A Literature Survey: Semantic Technology Approach in Machine Learning

Polycystic ovarian syndrome is an endocrine issue attacking ladies at the age of reproduction. This indication has primarily found in ladies whose age is in the middle of 25 and 35. It is essential to diagnose and recognize diverse types of ovulatory failure that can add to infertility. There are numerous clarifications for ovulation failure. Without distinguishing the correct locality of the follicle, the risk seriousness of the patient can’t reveal. In line with this, many of the researchers focusing their research interest in PCOS. In this paper, literature review on polycystic ovarian syndrome using machine learning and image processing has exhibited.

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Literature Survey—Food Recognition and Calorie Measurement Using Image Processing and Machine Learning Techniques

Lecture Notes in Electrical Engineering - ICCCE 2019 ◽

10.1007/978-981-13-8715-9_4 ◽

2019 ◽

pp. 23-37

Author(s):

V. Hemalatha Reddy ◽

Soumya Kumari ◽

Vinitha Muralidharan ◽

Karan Gigoo ◽

Bhushan S. Thakare

Keyword(s):

Machine Learning ◽

Image Processing ◽

Literature Survey ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Food Recognition

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A Decadal Walk on BCI Technology

Advances in Data Mining and Database Management - Analyzing Data Through Probabilistic Modeling in Statistics ◽

10.4018/978-1-7998-4706-9.ch006 ◽

2021 ◽

pp. 158-183

Author(s):

Ahan Chatterjee ◽

Aniruddha Mandal ◽

Swagatam Roy ◽

Shruti Sinha ◽

Aditi Priya ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Evoked Potential ◽

Final Section ◽

Literature Survey ◽

Feature Engineering ◽

Visually Evoked Potential

In this chapter, the authors take a walkthrough in BCI technology. At first, they took a closer look into the kind of waves that are being generated by our brain (i.e., the EEG and ECoG waves). In the next section, they have discussed about patients affected by CLIS and ALS-CLIS and how they can be treated or be benefitted using BCI technology. Visually evoked potential-based BCI technology has also been thoroughly discussed in this chapter. The application of machine learning and deep learning in this field are also being discussed with the need for feature engineering in this paradigm also been said. In the final section, they have done a thorough literature survey on various research-related to this field with proposed methodology and results.

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A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Healthcare ◽

10.3390/healthcare9070914 ◽

2021 ◽

Vol 9 (7) ◽

pp. 914

Author(s):

Anita Ramachandran ◽

Anupama Karuppiah

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sleep Apnea ◽

Gold Standard ◽

Large Population ◽

Literature Survey ◽

Feature Engineering ◽

Detection Systems ◽

Recent Advances ◽

Apnea Detection

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

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