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An Intelligent Predictive Model-Based Multi-response Optimization of EDM Process

computer Modeling in engineering & Sciences 2020年第8期124卷 459-476页

作者： N.Ganesh r.K.Ghadai A.K.Bhoi K.Kalita Xiao-Zhi Gao Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering Sikkim Manipal Institute of TechnologySikkim Manipal UniversityMajhitar737136India Department of Electrical and Electronics Engineering Sikkim Manipal Institute of TechnologySikkim Manipal UniversityMajhitar737136India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India School of Computing University of Eastern FinlandKuopio70211Finland

Electrical Discharge Machining(EDM)is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle *** does not require a cutting tool and can machine complex geometries ***,it suffers from drawbacks like a poor rate of machining and excessive tool *** this research,an attempt is made to address these issues by using an intelligent predictive model coupled global optimization appr.a.h to predict suitable combinations of input parameters(current,pulse on-time and pulse off-time)that would effectively incr.a.e the mater.a. removal rate and minimize the tool *** predictive models,which are based on the symbolic regression appr.a.h exploit the machine intelligence of Genetic Programming(GP).As compared to traditional polynomial response sur.a.e(PrS)predictive models,the GP predictive models show compactness as well as better prediction *** developed GP predictive models are deployed in conjunction with NSGA-II to predict Pareto optimal solutions.

关键词： EDM genetic algorithm genetic programming micro-machining optimization

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A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microor.a.ism Biovolume Measurements

arXiv

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arXiv 2022年

作者： Zhang, Jiawei Li, Chen rahaman, Md Mamunur Yao, Yudong Ma, Pingli Zhang, Jinghua Zhao, Xin Jiang, Tao Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Resources and Civil Engineering Northeastern University Shenyang110004 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Institute of Medical Informatics University of Luebeck Luebeck23538 Germany School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

With the acceleration of ur.a.ization and living standards, microor.a.isms play an incr.a.ingly impor.a.t role in industr.a. production, bio-technique, and food safety testing. Microor.a.ism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microor.a.ism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microor.a.ism biovolume measurements using digital image analysis methods and potential applications. © 2022, CC0.

关键词： Microor.a.isms

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The Playful Chronicles: Investigating Adolescents' Video Game Engagement with an Enter.a.nment Flair

SSRN

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SSrN 2024年

作者： Balamurugan, A. Varshney, Sudeep raghuveer, K. Kumar, Kishore M. Misra, Siddharth Prabhu, M. ramkumar Department of Management Studies School of Commerce and Management Bharath Institute of Higher Education and Research Agarm-then Road Tamilndu Selaiyur Chennai79 India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida201301 India Department of Business Management V.R.Siddhartha Engineering College A.P Kanuru20007 India CMR Technical Campus Telangana Hyderabad501401 India Marketing Birla Global University Odisha Bhubaneswar India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

In the era of expanding digital enter.a.nment of video games, understanding their impact on students and identifying associated factors is crucial for educational and developmental considerations. Video game use has an impact on social conduct, academic achievement, and general well-being in addition to being merely enter.a.ning. The study's objective is to provide insights into the complex nature of video game engagement among the demographic by examining var.a.les like social media usage, parental involvement, and device ownership. To determine the extent to students use video games and factors influence their gaming habits. The study utilized a Video Game Addiction Scale alongside a comprehensive study. Participants were extended to 200 students enrolled in 8th and 9th grades across multiple schools to participate in the survey. To investigate relationships and associations among var.a.les, statistical studies are performed, such as inferential tests and descriptive statistics. The findings provide insight into potential var.a.les that might influence both favourable and unfavourable features of video game participation. Participants can develop an environment that motivates students to play video games responsibly and in a balanced manner by acknowledging the complexity of the elements that influence gaming behaviour © 2024, The Authors. All rights reserved.

关键词： Students

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User Persona Mapping Technique for Human Interaction with Augmented and Virtual r.a.ity Environments

User Persona Mapping Technique for Human Interaction with Au...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), Inter.a.ional Conference on

作者： Nandhini Devi S Shiny. A J Sureshbabu Mahalakshmi S Sundara Bala Murugan P r. Priya Department of MTECH-C.S.E. Sri Sairam Engineering College Tambaram Chennai India Department of E.C.E. Nanjappa Polytechnic College Coimbatore Tamilnadu India Department of Computer Science and Design R.M.K. Engineering College Chennai Tamilnadu India Department of Management Studies St. Joseph's Institute of Technology Chennai Tamil Nadu India Department of Information Technology Mohamed Sathak A.J.College of Engineering Chennai Tamilnadu India

An in-depth analysis of user-centered design's function in the context of augmented and virtual r.a.ity demonstrates how “User Persona Mapping” can help satisfy the target audience's requirements. As shown by this study, it is crucial to consider user needs and preferences throughout the design phase of augmented and virtual r.a.ity (Ar/Vr) inter.a.es. Essential to this analysis is the “User Persona Mapping” technique, which entails the construction of detailed user personas based on extensive user research. By synthesizing user.a.tributes, behaviors, and preferences, designers can better meet the expectations of their end users if they first construct a concrete representation of that demographic. Through r.a.-world examples, this article demonstrates how this strategy can be used to improve the usability of augmented and virtual r.a.ity inter.a.es. This study contends that in the future of interactive technology, empathic design will be essential for ensuring that extended and virtual r.a.ity experiences are not only innovative but also authentically user-centric and engaging.

