scholarly journals On the benefit of logic-based machine learning to learn pairwise comparisons

2020 ◽  
Vol 9 (6) ◽  
pp. 2637-2649
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov ◽  
Ahmad Nurul Fajar
Keyword(s):  
Machine Learning ◽  
Description Logic ◽  
User Study ◽  
Inductive Learning ◽  
The Other ◽  
User Preference ◽  
Machine Learning Techniques ◽  
Web Searching ◽  
Learning To Learn ◽  
Software Models

In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.

scholarly journals Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Osama Siddig ◽  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
The Other ◽  
Well Logs ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Average Error ◽  
Support Vector ◽  
Empirical Correlations

Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods’ parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.

scholarly journals Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

2021 ◽  
Author(s):  
Mehmet Akif Cifci
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
The Other ◽  
Machine Learning Techniques ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Fundus Photographs ◽  
Processing Techniques ◽  
Time And Energy

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

scholarly journals Classifying Vertical Facial Deformity using Supervised and Unsupervised Learning

2001 ◽  
Vol 40 (05) ◽  
pp. 365-372 ◽  
Author(s):  
T. J. Hutton ◽  
Z. L. Nelson-Moon ◽  
N. P. Hunt ◽  
A. J. A. Madgwick ◽  
P. Hammond
Keyword(s):  
Machine Learning ◽  
Clinical Application ◽  
The Other ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Additional Patient ◽  
Facial Deformity ◽  
Feature Maps ◽  
Point Distribution ◽  
Objective Classification

Summary Objectives: To evaluate the potential for machine learning techniques to identify objective criteria for classifying vertical facial deformity. Methods: 19 parameters were determined from 131 lateral skull radiographs. Classifications were induced from raw data with simple visualisation, C5.0 and Kohonen feature maps; and using a Point Distribution Model (PDM) of shape templates comprising points taken from digitised radiographs. Results: The induced decision trees enable a direct comparison of clinicians’ idiosyncrasies in classification. Unsupervised algorithms induce models that are potentially more objective, but their blackbox nature makes them unsuitable for clinical application. The PDM methodology gives dramatic visualisations of two modes separating horizontal and vertical facial growth. Kohonen feature maps favour one clinician and PDM the other. Clinical response suggests that while Clinician 1 places greater weight on 5 of 6 parameters, Clinician 2 relies on more parameters that capture facial shape. Conclusions: While machine learning and statistical analyses classify subjects for vertical facial height, they have limited application in their present form. The supervised learning algorithm C5.0 is effective for generating rules for individual clinicians but its inherent bias invalidates its use for objective classification of facial form for research purposes. On the other hand, promising results from unsupervised strategies (especially the PDM) suggest a potential use for objective classification and further identification and analysis of ambiguous cases. At present, such methodologies may be unsuitable for clinical application because of the invisibility of their underlying processes. Further study is required with additional patient data and a wider group of clinicians.

scholarly journals Competitor Identification by Use The Sentiment Classification Based on the User Research

2019 ◽  
Vol 8 (9S2) ◽  
pp. 871-874
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
The Other ◽  
Machine Learning Techniques ◽  
Single Product ◽  
User Research ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Wide Range

Online shopping's have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.

scholarly journals COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

2021 ◽  
Vol 3 ◽  
Author(s):  
Ahmed Al-Hindawi ◽  
Ahmed Abdulaal ◽  
Timothy M. Rawson ◽  
Saleh A. Alqahtani ◽  
Nabeela Mughal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Data Science ◽  
The Other ◽  
Machine Learning Techniques ◽  
Prognostic Models ◽  
Learning Techniques ◽  
Classical Statistics ◽  
Open Datasets ◽  
The Impact

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

scholarly journals Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques

2021 ◽  
Vol 11 (4) ◽  
pp. 286-290
Author(s):  
Md. Golam Kibria ◽  
◽  
Mehmet Sevkli
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Credit Card ◽  
Learning Algorithms ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model

The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.

scholarly journals Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Study

2020 ◽  
Vol 9 (3) ◽  
pp. 635-641
Keyword(s):  
Machine Learning ◽  
Proper Time ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Techniques ◽  
Entire Body ◽  
Time Data ◽  
Learning Techniques ◽  
Health Related

Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking out and refining of useful information from a massive amount of data. It is a basic and primary process in defining and discovering useful information and hidden patterns from databases. The flexibility and adaptability of optimization algorithms find its use in dealing with complex non -linear problems. Machine Learning techniques find its use in medical sciences in solving real health-related issues by early prediction and treatment of various diseases. In this paper, six machine learning algorithms are used and then compared accordingly based on the evaluation of performance. Among all classifiers, decision tree outperforms over the other algorithms with a testing accuracy of 97.29%.

