Готові списки джерел за темами / ML classification algorithms

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Добірка наукової літератури з теми "ML classification algorithms"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "ML classification algorithms"

shehab, Sara, and Arabi Keshk. "Breast Cancer Classification Using Ml Algorithms." Kafrelsheikh Journal of Information Sciences 3, no. 1 (June 7, 2022): 1–7. http://dx.doi.org/10.21608/kjis.2022.159008.1010.

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Patidar, Muskan. "Cyber Bullying Detection for Twitter Using ML Classification Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 24–29. http://dx.doi.org/10.22214/ijraset.2021.38701.

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Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

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Dey, Sumagna. "DIABETES PREDICTION AND VALIDATION MODEL USING ML CLASSIFICATION ALGORITHMS." International Journal of Advanced Research in Computer Science 11, no. 5 (October 20, 2020): 59–63. http://dx.doi.org/10.26483/ijarcs.v11i5.6654.

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Pathan, Munir S., S. M. Pradhan, and T. Palani Selvam. "MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS." Radiation Protection Dosimetry 190, no. 3 (July 2020): 342–51. http://dx.doi.org/10.1093/rpd/ncaa108.

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Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

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Sipper, Moshe. "High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms." Algorithms 15, no. 9 (September 2, 2022): 315. http://dx.doi.org/10.3390/a15090315.

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Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. However, just how useful is said tuning? While smaller-scale experiments have been previously conducted, herein we carry out a large-scale investigation, specifically one involving 26 ML algorithms, 250 datasets (regression and both binary and multinomial classification), 6 score metrics, and 28,857,600 algorithm runs. Analyzing the results we conclude that for many ML algorithms, we should not expect considerable gains from hyperparameter tuning on average; however, there may be some datasets for which default hyperparameters perform poorly, especially for some algorithms. By defining a single hp_score value, which combines an algorithm’s accumulated statistics, we are able to rank the 26 ML algorithms from those expected to gain the most from hyperparameter tuning to those expected to gain the least. We believe such a study shall serve ML practitioners at large.

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Ambavkar, Om, Prathmesh Bharti, Amit Chaurasiya, Roshan Chauhan, and Mahalaxmi Palinje. "Review on IDS based on ML Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 169–74. http://dx.doi.org/10.22214/ijraset.2022.47284.

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Abstract: Intrusion detection is one of the challenging problems encountered by the modern network security industry. The developing pace of digital assaults on framework networks as of late compounds the protection and security of PC foundation and PCs. Intrusion Detection and Prevention systems are transforming into a critical part of PC organizations and network safety. Various approaches have been proposed to determine the most effective features and hence enhance the efficiency of intrusion detection systems, the methods include, machine learning-based (ML), Bayesian based algorithm, Random Forest, SVM, Decision Tree. This paper presents an intensive survey on different examination articles that utilized single, hybrid and ensemble classification algorithms. The outcomes measurements, weaknesses and datasets involved by the concentrated on articles in the advancement of IDS were looked at. A future heading for potential explores is likewise given. The paper addressed latest research papers written from the use of machine learning classifiers in intrusion detection systems.

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Camele, Genaro, Waldo Hasperué, Franco Ronchetti, and Facundo Manuel Quiroga. "Statistical analysis of the performance of four Apache Spark ML algorithms." Journal of Computer Science and Technology 22, no. 2 (October 17, 2022): e14. http://dx.doi.org/10.24215/16666038.22.e14.

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Feature selection (FS) techniques generally require repeatedly training and evaluating models to assess theimportance of each feature for a particular task. However, due to the increasing size of currently availabledatabases, distributed processing has become a necessity for many tasks. In this context, the Apache SparkML library is one of the most widely used libraries for performing classification and other tasks with largedatasets. Therefore, knowing both the predictive performance and efficiency of its main algorithms beforeapplying a FS technique is crucial to planning computations and saving time. In this work, a comparativestudy of four Spark ML classification algorithms is carried out, statistically measuring execution times andpredictive power based on the number of attributes from a colon cancer database. Results were statistically analyzed, showing that, although Random Forest and Na¨ıve Bayes are the algorithms with the shortest execution times, Support Vector Machine obtains models with the best predictive power. The study of the performance of these algorithms is interesting as they are applied in many different problems, such as classification of pathologies from epigenomic data, image classification, prediction of computer attacks in network security problems, among others.