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Dual Z-scheme Pr2Sn2O7/P@g-C3N4/SnS2 heterojunctions for the removal of tetracycline antibiotic by persulfate activation: Kinetics, thermodynamic parameters, density functional theory, and toxicity studies

引用

Chemical engineering Jour.a. 2024年 479卷

作者： Prasanna, Sanjay Ballur Sakthivel, rajalakshmi Shanthakumar, Karthik Chimatahalli Shivamurthy, Santhosh Arehalli Lin, Yu-Chien Dhawan, Udesh Liu, Xinke Chung, ren-Jei Taipei10608 Taiwan Department of Chemistry SJCE JSS Science and Technology University Karnataka570006 India N.M.K.R.V. College for Women Karnataka 560011 India Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Taipei10608 Taiwan

In this study, a Pr2Sn2O7/P@g-C3N4/SnS2 ter.a.y nanocomposite photocatalyst is obtained by hydrother.a. synthesis followed by mechanical grinding. By separating electrons and holes (e−/h+) effectively through the dual Z-scheme heterojunction and activating persulfate (PS), the heterojunction demonstrated superior visible light-driven catalytic activity. Tetracycline (TC) was degraded (89.48%) in 60 min using 30 mg/L of ter.a.y composite and 30 mg/L of PS. This system displayed the highest pseudo-first-order kinetic constant (0.0989 min−1 and r2 = 0.969). The electron paramagnetic resonance analysis showed that SO4[rad]−, [rad]OH, [rad]O2−, and 1O2 were involved in TC degradation. TC's vulnerability positions and degradation pathways were analyzed using DFT and gas chromatography-mass spectrometry analysis, while toxicity assessment software was used to determine the toxicity of intermediate products. Thus, Pr2Sn2O7/P@g-C3N4/SnS2 was established to be a promising photocatalyst template for pollutant remediation. © 2023 Elsevier B.V.

关键词： Density functional theory

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Dermoscopic Image Classification Using Attention Mechanism and Ensemble Learning Appr.a.hes

Dermoscopic Image Classification Using Attention Mechanism a...

引用

IEEE Inter.a.ional Conference on Big Data

作者： Shanchuan Huang Hongwei Lei Liuhan Jin Jinzhu Yang Tao Jiang Yudong Yao Marcin Grzegorzek Chen Li Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang Liaoning China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China Department of Electrical and Computer Engineering Stevens Institute of Technology USA Institute of Medical Informatics University of Luebeck Luebeck Germany

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a thr.a. to human health, malignant ones can develop into skin cancer.a.d become life-thr.a.ening if left untr.a.ed. Early detection of the disease is impor.a.t for the tr.a.ment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification perfor.a.ce by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the perfor.a.ce of Deep-skin with other classification models. results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better perfor.a.ce. In the future, we intend to investigate better classification models for.a.tomatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings.

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Mantaray Foraging Optimization based Makespan Enhancement in Cloud based Scheduling Environment

Mantaray Foraging Optimization based Makespan Enhancement in...

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Automation, Computing and renewable Systems (ICACrS), Inter.a.ional Conference on

作者： J Aswini K. Johny Elma P. John Augustine N. Kopperundevi S. M. Chithra T. Parasuraman Saveetha Engineering College (Autonomous) Affiliated to Anna University Information Technology Easwari Engineering College Department of Information Technology Sri Eshwar College of Engineering Vadasithur-Via Kinathukadavu Coimbatore School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Department Of Mathematics R. M. K. College of Engineering and Technology School of Physical Education & Sports Sciences Hindustan Institute of Technology & Science Chennai Tamil Nadu

ISBN: (纸本)9781665460859

Cloud computing (CC) delivers on-demand and powerful business environments. It can be constructed on top of virtualized data hubs. Virtualization renders a flexible infrastructure for cloud. However, scheduling the tasks and managing the resources in virtualized data hubs will be difficult. Maximizing the virtual machine utilization and minimizing the total makespan is imperative in the cloud environment. The task scheduling issue can be indicated as multi-objective optimization problem, which is solved by utilizing optimization methods. This study develops a Mantaray Foraging Optimization based Makespan Enhancement in Cloud based Scheduling Environment (MrOME-CSE) technique. The presented MrOME-CSE technique aims to allocate the resources proficiently in the cloud environment. In the presented MrOME-CSE technique, the behavior of manta rays is mainly involved. Furthermore, the MrOME-CSE method derived an objective function to allocate n individual tasks to m heterogeneous accessible resources, thereby task conclusion period was minimalized and resource was fully exploited. The experimental evaluation of the MrOME-CSE technique is examined briefly. The experimental outcomes stated the superior perfor.a.ce of the MrOME-CSE technique over other recent models.

