Genetic Method of Image Processing for Motor Vehicle Recognition

Fedorchenko, Ievgen; Oliinyk, Аndrii; Stepanenko, Alexander; Zaiko, Tetiana; Svyrydenko, Anton; Goncharenko, Dmytro

doi:10.32782/cmis/2353-17

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…Thinning is applied only during model training. In order to compensate for the effect of reducing the number of features during thinning, the features that have not been zeroed are multiplied by 1/(1 -R), where R is the thinning coefficient [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Preventing Overfittingmentioning

confidence: 99%

Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images

Fedorchenko,

Oliinyk,

Chornobuk

et al. 2024

IJC

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The paper discusses methods of detecting cervical spine fractures based on computed tomography images using machine learning algorithms. Solving such a problem is important in the context of providing emergency care to patients with suspected spinal fractures, when accurate decision-making based on radiological data can be critical. In this case, such a machine learning model can speed up the work of a radiologist and reduce the importance of the human factor in making important decisions. After a review of analogs from the literature, it has been found that convolutional neural networks appear to be the most promising method. Using a publicly available dataset, a model "Fracture detection 3" based on a convolutional neural network is developed to solve the problem. The model demonstrates a classification accuracy of 98.25%, sensitivity of 99%, and specificity of 97.5%, which is ahead of the literature. For comparison with traditional methods, models based on the support vector method, decision tree, and k-nearest method are developed using a similar dataset. "Fracture detection 3" outperforms all developed models based on traditional methods in terms of classification accuracy.

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Section: Preventing Overfittingmentioning

confidence: 99%

Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images

Fedorchenko,

Oliinyk,

Chornobuk

et al. 2024

IJC

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“…The authors devised a plethora of sensors to keep a watchful eye on the real-time health of animals. Their study utilized an array of sensors, including those that measured temperature, respiration rate, heart rate, and humidity rate [9] [10]. To control this project, they employed the Raspberry Pi, complete with its built-in Wi-Fi module.…”

Section: B Wearable Smart Health Monitoringmentioning

confidence: 99%

Patient Health Monitoring System Development using ESP8266 and Arduino with IoT Platform

Alsayaydeh¹,

Yusof²,

Halim³

et al. 2023

IJACSA

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The Internet of Things (IoT) has emerged as a transformative technology that has revolutionized the field of healthcare. One of the most promising applications of Internet of Things (IoT) in healthcare is patient health monitoring, which allows healthcare providers to remotely monitor patients' health and provide prompt medical attention when needed. This research work focuses on developing an Internet of Things (IoT)based patient health monitoring system aimed at providing a solution for patients, particularly the elderly, who face the risk of unexpected death due to the lack of medical attention. The proposed system utilizes a heartbeat sensor and an Infrared IR temperature sensor connected to Arduino UNO and Nodemcu, respectively, to monitor the patient's vital signs. The sensors collect the data, which is then sent to an Internet of Things (IoT) web platform via a Wi-Fi connection. The Internet of Things (IoT) platform displays the real-time data of the patient's health status, including the temperature and heartbeat rate, which can be monitored by doctors and nurses. The system is designed to send alerts to healthcare providers in the event of any medical emergency, ensuring that prompt medical attention can be provided to the patient. The significance of this research work lies in its potential to revolutionize the healthcare industry by providing a more efficient and effective means of patient health monitoring. The system can be used to monitor a large number of patients simultaneously, which is particularly beneficial in hospitals with a large patient load. Moreover, it can reduce the workload of healthcare providers, allowing them to focus on other critical tasks. This innovative system has the potential to improve the overall quality of healthcare services and lead to better health outcomes for the society.

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“…Вона протестована на двох наборах даних, що були отримані на основі простої попередньої обробки публічно доступного датасету [5]: набір зображеннями розміром 50×50 і набір із зображеннями розміром 100×100. Використовувалася виправлена версія датасету, оскільки в оригінальному датасеті були допущені помилки в попередній класифікації навчальних даних [13][14][15][16][17][18][19][20].…”

Section: висновкиunclassified

Розробка Та Дослідження Модифікованої Згорткової Нейронної Мережи Для Розпізнавання Образів Малярійних Клітин

Федорченко,

Олійник,

Степаненко

et al. 2023

РЗОД

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Проведено огляд та аналіз відомих рішень для проблеми виявлення малярії за знімками крові пацієнтів з використанням алгоритмів машинного навчання. Після розробки моделі машинного навчання для вирішення поставленої задачі на основі згорткових нейронних мереж проведене тестування точності роботи моделі та її порівняння з розглянутими аналогами. За результатами тестування було встановлено, що за показником точності класифікації модель знаходиться на рівні найкращих розглянутих моделей, маючи точність класифікації 98,08 %.

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Genetic Method of Image Processing for Motor Vehicle Recognition

Cited by 10 publications

References 14 publications

Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images

Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images

Patient Health Monitoring System Development using ESP8266 and Arduino with IoT Platform

Розробка Та Дослідження Модифікованої Згорткової Нейронної Мережи Для Розпізнавання Образів Малярійних Клітин

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