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Ã–zsoy, Salih, GÃ¶khan GÃ¼mÃ¼ÅŸ, and Savriddin KHALILOV. "C4.5 Versus Other Decision Trees: A Review." Computer Engineering and Applications Journal 4, no. 3 (September 20, 2015): 173–82. http://dx.doi.org/10.18495/comengapp.v4i3.141.

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In this study, Data Mining, one of the latest technologies of the Information Systems, was introduced and Classification a Data Mining method and the Classification algorithms were discussed. A classification was applied by using C4.5 decision tree algorithm on a dataset about Labor Relations from http://archive.ics.uci.edu/ml/datasets.html. Finally, C4.5 algorithm was compared to some other decision tree algorithms. C4.5 was the one of the successful classifier.

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Lee, Sangwoo, Eun Choe, and Boram Park. "Exploration of Machine Learning for Hyperuricemia Prediction Models Based on Basic Health Checkup Tests." Journal of Clinical Medicine 8, no. 2 (February 2, 2019): 172. http://dx.doi.org/10.3390/jcm8020172.

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Background: Machine learning (ML) is a promising methodology for classification and prediction applications in healthcare. However, this method has not been practically established for clinical data. Hyperuricemia is a biomarker of various chronic diseases. We aimed to predict uric acid status from basic healthcare checkup test results using several ML algorithms and to evaluate the performance. Methods: We designed a prediction model for hyperuricemia using a comprehensive health checkup database designed by the classification of ML algorithms, such as discrimination analysis, K-nearest neighbor, naïve Bayes (NBC), support vector machine, decision tree, and random forest classification (RFC). The performance of each algorithm was evaluated and compared with the performance of a conventional logistic regression (CLR) algorithm by receiver operating characteristic curve analysis. Results: Of the 38,001 participants, 7705 were hyperuricemic. For the maximum sensitivity criterion, NBC showed the highest sensitivity (0.73), and RFC showed the second highest (0.66); for the maximum balanced classification rate (BCR) criterion, RFC showed the highest BCR (0.68), and NBC showed the second highest (0.66) among the various ML algorithms for predicting uric acid status. In a comparison to the performance of NBC (area under the curve (AUC) = 0.669, 95% confidence intervals (CI) = 0.669–0.675) and RFC (AUC = 0.775, 95% CI 0.770–0.780) with a CLR algorithm (AUC = 0.568, 95% CI = 0.563–0.571), NBC and RFC showed significantly better performance (p < 0.001). Conclusions: The ML model was superior to the CLR model for the prediction of hyperuricemia. Future studies are needed to determine the best-performing ML algorithms based on data set characteristics. We believe that this study will be informative for studies using ML tools in clinical research.

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Jafarzadeh, Hamid, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, and Saeid Homayouni. "Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation." Remote Sensing 13, no. 21 (November 2, 2021): 4405. http://dx.doi.org/10.3390/rs13214405.

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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.

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Дисертації з теми "ML classification algorithms"

Wessman, Filip. "Advanced Algorithms for Classification and Anomaly Detection on Log File Data : Comparative study of different Machine Learning Approaches." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43175.

