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Materials property prediction using feature selection based machine learning technique

Materials Today: Proceedings 2022年 69卷 710-715页

作者： Valli Priyadharshini, K. Vijay, A. Swaminathan, K. Avudaiappan, T. Banupriya, V. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Information Technology M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India

An essential role in increasing quality and reducing environmental damage and public safety is played by material performance prediction in materials research. There is still a long way to go before traditional regression analysis models in materials science can accurately capture non-linearities in data, time-consuming predictions, and enormous amounts of data. Because the more features you have, the less accurate your classification will be, hence you need a feature selection method to improve your results. This necessitates the use of modern computer science methods to analyse materials data. Machine learning (ML) has arose in fresh years as a powerful tool for establishing correlations between data and is finding application in the field of materials science due to its generalizability, noise tolerance, and error tolerance. Different materials servicing varied settings can be predicted for their various properties using machine learning techniques based on feature selection. In this research, feature selection-based ML techniques are demonstrated. Comparisons with various computing and input-ranking approaches demonstrate the importance of the suggested model in materials-related challenges. © 2023

关键词： Forecasting

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Paddy Leaf Disease Identification on Infrared Images based on CNN with Auto Encoders 2

Paddy Leaf Disease Identification on Infrared Images based o...

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Kandukuri, Prabhakar Sowrab, B. Sameer Ramesh, G. Singamaneni, Kranthi Kumar Koneru Lakshmaiah Education Foundation Computer Science and Engineering Guntur India Gitam Deemed to Be University Computer Science and Engineering Visakhapatnam India Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Jain Deemed-to-be University Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350325799

The main food that people in India consume on a daily basis is paddy. According to data, the stress caused by rice illnesses, which reduce yields by 70%, was felt by the paddy farmers. If not controlled within a certain time frame, these plant diseases, which are typically brought on by pests, insects, and pathogens, have a significant negative impact on productivity. This research proposed auto encoders rooting convolutional neural network model to control these paddy diseases, and it has been implemented with various per-trained models like Vgg16, Resnet50, and inceptionV6. This model accurately determines whether a paddy plant is healthy or has one of four different types of paddy diseases. These models were developed, tested using auto encoders and a dataset of thermal pictures that was made available to the public, and they achieved the maximum model accuracy of 90.6%. A 84.8% was attained in the prior iteration without the use of auto encoders. With the use of this model, people can quickly detect paddy illness and assist farmers in raising productivity and yield. © 2023 IEEE.

关键词： Neural network models

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Wideband Matched Filtering Antennas with Absorptive Resonators

Wideband Matched Filtering Antennas with Absorptive Resonato...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Cui, Jiaqian Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9781733509657

This paper presents a design method of filtering antennas with wide matching bands to realize the integration of multiple functions such as absorbing, filtering, and radiating, and protect the system as well. In our design, an absorptive stub is employed to absorb the out-of-band signals. As such, a cascaded topology with lossy stubs, lossless stubs, and the resonant antenna is proposed to acquire the absorptive filtering antenna. The characteristic of the absorptive stub, and the response of the equivalent circuit are specifically analyzed here. Finally, an absorptive filtering slot antenna is implemented to prove that the method can be used to achieve a matching bandwidth far beyond that of a conventional resonant antenna. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Topology

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Evaluating Artificial Neural Networks and Emerging Machine Learning Methods for Engine Performance and Emissions Prediction: A Review 4

Evaluating Artificial Neural Networks and Emerging Machine L...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Galat, Umesh Rewatkar, Rajendra Kothare, Chandrakant Datta Meghe Institute of Research and Higher Education Aids Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Shri Shankarprasad Agnihotri Collge of Engineering Department of Mechanical Engineering Maharashatra Wardha India

