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9th IEEE/EMBS International Conference on Neural Engineering (NER)

作者： LeMoyne, Robert Mastroianni, Timothy McCandless, Cyrus Whiting, Donald Tomycz, Nestor No Arizona Univ Dept Biol Sci Box 5640 Flagstaff AZ 86011 USA No Arizona Univ Ctr Bioengn Box 5640 Flagstaff AZ 86011 USA Sentient Decis Sci Behav Sci Portsmouth NH 03801 USA AHN Neurosci Inst Dept Neurosurg Pittsburgh PA 15212 USA West Penn Allegheny Hlth Syst Pittsburgh PA 15212 USA

ISBN: (纸本)9781538679210

Essential tremor is a prevalent neurodegenerative movement disorder. Deep brain stimulation represents a highly effective means of treatment, especially for scenarios for which traditional medical intervention is no longer feasible. One of the major post-operative challenges is the determination of an optimal set of tuning parameters. Optimizing the deep brain stimulation parameters can impart a time-intensive task to the clinician. The smartphone in the context of a wearable and wireless inertial sensor system offers the capability to objectively quantify the characteristics of the tremor. machine learning in conjunction with a wearable and wireless inertial sensor system, such as a smartphone, can distinguish between disparate states, such as deep brain stimulation in 'On' and 'Off' status. Multiple machine learning classification techniques are available, such as the multilayer perceptron neural network, support vector machine, K-nearest neighbors, logistic regression, J-48 decision tree, and random forest. The objective of this research endeavor is to evaluate these six machine learning classification algorithms for classification of deep brain stimulation regarding 'On' and 'Off' status for Essential tremor during a reach and grasp task. The reach and grasp task is quantified through the smartphone as wearable and wireless inertial sensor system mounted to the dorsum of the hand and secured by latex glove. Multiple feature set scenarios are considered, such as recordings from both the accelerometer and gyroscope, accelerometer, and gyroscope. These scenarios facilitate the determination of the most robust machine learning algorithms. The multilayer perceptron neural network, support vector machine, K-nearest neighbors, and logistic regression achieve the highest classification accuracy for three feature set scenarios derived by recordings from both accelerometer and gyroscope, accelerometer, and gyroscope.

关键词： Brain stimulation Wireless communication Wireless sensor networks machine learning machine learning algorithms Task analysis Gyroscopes

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Predicting the Tensile Strength of Extrusion-blown High Density Polyethylene Film Using machine learning algorithms

Predicting the Tensile Strength of Extrusion-blown High Dens...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Alhindawi, F. Altarazi, S. German Jordanian Univ Ind Engn Dept Amman Jordan

ISBN: (纸本)9781538667866

This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and k-Nearest Neighbors. Eleven input parameters, five materials related and six process related;were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

关键词： machine learning algorithms extrusion-blown molding tensile strength HDPE film

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Demand forecasting based machine learning algorithms on customer information: an applied approach

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International Journal of Information Technology (Singapore) 2022年第4期14卷 1937-1947页

作者： Zohdi, Maryam Rafiee, Majid Kayvanfar, Vahid Salamiraad, Amirhossein Department of Industrial Engineering Sharif University of Technology Tehran Iran

Demand forecasting has always been a concern for business owners as one of the main activities in supply chain management. Unlike the past, that forecasting was done with the help of a limited amount of information, today, using advanced technologies and data analytics, forecasting is performed with machine learning algorithms and data-driven methods. Patterns and trends of demand, customer information, preferences, suggestions, and post-consumption feedbacks are some types of data that are used in various demand forecasting efforts. Traditional statistical methods and techniques are biased in demand prediction and are not accurate;so, machine learning algorithms as more popular techniques have been replaced in recent researches in the literature. Until the time of conducting this research, extreme learning machine has not been used for intermittent demand prediction, so the novelty of our research is to adopt this algorithm and also other machine learning algorithms such as K-nearest neighbors, decision tree, gradient boosting, and multi-layer perceptron to examine its accuracy and performance in comparison to other approaches. Finally, it is demonstrated that artificial neural network-based methods outperform the other employed techniques through conducting a comparison among the above-mentioned predictors in terms of mean squared error, mean absolute error, coefficient of determination, and computational time. Furthermore, extreme learning machine is the best or at least among the best predictors. At last, for determining whether the obtained results are statistically significant or not, analysis of variance is conducted and the Kolmogorov–Smirnov technique is adopted to test the normality of outcomes. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Big data analytics Customer information Demand forecasting machine learning algorithms Supply chain management

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House Prices Prediction with machine learning algorithms 2018