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Background: A problematic area in today’s large scale distributed systems is the exponential amount of growing log data. Finding anomalies by observing and monitoring this data with manual human inspection methods becomes progressively more challenging, complex and time consuming. This is vital for making these systems available around-the-clock. Aim: The main objective of this study is to determine which are the most suitable Machine Learning (ML) algorithms and if they can live up to needs and requirements regarding optimization and efficiency in the log data monitoring area. Including what specific steps of the overall problem can be improved by using these algorithms for anomaly detection and classification on different real provided data logs. Approach: Initial pre-study is conducted, logs are collected and then preprocessed with log parsing tool Drain and regular expressions. The approach consisted of a combination of K-Means + XGBoost and respectively Principal Component Analysis (PCA) + K-Means + XGBoost. These was trained, tested and with different metrics individually evaluated against two datasets, one being a Server data log and on a HTTP Access log. Results: The results showed that both approaches performed very well on both datasets. Able to with high accuracy, precision and low calculation time classify, detect and make predictions on log data events. It was further shown that when applied without dimensionality reduction, PCA, results of the prediction model is slightly better, by a few percent. As for the prediction time, there was marginally small to no difference for when comparing the prediction time with and without PCA. Conclusions: Overall there are very small differences when comparing the results for with and without PCA. But in essence, it is better to do not use PCA and instead apply the original data on the ML models. The models performance is generally very dependent on the data being applied, it the initial preprocessing steps, size and it is structure, especially affecting the calculation time the most.

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Rasool, Raihan Ur. "CyberPulse: A Security Framework for Software-Defined Networks." Thesis, 2020. https://vuir.vu.edu.au/42172/.

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Software-Defined Networking (SDN) technology provides a new perspective in traditional network management by separating infrastructure plane from the control plane which facilitates a higher level of programmability and management. While centralized control provides lucrative benefits, the control channel becomes a bottleneck and home to numerous attacks. We conduct a detailed study and find that crossfire Link Flooding Attacks (LFA) are one of the most lethal attacks for SDN due to the utilization of low-rate traffic and persistent attacking nature. LFAs can be launched by the malicious adversaries to congest the control plane with low-rate traffic which can obstruct the flow rule installation and can ultimately bring down the whole network. Similarly, the adversary can employ bots to generate low-rate traffic to congest the control channel, and ultimately bring down the control plane and data plane connection causing service disruption. We present a systematic and comparative study on the vulnerabilities of LFAs on all the SDN planes, elaborate in detail the LFA types, techniques, and their behavior in all the variant of SDN. We then illustrate the importance of a defense mechanism employing a distributed strategy against LFAs and propose a Machine Learning (ML) based framework namely CyberPulse. Its detailed design, components, and their interaction, working principles, implementation, and in-depth evaluation are presented subsequently. This research presents a novel approach to write anomaly patterns and makes a significant contribution by developing a pattern-matching engine as the first line of defense against known attacks at a line-speed. The second important contribution is the effective detection and mitigation of LFAs in SDN through deep learning techniques. We perform twofold experiments to classify and mitigate LFAs. In the initial experimental setup, we utilize Artificial Neural Networks backward propagation technique to effectively classify the incoming traffic. In the second set of experiments, we employ a holistic approach in which CyberPulse demonstrates algorithm agnostic behavior and employs a pre-trained ML repository for precise classification. As an important scientific contribution, CyberPulse framework has been developed ground up using modern software engineering principles and hence provides very limited bandwidth and computational overhead. It has several useful features such as large-scale network-level monitoring, real-time network status information, and support for a wide variety of ML algorithms. An extensive evaluation is performed using Floodlight open-source controller which shows that CyberPulse offers limited bandwidth and computational overhead and proactively detect and defend against LFA in real-time. This thesis contributes to the state-of-the-art by presenting a novel framework for the defense, detection, and mitigation of LFA in SDN by utilizing ML-based classification techniques. Existing solutions in the area mandate complex hardware for detection and defense, but our presented solution offers a unique advantage in the sense that it operates on real-time traffic scenario as well as it utilizes multiple ML classification algorithms for LFA traffic classification without necessitating complex and expensive hardware. In the future, we plan to implement it on a large testbed and extend it by training on multiple datasets for multiple types of attacks.

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Частини книг з теми "ML classification algorithms"

Zoungrana, Wend-Benedo, Abdellah Chehri, and Alfred Zimmermann. "Automatic Classification of Rotating Machinery Defects Using Machine Learning (ML) Algorithms." In Human Centred Intelligent Systems, 193–203. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5784-2_16.

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Bayas, Shiwani, Suraj Sawant, Ishwari Dhondge, Priyanka Kankal, and Amit Joshi. "Land Use Land Cover Classification Using Different ML Algorithms on Sentinel-2 Imagery." In Lecture Notes in Electrical Engineering, 761–77. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0840-8_59.

