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Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

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computers, Materials & Continua 2024年第4期79卷 1727-1755页

作者： Parth Khandelwal Harshit Indranil Manna Metallurgical&Materials Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Computer Science&Engineering Department Indian Institute of TechnologyKharagpurWest Bengal721302India Vice Chancellor Office Birla Institute of Technology(BIT)MesraRanchiJharkhand835215India

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation ***, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data *** examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

关键词： Machine learning genetic algorithm solid-solution precipitation strengthening pareto front data augmentation

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Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Enhancing Generalized EEG Classification with Decomposed Sta...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Yubin Chen, C.L. Philip Chen, Bianna Zhang, Tong School of Computer Science&Engineering South China University of Technology Guangdong Guangzhou510006 China Pazhou Lab Guangdong Guangzhou510335 China

ISBN: (纸本)9798350368741

Learning a generalized EEG representation under limited data and subject variability is a long-standing challenge. Most studies utilized data augmentation to extend the distribution of training data, which may hinder the diversity of augmented samples to cover more subject variability. In this paper, we propose a decomposed statistics-diverse feature augmentation (DSFA) framework for generalized EEG learning. The wavelet-based decomposed representation module decomposes signals into approximation and detail features, thus deriving semantics from original signals into approximation features to avoid over-transformation. The statistics-diverse feature augmentation module augments features to extend beyond the original feature space by manipulating the statistics of features. Extensive experiments on three datasets demonstrate that our approach achieves state-of-the-art performance in different classification tasks. Our repository is public at https://***/1940653868/DSFA. © 2025 IEEE.

关键词： EEG Classification Feature Augmentation Model Generalization Signal Decomposition Transformer

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Secure Rotation Invariant Face Detection System for Authentication

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computers, Materials & Continua 2022年第1期70卷 1955-1974页

作者： Amit Verma Mohammed Baljon Shailendra Mishra Iqbaldeep Kaur Ritika Saini Sharad Saxena Sanjay Kumar Sharma Department of Computer Science&Engineering Chandigarh Group of CollegesMohali140317India Department of Computer Engineering College of Computer&Information ScienceMajmaah University11952Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering&TechnologyPatiala147004India Department of Computer Centre&Campus Network Facility Science Complex PO Central UniversityGachibowliHyderabad500046TelanganaIndia

Biometric applications widely use the face as a component for recognition and automatic *** rotation is a variable component and makes face detection a complex and challenging task with varied angles and *** problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been *** objective of the paper is to implement a robust method for face detection taken at various *** to achieve better results than known algorithms for face *** RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid *** MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation *** this manner,RIFDS can detect human faces at different rotations and with different facial *** RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena *** results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

关键词： Pose variations face detection frontal faces facial expressions emotions

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Handling class imbalance problem in software maintainability prediction:an empirical investigation

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Frontiers of computer science 2022年第4期16卷 5-18页

作者： Ruchika Malhotra Kusum Lata Discipline of Software Engineering Department of Computer Science&EngineeringDelhi Technological UniversityDelhi110042India University School of Management and Entrepreneurship East Delhi CampusDelhi Technological UniversityDelhi110095India

As the complexity of software systems is increasing;software maintenance is becoming a challenge for software *** prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality *** research of software engineering predictive modeling,various software maintainability prediction(SMP)models are evolved to forecast *** develop a maintainability prediction model,software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability *** condition gives rise to a class imbalance problem(CIP).In this situation,the minority classes’prediction,i.e.,the classes demanding high maintainability effort,is a ***,in this direction,this study investigates three techniques for handling the CIP on ten open-source software to predict software *** empirical investigation supports the use of resampling with replacement technique(RR)for treating CIP and develop useful models for SMP.

关键词： sofiware maintenance software maintainability imbalanced learning

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Deep Learning for Automatic Diagnosis of Skin Cancer Using Dermoscopic Images

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 114-123页

作者： S.Rama Krishna Anand Gudur Siddharth Jain Shanmugavel Deivasigamani Mohit Tiwari K.G.S.Venkatesan Karthik Raj V Department of Computer Science&Engineering GSoT GITAM(Deemed-to-be-University)RudraramTelanganaIndia Department of Onchology Krishna Institute of Medical SciencesKrishna Vishwa Vidya PeethKaradMSIndia Inueron Flat 401Natraj TowerWaltair UplandsVisakhapatnamIndia Computer Science and Engineering Sri Muthukumaran Institute of TechnologyAnna UniversityIndia Department of Computer Science and Engineering Bharati Vidyapeeth’s College of EngineeringA-4Rohtak RoadPaschim ViharDelhiIndia Department of Computer Science&Engineering MEGHA Institute of Engineering&Technology for WomenEdulabadHyderabadTelanganaIndia Department of Accreditations and Trainings Andhra Pradesh MedTech Zone Limited(AMTZ)AMTZ CampusPragati MaidanVM steel Project SOVisakhapatnamIndia

Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence *** developments are made to better the standard of medical decision-making and the standard of treatment given to *** boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin ***,these difficulties may be conquered by employing image processing *** address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image *** thresholding and DL are brought together in this *** hat filtering and inpainting have been included into IMLT-DL for use in image *** addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble *** further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial *** advances are a major step forward in the quest for faster and more accurate skin lesion detection.

