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11th International Conference on Computational Collective Intelligence (ICCCI)

作者： Mansurova, Madina Barakhnin, Vladimir Khibatkhanuly, Yerzhan Pastushkov, Ilya Al Farabi Kazakh Natl Univ Alma Ata 050040 Kazakhstan Inst Computat Technol SB RAS Novosibirsk Russia Novosibirsk State Univ Novosibirsk Russia

ISBN: (纸本)9783030283742;9783030283735

The modern society have been witnessed that intensive development of Internet technologies had followed to information explosion during last decades. This explosion had been expressing by an exponential growth of data volume among the low-quality information. This paper is designed to provide detailed information about some intellectual tools which are support decision taking by automatic knowledge extraction. In the first part of paper, we considered a preprocessing contains morphological analysis of texts. Then we had considered the model of text documents in the form of a hypergraph and implementation of the random walk method to extract semantically close word's pairs, in other words, pairs that often appears together. Result of calculations is matrix with word affinity coefficients corresponding to each other component of vocabulary vector. In the second part we describe training of neural network for linguistic constructions extraction. These ones include possible values of text named entities descriptors. The neural network enables to retrieve information on one preselected descriptor, for example, location, in the form of the final result of the name of geographical objects. In a general case, the neural network can retrieve information on several descriptors simultaneously.

关键词： Entity extraction Semi-structured data machine learning algorithms Random walk method Neural networks

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machine learning Security algorithms and Framework for IOT System

Machine Learning Security Algorithms and Framework for IOT S...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Megha Mishra Vishnu Kumar Mishra Sunil Tekale T. NagaPraveena Kimidi Parijatha Bhupesh Dewangan Saddamhusian Hadimani Computer Science Engineering Shri Shankaracharya Technical campus CSVTU University Bhilai Chhattisgarh India Computer Science & Engineering Malla Reddy College of Engineering JNTUH University Secunderabad Telangana India Computer Science & Engineering OP Jindal University Raigarh Chhattisgarh India

This paper cover different security issues needs along with attack and vectors attacks and also highlighted current issues and solutions for IoT security for networks are all thoroughly reviewed in this study. Here we cover ML and DL methods for holes in these security solutions that demand strategies are then highlighted. We also go into great detail about the ML and DL technologies that are now being used to solve a range of security harms to different IoT networks. In this study we highlighted the future research prospects for ML (machine Leraning) and DL (Deep learning) based IoT security based on the thorough analysis of the already published solutions. This work focused on a brand-new (ML) machine learning based security framework that adapts automatically to the evolving security needs of the IoT domain. For the purpose of reducing various vulnerabilities, this framework formulate utilize of both (NFV) Network Function Virtualization enabler’s and (SDN) Software Defined Networking. To accomplish this result we are using supervised learning and their framework that system more secures and distributed data mining system, and neural networks.

关键词： machine learning algorithms Supervised learning Software algorithms Web servers Network function virtualization Security Internet of Things

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Cost effective and efficient screening of tuberculosis disease with Raman spectroscopy and machine learning algorithms

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PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY 2020年 32卷 101963-101963页

作者： Ullah, Rahat Khan, Saranjam Chaudhary, Ishtiaq Shahzad, Shaheen Ali, Hina Bilal, Muhammad Pakistan Inst Engn & Appl Sci Natl Inst Lasers & Optron Coll Agri & Biophoton Div Islamabad 45650 Pakistan Islamia Coll Peshawar Dept Phys Peshawar Pakistan Int Islamic Univ Dept Bioinformat & Biotechnol Islamabad Pakistan

