scholarly journals Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4291
Author(s):  
Homa Arab ◽  
Iman Ghaffari ◽  
Lydia Chioukh ◽  
Serioja Tatu ◽  
Steven Dufour
Keyword(s):  
Machine Learning ◽  
Cross Sections ◽  
Binary Classification ◽  
Low Noise ◽  
Supervised Machine Learning ◽  
Radar Sensor ◽  
Radar Cross Sections ◽  
Machine Learning Model ◽  
Wave Radar ◽  
The One

A target’s movements and radar cross sections are the key parameters to consider when designing a radar sensor for a given application. This paper shows the feasibility and effectiveness of using 24 GHz radar built-in low-noise microwave amplifiers for detecting an object. For this purpose a supervised machine learning model (SVM) is trained using the recorded data to classify the targets based on their cross sections into four categories. The trained classifiers were used to classify the objects with varying distances from the receiver. The SVM classification is also compared with three methods based on binary classification: a one-against-all classification, a one-against-one classification, and a directed acyclic graph SVM. The level of accuracy is approximately 96.6%, and an F1-score of 96.5% is achieved using the one-against-one SVM method with an RFB kernel. The proposed contactless radar in combination with an SVM algorithm can be used to detect and categorize a target in real time without a signal processing toolbox.

scholarly journals SENTIMENT ANALYSIS OF CUSTOMER REVIEWS

2021 ◽  
Vol 4 (1) ◽  
pp. 113-125
Author(s):  
Syed Rashiq Nazar ◽  
◽  
Tapalina Bhattasali
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Binary Classification ◽  
Classification Problem ◽  
Supervised Machine Learning ◽  
Frequency Model ◽  
Customer Reviews ◽  
Machine Learning Model ◽  
Logistic Regression Algorithm

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

Relationship Between Wellness Index and Internal Training Load in Soccer: Application of a Machine Learning Model

2020 ◽  
pp. 1-9
Author(s):  
Enrico Perri ◽  
Carlo Simonelli ◽  
Alessio Rossi ◽  
Athos Trecroci ◽  
Giampietro Alberti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perceived Exertion ◽  
Training Load ◽  
Supervised Machine Learning ◽  
Training Time ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
The One ◽  
The Relationship

Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi) and WI measured the day after (WIMDi+1) (r = .72, P < .001). Additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and WI. Nevertheless, the higher accuracy of ordinal regression (39% [2%]) compared with the results obtained by baseline B1 (21% [1%]) demonstrated that the machine learning approach used in this study can predict the WI according to the TL performed the day before (MD<i). Conclusion: The machine learning technique can be used to predict the WI based on a targeted weekly TL. Such an approach may contribute to enhancing the training-induced adaptations, maximizing the players’ readiness and reducing the potential drops in performance associated with poor wellness scores.

Predicting Convective Storm Characteristics using Machine Learning from Hi-Resolution NWP Forecasts

2021 ◽  
Author(s):  
Aniel Jardines
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Binary Classification ◽  
Air Traffic ◽  
Mitigation Strategies ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Traffic Flow Management ◽  
Storm Characteristics ◽  
Convective Weather

&lt;p&gt;Convective weather&amp;#160;represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms&amp;#160;are the cause for a substantial amount of delay in&amp;#160;both the en-route and airport&amp;#160;environment. Before the day of operations, poor prediction capability of convective weather prohibits traffic managers from considering weather mitigation strategies during the pre-tactical phase of ATFM planning. As a result, convective weather is mitigated tactically, possibly leading to excessive delays. &amp;#160;&lt;/p&gt;&lt;p&gt;The skill of weather forecasting has greatly improved in recent years. Hi-resolution weather models can predict the future state of the atmosphere for some weather parameters. However, incorporating the output from these sophisticated weather products into an ATFM solution that provides easily interpreted information by the air traffic managers remains a challenge.&amp;#160;&lt;/p&gt;&lt;p&gt;This paper combines data from high-resolution&amp;#160;numerical&amp;#160;weather&amp;#160;predictions&amp;#160;with actual storm observations from lightning detecting and satellite images. It applies supervised machine learning techniques such as binary classification, multiclass classification, and regression to train neural networks to predict the occurrence, severity, and altitude of thunderstorms. The model predictions are given up to 36hr in advance, within timeframes necessary for pre-tactical planning of ATFM, providing traffic managers with valuable information for developing weather mitigation plans.&amp;#160;&lt;/p&gt;

