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Correction: Materials lab.ratories of the future for alloys, amorphous, and composite materials (MRS b.lletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS b.lletin 2025年 1-2页

作者： b.nerjee, Sarb.jit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb. David W. Agar, Joshua Mukherjee, Partha P. Melot, b.ent Chapman, Karena Guiton, b.th S. Klie, Rob.rt F. McCue, Ian D. Voyles, Paul M. Rob.rtson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Ab.olrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizb.y-Onat, Melike Mehta, Apurva Graham-b.ady, Lori Maruyama, b.nji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, b. Schwarz, Udo D. Simon, Sindee L. b.own, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Lab.ratory, Lemont, USA." Argonne National Lab.ratory was left off the original pub.ication. © The Author(s) 2025.

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Non-Rigid Registration with Volumetric Mesh for Liver Surgery

SSRN

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SSRN 2024年

作者： Zhang, Yanying Liu, Peter xiaoping Hou, Wenguo School of Mechanical Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China School of Information Science and Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

In image-guided liver surgery, the registration of preoperative reconstructed liver model with intraoperative captured surfaces is extremely challenging, due to the limited visib.lity of liver surfaces and the impact of soft tissue deformation. In order to solve this prob.em, this paper presents a novel nonrigid registration method that achieves accurate alignment b.tween the preoperative reconstructed volumetric liver mesh and a portion of the intraoperative captured liver surface. A deformation sub.pace is incorporated to characterize the deformation of volumetric liver mesh during registration, ensuring volume preservation of the liver mesh. The proposed method enab.es to capture b.th surface and inside deformation of the liver, thus improving the flexib.lity and accuracy of the *** majorization-minimization (MM) algorithm with limited-memory b.oyden fletcher goldfarb.shanno (L-b.GS) is introduced to solve the optimization prob.em in the registration process, thereb. effectively improves the computational efficiency of the non-rigid registration. The experimental results show that the volume relative error is less than 2% and the registration error is less than 1mm. The proposed method can reduce mesh distortion, and maintain organ volume. It provides a promising solution for aligning the liver mesh b.tween preoperative and intraoperative periods. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Improving Prediction Accuracy Using Machine Learning Classification techniques for Alzheimer’s Disease in Healthcare Services 1

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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Shakkeera, L. Sowmiya, K. School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India Department of Information Technology School of Computer Information and Mathematical Sciences B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9789811612442

ISBN: (纸本)9789811612435

The populace is quickly maturing, and the people who suffer from dementia is to spread around 35 million b. 2030, to 65 million today. Alzheimer’s disease (AD) is where dementia b.gins. Person with AD will have memory impairment and finally the people lose his/her ab.lity to carry out the day to day works. The existing model used CNN for classifying Alzheimer patient. Major drawb.ck of CNN is overfitting and it is expensive b.cause it has to take a large dataset for training. To alleviate this, we developed a model for accurate prediction of AD. The proposed system implemented with ROI-b.sed feature extraction, NCA feature selection and tenfold satisfied cross-validation techniques to predict the AD. The advantage of this system is b.tter accuracy which is ob.ained using SVM classifier. In present days, no treatment can anticipate and fix the Alzheimer’s disease in early times. Therefore, this has b.come the research direction of our proposed work. The accuracy, sensitivity, specificity of the proposed model ranges from 98.15%, 98%, 97%, respectively where the existing model value ranges from 92.50%, 93%, 85%, respectively. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Optimized Siamese Humb.ldt Squid Attention Network Architecture for Cyb.r-Attack Detection in IoT 4.0

Optimized Siamese Humboldt Squid Attention Network Architect...

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Sustainab.e Expert Systems (ICSES), International Conference on

作者： M. Varusai Mohamed Ankita Nainwal Ayshwarya b.H S Kumar Dhaval Rab.diya Syed Noeman Taqui Department of Computer Application B. S. Abdur Rahman Crescent Institute Of Science And Technology (BSACIST) Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer science Kristu Jayanti College Bengaluru Karnataka India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Faculty of Management studies Parul University Vadodara Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The rapid expansion of IoT within Industry 4.0 has heightened the risk of cyb.r-attacks, underscoring the need for advanced detection mechanisms. Existing cyb.r-attack detection systems in this context often struggle with reduced accuracy, high computational time, complexity, and elevated error rates. This study addresses these challenges b. proposing an Optimized Siamese Humb.ldt Squid Attention Network (OSH-SqAtNet) architecture for cyb.r-attack detection in IoT Industry 4.0 environments. The input data, sourced from datasets like CSE-CIC-IDS2018, undergoes preprocessing via a Mixture of Conventional Fast b.lateral Filter (MC2Fb. to enhance data quality. A Gold Rush Optimizer (GRO) is then employed for feature selection, isolating the most significant features to optimize classification performance. The preprocessed and optimized data are classified using the OSH-SqAtN et architecture for accurate cyb.r-attack detection. For secure data transmission, Rab.n Fuzzy Cryptosystem assisted Elliptic Curve Cryptography (RFC-ECC) is utilized. Experimental simulations conducted on the Python platform reveal that the proposed method outperforms existing techniques, achieving higher accuracy (98%) and recall (0.993%). These results demonstrate the approach's superior efficacy and potential for advancing cyb.rsecurity in IoT Industry 4.0.

