scholarly journals Performance Analysis of ML Algorithms to Detect Gender Based on Voice

2021 ◽  
Author(s):  
Raz Mohammad Sahar ◽  
T. Srivinasa Rao ◽  
S. Anuradha ◽  
B. Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Classification Algorithms ◽  
Gender Classification ◽  
Gender Based ◽  
And Gender

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.

scholarly journals Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Susan Idicula-Thomas ◽  
Ulka Gawde ◽  
Prabhat Jha
Keyword(s):  
Machine Learning ◽  
Verbal Autopsy ◽  
Causes Of Death ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Diarrhoeal Diseases

Abstract Background Machine learning (ML) algorithms have been successfully employed for prediction of outcomes in clinical research. In this study, we have explored the application of ML-based algorithms to predict cause of death (CoD) from verbal autopsy records available through the Million Death Study (MDS). Methods From MDS, 18826 unique childhood deaths at ages 1–59 months during the time period 2004–13 were selected for generating the prediction models of which over 70% of deaths were caused by six infectious diseases (pneumonia, diarrhoeal diseases, malaria, fever of unknown origin, meningitis/encephalitis, and measles). Six popular ML-based algorithms such as support vector machine, gradient boosting modeling, C5.0, artificial neural network, k-nearest neighbor, classification and regression tree were used for building the CoD prediction models. Results SVM algorithm was the best performer with a prediction accuracy of over 0.8. The highest accuracy was found for diarrhoeal diseases (accuracy = 0.97) and the lowest was for meningitis/encephalitis (accuracy = 0.80). The top signs/symptoms for classification of these CoDs were also extracted for each of the diseases. A combination of signs/symptoms presented by the deceased individual can effectively lead to the CoD diagnosis. Conclusions Overall, this study affirms that verbal autopsy tools are efficient in CoD diagnosis and that automated classification parameters captured through ML could be added to verbal autopsies to improve classification of causes of death.

scholarly journals Comparative Analysis of Machine Learning Algorithms for Multi-Syndrome Classification of Neurodegenerative Syndromes

2021 ◽  
Author(s):  
Leonie Lampe ◽  
Sebastian Niehaus ◽  
Hans-Jürgen Huppertz ◽  
Alberto Merola ◽  
Janis Reinelt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Performance Measures ◽  
Careful Consideration ◽  
Machine Learning Algorithms ◽  
Classification Task ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods

Abstract Importance The entry of artificial intelligence into medicine is pending. Several methods have been used for predictions of structured neuroimaging data, yet nobody compared them in this context.Objective Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.Design, Setting, and Participants Atlas-based volumetry was performed on multi-centric T1weighted MRI data from 940 subjects, i.e. 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.Interventions n.a.Main Outcomes and Measures Cohen’s Kappa, Accuracy, and F1-score to assess model performance.Results Over all, the neural network produced both the best performance measures as well as the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with wide-spread and rather weak atrophy.Conclusions and Relevance Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.Trial Registration n.a.

scholarly journals Fine-grained classification of social science journal articles using textual data: A comparison of supervised machine learning approaches

2020 ◽  
pp. 1-26
Author(s):  
Joshua Eykens ◽  
Raf Guns ◽  
Tim C.E. Engels
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Social Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Fine Grained ◽  
Textual Data

We compare two supervised machine learning algorithms—Multinomial Naïve Bayes and Gradient Boosting—to classify social science articles using textual data. The high level of granularity of the classification scheme used and the possibility that multiple categories are assigned to a document make this task challenging. To collect the training data, we query three discipline specific thesauri to retrieve articles corresponding to specialties in the classification. The resulting dataset consists of 113,909 records and covers 245 specialties, aggregated into 31 subdisciplines from three disciplines. Experts were consulted to validate the thesauri-based classification. The resulting multi-label dataset is used to train the machine learning algorithms in different configurations. We deploy a multi-label classifier chaining model, allowing for an arbitrary number of categories to be assigned to each document. The best results are obtained with Gradient Boosting. The approach does not rely on citation data. It can be applied in settings where such information is not available. We conclude that fine-grained text-based classification of social sciences publications at a subdisciplinary level is a hard task, for humans and machines alike. A combination of human expertise and machine learning is suggested as a way forward to improve the classification of social sciences documents.

scholarly journals Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

2021 ◽  
Vol 12 (6) ◽  
pp. 1-28
Author(s):  
Munder Abdulatef Al-Hashem ◽  
Ali Mohammad Alqudah ◽  
Qasem Qananwah
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

scholarly journals Classification of Liver Patient Dataset Using Machine Learning Algorithms

2018 ◽  
Vol 7 (3.34) ◽  
pp. 323
Author(s):  
S Muthuselvan ◽  
S Rajapraksh ◽  
K Somasundaram ◽  
K Karthik
Keyword(s):  
Machine Learning ◽  
Medical Information ◽  
Learning Algorithms ◽  
Medical Industry ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Successful Implementation ◽  
Computer Aided ◽  
Aided Diagnosis

