Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал, № 3

Постійне посилання зібрання

https://ela.kpi.ua/handle/123456789/63059

Переглянути

Перегляд Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал, № 3 за Ключові слова "62-50"

Зараз показуємо 1 - 3 з 3
  • Ескіз
    ДокументВідкритий доступ
    A concatenation approach-based disease prediction model for sustainable health care system
    (КПІ ім. Ігоря Сікорського, 2023) Tharageswari, K.; Sundaram, N. Mohana; Santhosh, R.
    Abstract. In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF).
  • Ескіз
    ДокументВідкритий доступ
    Augmented security scheme for shared dynamic data with efficient lightweight elliptic curve cryptography
    (КПІ ім. Ігоря Сікорського, 2023) Dharmadhikari, Dipa D.; Tamane, Sharvari C.
    Abstract. Technology for Cloud Computing (CC) has advanced, so Cloud Computing creates a variety of cloud services. Users may receive storage space from the provider as Cloud storage services are quite practical; many users and businesses save their data in cloud storage. Data confidentiality becomes a larger risk for service providers when more information is outsourced to Cloud storage. Hence in this work, a Ciphertext and Elliptic Curve Cryptography (ECC) with Identity-based encryption (CP-IBE) approaches are used in the cloud environment to ensure data security for a healthcare environment. The revocation problem becomes complicated since characteristics are used to create cipher texts and secret keys; therefore, a User revocation algorithm is introduced for which a secret token key is uniquely produced for each level ensuring security. The initial operation, including signature, public audits, and dynamic data, are sensible to Sybil attacks; hence, to overcome that, a Sybil Attack Check Algorithm is introduced, effectively securing the system. Moreover, the conditions for public auditing using shared data and providing typical strategies, including the analytical function, security, and performance conditions, are analyzed in terms of accuracy, sensitivity, and similarity.
  • Ескіз
    ДокументВідкритий доступ
    Identification of lung disease types using convolutional neural network and VGG-16 architecture
    (КПІ ім. Ігоря Сікорського, 2023) Bukhori, S.; Verdy, B. Y. N.; Eka, Y. R. Windi; Januar, A. P.
    Abstract. Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing different diagnostic results of X-ray photos. This research classifies lung images into four categories: normal lungs, tuberculosis, pneumonia, and Covid-19 using the Convolutional Neural Network method and VGG-16 architecture. The results of the research with models and scenarios without pre-trained use data with a ratio of 9:1 at epoch 50, an accuracy of 94%, while the lowest results are in scenarios using data with a ratio of 8:2 at epoch 50, non-pre-trained models, accuracy by 87%.