The current study presents Raman Spectroscopy (RS) accompanied by machine learning algorithms based on Principle Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) for analysis of tuberculosis (TB). TB positive (diseased), TB negative (cured) and control (healthy) serum samples are considered for inter and intra comparative analysis. Raman spectral differences observed between both TB group and control samples spectra attributed probably to the changes in biomolecules like higher lactate concentration, lowering level of beta-carotene and amide-I band of protein in TB patient's blood samples. Inter comparison between control and TB positive sera samples shows prominent decrease in three extremely intense Raman peaks associated to beta-carotene concentration. Noteworthy spectral differences are also observed among TB positive and TB negative sera samples. The comparison of these Raman results clearly indicate that the blood composition of TB negative patients still showing irregularities in some important elements. Moreover, the Raman spectral differences observed in the data of the control and diseased samples are further highlighted with the help of the machine learning algorithms. In general, a fine correlation has been observed between PCA score plot as well as HCA dendogram with the original Raman findings. Further investigation of such noticeable differences could help in understandings regarding the existing threshold levels. Moreover in future, it can contribute a lot towards the development of new, modified and more effective screening options.

关键词： Raman spectroscopy Tuberculosis disease machine learning algorithms Risk prediction

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Software Defect Estimation Using machine learning algorithms 4

Software Defect Estimation Using Machine Learning Algorithms

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4th International Conference on Computer Science and Engineering (UBMK)

作者： Yalciner, Burcu Ozdes, Merve Hacettepe Univ Comp Engn Ankara Turkey

ISBN: (纸本)9781728139647

Software Engineering is a comprehensive domain since it requires a tight communication between system stakeholders and delivering the system to be developed within a determinate time and a limited budget. Delivering the customer requirements include procuring high performance by minimizing the system. Thanks to effective prediction of system defects on the front line of the project life cycle, the project's resources and the effort or the software developers can be allocated more efficiently for system development and quality assurance activities. The main aim of this paper is to evaluate the capability of machine learning algorithms in software defect prediction and find the best category while comparing seven machine learning algorithms within the context of four NASA datasets obtained from public PROMISE repository [12]. All in all, the results of ensemble learners category consisting of Random Forests (RF) and Bagging in defect prediction is pretty much its counterparts.

关键词： Software quality metrics Software defect predic-tion Software fault prediction machine learning algorithms

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OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

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PLOS COMPUTATIONAL BIOLOGY 2022年第1期18卷 e1009731-e1009731页

作者： Krishnakumar, Raga Ruffing, Anne M. Sandia Natl Labs Syst Biol Dept Livermore CA 94551 USA Sandia Natl Labs Mol & Microbiol Dept Albuquerque NM USA

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

关键词： Operons machine learning algorithms algorithms machine learning Forecasting Escherichia coli RNA structure Prokaryotic cells

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Approaches for Using machine learning algorithms with Large Label Sets for Rotorcraft Maintenance

Approaches for Using Machine Learning Algorithms with Large ...

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IEEE Aerospace Conference

作者： Seale, Maria Hines, Amanda Nabholz, Grace Ruvinsky, Alicia Eslinger, Owen Rigoni, Nathan Vega-Maisonet, Luis Us Army Engineer Res & Dev Ctr 3909 Halls Ferry Rd Vicksburg MS 39180 USA Aviat & Missile Res Dev & Engn Ctr Huntsville AL USA Univ Puerto Rico PR-108 Mayaguez PR 00682 USA

ISBN: (纸本)9781538668542

The US Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC), in collaboration with the US Army Engineer Research and Development Center (ERDC), is using machine learning (ML) to transform the way rotorcraft maintenance logbook event data is scored for reporting purposes. Traditionally, human analysts manually inspect data fields completed by maintenance personnel and, using published guidelines in conjunction with their personal expertise, provide sets of labels or scores for each event. These labels are stored with the data and provide valuable insight into maintenance event histories. However, the inequity between the enormous volume of maintenance data generated daily and the ability of analysts to score the data results in only 10% of all data receiving scores;therefore, 90% of the recorded data does not contain this important value added feature. Classification algorithms for automating this scoring process trained on existing labeled data sets have been implemented with promising results. A particularly challenging element of this problem, however, involves the classification of the specific component on which maintenance was performed. Greater than 1200 unique labels exist that can be used to describe a rotorcraft component that is the subject of a maintenance action. Furthermore, the component labels are hierarchically structured, resulting in the occurrence of multiple levels of precision in identifying a component in the expert-labeled data used for training. Although computational efficiency of common classification algorithms has improved considerably, it is still quite challenging to harness these methods for problems that include large numbers of unique class labels. This paper describes several novel strategies for solving this problem, based on hierarchical ensemble models and strategic label set segmentation. Through these approaches, a best overall total component classification accuracy of 96% was achieved, in conjuncti

