scholarly journals Current Techniques for Diabetes Prediction: Review and Case Study

2019 ◽  
Vol 9 (21) ◽  
pp. 4604 ◽  
Author(s):  
Larabi-Marie-Sainte ◽  
Aburahmah ◽  
Almohaini ◽  
Saba
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recent Survey ◽  
Combined Model ◽  
Common Diseases ◽  
Combined Models ◽  
Diabetes Prediction

Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML) techniques have been utilized in predicting diabetes in the last couple of years. The increasing complexity of this problem has inspired researchers to explore the robust set of Deep Learning (DL) algorithms. The highest accuracy achieved so far was 95.1% by a combined model CNN-LSTM. Even though numerous ML algorithms were used in solving this problem, there are a set of classifiers that are rarely used or even not used at all in this problem, so it is of interest to determine the performance of these classifiers in predicting diabetes. Moreover, there is no recent survey that has reviewed and compared the performance of all the proposed ML and DL techniques in addition to combined models. This article surveyed all the ML and DL techniques-based diabetes predictions published in the last six years. In addition, one study was developed that aimed to implement those rarely and not used ML classifiers on the Pima Indian Dataset to analyze their performance. The classifiers obtained an accuracy of 68%–74%. The recommendation is to use these classifiers in diabetes prediction and enhance them by developing combined models.

A Comparative Analysis of Early Stage Diabetes Prediction using Machine Learning and Deep Learning Approach

2021 ◽  
Author(s):  
Md Abu Rumman Refat ◽  
Md. Al Amin ◽  
Chetna Kaushal ◽  
Mst Nilufa Yeasmin ◽  
Md Khairul Islam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Early Stage ◽  
Learning Approach ◽  
Diabetes Prediction

Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study

2021 ◽  
pp. 175-198
Author(s):  
Roopa B. Hegde ◽  
Vidya Kudva ◽  
Keerthana Prasad ◽  
Brij Mohan Singh ◽  
Shyamala Guruvare
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Conventional Machine

scholarly journals A Comparative Analysis for 2D Object Recognition: A Case Study with Tactode Puzzle-Like Tiles

2021 ◽  
Vol 7 (4) ◽  
pp. 65
Author(s):  
Daniel Silva ◽  
Armando Sousa ◽  
Valter Costa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Object Recognition ◽  
Template Matching ◽  
Recognition Performance ◽  
Regions Of Interest ◽  
The Other ◽  
Classification Methods

Object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM; (ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSD and YOLOv4; (iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB; and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSD and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively; VGG19 with an accuracy of 99.96% and 99.68% for the same datasets; and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

scholarly journals Deep Learning for Recommender Systems: A Netflix Case Study

AI Magazine ◽  
2022 ◽  
Vol 42 (3) ◽  
pp. 7-18
Author(s):  
Harald Steck ◽  
Linas Baltrunas ◽  
Ehtsham Elahi ◽  
Dawen Liang ◽  
Yves Raimond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommender Systems ◽  
Input Data ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Methods ◽  
Recommendation Algorithms

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

scholarly journals Existential Methods on Diabetes Detection using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3034-3039
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Literature Review ◽  
Learning Techniques ◽  
The World ◽  
Diabetes Prediction

Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.

scholarly journals Artificial Intelligence-Enabled and Period-Aware Forecasting COVID-19 Spread

2021 ◽  
Vol 26 (1) ◽  
pp. 47-57
Author(s):  
Paul Menounga Mbilong ◽  
Asmae Berhich ◽  
Imane Jebli ◽  
Asmae El Kassiri ◽  
Fatima-Zahra Belouadha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Health Management ◽  
Negative Impact ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Time Lags ◽  
Context Sensitive ◽  
Learning Techniques

Coronavirus 2019 (COVID-19) has reached the stage of an international epidemic with a major socioeconomic negative impact. Considering the weakness of the healthy structure and the limited availability of test kits, particularly in emerging countries, predicting the spread of COVID-19 is expected to help decision-makers to improve health management and contribute to alleviating the related risks. In this article, we studied the effectiveness of machine learning techniques using Morocco as a case-study. We studied the performance of six multi-step models derived from both Machine Learning and Deep Learning regards multiple scenarios by combining different time lags and three COVID-19 datasets(periods): confinement, deconfinement, and hybrid datasets. The results prove the efficiency of Deep Learning models and identify the best combinations of these models and the time lags enabling good predictions of new cases. The results also show that the prediction of the spread of COVID-19 is a context sensitive problem.

scholarly journals Development of the "smart library" application using the Intel Distribution of OpenVINO toolkit

2019 ◽  
Author(s):  
Евгений Васильев ◽  
Evgeniy Vasil'ev ◽  
Валентина Кустикова ◽  
Valentina Kustikova ◽  
Иван Вихрев ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Source Code ◽  
Face Features ◽  
Machine Learning Model ◽  
Library Users ◽  
The Face

We represent a case study of using deep learning and computer vision library - the Intel Distribution of OpenVINO toolkit. We develop the automated “smart library” using DL and computer vision methods implemented in OpenVINO toolkit. The application involves the registration of the reader (adding information and photos of the new user); updating the machine learning model that describes the face features of the library users; authorization of the reader through face recognition; receiving and returning books by comparing the cover image with the database of flat images available in the library of books. The source code of the application is free available on GitHub: https://github.com/itlab-vision/openvino-smart-library. The developed application is planned to be published as a sample of the OpenVINO toolkit.

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