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Wyszukujesz frazę "Pande, Sagar" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Soft computing techniques-based digital video forensics for fraud medical anomaly detection
Autorzy:
Nanda, Sunpreet Kaur
Ghai, Deepika
Ingole, P.V.
Pande, Sagar
Powiązania:
https://bibliotekanauki.pl/articles/38701161.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
smart healthcare system
medical imaging
healthcare fraud
MRI imaging
digital image forensics
object detection
YOLO architecture
customized CNN
inteligentny system opieki zdrowotnej
obrazowanie medyczne
oszustwo w służbie zdrowia
obrazowanie MRI
kryminalistyka obrazu cyfrowego
detekcja obiektów
architektura YOLO
dostosowanie CNN
Opis:
The current pandemic situation has made it important for everyone to wear masks. Digital image forensics plays an important role in preventing medical fraud and in object detection. It is helpful in avoiding the high-risk situations related to the health and security of the individuals or the society, including getting the proper evidence for identifying the people who are not wearing masks. A smart system can be developed based on the proposed soft computing technique, which can be helpful to detect precisely and quickly whether a person wears a mask or not and whether he/she is carrying a gun. The proposed method gave 100% accurate results in videos used to test such situations. The system was able to precisely differentiate between those wearing a mask and those not wearing a mask. It also effectively detects guns, which can be used in many applications where security plays an important role, such as the military, banks, etc.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 111-130
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Explainable deep neural network-based analysis on intrusion-detection systems
Autorzy:
Pande, Sagar Dhanraj
Khamparia, Aditya
Powiązania:
https://bibliotekanauki.pl/articles/27312883.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
IDS
deep neural network
explainable AI
NSL-KDD
local explainability
global explainability
Opis:
The research on intrusion-detection systems (IDSs) has been increasing in recent years. Particularly, this research widely utilizes machine-learning concepts, and it has proven that these concepts are effective with IDSs – particularly, deep neural network-based models have enhanced the rates of the detection of IDSs. In the same instance, these models are turning out to be very complex, and users are unable to track down explanations for the decisions that are made; this indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, this article deals with a proposed model that can explain the obtained predictions. The proposed framework is a combination of a conventional IDS with the aid of a deep neural network and the interpretability of the model predictions. The proposed model utilizes Shapley additive explanations (SHAPs) that mixes the local explainability as well as the global explainability for the enhancement of interpretations in the case of IDS. The proposed model was implemented by using popular data sets (NSL-KDD and UNSW-NB15), and the performance of the framework was evaluated by using their accuracy. The framework achieved accuracy levels of 99.99 and 99.96%, respectively. The proposed framework can identify the top-4 features using local explainability and the top-20 features using global explainability.
Źródło:
Computer Science; 2023, 24 (1); 97--111
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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