关键词： deep learning dermoscopic images skin cancer

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Stock Prediction Based on Technical Indicators Using Deep Learning Model

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computers, Materials & Continua 2022年第1期70卷 287-304页

作者： Manish Agrawal Piyush Kumar Shukla Rajit Nair Anand Nayyar Mehedi Masud Department of Computer Science&Engineering University Institute of TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopal-462033Madhya PradeshIndia Faculty of Department of Computer Science&Engineering University Institute of TechnologyRajiv Gandhi Proudyogiki VishwavidyalayaBhopal-462033Madhya PradeshIndia Faculty of Department of Computer Science&Engineering School of Engineering and TechnologyJagran Lakecity UniversityBhopal462026Madhya PradeshIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable *** stock data is usually non-stationary,and attributes are non-correlative to each *** traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket *** study the stock market characteristics using STIs and make efficient trading decisions,a robust model is *** paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using *** proposed model has implemented the Deep Learning(DL)model to establish the concept of *** analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is *** datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov *** work also conducted exhaustive experiments to study the correlation of various STIs with stock price *** model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning *** proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,*** indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

关键词： Long short term memory evolutionary deep learning model national stock exchange stock technical indicators predictive modelling prediction accuracy

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Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

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computers, Materials & Continua 2023年第11期77卷 2097-2121页

作者： Deepak Kumar Vinay Kukreja Ayush Dogra Bhawna Goyal Talal Taha Ali Department of Computer Science&Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Electronics&Communication Engineering Chandigarh UniversityPunjab140413India Department of Dental Industry Techniques Al-Noor University CollegeNineveh41018Iraq

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every *** wheat rust diseases are identified either through experienced evaluators or computerassisted *** experienced evaluators take time to identify the disease which is highly laborious and too *** wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield *** solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease *** the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation *** augmented images are preprocessed by histogram *** a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch *** layers of region extraction models construct a feature vector that is later passed to the CE-SVM *** a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

关键词： Wheat rust diseases agricultural region extraction models intercropping image processing feature extraction precision agriculture

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Boosting efficiency and stability of 2D alternating cation perovskite solar cells via rational surface-modification: Marked passivation efficacy of anion

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Journal of Energy Chemistry 2023年第9期84卷 354-362页

作者： Hualin Zheng Xuefeng Peng Tingxi Chen Ting Zhang Shihao Yuan Lei Wang Feng Qian Jiang Huang Xiaodong Liu Zhi David Chen Yanning Zhang Shibin Li State Key Laboratory of Electronic Thin Films and Integrated Devices and School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Department of Electrical&Computer Engineering and Center for Nanoscale Science&EngineeringUniversity of KentuckyLexingtonKY 40506USA Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808GuangdongChina

Two-dimensional(2D) alternating cation(ACI) perovskite surface defects,especially dominant iodine vacancies(V_Ⅰ) and undercoordinated Pb^(2+),limit the performance of perovskite solar cells(PVSCs).To address the issue,1-butyl-3-methylimidazolium trifluoro-methane-sulfonate(BMIMOTF) and its iodide counterpart(BMIMI) are utilized to modify the perovskite surface *** find that BMIMI can change the perovskite surface,whereas BMIMOTF shows a nondestructive and more effective defect passivation,giving significantly reduced defect density and suppressed charge-carrier nonradiative *** mainly attributes to the marked passivation efficacy of OTF-anion on V_Ⅰ and undercoordinated Pb^(2+),rather than BMIMI^(+) *** from the rational surface-modification of BMMIMOTF,the films exhibit an optimized energy level alignment,enhanced hydrophobicity and suppressed ion ***,the BMIMOTF-modified devices achieve an impressive efficiency of 21.38% with a record open-circuit voltage of 1.195 V,which is among the best efficiencies reported for 2D PVSCs,and display greatly enhanced humidity and thermal stability.

关键词： 2D ACI perovskite solar cells Charge-carrier nonradiative recombination Surface defects passivation Energy level alignment Ionic migration Stability

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Hybrid Sampling and Variant Deep Learning Models for Credit Card Fraud Detection 10

Hybrid Sampling and Variant Deep Learning Models for Credit ...

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10th International Conference on Platform Technology and Service, PlatCon 2024

作者： Le, Thi-Thu-Huong Adiputra, Andro Aprila Iqbal, Muhammad Hwang, Yeonjeong Kim, Howon Blockchain Platform Research Center Pusan National University Busan Korea Republic of Dept. of Computer Science&Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9798350367874

Credit card fraud detection poses significant challenges due to transaction data’s imbalanced nature and fraudsters’ continuously evolving strategies. This paper proposes an efficient approach to fraud detection by integrating hybrid sampling techniques with advanced deep-learning models. By combining under-sampling and over-sampling methods, we address data imbalance and enhance the performance of our detection models. Our approach employs various Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Experimental results demonstrate that hybrid sampling techniques, combined with these DL models, significantly improve detection accuracy and reduce false positives, offering a robust solution for real-world credit card fraud detection. Among DL models, GRU outperformed the others, achieving 99.51% precision, 99.96% recall, 99.73% accuracy, and 99.73% F1-score on the credit card fraud dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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