Landmark Recognition Using Ensemble-Based Machine Learning Models

2021 ◽  
pp. 64-74
Author(s):  
Kanishk Bansal ◽  
Amar Singh Rana
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Classification ◽  
Digital Image ◽  
Subject Area ◽  
Learning Models ◽  
Landmark Recognition ◽  
Important Field ◽  
Definition Of ◽  
Machine Learning Models

Recognizing landmarks in images with machine learning is an excellent topic for research today. Landmark recognition is an important field in computer vision. In this field, we train the machine learning models to identify and recognize the closed distinctly distinguishable objects in a digital image. In general, if we consider a digital image to be a set of coordinates of different pixels, a landmark is said to be enclosed in that closed polygon formed by the pixels that may be considered as a distinct and distinguishable thing in one or the other sense. Landmark recognition is an important subject area of image classification since it is considered as one of the first steps towards reaching complete computer vision. The extremely broad definition of a landmark makes it eligible to be considered as one of the leading problems in image classification tasks. Since the task is considered to be a very broad one, the solutions to the task hold no easy procedures. This chapter explores landmark recognition using ensemble-based machine learning models.

scholarly journals End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

scholarly journals A survey of surveys on the use of visualization for interpreting machine learning models

2020 ◽  
Vol 19 (3) ◽  
pp. 207-233 ◽  
Author(s):  
Angelos Chatzimparmpas ◽  
Rafael M. Martins ◽  
Ilir Jusufi ◽  
Andreas Kerren
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Original Research ◽  
Learning Models ◽  
Visual Interpretation ◽  
Research Papers ◽  
Black Boxes ◽  
Internal Operations ◽  
Definition Of ◽  
Machine Learning Models

Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

Towards synthetic data generation for machine learning models in weather and climate

2020 ◽  
Author(s):  
David Meyer
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Climate Models ◽  
Synthetic Data ◽  
Real Data ◽  
Data Generation ◽  
Learning Models ◽  
Synthetic Data Generation ◽  
Weather And Climate ◽  
Machine Learning Models

<p>The use of real data for training machine learning (ML) models are often a cause of major limitations. For example, real data may be (a) representative of a subset of situations and domains, (b) expensive to produce, (c) limited to specific individuals due to licensing restrictions. Although the use of synthetic data are becoming increasingly popular in computer vision, ML models used in weather and climate models still rely on the use of large real data datasets. Here we present some recent work towards the generation of synthetic data for weather and climate applications and outline some of the major challenges and limitations encountered.</p>

scholarly journals Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction

Healthcare ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1662
Author(s):  
Tao Han Lee ◽  
Jia-Jin Chen ◽  
Chi-Tung Cheng ◽  
Chih-Hsiang Chang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Kidney Injury ◽  
Prediction Models ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Current Evidence ◽  
Learning Models ◽  
Definition Of ◽  
Machine Learning Models

Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.

scholarly journals Explaining Recurrent Machine Learning Models: Integral Privacy Revisited

2020 ◽  
pp. 62-73
Author(s):  
Vicenç Torra ◽  
Guillermo Navarro-Arribas ◽  
Edgar Galván
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Minimal Sets ◽  
Number Of Generators ◽  
Privacy Model ◽  
Definition Of ◽  
Machine Learning Models

Abstract We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem.

scholarly journals A Visualization System for Performance Analysis of Image Classification Models

2020 ◽  
Vol 2020 (1) ◽  
pp. 375-1-375-9
Author(s):  
Chanhee Park ◽  
Hyojin Kim ◽  
Kyungwon Lee
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Image Classification ◽  
Visual Analytics ◽  
Model Performance ◽  
Learning Models ◽  
Classification Problems ◽  
Visualization System ◽  
Wide Range ◽  
Machine Learning Models

Developing machine learning models for image classification problems involves various tasks such as model selection, layer design, and hyperparameter tuning for improving the model performance. However, regarding deep learning models, insufficient model interpretability renders it infeasible to understand how they make predictions. To facilitate model interpretation, performance analysis at the class and instance levels with model visualization is essential. We herein present an interactive visual analytics system to provide a wide range of performance evaluations of different machine learning models for image classification. The proposed system aims to overcome challenges by providing visual performance analysis at different levels and visualizing misclassification instances. The system which comprises five views - ranking, projection, matrix, and instance list views, enables the comparison and analysis different models through user interaction. Several use cases of the proposed system are described and the application of the system based on MNIST data is explained. Our demo app is available at https://chanhee13p.github.io/VisMlic/.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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