scholarly journals Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7565
Author(s):  
Varad Kabade ◽  
Ritika Hooda ◽  
Chahat Raj ◽  
Zainab Awan ◽  
Allison S. Young ◽  
...  
Keyword(s):  
Machine Learning ◽  
Correct Diagnosis ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Teaching Tool ◽  
Common Symptom ◽  
Vestibular Disorder ◽  
Future Directions ◽  
Learning Techniques ◽  
Work Done

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.

scholarly journals Will the Machine Like Your Image?Automatic Assessment of Beauty in Images with Machine Learning Techniques †

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 34 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Processing System ◽  
Machine Learning Techniques ◽  
Community Studies ◽  
Automatic Assessment ◽  
Future Directions ◽  
Computer Methods ◽  
Learning Techniques ◽  
Work Done

Although the concept of image quality has been a subject of study for the image processing community for more than forty years (where, with the term “quality”, we are referring to the accuracy with which an image processing system captures, processes, stores, compresses, transmits, and displays the signals that compose an image), notions related to aesthetics of photographs and images have only appeared for about ten years within the community. Studies devoted to aesthetics of images are multiplying today, taking advantage of the latest machine learning techniques and mostly due to the proliferation of huge communities and websites, specialized in digital photography sharing and archiving, such as Flickr, Imgur, DeviantArt, and Instagram. In this review, we examine the latest advances of computer methods that aim at computationally distinguishing high-quality from low-quality photos and images, relying on machine learning techniques. The paper is organized as follows: First, we introduce many approaches to aesthetics, studied in philosophy, neurobiology, experimental psychology, and sociology, to see what lighting they propose to researchers. Such points of view let us explain the weakness of the current consensus on the difficult aesthetics problem and the importance of the ongoing debates on it. Then, we analyze the work done in the community of pattern recognition and artificial intelligence on the task of automatic aesthetic assessment, and we both compare and critically examine the presented results. Finally, we describe many issues that have not been addressed, and starting from these, we outline some possible future directions.

Educational Software Based on Matlab GUIs for Neural Networks Courses

2016 ◽  
pp. 333-358 ◽  
Author(s):  
Pablo Díaz-Moreno ◽  
Juan José Carrasco ◽  
Emilio Soria-Olivas ◽  
José M. Martínez-Martínez ◽  
Pablo Escandell-Montero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Educational Software ◽  
Machine Learning Techniques ◽  
Teaching Tool ◽  
Neural Models ◽  
Learning Techniques ◽  
The World ◽  
Programming Skills

Neural Networks (NN) are one of the most used machine learning techniques in different areas of knowledge. This has led to the emergence of a large number of courses of Neural Networks around the world and in areas where the users of this technique do not have a lot of programming skills. Current software that implements these elements, such as Matlab®, has a number of important limitations in teaching field. In some cases, the implementation of a MLP requires a thorough knowledge of the software and of the instructions that train and validate these systems. In other cases, the architecture of the model is fixed and they do not allow an automatic sweep of the parameters that determine the architecture of the network. This chapter presents a teaching tool for the its use in courses about neural models that solves some of the above-mentioned limitations. This tool is based on Matlab® software.

scholarly journals Collaboration of Blockchain and Machine Learning in Healthcare Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 2642-2645
Keyword(s):  
Machine Learning ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Healthcare Sector ◽  
Healthcare Industry ◽  
Sensitive Data ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Learning Techniques

The purpose of this paper is to explore the applications of blockchain in the healthcare industry. Healthcare sector can become an application domain of blockchain as it can be used to securely store health records and maintain an immutable version of truth. Blockchain technology is originally built on Hyperledger, which is a decentralized platform to enable secure, unambiguous and swift transactions and usage of medical records for various purposes. The paper proposes to use blockchain technology to provide a common and secured platform through which medical data can be accessed by doctors, medical practitioners, pharma and insurance companies. In order to provide secured access to such sensitive data, blockchain ensures that any organization or person can only access data with consent of the patient. The Hyperledger Fabric architecture guarantees that the data is safe and private by permitting the patients to grant multi-level access to their data. Apart from blockchain technology, machine learning can be used in the healthcare sector to understand and analyze patterns and gain insights from data. As blockchain can be used to provide secured and authenticated data, machine learning can be used to analyze the provided data and establish new boundaries by applying various machine learning techniques on such real-time medical data.

scholarly journals Bridging the Gap between Energy Consumption and Distribution through Non-Technical Loss Detection

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1748 ◽  
Author(s):  
Bernat Coma-Puig ◽  
Josep Carmona
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Energy Consumption ◽  
Energy Distribution ◽  
Machine Learning Techniques ◽  
Artificial Intelligence Techniques ◽  
Learning Techniques ◽  
Distribution Company ◽  
Energy Companies ◽  
Work Done

The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides.

scholarly journals Survey Paper On “Web Page Content Visualization”

1970 ◽  
Vol 8 (1) ◽  
pp. 125-133
Author(s):  
Sushil Shrestha
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Web Page ◽  
Semantic Graph ◽  
Survey Paper ◽  
Learning Techniques ◽  
Future Direction ◽  
Work Done

The paper is a survey of a work done in the field of web page content vuisualization. The paper start with summary of the semantic graph. Then it describe the process of generation of semantic graph by natural language processing and machine learning techniques and then enriching text with RDF/OWL Encoded Sense as the enhancement of the existing enrycher text conversion and ends with the possible future direction and conclusion. DOI: http://dx.doi.org/10.3126/kuset.v8i1.6052 KUSET 2012; 8(1): 125-133

Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms

2021 ◽  
pp. 150-182
Author(s):  
Jayashree M. Kudari
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Huge Data ◽  
Learning Techniques ◽  
Classification And Regression ◽  
Robust Prediction

Developments in machine learning techniques for classification and regression exposed the access of detecting sophisticated patterns from various domain-penetrating data. In biomedical applications, enormous amounts of medical data are produced and collected to predict disease type and stage of the disease. Detection and prediction of diseases, such as diabetes, lung cancer, brain cancer, heart disease, and liver diseases, requires huge tests and that increases the size of patient medical data. Robust prediction of a patient's disease from the huge data set is an important agenda in in this chapter. The challenge of applying a machine learning method is to select the best algorithm within the disease prediction framework. This chapter opts for robust machine learning algorithms for various diseases by using case studies. This usually analyzes each dimension of disease, independently checking the identified value between the limits to monitor the condition of the disease.

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