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Tiwari, Manu, Piyush Nagar, Gautam Arya, and Surendra Singh Chauhan. "Road Accident Analysis Using ML Classification Algorithms and Plotting Black Spot Areas on Map." In Micro-Electronics and Telecommunication Engineering, 685–701. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8721-1_64.

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Łukasik, Michał, Tomasz Kuśmierczyk, Łukasz Bolikowski, and Hung Son Nguyen. "Hierarchical, Multi-label Classification of Scholarly Publications: Modifications of ML-KNN Algorithm." In Intelligent Tools for Building a Scientific Information Platform, 343–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35647-6_22.

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Ghodke, Tejashri, and V. M. Khadse. "Effective Text Comment Classification Using Novel ML Algorithm—Modified Lazy Random Forest." In Lecture Notes in Electrical Engineering, 81–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3690-5_9.

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Almeida, Thissiany Beatriz, and Helyane Bronoski Borges. "An Adaptation of the ML-kNN Algorithm to Predict the Number of Classes in Hierarchical Multi-label Classification." In Modeling Decisions for Artificial Intelligence, 77–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67422-3_8.

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Dharmapala, Parakramaweera Sunil. "Employee Classification in Reward Allocation Using ML Algorithms." In Encyclopedia of Data Science and Machine Learning, 3102–19. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch186.

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This work discussed an application of machine learning algorithms in predicting employee categories in reward allocation based on input features determined from survey responses. The results reported in this article are primarily based on beliefs and perceptions of the survey respondents about the four categories of employees, namely performer, needy, starter, and senior. The authors considered two classification models—full model with 10 input features and the reduced model with seven input features—and the results show that the reduced model performed better than the full model, indicating that three qualitative input features bear no relevance to predicting the employee categories. Both models selected optimizable ensemble and optimizable SVM as best machine learning classifiers, based on accuracy rates and AUC scores. Finally, using the reduced model on out-of-sample observations, employee categories were correctly predicted matching the actual categories.

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Mohammad Sahar, Raz, T. Srivinasa Rao, S. Anuradha, and B. Srinivasa Rao. "Performance Analysis of ML Algorithms to Detect Gender Based on Voice." In Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210192.

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Gender classification is amongst the significant problems in the area of signal processing; previously, the problem was handled using different image classification methods, which mainly involve data extraction from a collection of images. Nevertheless, researchers over the globe have recently shown interest in gender classification using voiced features. The classification of gender goes beyond just the frequency and pitch of a human voice, according to a critical study of some of the human vocal attributes. Feature selection, which is from a technical point of view termed dimensionality reduction, is amongst the difficult problems encountered in machine learning. A similar obstacle is encountered when choosing gender particular features—which presents an analytical purpose in analyzing a human’s gender. This work will examine the effectiveness and importance of classification algorithms to the classification of gender via voice problems. Audial data, for example, pitch, frequency, etc., help in determining gender. Machine learning offers encouraging outcomes for classification problems in all domains. An area’s algorithms can be evaluated using performance metrics. This paper evaluates five different classification Algorithms of machine learning based on the classification of gender from audial data. The plan is to recognize gender using five different algorithms: Gradient Boosting, Decision Trees, Random Forest, Neural network, and Support Vector Machine. The major parameter in assessing any algorithm must be performance. Misclassifying rate ratio should not be more in classifying problems. In business markets, the location and gender of people are essentially related to AdSense. This research aims at comparing various machine learning algorithms in order to find the most suitable fitting for gender identification in audial data.

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Costa, Igor Pinheiro de Araújo, Marcio Pereira Basílio, Sérgio Mitihiro do Nascimento Maêda, Marcus Vinícius Gonçalves Rodrigues, Miguel Ângelo Lellis Moreira, Carlos Francisco Simões Gomes, and Marcos dos Santos. "Algorithm Selection for Machine Learning Classification: An Application of the MELCHIOR Multicriteria Method." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210243.