House Prices Prediction with Machine Learning Algorithms

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10th International Conference on machine learning and Computing (ICMLC)

作者： Fan, Chenchen Cui, Zechen Zhong, Xiaofeng Tsinghua Univ Elect Engn Dept ShuangQing Rd 30 Beijing Peoples R China Beijing Dongzhimen Middle Sch ShuangQing Rd 30 Beijing Peoples R China

ISBN: (纸本)9781450363532

Based on the data set compiled by D. D. Cock and the competition run by ***, we propose a house prices prediction algorithm in Ames, lowa by deliberating on data processing, feature engineering and combination forecasting. Our prediction ranks the 35th of the total 2221 results on the public leaderboard of *** and the RMSE of predicted results after taking logarithm from all the test data is 0.12019, which shows good performance and small of over-fitting.

关键词： House Prices Prediction machine learning algorithms Combination Forecasting Kaggle Competition

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Detecting Fake News About Covid-19 on Small Datasets with machine learning algorithms 30

Detecting Fake News About Covid-19 on Small Datasets with Ma...

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30th Conference of Open-Innovations-Association (FRUCT)

作者： Shushkevich, Elena Cardiff, John Technol Univ Dublin Dublin Ireland

ISBN: (纸本)9789526924465

Nowadays the problem of fake news in social media is dramatically increasing, especially when it refers to fake news about Covid-19, as it is a recent and global problem. Because of this fact, it is important to have the ability to detect and delete such news immediately. In our research we concentrate our efforts on detecting fake news about Coronavirus on small datasets, using the Constraint-2021 corpus: the full dataset (10,700 messages) and the limited dataset (1,000 messages). We compare classical machine learning algorithms (4 algorithms: Logistic Regression, Support Vectors machine, Gradient Boosting, Random Forest) - algorithms of classification from the Scikit-learn library, GMDH-Shell tool (2 algorithms: Combi and Neuro), and Deep Neural Network (LSTM model). The results show that GMDH algorithms outperform traditional machine learning algorithms and are comparable with Neural Networks model's results on the limited dataset.

关键词： COVID-19 Support vector machines Technological innovation machine learning algorithms Social networking (online) Neural networks Tools

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A Comparative Study of machine learning algorithms for Prior Prediction of UFC Fights 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Hitkul Aggarwal, Karmanya Yadav, Neha Dwivedy, Maheshwar BML Munjal Univ Sch Engn & Technol Gurugram 122413 Haryana India

ISBN: (纸本)9789811307614;9789811307607

Mixed Martial Arts is a rapidly growing combat sport that has a highly multi-dimensional nature. Due to a large number of possible strategies available to each fighter, and multitude of skills and techniques involved, the potential for upset in any fight is very high. That is the chance of a highly skilled, veteran athlete being defeated by an athlete with significantly less experience is possible. This problem is further exacerbated by the lack of a well-defined, time series database of fighter profiles prior to every fight. In this paper, we attempt to develop an efficient model based on the machine learning algorithms for the prior prediction of UFC fights. The efficacy of various machine learning models based on Perceptron, Random Forests, Decision Trees classifier, Stochastic Gradient Descent (SGD) classifier, Support Vector machine (SVM), and K-Nearest Neighbor (KNN) classifiers is tested on a time series set of a fighter's data before each fight.

关键词： machine learning algorithms Mixed martial arts Classifiers

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Alzheimer Disease Prediction using machine learning algorithms 6

Alzheimer Disease Prediction using Machine Learning Algorith...

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6th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Neelaveni, J. Devasana, M. S. Geetha Sri Ramakrishna Engn Coll Comp Sci & Engn Dept Coimbatore 641022 Tamil Nadu India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151977;9781728151960

Alzheimer disease is the one amongst neurodegenerative disorders. Though the symptoms are benign initially, they become more severe over time. Alzheimer's disease is a prevalent sort of dementia. This disease is challenging one because there is no treatment for the disease. Diagnosis of the disease is done but that too at the later stage only. Thus if the disease is predicted earlier, the progression or the symptoms of the disease can be slow down. This paper uses machine learning algorithms to predict the Alzheimer disease using psychological parameters like age, number of visit, MMSE and education.