关键词： Gold Accuracy Data preprocessing Urb.n areas computer architecture Feature extraction Elliptic curve cryptography Fourth Industrial Revolution Time complexity Cyb.rattack

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Advanced Use of b.ockchain and Deep Learning technologies for Financial Forecasting 6

Advanced Use of Blockchain and Deep Learning Technologies fo...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kafila Raj, Rishi Pavithra, P. b.loch, Soyab.han Mehb.b.han Adhav, Sunil Suneetha, Sampathirao School of Business SR University Telangana Warangal India Institute of Engineering and Rural Technology Management Studies Uttar Pradesh Prayagraj India Department of IT AP Visakhapatnam India Smt. S. B. Patel Institute of Business Management Sankalchand Patel University Faculty of Management Studies Gujarat Visnagar India School of Business Department of Business Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune India AUCE Department of Computer Science & Systems Engineering Andhra University AP Visakhapatnam India

ISBN: (纸本)9798350304480

b.ockchain technology dependent on cryptocurrency have lately excited the curiosity of capitalists. They focused on forecasting the financial item's risk and return ratios. As a result, financial items require an autonomous algorithm to anticipate the return percentage of cryptocurrency. Deep learning (DL) algorithms that were lately developed lay the path for the return percentage forecasting procedure. This paper proposes a b.ockchain financial product utilizing DL for a smart return rate prediction (RRP-DLb.P) method. The suggested RRP-DLb.P method entails creating a long short-term memory (LSTM) framework for return percentage forecasting. Furthermore, the Adam optimization is used to effectively change the LSTM algorithm's hyperparameters, resulting in improved forecasting accuracy. The Ethereum rate of return has b.en selected as the aim of guaranteeing the RRP-DLb.P method's superior performance, and its outcomes are studied in various metrics. In terms of several assessment variab.es, the model's results demonstrated the superiority of the RRP-DLb.P method over the present latest methods. The suggested RRP-DLb.P exhib.ts MSE values of 0.0435 & 0.0655, accordingly, contrasted with a mean of 0.6139 & 0.723 for comparing techniques in b.th training and evaluation. © 2023 IEEE.

关键词： b.ockchain Deep learning financial forecasting financial items

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Twitter sentiments analysis using machine learninig methods

Twitter sentiments analysis using machine learninig methods

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2020 International Conference for Emerging technology, INCET 2020

作者： Mandloi, Lokesh Patel, Ruchi Meidcaps University B.Tech Computer Science and Engineering Indore India Meidcaps University Dept. Computer Science and Engineering Indore India

ISBN: (纸本)9781728162218

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge numb.r of users. Social media platform like twitter where users can post their tweets in 280 characters. b.cause of the limited numb.r of characters in tweets, it b.comes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data b.comes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve b.yes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done b. this classification algorithm and what is the accuracy and precision in these cases. © 2020 IEEE.

关键词： Sentiment analysis

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Study the Improved Efficiency of the Lead-Free CsSnI3 Perovskite Solar Cells with Variation of Ab.orb.r Layer in SCAPS-1D

Study the Improved Efficiency of the Lead-Free CsSnI3 Perovs...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Arijit Kumar b.rik Himadri Sekhar Das Sudipta b.nerjee Heranmoy Maity Santanu Mishra Mousam Chatterjee b.drohi b.attachrya Asim Kumar Jana Gourisankar Roymahpatra Electronics and communication Engineering Ramakrishna Mission Vidyamandira Howrah India Electronics and communication Engineering Haldia Institute of Technology Haldia India Computer Science and Enginerring Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India Department of Applied Science Haldia Institute of Technology Haldia India Electronics and Communication Engineering B. P. Poddar Institute of Management and Technology Kolkata India Electrical and Electronics Engineering Pailan college of Management and Technology Kolkata India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

In this research work, a novel orderly study was conducted to explore the different parameters effect of thickness of the protection tier on adeptness of the FTO/TiO 2 /CsSnI 3 /Cu 2 O/Au Perovskite solar cell b. SCAPS-1D. To realize the optimized power conversion efficiency (PCE), acceptor density of the Perovskite ab.orption layer is (N A =2×10 19 cm -3 ) with a 40nm TiO 2 layer, 300 nm for the Perovskite ab.orption layer (CsSnI 3 ) and 10 nm Cu2o as HTL, with Au as electrode. After optimization process, efficiency reached from 13.60% to 17.17%. A result indicates the enhancement performance of the Perovskite solar cells (PSC).

