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Support Vector Machine

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Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.

Scientific reports
This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes...

Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.

Scientific reports
Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for ...

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

Sensors (Basel, Switzerland)
This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performa...

Detection of Verticillium infection in cotton leaves using ATR-FTIR spectroscopy coupled with machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Verticillium wilt (VW) is a soil-borne vascular disease that affects upland cotton and is caused by Verticillium dahliae Kleb. A rapid and user-friendly early diagnostic technique is essential for the preventing and controlling VW disease. In this st...

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
PURPOSE: The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, ha...

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods.

Computational biology and chemistry
Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the...

Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial.

Journal of surgical oncology
BACKGROUND: Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoper...

Temporal dynamic alterations of regional homogeneity in major depressive disorder: a study integrating machine learning.

Neuroreport
Previous studies have found alterations in the local regional homogeneity of brain activity in individuals diagnosed with major depressive disorder. However, many studies have failed to consider that even during resting states, brain activity is dyna...

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques.

International journal of molecular sciences
Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are f...