D-PPSOK clustering algorithm with data sampling for clustering big data analysis

Dhas, C. Suresh Gnana; Yuvaraj, N.; Kousik, N.V.; Kaliyaperumal, Karthikeyan

doi:10.1016/b978-0-323-90240-3.00027-8

Cited by 8 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is is one of the few things that IoMT can do by developing established best practices [6]. Another important way IoMT works in the medical field is to help provide greater flexibility for physicians and other medical professionals [7].…”

Section: Introductionmentioning

confidence: 99%

“…(1) Initialize input IoT sensors value (2) Store the image value and send it for computation (3) if (Indicated images � database) (4) en move to image comparison (5) Update the values of p and q (6) if (Indicated images � IoT indicated) (7) en move to data validation module ( 8) if (data validation >80%) (9) en declare type 4 tumor (10) else if (data validation ≤ 79% and > 60%) (11) en declare type 3 tumor ( 12) else if (data validation ≤ 59% and > 40%) (13) en declare type 2 tumor (14) else declare type 1 tumor (15) else go to step 1 (16) (…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients’ health report and provides better treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…ese cells are called melanocytes [14][15][16][17][18][19]. Melanin is the result of these cells responsible for the color of the skin, eyes, and hair.…”

Section: Introductionmentioning

confidence: 99%

“…Melanoma begins in cells that produce skin cancer pigment. These cells are called melanocytes [ 14 – 19 ]. Melanin is the result of these cells responsible for the color of the skin, eyes, and hair.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques

Arivazhagan

Mukunthan

Sundaranarayana

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Skin cancer is the uncontrolled growth of irregular cancer cells in the human-skin's outer layer. Skin cells commonly grow in an uneven pattern on exposed skin surfaces. The majority of melanomas, aside from this variety, form in areas that are rarely exposed to sunlight. Harmful sunlight, which results in a mutation in the DNA and irreparable DNA damage, is the primary cause of skin cancer. This demonstrates a close connection between skin cancer and molecular biology and genetics. Males and females both experience the same incidence rate. Avoiding revelation to ultraviolet (UV) emissions can lower the risk rate. This needed to be known about in order to be prevented from happening. To identify skin cancer, an improved image analysis technique was put forth in this work. The skin alterations are routinely monitored by this proposed skin cancer categorization approach. Therefore, early detection of suspicious skin changes can aid in the early discovery of skin cancer, increasing the likelihood of a favourable outcome. Due to the blessing of diagnostic technology and recent advancements in cancer treatment, the survival rate of patients with skin cancer has grown. The strategy for detecting skin cancer using image processing technologies is presented in this paper. The system receives the image of the skin lesion as an input and analyses it using cutting-edge image processing methods to determine whether skin cancer is present. The Lesion Image Analysis Tools use texture, size, and shape assessment for image segmentation and feature phases to check for various cancer criteria including asymmetries, borders, pigment, and diameter. The image is classified as Normal skin and a lesion caused by skin cancer using the derived feature parameters.

show abstract

Enhancing Efficiency in Aviation and Transportation Through Intelligent Radial Basis Function

Thangarasu,

Alla

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

D-PPSOK clustering algorithm with data sampling for clustering big data analysis

Cited by 8 publications

References 3 publications

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques

Enhancing Efficiency in Aviation and Transportation Through Intelligent Radial Basis Function

Contact Info

Product

Resources

About