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This paper aims to select an algorithm for the Machine Learning (ML) classification task. For the proposed analysis, the Multi-criteria Decision Aid (MCDA) Méthode d’ELimination et de CHoix Includent les relations d’ORdre (MELCHIOR) method was applied. The experiment considered the following criteria as relevant: Accuracy, sensitivity, and processing time of the algorithms. The data used refers to the intention of buying on the Internet and the purpose is to predict whether the customer will finalize a particular purchase. Among various MCDA techniques available, MELCHIOR was chosen to support the decision-making process because this method provides the evaluation of alternatives without the need to elicit the weights of the criteria. As a result, the Gradient Boosting Decision Tree algorithm has been selected as the most suitable for the ML classification task.

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Allam, Mohan, M. Nandhini, and M. Thangadarshini. "Optimization of Machine Learning Models for Early Diagnosis of Autism Spectrum Disorder." In Advances in Medical Diagnosis, Treatment, and Care, 138–66. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7460-7.ch009.

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Autism spectrum disorder is a syndrome related to interaction with people and repetitive behavior. ASD is diagnosed by health experts with the help of special practices that can be prolonged and costly. Researchers developed several ASD detection techniques by utilizing machine learning tools. ML provides the advanced algorithms that build automatic classification models. But disease prediction is a challenge for ML models due to the majority of the medical datasets including irrelevant features. Feature selection is a critical job in the predictive modeling for selecting a subset of significant features from the dataset. Recent feature selection techniques are using the optimization algorithms to improve the prediction rate of classification models. Most of the optimization algorithms make use of several controlling parameters that have to be tuned for improved productivity. In this chapter, a novel feature selection technique is proposed using binary teaching learning-based optimization algorithm that requires standard controlling parameters to acquire optimum features from ASD data.

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Тези доповідей конференцій з теми "ML classification algorithms"

Majumder, Uttam K. "Machine Learning (ML) Algorithms: An overview of various techniques for target detection and classification (Conference Presentation)." In Algorithms for Synthetic Aperture Radar Imagery XXIV, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2017. http://dx.doi.org/10.1117/12.2263216.

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Reddy, Laxmi Narsimha, Sergey Butakov, and Pavol Zavarsky. "Applying ML Algorithms to improve traffic classification in Intrusion Detection Systems." In 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2020. http://dx.doi.org/10.1109/iccicc50026.2020.9450218.

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de Souza, Bruno Feres, Andre C. P. L. F. de Carvalho, and Carlos Soares. "A comprehensive comparison of ML algorithms for gene expression data classification." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596651.

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Barreto, Cephas A. S., Victor V. Targino, Tales V. de M. Alves, Lucas V. Bazante, Rafael V. R. de Oliveira, Ricardo A. R. do A. Junior, João C. Xavier-Júnior, and Anne Magály de P. Canuto. "Applying Feature Selection Combination in Audios of Whale for Improving Classification." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/eniac.2022.227616.

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Audio signal processing has been under investigation for the last decades. The majority of the works found in literature focus on signal analysis and classification. Most of them integrate Machine Learning (ML) algorithms with the audio signal processing techniques. As the performance of any ML algorithm depends on the features of a dataset used for training and testing purposes, using a dataset derived from the extraction of features from an audio is not trivial due to the fact that the correct combination of extraction techniques with the selection of the most relevant attributes needs to take place. In this sense, this paper proposes an empirical analysis on different audio extraction techniques combined with feature selection for improving Whale audio classification. Usually, the application of audio extraction techniques results in poor classification performance. However, the combination of feature selection can achieve better results. The experimental results have been promising, indicating that the idea of combining different audio extraction techniques with feature selection can improve the performance of ML classification algorithms over whales’ audios by 22 percentage points.

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Priya, Kamath B., M. Geetha, and Acharya U. Dinesh. "Empirical Evaluation of various ML algorithms for classification of online Restaurant reviews." In 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2021. http://dx.doi.org/10.1109/icaect49130.2021.9392457.

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Amer, Mustafa Mohamed, Bader M. Otaibi, and Amr I. Othman. "Automatic Drilling Operations Coding and Classification Utilizing Text Recognition Machine Learning Algorithms." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211851-ms.