Prediction of the disease in the human being is the very long and difficult process in early days. Now a days, computer aided diagnosis is the important role in the medical industry for predicting, analyzing and storing medical information with the images. In this paper will discuss and classify the liver patients with the help of the liver patient dataset with the help of the machine learning algorithms. WEKA is the software used here for implement the some of the classification algorithms with the data selected from the liver disease dataset. After the successful implementation of the all the algorithms, the best algorithms selected from the output of the all the algorithms execution. 

scholarly journals Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

2020 ◽  
Vol 48 (4) ◽  
pp. 2316-2327
Author(s):  
Caner KOC ◽  
Dilara GERDAN ◽  
Maksut B. EMİNOĞLU ◽  
Uğur YEGÜL ◽  
Bulent KOC ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ensemble Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Performance Criteria ◽  
Gradient Boosting ◽  
Data Set

Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

scholarly journals Machine learning classification of Kuiper belt populations

2020 ◽  
Vol 497 (2) ◽  
pp. 1391-1403
Author(s):  
Rachel A Smullen ◽  
Kathryn Volk
Keyword(s):  
Machine Learning ◽  
Kuiper Belt ◽  
Giant Planet ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Gradient Boosting ◽  
Class Membership ◽  
Machine Learning Classification ◽  
Tree Classifier

ABSTRACT In the outer Solar system, the Kuiper belt contains dynamical subpopulations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration. The subdivision of observed Kuiper belt objects (KBOs) into different dynamical classes is based on their current orbital evolution in numerical integrations of their orbits. Here, we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification. Using a Gradient Boosting Classifier, a type of machine learning regression tree classifier trained on features derived from short numerical simulations, we sort observed KBOs into four broad, dynamically distinct populations – classical, resonant, detached, and scattering – with a >97 per cent accuracy for the testing set of 542 securely classified KBOs. Over 80 per cent of these objects have a >3σ probability of class membership, indicating that the machine learning method is classifying based on the fundamental dynamical features of each population. We also demonstrate how, by using computational savings over traditional methods, we can quickly derive a distribution of class membership by examining an ensemble of object clones drawn from the observational errors. We find two major reasons for misclassification: inherent ambiguity in the orbit of the object – for instance, an object that is on the edge of resonance – and a lack of representative examples in the training set. This work provides a promising avenue to explore for fast and accurate classification of the thousands of new KBOs expected to be found by surveys in the coming decade.

scholarly journals Because the Machine Can Discriminate: Machine Learning, Neuroimaging, and Epistemologies of Sex

2021 ◽  
Author(s):  
Jeffrey W Lockhart
Keyword(s):  
Machine Learning ◽  
Full Range ◽  
Machine Learning Algorithms ◽  
Social Structures ◽  
Classification Algorithms ◽  
Sex And Gender ◽  
Theoretical Understanding ◽  
Brain Scans ◽  
And Gender ◽  
Society Research

Algorithms and society research has focused largely on algorithms for production, resulting in constrained theoretical understanding of the full range of ways algorithms may reinforce social structures of inequality. Algorithms for discovery illustrate an alternative to the prevailing emphasis on inattention and thoughtlessness in algorithms and society literature and the scientific bias literature more generally. Using the case of machine learning algorithms trained to predict sex from brain scans, I argue that increasing intentionality and theory-method alignment in algorithmic systems are not necessarily cures. Instead, scientists design and interpret classification algorithms to reinforce conservative notions of sex and gender.

scholarly journals Monitoring and Smart Decision Architecture for DRONE-FOG Integrated Environment

2021 ◽  
Author(s):  
Wendel Serra ◽  
Warley Junior ◽  
Isaac Barros ◽  
Hugo Kuribayashi ◽  
João Carmona
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Performance Metrics ◽  
Weather Conditions ◽  
Computational Effort ◽  
Machine Learning Algorithms ◽  
Computation Offloading ◽  
Classification Algorithms ◽  
Multi Layer Perceptron ◽  
Integrated Environment

Due to the limited computing resources of drones, it is difficult to handle computation-intensive tasks locally, hence, fog-based computation offloading has been widely adopted. The effectiveness of an offloading operation, however, is determined by its ability to infer where the execution of code/data represents less computational effort for the drone, so that, by deciding where to offload correctly, the device benefits. Thus, this paper proposes MonDroneFog, a novel fog-based architecture that supports image offloading, as well as monitoring and storing the performance metrics related to the drone, wireless network, and cloudlet. It takes advantage of the main machine-learning algorithms to provide offloading decisions with high levels of accuracy, F1, and G-mean. We evaluate the main classification algorithms under our database and the results show that Multi-Layer Perceptron (MLP) and Logistic Regression classifiers achieve 99.64% and 99.20% accuracy, respectively. Under these conditions, MonDrone-Fog works well in dense forests when weather conditions are favorable and can be useful as a support system for SAR missions by providing a shorter runtime for image operations.

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