关键词： Maintenance engineering Classification algorithms Prediction algorithms machine learning algorithms Reliability Aircraft machine learning

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Identifying Students At-Risk with an Ensemble of machine learning algorithms 3

Identifying Students At-Risk with an Ensemble of Machine Lea...

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3rd Annual Conference on Information-Communications-Technology-and-Society (ICTAS)

作者： Soobramoney, Ranjin Singh, Alveen Durban Univ Technol Dept Informat Technol Durban South Africa Durban Univ Technol Dept Informat Technol ICT & Soc Res Grp Durban South Africa

ISBN: (纸本)9781538673652

Accurately predicting the academic performance of students is an important process for the long term sustainability of higher education institutions (HEIs). A reliable and timely identification of students at-risk will enable HEIs to take proactive measures to assist these students. Owing to large student numbers with varying backgrounds, identifying students at-risk is often primarily a manual process involving an analysis of students' prior academic results and little else. While prior academic results do play an important role in indentifying students at-risk, there could be several other factors that may be overlooked. The demographic and financial circumstances of the student for instance could play a key role in more accurately identifying students at-risk. It may be impractical to consider all of these factors when manually trying to identify students at-risk. We propose that classification models using an ensemble of machine learning algorithms (MLAs) have promising prospects to more effectively predict students at-risk by including several factors that may often not even be practical with manual methods.

关键词： Classification Prediction machine learning algorithms At-risk Feature Selection Ensemble methods

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Evaluation of machine learning algorithms for Classifying Deep Brain Stimulation Respective of 'On' and 'Off' Status 9

Evaluation of Machine Learning Algorithms for Classifying De...

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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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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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Classification of Rheumatoid Arthritis using machine learning algorithms 17

Classification of Rheumatoid Arthritis using Machine Learnin...

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17th IEEE Student Conference on Research and Development (SCOReD)

作者： Sharon, Ho Elamvazuthi, I Lu, C. K. Parasuraman, S. Natarajan, Elango Univ Teknol PETRONAS Dept Elect & Elect Engn Smart Assist & Rehabil Technol SMART Res Grp Bandar Seri Iskandar 32610 Malaysia Monash Univ Malaysia Sch Engn Bandar Sunway 46150 Malaysia UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur Malaysia

ISBN: (纸本)9781728126135

Rheumatoid Arthritis (RA) is a persistent provocative ailment that effects and decimates the joints of wrist, finger, and feet. If left untreated, one can lose their ability to lead a normal life. RA is the most typical fiery joint inflammation, influencing around 1-2% of the total populace. Throughout the years, soft computing played an important part in helping ailment analysis in doctor's decision process. The main aim of this study is to investigate the possibility of applying machine learning techniques to the analysis of RA characteristics. As a preliminary work, a credible database has been identified to be used for this research. The database has outputs of array temperature values from thermal imaging for the joints of hand. Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. In this preliminary work, ensemble algorithms such as bagging, AdaBoost and random subspace with base classifier such as random forest and SVM were trained and tested using the assessment criteria such as accuracy, precision, sensitivity and AUC using Weka tool. From the preliminary finding of this paper, it can be concluded that with base classifier SVM, bagging has better classification accuracy over the others and with base classifier random forest Adaboost slightly outperformed other models for rheumatoid arthritis dataset.

关键词： Classification Rheumatoid Arthritis machine learning algorithms

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