scholarly journals GAVIN

2021 ◽  
Vol 28 (4) ◽  
pp. 1-32
Author(s):  
Anam Ahmad Khan ◽  
Joshua Newn ◽  
Ryan M. Kelly ◽  
Namrata Srivastava ◽  
James Bailey ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Study ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Note Taking ◽  
Digital Text ◽  
Digital Documents ◽  
Learning Techniques ◽  
Text Passage ◽  
Minimal Effort

Annotation is an effective reading strategy people often undertake while interacting with digital text. It involves highlighting pieces of text and making notes about them. Annotating while reading in a desktop environment is considered trivial but, in a mobile setting where people read while hand-holding devices, the task of highlighting and typing notes on a mobile display is challenging. In this article, we introduce GAVIN, a gaze-assisted voice note-taking application, which enables readers to seamlessly take voice notes on digital documents by implicitly anchoring them to text passages. We first conducted a contextual enquiry focusing on participants’ note-taking practices on digital documents. Using these findings, we propose a method which leverages eye-tracking and machine learning techniques to annotate voice notes with reference text passages. To evaluate our approach, we recruited 32 participants performing voice note-taking. Following, we trained a classifier on the data collected to predict text passage where participants made voice notes. Lastly, we employed the classifier to built GAVIN and conducted a user study to demonstrate the feasibility of the system. This research demonstrates the feasibility of using gaze as a resource for implicit anchoring of voice notes, enabling the design of systems that allow users to record voice notes with minimal effort and high accuracy.

scholarly journals Android Security Analysis using Mobile Sandbox

2021 ◽  
Vol 1 (3) ◽  
pp. 41-46
Author(s):  
Munishka Vijayvergiya ◽  
Abhignya Tayi ◽  
Sanya jain ◽  
Sanjana Reddy
Keyword(s):  
Machine Learning ◽  
Security Analysis ◽  
The Other ◽  
Machine Learning Techniques ◽  
Android Security ◽  
After Effects ◽  
Learning Techniques ◽  
Dynamic Examination ◽  
Android Applications ◽  
Dynamic Investigation

Today, smartphones and Android devices are effectively in development like never before and have become the easiest cybercrime forum. It is necessary for security experts to investigate the vengeful programming composed for these frameworks if we closely observe the danger to security and defence. The main objective of this paper was to describe Mobile Sandbox, which is said to be a platform intended to periodically examine Android applications in new ways. First of all in the essence of the after-effects of static analysis that is used to handle the dynamic investigation, it incorporates static and dynamic examination and attempts to justify the introduction of executed code. On the other hand, to log calls to native APIs, it uses those techniques, and in the end, it combines the end results with machine learning techniques to collect the samples analysed into dangerous ones. We reviewed the platform for more than 69, 000 applications from multi-talented Asian international businesses sectors and found that about 21% of them officially use the local calls in their code

scholarly journals A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4512
Author(s):  
Junqi Guo ◽  
Lan Yang ◽  
Anton Umek ◽  
Rongfang Bie ◽  
Sašo Tomažič ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Hand Movement ◽  
Training System ◽  
The Other ◽  
Machine Learning Techniques ◽  
Movement Error ◽  
Learning Techniques ◽  
Biofeedback System

In the military, police, security companies, and shooting sports, precision shooting training is of the outmost importance. In order to achieve high shooting accuracy, a lot of training is needed. As a result, trainees use a large number of cartridges and a considerable amount of time of professional trainers, which can cost a lot. Our motivation is to reduce costs and shorten training time by introducing an augmented biofeedback system based on machine learning techniques. We are designing a system that can detect and provide feedback on three types of errors that regularly occur during a precision shooting practice: excessive hand movement error, aiming error and triggering error. The system is designed to provide concurrent feedback on the hand movement error and terminal feedback on the other two errors. Machine learning techniques are used innovatively to identify hand movement errors; the other two errors are identified by the threshold approach. To correct the excessive hand movement error, a precision shot accuracy prediction model based on Random Forest has proven to be the most suitable. The experimental results show that: (1) the proposed Random Forest (RF) model achieves the prediction accuracy of 91.27%, higher than any of the other reference models, and (2) hand movement is strongly related to the accuracy of precision shooting. Appropriate use of the proposed augmented biofeedback system will result in a lower number of rounds used and shorten the precision shooting training process.

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