ISBN: (纸本)9798331523923

These due to the shift towards the cleaner energy generation and newer and sophisticated engine technologies the prediction of the performance and emission of engine is of vital importance for the designer for the purpose of ANR as well as the emission control standards. This review also assesses the performance of ANNs and the new machine learning techniques in this area. ANNs especially the single layer and multi-layer networks have shown good levels of predictions of engine performance and emission for ethanol-gasoline, bioethanol and other fuel blends. For example, the models based on Levenberg-Marquardt optimization and the Bayesian regularization have revealed essential correlation coefficients and minimized mean squared errors. Based on comparative analyses, the further improvement of the predictive ability and speed of ANNs is stressed, including applications of ANNs integrated with other approaches like Response Surface Methodology (RSM), and other developing approaches like Extreme Learning Machines (ELM) and Support Vector Regression (SVR). However, challenges are concluded to remains including data quality, model complexities and computational resources. This review reveals that, for tackling these issues and enhancing the progress in developing efficient and sustainable engine solutions, detailed databases and refined mathematical modeling approaches are required. The directions for future research are to obtain better quality data, enhance computational time, and determine more fuel blends and engine settings for the model. © 2025 IEEE.

关键词： Adversarial machine learning

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Analysis of Sensing Matrix Design for Under-Sampling MRI 9

Analysis of Sensing Matrix Design for Under-Sampling MRI

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9th International Conference on Communication and Information Processing, ICCIP 2023

作者： Andryani, Nur afny catur Oey, Endra Laurent, Velix Arvino Nasya, Alexandra Husin, Husna Sarirah Computer Science Department School of Computer Science Bina Nusantara University Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Indonesia Taylor's University Malaysia

ISBN: (纸本)9798400708909

MRI currently is the most powerful medical diagnostic tomography system. It provides high resolution with high detail medical image. Its magnetization sensing is also free from radiation impact hence safe for the patient. However, the data acquisition process takes quite a long time with high-rate sampling. The sensing patient should stay still in the MRI sensing device for 20 to 40 minutes. It can affect claustrophobia for some patients. The high-rate sampling will also linearly affect the cost, which is considerably high. Hence, the research on enabling under-sampling MRI imaging has been raised. The main objective of under-sampling MRI Imaging is reducing the scanning time by under-sampling the data acquisition while at the same time the resulting qualified reconstructed image can be maintained. Compressive Sensing (CS) framework is one of the approaches that can be utilized on solving projection with less measurement. It can recover signals which have sparsity property. The signal is sparse if the signal being sensed has low information rate. Thus, the framework of Compressive Sensing matches under-sampling MRI Imaging. The sparsity requirement is closely related to the design of sensing matrix and dictionary on the Compressive Sensing framework. This paper presents analysis of two proposed sensing matrix designs for Under-sampling MRI Imaging: square sensing matrix and sorting-based sensing matrix designs. The proposed sensing matrix design is intended to appropriately sample the data measurement on certain sampling rate in frequency domain to maintain the quality of the reconstructed MRI image closely to the ground truth (full sampling MRI Imaging). The simulations show that both proposed sensing matrix designs are able to conduct under-sampling MRI Imaging for at least at 40% sampling rate with average of PSNR 27.72 and 33.07 respectively subject to the ground truth. © 2023 Copyright held by the owner/author(s)

关键词： Medical imaging

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Simulation of a Pick and Place System for Electronic Cards Using a YuMi Cobot 10

Simulation of a Pick and Place System for Electronic Cards U...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Sze, Derrick L. Reyes, Rosula S.J. Abu, Patricia Angela R. Computer and Communications Engineering Department of Electronics United States Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9798350394245

Collaborative Robots are one of the main drivers of Industry 4.0, which started as a vision focusing on industrial production. It addresses several challenges in the current manufacturing industry such as performing repetitive work and requiring highly skilled workers. The goal of the research is to be able to simulate a pick and place environment with electronic cards using a YuMi cobot and mobile platforms in Coppeliasim. The mobile robot is responsible for transporting the electronic cards to the target location through path planning implemented using the OMPL plug-in. After arriving at the target location, YuMi will then perform the pick and place task from vertically stacked cards through inverse kinematics. Use cases were considered on the chosen mobile platform and robotic arms to determine the capabilities and limitations of the hardware involved. In addition, the total power consumption was obtained for energy assessment when the setup is performed in a real-world setting. For this particular setup, the fourth joint starting from the base consumed the most power and is therefore the primary target for optimization. © 2024 IEEE.

