Порівняльний аналіз алгоритмів класифікації при аналізі медичних зображень за відеоданими спекл-трекінг ехокардіографії

Проблематика. Машинне навчання дає змогу застосувати різні інтелектуальні алгоритми для отримання діагностичних та(або) прогностичних моделей. Подібні моделі можуть бути використані для визначення функціонального стану серця, який діагностується за допомогою спекл-трекінг ехокардіографії. Для того щоб детально визначити стан серця пацієнта, в машинному навчанні використовується підхід класифікації. Кожен із алгоритмів класифікації має різну ефективність при застосуванні в певних ситуаціях. Тому актуальною задачею є визначення найбільш ефективного алгоритму для розвʼязання конкретної задачі класифікації стану серця пацієнта при застосуванні однакового масиву даних спекл-трекінг ехокардіографії. Мета. Оцінити ефективність застосування прогностичних моделей логістичної регресії, методу групового урахування аргументів (МГУА), випадкового лісу і адаптивного бустингу (AdaBoost) при побудові алгоритмів підтримки прийняття медичних рішень щодо діагностики ішемічної хвороби серця.Background. Machine learning allows applying various intelligent algorithms to produce diagnostic and/or prognostic models. Such models can be used to determine the functional state of the heart, which is diagnosed by speckle-tracking echocardiography. To determine the patient's heart condition in detail, a classification approach is used in machine learning. Each of the classification algorithms has a different performance when applied to certain situations. Therefore, the actual task is to determine the most efficient algorithm for solving a specific task of classifying the patient's heart condition when applying the same speckle-tracking echocardiography data set. Objective. We are aimed to evaluate the effectiveness of the application of prognostic models of logistic regression, the group method of data handling (GMDH), random forest, and adaptive boosting (AdaBoost) in the construction of algorithms to support medical decision-making on the diagnosis of coronary heart disease. Methods. Video data from speckle-tracking echocardiography of 40 patients with coronary heart disease and 16 patients without cardiac pathology were used for the study. Echocardiography was recorded in B-mode in three positions: long axis, 4-chamber, and 2-chamber. Echocardiography frames that reflect the systole and diastole of the heart (308 samples in total) were taken as objects for classification. To obtain informative features of the selected objects, the genetic GMDH approach was applied to identify the best structure of harmonic textural features. We compared the efficiency of the following classification algorithms: logistic regression method, GMDH classifier, random forest method, and AdaBoost method.Проблематика. Машинное обучение позволяет применить различные интеллектуальные алгоритмы для получения диагности- ческих и (или) прогностических моделей. Подобные модели могут быть использованы для определения функционального состояния сердца, которое диагностируется с помощью спекл-трекинг эхокардиографии. Для того чтобы детально определить состояние сердца пациента, в машинном обучении используется подход классификации. Каждый из алгоритмов классификации имеет разную эффективность при применении в определенных ситуациях. Поэтому актуальной задачей является определение наиболее эффективного алгоритма для решения конкретной задачи классификации состояния сердца пациента при применении одинакового массива данных спекл-трекинг эхокардиографии. Цель. Оценить эффективность применения прогностических моделей логистической регрессии, метода группового учета аргументов (МГУА), случайного леса и адаптивного бустинга (AdaBoost) при построении алгоритмов поддержки принятия медицинских решений по диагностике ишемической болезни сердца

Transcriptome analysis of a spontaneous mutant in sweet orange [Citrus sinensis (L.) Osbeck] during fruit development

Bud mutations often arise in citrus. The selection of mutants is one of the most important breeding channels in citrus. However, the molecular basis of bud mutation has rarely been studied. To identify differentially expressed genes in a spontaneous sweet orange [C. sinensis (L.) Osbeck] bud mutation which causes lycopene accumulation, low citric acid, and high sucrose in fruit, suppression subtractive hybridization and microarray analysis were performed to decipher this bud mutation during fruit development. After sequencing of the differentially expressed clones, a total of 267 non-redundant transcripts were obtained and 182 (68.2%) of them shared homology (E-value ≤1×10−10) with known gene products. Few genes were constitutively up- or down-regulated (fold change ≥2) in the bud mutation during fruit development. Self-organizing tree algorithm analysis results showed that 95.1% of the differentially expressed genes were extensively coordinated with the initiation of lycopene accumulation. Metabolic process, cellular process, establishment of localization, response to stimulus, and biological regulation-related transcripts were among the most regulated genes. These genes were involved in many biological processes such as organic acid metabolism, lipid metabolism, transport, and pyruvate metabolism, etc. Moreover, 13 genes which were differentially regulated at 170 d after flowering shared homology with previously described signal transduction or transcription factors. The information generated in this study provides new clues to aid in the understanding of bud mutation in citrus

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade’s Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets