scholarly journals Medibot: A Predictive Generic Diabetic Chatbot using Bagging Ensemble/Hybrid Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1518-1523
Keyword(s):  
Side Effects ◽  
Human Life ◽  
Clinical Information ◽  
Gradient Boosting ◽  
Support Vector ◽  
Prevention Measures ◽  
Vector Machines ◽  
Normal Man ◽  
High Level ◽  
Bagging Ensemble

Clinical chatbots are conversational operators worked in light of clinical applications. They can possibly lessen medicinal services costs and improve availability of clinical information to basic man. There are different methods accessible for planning chatbots for anticipating an infection. In any case, a client can accomplish the genuine advantage of a chatbot just when he can connect with it in a simple manner and it ready to foresee the infection with high level of exactness while simultaneously give all important data being looked for by the patient. Chatbots can either be conventional or sickness explicit in nature. Diabetes is a non-infectious ceaseless human issue. Early forecast of this issue can uncover the deplorable intricacies and help to spare human life. Right now, have first built up a conventional book to-content 'Medibot' – a chatBOT which connects with patients in discussion utilizing propelled Natural Language Understanding (NLU) methods to give customized forecast dependent on the different side effects shared by the patient. The plan is additionally stretched out as a chatBOT to diagonise particular Diabetes type expectation and for proposing prevention measures to be taken. For expectation, there exists various grouping calculations in ML Ways which can be utilized dependent on their exactness. Nonetheless, as opposed to thinking about just one model and trusting this model is the best/most exact indicator we can make, the curiosity right now in Hybrid Algo realizing which is a meta-calculation that joins a bunch of models and midpoints them to create one last model to diminish change (stowing), predisposition (boosting), or improve expectations (stacking). From writing surveys, it is seen that almost no exertion has been placed into utilizing troupe techniques to expand expectation precision. The paper introduces a cutting edge Medibot plan with an undemanding front-end interface for normal man utilizing UI, NLU based content pre-preparing, quantitative execution examination of different AI calculations like Gaussian Naïve Bayes , Entropy Decision tree, Random Forest, K- NN, Support Vector Machines, Logistic and X-Gradient boosting as independent classifiers and joining them all in a dominant part casting a ballot troupe for adjusted outcomes. It is seen that the chatbot can interface consistently with any patient and dependent on the side effects shared, anticipate and rank the most likely ailment precisely utilizing the nonexclusive model and explicitly diabetes dependent on a strong outfit learning model.

The impact of different parameter sets on the classification of asteroid types

2021 ◽  
Author(s):  
Hanna Klimczak ◽  
Wojciech Kotłowski ◽  
Dagmara Oszkiewicz ◽  
Francesca DeMeo ◽  
Agnieszka Kryszczyńska ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Science Centre ◽  
The Difference ◽  
The Impact

<p>The aim of the project is the classification of asteroids according to the most commonly used asteroid taxonomy (Bus-Demeo et al. 2009) with the use of various machine learning methods like Logistic Regression, Naive Bayes, Support Vector Machines, Gradient Boosting and Multilayer Perceptrons. Different parameter sets are used for classification in order to compare the quality of prediction with limited amount of data, namely the difference in performance between using the 0.45mu to 2.45mu spectral range and multiple spectral features, as well as performing the Prinicpal Component Analysis to reduce the dimensions of the spectral data.</p> <p> </p> <p>This work has been supported by grant No. 2017/25/B/ST9/00740 from the National Science Centre, Poland.</p>

scholarly journals Exploiting Visual Features in Financial Time Series Prediction

2020 ◽  
Vol 14 (2) ◽  
pp. 61-76
Author(s):  
Adil Gürsel Karaçor ◽  
Turan Erman Erkan
Keyword(s):  
Financial Time Series ◽  
Time Series Prediction ◽  
Visual Features ◽  
Gradient Boosting ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Extreme Gradient Boosting ◽  
Vector Machines ◽  
Direction Of Movement ◽  
Visual Properties

The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.

scholarly journals Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 522
Author(s):  
Congcong Sun ◽  
Hui Tian ◽  
Chin-Chen Chang ◽  
Yewang Chen ◽  
Yiqiao Cai ◽  
...  
Keyword(s):  
Statistical Characteristics ◽  
Gradient Boosting ◽  
Support Vector ◽  
Final State ◽  
Markov Transition Matrix ◽  
Markov Transition ◽  
Extreme Gradient Boosting ◽  
Vector Machines ◽  
Feature Based ◽  
Series Of Experiments

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams.

scholarly journals A Study on Machine Vision Techniques for the Inspection of Health Personnels’ Protective Suits for the Treatment of Patients in Extreme Isolation

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 743 ◽  
Author(s):  
Alice Stazio ◽  
Juan G. Victores ◽  
David Estevez ◽  
Carlos Balaguer
Keyword(s):  
Logistic Regression ◽  
Machine Vision ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Classification Algorithms ◽  
Adaptive Boosting ◽  
Blood Stains ◽  
Extreme Gradient Boosting ◽  
Vector Machines

The examination of Personal Protective Equipment (PPE) to assure the complete integrity of health personnel in contact with infected patients is one of the most necessary tasks when treating patients affected by infectious diseases, such as Ebola. This work focuses on the study of machine vision techniques for the detection of possible defects on the PPE that could arise after contact with the aforementioned pathological patients. A preliminary study on the use of image classification algorithms to identify blood stains on PPE subsequent to the treatment of the infected patient is presented. To produce training data for these algorithms, a synthetic dataset was generated from a simulated model of a PPE suit with blood stains. Furthermore, the study proceeded with the utilization of images of the PPE with a physical emulation of blood stains, taken by a real prototype. The dataset reveals a great imbalance between positive and negative samples; therefore, all the selected classification algorithms are able to manage this kind of data. Classifiers range from Logistic Regression and Support Vector Machines, to bagging and boosting techniques such as Random Forest, Adaptive Boosting, Gradient Boosting and eXtreme Gradient Boosting. All these algorithms were evaluated on accuracy, precision, recall and F 1 score; and additionally, execution times were considered. The obtained results report promising outcomes of all the classifiers, and, in particular Logistic Regression resulted to be the most suitable classification algorithm in terms of F 1 score and execution time, considering both datasets.