关键词： Motion planning

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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computer systems science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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Comparative Analysis of Deep Learning and Machine Learning Analysis for Kidney Tumor Analysis 7

Comparative Analysis of Deep Learning and Machine Learning A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Yazhini, A. Kalpana Devi, M. Anitha, M. Praveena, R. Mol, Maheswari K Lawrence, Jinsha Anna University Faculty of Electrical Engineering College of Engineering Chennai600025 India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore10 India KIT - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore641402 India Jai Shriram Engineering College Department of Computer Science and Engineering Tirupur638660 India Veterinary College and Research Institute Thanjavur614625 India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

In order to lower death risks, provide the most effective course of treatment, and improve community healthcare, the majority of recent research has concentrated on examining prevalent illnesses in the population. One of the prevalent ailments that has impacted our society is kidney disease. Around the world, kidney tumors (KT) rank tenth in terms of frequency in both men and women. The organization of cancer is a problem that is attracting increasing attention in the domains of bioinformatics and computational biology. This study presents a comparative analysis of the latest developments in deep learning (DL) and machine learning (ML). While several methods have been proposed to tackle the cancer classification challenge, new research indicates that supervised and deep learning-based methods are the most effective. Healthcare has advanced as a result of the creation of numerous machine learning techniques for awareness evaluation in cancer categorization. It seems that there is a high requirement for the ongoing development of efficient categorization algorithms in order to handle the growth in healthcare applications. The comparative analysis of the proposed classification shows the accuracy as 0.92%. © 2024 IEEE.

关键词： Adversarial machine learning

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Probabilistic Co-Optimization of Hybrid Battery/Super-Capacitor systems Integrated into Microgrids

Probabilistic Co-Optimization of Hybrid Battery/Super-Capaci...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Mohseni, Soheil Brent, Alan C School of Engineering and Computer Science Sustainable Energy Systems Victoria University of Wellington Wellington Faculty of Engineering Wellington6140 New Zealand

ISBN: (纸本)9781728170251

While long-Term strategic investment modelling frameworks tailored to energy storage system-supported grid-connected microgrids are widely studied in the literature, the co-optimization of the integration of hybrid battery/super-capacitor systems is not well explored, particularly in terms of the impact analysis of the parametric uncertainties involved. In response, this paper introduces a novel stochastic energy storage sizing modelling framework for hybrid battery/super-capacitor systems. The proposed framework concurrently optimizes the operational scheduling and investment planning of the system, whilst employing the 'low-pass energy filter' concept to determine the optimal share of each energy storage medium in addressing the supply-demand mismatches within the system. Collectively, the quantification of various sources of uncertainty and the intelligent, optimization-based scheduling of storage devices allow for better nearing the global optimality. To show the effectiveness of the model, a case study is performed for a hypothetical microgrid system to be installed in a residential subdivision in Aotearoa-New Zealand. Importantly, the simulation results indicate a significant \sim\mathbf{36}\% underestimation of the total discounted cost of the system by business-As-usual deterministic frameworks compared to the worst-case probabilistic scenario obtained using the proposed probabilistic optimization method. © 2023 IEEE.

关键词： Optimization

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Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

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computer systems science & engineering 2023年第5期45卷 2265-2280页

作者： P.M.Arunkumar Mehedi Masud Sultan Aljahdali Mohamed Abouhawwash Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Nowadays,the cloud environment faces numerous issues like synchronizing information before the switch over the data *** requirement for a centralized internet of things(IoT)-based system has been restricted to some *** to low scalability on security considerations,the cloud seems *** healthcare networks demand computer operations on large amounts of data,the sensitivity of device latency evolved among health networks is a challenging *** comparison to cloud domains,the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing *** fog computing frameworks have various flaws,such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network *** this proposed work,Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart ***,the data is collected,and then pre-processing is done by Linear Discriminant *** the features are extracted and optimized using Galactic Swarm *** optimized features are given into the Health Fog framework for diagnosing heart disease *** uses ensemble-based deep learning in edge computing devices,which automatically monitors real-life health networks such as heart disease ***,the classifiers such as bagging,boosting,XGBoost,Multi-Layer Perceptron(MLP),and Partitions(PART)are used for classifying the *** the majority voting classifier predicts the *** work uses FogBus architecture and evaluates the execution of power usage,bandwidth of the network,latency,execution time,and accuracy.

关键词： Healthfog FogBus cascaded convolution neural network cloud computing heart disease automatic health monitoring internet of things

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