Prediction of Compound-Protein Interactions with Machine Learning Methods

2011 ◽  
pp. 304-317
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

scholarly journals Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches

2020 ◽  
Vol 28 (4) ◽  
pp. 532-551
Author(s):  
Blake Miller ◽  
Fridolin Linder ◽  
Walter R. Mebane
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Random Sampling ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Simulation Studies ◽  
Text Data ◽  
Passive Learning ◽  
Machine Learning Model ◽  
The Cost

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling.

scholarly journals Automatic recognition of self-acknowledged limitations in clinical research literature

2018 ◽  
Vol 25 (7) ◽  
pp. 855-861 ◽  
Author(s):  
Halil Kilicoglu ◽  
Graciela Rosemblat ◽  
Mario Malički ◽  
Gerben ter Riet
Keyword(s):  
Machine Learning ◽  
Clinical Research ◽  
Binary Classification ◽  
Classification Performance ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Rule Based ◽  
Research Transparency

Abstract Objective To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Methods To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results Annotators had good agreement in labeling limitation sentences (Krippendorff’s α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]). Conclusions The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

scholarly journals Machine learning model for rice yield prediction using KNN regression.

2021 ◽  
Author(s):  
Akhil Wilson ◽  
Raji Sukumar ◽  
N. Hemalatha
Keyword(s):  
Machine Learning ◽  
Rice Yield ◽  
Support Vector ◽  
Yield Prediction ◽  
Challenging Problem ◽  
Random Forest Regression ◽  
Machine Learning Model ◽  
Smart Farming ◽  
Regression Algorithms ◽  
The One

Abstract The prediction of agriculture yield is the one of the challenging problem in smart farming, we have predicted the yield of rice in the state of Kerala, India with the help of Machine Learning by considering the soil properties, micro climatic condition and area of the rice. Here we have used Decision Tree Regression, Random Forest Regression, Linear Regression, K Nearest Neighbour Regression, Xgboost Regression and Support Vector Regression algorithms in order to predict the rice yield. From the experiments we got KNN regression to be the best with 98.77% accuracy.

scholarly journals The impact of data-complexity and team characteristics on performance in the classification model

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Class Imbalance ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Classification Model ◽  
Data Complexity ◽  
High Performing ◽  
Machine Learning Model ◽  
The Impact

This article investigates the impact of data-complexity and team-specific characteristics on machine learning competition scores. Data from five real-world binary classification competitions hosted on Kaggle.com were analyzed. The data-complexity characteristics were measured in four aspects including standard measures, sparsity measures, class imbalance measures, and feature-based measures. The results showed that the higher the level of the data-complexity characteristics was, the lower the predictive ability of the machine learning model was as well. Our empirical evidence revealed that the imbalance ratio of the target variable was the most important factor and exhibited a nonlinear relationship with the model’s predictive abilities. The imbalance ratio adversely affected the predictive performance when it reached a certain level. However, mixed results were found for the impact of team-specific characteristics measured by team size, team expertise, and the number of submissions on team performance. For high-performing teams, these factors had no impact on team score.

scholarly journals Translationese and Post-editese: How comparable is comparable quality?

2018 ◽  
Vol 16 ◽  
Author(s):  
Joke Daems ◽  
Orphée De Clercq ◽  
Lieve Macken
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Original Text ◽  
High Quality ◽  
Machine Learning Model ◽  
Comparable Quality

Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example, are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text, computers have been trained to detect Translationese successfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text.

Sign in / Sign up

Export Citation Format

Share Document