scholarly journals Community-Associated Methicillin-Resistant Staphylococcus aureus Infections in the Athlete

2009 ◽  
Vol 1 (5) ◽  
pp. 405-410 ◽  
Author(s):  
Kathleen Weber
Keyword(s):  
Wound Care ◽  
Clinical Information ◽  
Personal Hygiene ◽  
Prevention Measures ◽  
Effective Prevention ◽  
Methicillin Resistant ◽  
Treatment And Prevention ◽  
Appropriate Treatment ◽  
Prevention Of Infections ◽  
High Level

Context: Community-associated methicillin-resistant Staphlococcus aureus (CA-MRSA) has become of increasing concern in the athletic setting. Appropriate recognition, treatment, and prevention measures are all paramount to protect individual athletes and teamwide outbreaks. Evidence Acquisition: Relevant electronic databases (Medline or PubMed) through 2008 were searched. Articles and studies relevant to this topic were reviewed for pertinent clinical information. Study Type: Clinical review. Results: CA-MRSA is an increasing problem both in the community at large and in the athletic population. Conclusion: Early infections based on methicillin-resistant Staphlococcus aureus are often misidentified, leading to delay in appropriate treatment. A high level of suspicion, prompt recognition, and appropriate treatment can minimize morbidity associated with CA-MRSA. Careful selection of antibiotics in suspected cases is important, with more severe infections requiring hospitalization and intravenous antibiotics. Eradication of bacteria in colonized patients has not yet proven to be effective. Prevention of infections is multifaceted, and it includes education, proper personal hygiene, routine cleaning of equipment, and proper wound care.

Support vector machines and gradient boosting for graphical estimation of a slate deposit

2004 ◽  
Vol 18 (5) ◽  
pp. 309-323 ◽  
Author(s):  
J.M. Mat�as ◽  
A. Vaamonde ◽  
J. Taboada ◽  
W. Gonz�lez-Manteiga
Keyword(s):  
Support Vector Machines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Vector Machines

scholarly journals Analyzing Fake News Based on Machine Learning Algorithms

2020 ◽  
Author(s):  
Pawar A B ◽  
Jawale M A ◽  
Kyatanavar D N
Keyword(s):  
Language Processing ◽  
Gradient Descent ◽  
Human Life ◽  
Stochastic Gradient ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fake News ◽  
Processing Techniques

Usages of Natural Language Processing techniques in the field of detection of fake news is analyzed in this research paper. Fake news are misleading concepts spread by invalid resources can provide damages to human-life, society. To carry out this analysis work, dataset obtained from web resource OpenSources.co is used which is mainly part of Signal Media. The document frequency terms as TF-IDF of bi-grams used in correlation with PCFG (Probabilistic Context Free Grammar) on a set of 11,000 documents extracted as news articles. This set tested on classification algorithms namely SVM (Support Vector Machines), Stochastic Gradient Descent, Bounded Decision Trees, Gradient Boosting algorithm with Random Forests. In experimental analysis, found that combination of Stochastic Gradient Descent with TF-IDF of bi-grams gives an accuracy of 77.2% in detecting fake contents, which observes with PCFGs having slight recalling defects

scholarly journals Applying Machine Learning for Healthcare: A Case Study on Cervical Pain Assessment with Motion Capture

2020 ◽  
Vol 10 (17) ◽  
pp. 5942 ◽  
Author(s):  
Juan de la Torre ◽  
Javier Marin ◽  
Sergio Ilarri ◽  
Jose J. Marin
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cervical Pain ◽  
K Nearest Neighbors ◽  
Network Algorithms ◽  
Vector Machines ◽  
Real Scenario

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources.

scholarly journals Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3598
Author(s):  
Jose R. Huerta-Rosales ◽  
David Granados-Lieberman ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez ◽  
Juan P. Amezquita-Sanchez ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Electrical Network ◽  
Support Vector ◽  
Fisher Score ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Field Programmable ◽  
Statistical Time ◽  
High Level

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.

scholarly journals Human Papillomavirus Targeted Immunotherapy Outcome Prediction Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3598-3611
Author(s):  
Vidya Moni
Keyword(s):  
Machine Learning ◽  
Human Papillomavirus ◽  
Outcome Prediction ◽  
Performance Comparison ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Nearest Neighbours ◽  
Vector Machines ◽  
Modern Machine

Warts caused by the Human Papillomavirus (HPV) is a highly contagious disease, and affects several million people across the globe every year, in the form of small lesions on the skin, commonly known as warts. Warts can be treated effectively with several methods, the most effective being Immunotherapy and Cryotherapy. Our research is focused on the performance comparison of modern Machine Learning classification techniques to predict the outcome (positive or negative) of Immunotherapy treatment given to a patient, by using patient data as input features to our classifiers. The precision, recall, f-measure and accuracy were used to compare the performance of the various classifiers considered in this study. We considered Logistic Regression, ZeroR, AdaBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Gradient Boosting, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Decision Trees and Random Forests. The ZeroR classifier was used as a baseline to provide us with insights into the skewed nature of the data, so as to enable us to better understand the comparison in performance of the various classifiers.

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