关键词： Cloud computing renewable energy sources Processor scheduling Computational modeling Optimization methods Linear programming Virtual machining

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Emerging Technologies for Carbon Capture and Storage in Energy Transition

Emerging Technologies for Carbon Capture and Storage in Ener...

引用

Technological engineering and its Applications in Sustainable Development (ICTEASD), Inter.a.ional Conference for

作者： Dharmesh Dhabliya Kushagra Kulshreshtha S. Prakaash Ashish Malik Visumathi J J. B. Sapaev Haider.a.abdeli Gajula ramesh Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Institute of Business Management GLA University Mathura India Mathematics Panimalar Engineering College Poonamallee Chennai India Mechanical Engineering Axis Institute of Technology and Management Kanpur India Computer Science and Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Western Caspian University Baku Azerbaijan Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

This article examines and evaluates the prominent characteristics of the continuing shift in energy from a higher-carbon to a low-carbon marketplace. The study indicates that to make this shift, it will be necessary to eliminate carbon emissions from the electricity, transpor.a.ion, and industr.a. sectors. The particular method for this change will vary depending on the nation. Carbon Capture and Storage (CCS) technology will significantly contribute to the power transition by reducing carbon emissions from current and future fossil fuel electricity plants and cr.a.ing low-carbon fossil-fuel-based blue hydrogen. Blue hydrogen is a ver.a.ile resource utilized in hydrogen fuel cell transpor.a.ion and as a source of heat and feedstock in many industr.a. applications. Current projections indicate that the world’s saltwater.a.uifers and oil and gas reserves can store carbon dioxide (CO2) emissions caused by human activities for 200 years. The sluggish progress with the execution of CCS is problematic. It can be attributed to the insufficient profitability of CO2-enhanced petroleum extraction caused by low oil prices, the absence of fiscal incentives for CO2 geological preser.a.ion, limited popularity, inconsistent government energy policies and CCS rules, and the substantial capital expenditure required. The research suggests many strategies to expedite the introduction of CCS. These measures include the cr.a.ion of local CCS regions to benefit from economies of scale, including the public in CCS initiatives, implementing carbon taxes, and applying public-private partnerships for funding, knowledge transfer, and collaboration among multiple stakeholders.

关键词： Profitability Oils Electricity Hydrogen Transpor.a.ion Carbon capture and storage Stakeholders

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Energetic Content based Image retrieval Scheme using Improved Deep Learning Strategy with Image Analysis Technique

Energetic Content based Image Retrieval Scheme using Improve...

引用

Inter.a.ional Conference on Smart Structures and Systems (ICSSS)

作者： S. Karkuzhali P. Malathi A Thenmozhi Velu Aiyyasamy Keerthika A G.S. Uthayakumar Department of General Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Department of Electronics and Communication Engineering Kings Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Meenakshi Sundararajan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of ECE St. Joseph’s Institute of Technology Chennai Tamil Nadu India

One popular.a.pr.a.h to retrieving images from huge, unprocessed datasets is content-based image retrieval, or CBIr. The conventional ways of data retrieval, however, are not meeting the needs of consumers. The number of consumer.a.cessible photographs also keeps growing, and there are an abundance of digital platforms for development and dissemination. Consequently, digital image processing is both ubiquitous and per.a.ent in many fields. Consequently, effective strategies are required for both the speedy access to these massive picture databases and the recovery of comparable photos from this massive number of images based on a specific picture (query). The computation of depiction of features and resemblance is crucial to the efficiency of a CBIr system. We introduce LAIrS, a cutting-edge deep learning method that integrates the extraction of features and aims to do this with classification to facilitate rapid picture retrieval. For.a.transparent evaluation of the proposed scheme’s efficiency, the suggested logic is cross-validated using Convolutional Neural Networks (CNNs), a classic deep learning paradigm. Several in-depth obser.a.ional experiments for various CBIr tasks employing an image database have shown encouraging results, teaching us impor.a.t lessons for making CBIr system more efficient. With the use of CBIr structures, a different collection can find similar pictures to the query image. There is little doubt that Google’s search per photo tool is per.a.s the most widely used CBIr.a.pr.a.h.

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Predictions of reynolds and Nusselt numbers in turbulent convection using machine-learning models

arXiv

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arXiv 2022年

作者： Bhattachar.a. Shashwat Ver.a. Mahendra K. Bhattachar.a. Ar.a. Institut für Thermo-und Fluiddynamik Technische Universität Ilmenau Postfach 100565 IlmenauD-98684 Germany Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur208016 India

In this paper, we develop a multivar.a.e regression model and a neural network model to predict the reynolds number (re) and Nusselt number in turbulent ther.a. convection. We compare their predictions with those of earlier models of convection: Grossmann-Lohse [Phys. rev. Lett. 86, 3316 (2001)], revised Grossmann-Lohse [Phys. Fluids 33, 015113 (2021)], and Pandey-Ver.a.[Phys. rev. E 94, 053106 (2016)] models. We observe that although the predictions of all the models are quite close to each other, the machine learning models developed in this work provide the best match with the experimental and numerical results. © 2022, CC BY.

关键词： Nusselt number

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