关键词： Aizheimer disease mild cognitive impairment machine learning algorithms psychological parameters

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Evaluation of machine learning algorithms for Automated Management of Wireless Links 1

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21st International Conference on Intelligent Data Engineering and Automated learning

作者： Frias, Francisca Marcal, Andre R. S. Prior, Rui Moreira, Waldir Oliveira, Antonio, Jr. Fraunhofer Portugal AICOS Porto Portugal Univ Porto Fac Ciencias Porto Portugal Inst Telecomunicacoes Porto Portugal Univ Fed Goias Inst Informat Goiania Go Brazil

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

machine learning, a subfield of artificial intelligence, has been widely used to automate tasks usually performed by humans. Some applications of these techniques are understanding network traffic behavior, predicting it, classifying it, fixing its faults, identifying malware applications, and preventing deliberate attacks. The goal of this work is to use machine learning algorithms to classify, in separate procedures, the errors of the network, their causes, and possible fixes. Our application case considers the WiBACK wireless system, from which we also obtained the data logs used to produce this paper. WiBACK is a collection of software and hardware with auto-configuration and self-management capabilities, designed to reduce CAPEX and OPEX costs. A principal components analysis is performed, followed by the application of decision trees, k nearest neighbors, and support vector machines. A comparison between the results obtained by the algorithms trained with the original data sets, balanced data sets, and the principal components data is performed. We achieve weighted F1-score between 0.93 and 0.99 with the balanced data, 0.88 and 0.96 with the original unbalanced data, and 0.81 and 0.89 with the Principal Components Analysis.

关键词： machine learning algorithms Principal components analysis Traffic classification Wireless links management

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Considerations in the Deployment of machine learning algorithms on Spaceflight Hardware

Considerations in the Deployment of Machine Learning Algorit...

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IEEE Aerospace Conference (AeroConf)

作者： McBee, Ryan Anderson, Joshua L. Koets, Michael A. Ramirez, Joshua Tschirhart, Zachary Southwest Res Inst 6220 Culebra Rd San Antonio TX 78238 USA

ISBN: (纸本)9781728174365

Recent advances in artificial intelligence (AI) and machine learning (ML) have revolutionized many fields. ML has many potential applications in the space domain. Next generation space instruments are producing data at rates that exceed the capabilities of current spacecraft to store or transmit to ground stations. Deployment of ML algorithms onboard future spacecraft could perform processing of sensor data as it is gathered, reducing data volume and providing a dramatic increase in throughput of meaningful data. ML techniques may also be used to enhance the autonomy of space missions. However ML techniques have not yet been widely deployed in space environments, primarily due to limitations on the computational capabilities of spaceflight hardware. The need to verify that high-performance computational hardware can reliably operate in this environment delays the adoption of these technologies. Nevertheless, the availability of advanced processing capabilities onboard spacecraft is increasing. These platforms may not provide the processing power of terrestrial equivalents, but they do provide the resources necessary for deploying real-time execution of ML algorithms. In this paper, we present results exploring the implementation of ML techniques on computationally-constrained, high-reliability spacecraft hardware. We show two ML algorithms utilizing deep learning techniques which illustrate the utility of these approaches for space applications. We describe implementation considerations when tailoring these algorithms for execution on computationally-constrained hardware and present a workflow for performing these optimizations. We also present initial results on characterizing the trade space between algorithm accuracy, throughput, and reliability on a variety of hardware platforms with current and anticipated paths to spaceflight.

关键词： Space vehicles machine learning algorithms Space missions Aerospace electronics Throughput Reliability engineering Hardware

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Particle Swarm Optimization-Based machine learning algorithms for Developing the Modified Proctor Compaction Parameter Prediction Software

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TRANSPORTATION INFRASTRUCTURE GEOTECHNOLOGY 2024年第4期11卷 1492-1519页

作者： Verma, Gaurav Kumar, Brind Ransinchung, R. N. G. D. Indian Inst Technol BHU Dept Civil Engn Varanasi 221005 Uttar Pradesh India Indian Inst Technol Dept Civil Engn Roorkee 247667 Uttarakhand India

Maximum dry density (MDD) and optimum moisture content (OMC) are two significant compaction criteria, especially for quality control and design engineers. Estimating laboratory proctor compaction test is rigorous, time-consuming, and expensive, hindering projects with limited budgets and tight schedules. This study proposed the novel application of hybrid particle swarm optimization (PSO) optimized Gaussian process regression (GPR), K-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGB) algorithms for predicting the soil compaction parameters. Analyzing 2148 in situ soil samples from various geological locations established the maximum proficiency of the XGB algorithm followed by KNN, GPR, and RF in MDD, whereas XGB, KNN, RF, and GPR in OMC. Furthermore, the level 1 and level 2 validation results ascertain the robustness of models in predicting MDD and OMC on different geological location datasets. Eventually, the AI-based computer software developed through this study offers reliable and efficient predictions for civil engineers.

关键词： machine learning algorithms Hyperparameter optimization Computer software Modified compaction parameters Borrow materials

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