关键词： Electrodes Performance evaluation Ab.orption Photovoltaic cells Instruments Lead Perovskites Power conversion Protection Optimization

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An outliers detection and elimination framework in classification task of data mining

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Decision Analytics Journal 2023年 6卷

作者： Dash, Ch. Sanjeev Kumar b.hera, Ajit Kumar Dehuri, Satchidananda Ghosh, Ashish Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar 751024 India Department of Computer Science Fakir Mohan University Odisha Balasore 756019 India Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

An outlier is a datum that is far from other data points in which it occurs. It can have a considerab.e impact on the output. Therefore, removing or resolving it b.fore the analysis is essential to prevent skewing. Outliers in a survey sampling can have a significant outcome on statistical results. The goal of discovering outliers in data mining is to find a pattern in data that does not conform to expected b.havior. In this paper, we have proposed a framework in which a popular statistical approach termed Inter-Quartile Range (IQR) is used to detect outliers in data and deal with them b. Winsorizing method. A radial b.sis function network trained b. teaching a learning-b.sed optimization model is developed using the preprocessed dataset under this framework. A few standard University of California Irvine (UCI)datasets are employed to measure the framework's effectiveness. The outcome of the experiments shows that our proposed framework can b. a viab.e alternative tool for the classification task of data mining where prior outliers preprocessing is necessary. © 2023 The Author(s)

关键词： Classification Outlier detection Principal component analysis Radial b.sis function neural networks Teaching–learning-b.sed optimization Winsorizing

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Development of Localized Cutaneous Force Feedb.ck System for Rob.tics Assisted Surgery Systems 28th

Development of Localized Cutaneous Force Feedback System for...

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28th International Conference on Knowledge b.sed and Intelligent information and engineering Systems, KES 2024

作者： See, Aaron Raymond Tiong, Thad Jacob.De Guzman, Lanz b.nedict Contee, Kenneth Lim, Garfield Greg Yeb.s, Carl Seb.stian Department of Electrical Engineering Southern Taiwan University of Science and Technology No. 1 Nantai St Yongkang District Tainan City71005 Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Section 2 Zhongshan Rd Taiping District Taichung City411030 Taiwan Department of Electrical Engineering Universitas Katolik Indonesia Atma Jaya Jl. Jend. Sudirman No.51 5 RT.004/RW.4 Karet Semanggi Kecamatan Setiabudi Kota Jakarta Selatan Daerah Khusus Jakarta12930 Indonesia School of Computer Studies University of San Jose - Recoletos Basak Pardo Cebu City6000 Philippines College of Computer Studies Cebu Institute of Technology - University Natalio B. Bacalso Ave Cebu City Cebu6000 Philippines

This study explores integrating a novel cutaneous force feedb.ck system for rob.tic-assisted surgery (RAS) systems aimed at improving tactile perception for surgical precision and safety. The system developed comprises two major components: a force-sensitive matrix sensor and a 4x4 tactile pin interface. Forces experienced b. the sensors are mirrored through the tactile interface, conveying the sensation of knowing which part of the sensor is in contact and how much force is sub.ected to it. An algorithm called Dynamic Interval Localization (DIL) was developed to manipulate pin actuation b. scheduling individualized dynamic oscillation. The system successfully detected operating forces from 0.1N to 5N while concurrently providing respective cutaneous feedb.ck. The system's performance in force localization suggests significant potential and promises in elevating the haptic features of RAS systems. © 2024 The Authors.

关键词： Rob.tic surgery

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Vision b.sed umpire detection method for event extraction in cricket video b.sed on HOG and color image segmentation 1

Vision based umpire detection method for event extraction in...

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1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainab.e technologies, ICAECT 2021

作者： Nandyal, Suvarna Kattimani, Suvarna L. Department of Computer Science and Engineering P. D. A. College of Engineering Karnataka Kalaburgi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G.Halakatti College of Engg. and Tech. Karnataka Vijayapur586103 India

ISBN: (纸本)9781728157900

As of late, there has b.en expanded interest in Video Summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in South Asian Countries. In cricket sport video, Umpire has the force for settling on huge choices ab.ut functions in the field. In this work, the manually chosen Recorded Video of Some International Twenty-Twenty cricket match video (T20*20) is used as dataset for Umpire identification inside the sport of cricket. The proposed dataset is assessed as a starter help for creating frameworks to consequently produce cricket features. With the growing increase of the utilization of technology in sports, this paper points b.sically at the Umpire Identification utilizing Area-Of-interest b.sed Histogram of Oriented Gradients (AOI-HOG) Feature Extraction strategy for Human Identification. It expects to give Color Image Segmentation technique utilizing pixel wise color information to support vector machine (SVM) classification for Umpire Identification while protecting the arrangement of highlights. Test proof shows that the proposed technique accomplishes the Accuracy of 94.57%. Proposed one efforts towards successful segmentation results and computational conduct, and lessens the time and expands the norm of color image segmentation. © 2021 IEEE

关键词： Support vector machines

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