system the patient's data set will be collected. The collected data set will be manipulated and cleaned using feature extraction process [1]. The data set attributes consist of the nuclei measurements information. Nuclei measurements include radius, texture, perimeter etc. Also, the data set has the information whether the women breast does have the cancer or not by means of already calculated data which is indicated as benign or malignant. Benign are the non-cancerous tumor cell and that does not invade the neighboring cell, but malignant are the cancercausing tumor and it invades the neighbor cell after the manipulation and feature extraction of the data, it is further processed by machine learning algorithms under neural network. The dataset will be split into dependent and independent data. Dependent data are the data expect id and diagnosis, where the independent are diagnosis data [2]. The split data is 75% of training data and 25 % is testing data. Then scale the data (feature scaling) to bring the training data to a particular range of 0 to 1. The logistic regression is a part of machine learning algorithm which is a subset of random forest classifier, confusion matrix, SVM classifier, KNN classifiers are made used for both training and testing the data set. With the result the accuracy will be calculated [3]. The highest accuracy method will be chosen and the result data and original data will be compare to find the accurate result that whether the women does have the cancer or not. © 2024 IEEE.

关键词： Lung cancer

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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A Real-Time Computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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An Efficient Technique for Feature Selection in Learning to Rank Using NSGA-II

An Efficient Technique for Feature Selection in Learning to ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framework of learning to rank, specifically emphasizing a multi-objective evolutionary algorithm utilizing the listwise approach. Conventional ranking algorithms frequently experience issues with superfluous or unnecessary features, undermining their efficacy. The suggested method employs a combination of instance selection, evolutionary optimization, and ensemble techniques to choose feature subsets. The methodology seeks to reduce the feature count while enhancing ranking precision, assessed via the Normalized Discounted Cumulative Gain (NDCG) metric. Experiments were performed using LETOR 4.0 dataset (MQ2007-list and MQ2008-list), evaluating proposed method against established listwise ranking algorithms like ListMLE, ListNet, RankCosine and p-ListMLE. The findings indicate that, although there are performance discrepancies relative to current algorithms, the suggested method attains substantial feature reduction up to 60 percent while maintaining competitive ranking accuracy. © 2024 IEEE.

关键词： Optimization algorithms

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