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Abstract Digital transformation is becoming a major goal for oil and gas (O&G) companies, and one major component of digital transformation initiatives is utilizing artificial intelligence (AI) and machine learning (ML) algorithms/techniques to automate critical business processes for the sake of consistency and objectivity. Drilling operations classification and coding is always challenging due to the subjectivity of end users; however, using machine learning and big data to automate operations classification based on natural language operations descriptions provided by drilling personnel can ensure objective and consistent classification. In this paper, a new approach is introduced for using ML algorithms to classify drilling operations based on the operation description provided by rig personnel in their morning reports, with time broken down using natural language. The new ML predictive model learns from historical data how to define the proper coding of drilling operations based on operation description to minimize human interaction with the coding system for drilling operations. The approach utilizes a set of prediction models to predict the proper code combinations that classify the activity carried out on the rig floor. Each model of these proposed models will feed its prediction into the next model to define the next level of code predictions. The model was trained using around 800,000 records to define the coding patterns based on operation remarks and then predicted a multi-level operational coding for any given operational remarks. The model has been tested and validated with around 800,000 records from datasets from multiple years, and showed significate results when compared to recent reports and subject matter expert evaluations showing a very good level of consistency and level of accuracy of more than 80%. The result of this work is an ML classification model that can reduce daily morning report data entry by 40% and improve the operational classification quality and consistency significantly. This approach also improves internal processes such as services invoicing verification, bit selection and end-of-well reporting.

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"CLASSIFICATION OF METHODS AND ALGORITHMS FOR DETECTION OF FALLS IN OLDER ADULTS." In 20th International Conference on e-Society (ES 2022) and 18th International Conference on Mobile Learning (ML 2022). IADIS Press, 2022. http://dx.doi.org/10.33965/es_ml2022_202202l010.

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Manikonda, Kaushik, Abu Rashid Hasan, Chinemerem Edmond Obi, Raka Islam, Ahmad Khalaf Sleiti, Motasem Wadi Abdelrazeq, and Mohammad Azizur Rahman. "Application of Machine Learning Classification Algorithms for Two-Phase Gas-Liquid Flow Regime Identification." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208214-ms.

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Abstract This research aims to identify the best machine learning (ML) classification techniques for classifying the flow regimes in vertical gas-liquid two-phase flow. Two-phase flow regime identification is crucial for many operations in the oil and gas industry. Processes such as flow assurance, well control, and production rely heavily on accurate identification of flow regimes for their respective systems' smooth functioning. The primary motivation for the proposed ML classification algorithm selection processes was drilling and well control applications in Deepwater wells. The process started with vertical two-phase flow data collection from literature and two different flow loops. One, a 140 ft. tall vertical flow loop with a centralized inner metal pipe and a larger outer acrylic pipe. Second, an 18-ft long flow loop, also with a centralized, inner metal drill pipe. After extensive experimental and historical data collection, supervised and unsupervised ML classification models such as Multi-class Support vector machine (MCSVM), K-Nearest Neighbor Classifier (KNN), K-means clustering, and hierarchical clustering were fit on the datasets to separate the different flow regions. The next step was fine-tuning the models' parameters and kernels. The last step was to compare the different combinations of models and refining techniques for the best prediction accuracy and the least variance. Among the different models and combinations with refining techniques, the 5- fold cross-validated KNN algorithm, with 37 neighbors, gave the optimal solution with a 98% classification accuracy on the test data. The KNN model distinguished five major, distinct flow regions for the dataset and a few minor regions. These five regions were bubbly flow, slug flow, churn flow, annular flow, and intermittent flow. The KNN-generated flow regime maps matched well with those presented by Hasan and Kabir (2018). The MCSVM model produced visually similar flow maps to KNN but significantly underperformed them in prediction accuracy. The MCSVM training errors ranged between 50% - 60% at normal parameter values and costs but went up to 99% at abnormally high values. However, their prediction accuracy was below 50% even at these highly overfitted conditions. In unsupervised models, both clustering techniques pointed to an optimal cluster number between 10 and 15, consistent with the 14 we have in the dataset. Within the context of gas kicks and well control, a well-trained, reliable two-phase flow region classification algorithm offers many advantages. When trained with well-specific data, it can act as a black box for flow regime identification and subsequent well-control measure decisions for the well. Further advancements with more robust statistical training techniques can render these algorithms as a basis for well-control measures in drilling automation software. On a broader scale, these classification techniques have many applications in flow assurance, production, and any other area with gas-liquid two-phase flow.

