AIMC Topic: Machine Learning

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Machine learning approach for discrimination of genotypes based on bright-field cellular images.

NPJ systems biology and applications
Morphological profiling is a combination of established optical microscopes and cutting-edge machine vision technologies, which stacks up successful applications in high-throughput phenotyping. One major question is how much information can be extrac...

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

PLoS computational biology
Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used...

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.

PloS one
This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify ...

PHOTONAI-A Python API for rapid machine learning model development.

PloS one
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm ...

Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images.

Clinical lymphoma, myeloma & leukemia
BACKGROUND: Conventional identification of blood disorders based on visual inspection of blood smears through microscope is time consuming, error-prone and is limited by hematologist's physical acuity. Therefore, an automated optical image processing...

Automating water quality analysis using ML and auto ML techniques.

Environmental research
Generation of unprocessed effluents, municipal refuse, factory wastes, junking of compostable and non-compostable effluents has hugely contaminated nature-provided water bodies like rivers, lakes and ponds. Therefore, there is a necessity to look int...

Adoption of New Technologies: Artificial Intelligence.

Gastrointestinal endoscopy clinics of North America
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterol...

The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Genes
Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has re...

Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices.

Sensors (Basel, Switzerland)
The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to cent...

MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks.

Sensors (Basel, Switzerland)
In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, ...