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The increasing global demand for clean and environmentally friendly energy resources has caused increased interest in harnessing solar power through photovoltaic (PV) systems for smart grids and homes. However, the inherent unpredictability…
Studies have shown that the traces people leave when browsing the internet may indicate the onset of diseases such as cancer. Here we show that the adaptive engines of advertising systems in conjunction with clinically verified…
In this paper we describe a novel implementation of adaboost for prediction of survival function. We take different variations of the algorithm and compare the algorithms based on system run time and root mean square error. Our construction…
Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accurate, available and inexpensive method to predict the…
Skin cancer is the most common human malignancy(American Cancer Society) which is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic(related to skin) analysis, a biopsy and…
Recent advances in computer vision and robotics enabled automated large-scale biological image analysis. Various machine learning approaches have been successfully applied to phenotypic profiling. However, it remains unclear how they…
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability…
The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in…
Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional…
Identifying factors that affect participation is key to a successful insurance scheme. This study's challenges involve using many factors that could affect insurance participation to make a better forecast.Huge numbers of factors affect…
Identifying the mutations that drive cancer growth is key in clinical decision making and precision oncology. As driver mutations confer selective advantage and thus have an increased likelihood of occurrence, frequency-based statistical…
In this paper we apply computer learning methods to diagnosing ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass-spectrometry. We are working with a new data set collected over a…
The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used…
Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities. Precise prediction and evaluation of…
Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized cancer immunotherapy. Therefore, we established a TCR generative specificity detection…
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and…
There exists unexplained diverse variation within the predefined colon cancer stages using only features either from genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved…
Machine learning can precisely identify different cancer tumors at any stage by classifying cancerous and healthy samples based on their genomic profile. We have developed novel methods of MLAC (Machine Learning Against Cancer) achieving…
In this paper we tackle the problem of point and probabilistic forecasting by describing a blending methodology of machine learning models that belong to gradient boosted trees and neural networks families. These principles were…
Computer-aided cancer survival risk prediction plays an important role in the timely treatment of patients. This is a challenging weakly supervised ordinal regression task associated with multiple clinical factors involved such as…