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Alquraini, Ali Haitham, Mohammad Saeed Al Kadem, and Ali Radhi Al Ssafwany. "Utilization of ML to Validate Pressure and Temperature Measurements." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211043-ms.

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Abstract The process of validating and monitoring pressure and temperature data is a key element in production engineering as it ensures proper well evaluation. Consequently, wells are frequently surveyed for better reservoir monitoring and accurate measurement of productivity. This study explores a validation method using advanced Artificial Intelligence (AI) and Machine Learning (ML) classification models that were developed utilizing historical data to automatically validate conducted pressure and temperature measurement and communicate observations and alerts to engineers. The proposed method validates pressure and temperature measurement using ML model based on previously conducted measurement using advanced algorithms. The developed model fed on pre-identified key production and pressure/temperature parameters that are used to classify surveys. Moreover, these parameters were selected based on historical data and measurement reports and then were analyzed and ranked to identify the most important parameters on the performance and accuracy of the model utilizing advance algorithm and correlation analysis. This is to predict and classify test measurement via the utilization of a non-linear relationship through the use of data-based analysis alongside physics-based analysis. The data set of conducted pressure and temperature measurement was split into two groups i.e. training and testing. In addition, a K-fold cross-validation was performed on the training set to validate the performance of all considered and selected ML models. The results of each ML model were then compared for accuracy and the Random Forest Classification algorithm was selected. The developed classification model achieved an overall accuracy level of more than 95%. Validating and testing the model on several cases showed promising results as irregularities are detected in advance before engineers evaluate these conducted measurements. The developed model enabled an effective utilization of previous measurements to validate newly conducted ones and, consequently, alert engineers of any detected anomalies in advance. This yielded significant impact on cost and time savings due the model's ability to automatically predict and validate the conducted measurements. The pressure and temperature validation model enhanced monitoring and interpreting the production/pressure and temperature measurements and resulted in a substantial improvement in timesaving. The model is developed to be run on the Cloud and it provides an automatic validation of the newly conducted measurements. In addition, it also delivers an alerting mechanism to engineers for any observed abnormalities.

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ABADÍA, JOSÉ JOAQUÍN PERALTA, HENRIEKE FRITZ, KOSMAS DRAGOS, and KAY SMARSLY. "SENSOR FAULT DIAGNOSIS COUPLING DEEP LEARNING AND WAVELET TRANSFORMS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36327.

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Sensor networks facilitate collecting measurement data necessary for decision making regarding structural maintenance and rehabilitation in structural health monitoring (SHM) systems. Nevertheless, the reliability of decision making in SHM systems depends on the proper operation of the sensors. Sensors may exhibit faults, entailing faulty data and incorrect judgment of structural conditions. Therefore, fault diagnosis (FD), comprising detection, isolation, identification, and accommodation of sensor faults, has been introduced in SHM systems, enabling timely detection of faulty data while advancing reliable operation of SHM systems. Traditional FD approaches based on “analytical redundancy” take advantage of correlated sensor data inherent to the SHM system, sometimes neglecting the fault identification step, and are implemented for specific sensor data. In this paper, an analytical redundancy FD approach for SHM systems, coupled with machine learning algorithms and wavelet transforms, capable of processing any type of sensor data is presented. A machine learning (ML) regression algorithm is proposed for fault detection, fault isolation, and fault accommodation, and an ML classification algorithm is proposed for fault identification. Continuous wavelet transform (CWT) is used as a preprocessing step of fault identification, exposing fault patterns in the data. The ML-CWT-FD approach is validated using data from a real-world SHM system in operation at a railway bridge implementing a deep neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. As a result of this paper, the ML-CWTFD approach is demonstrated to be capable of ensuring reliable SHM systems.

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