The research emphasizes the creation of a powerful and efficient system for the automaticextraction of contact information from physical calling cards through computer vision and information extraction techniques. Thi...
详细信息
Metaphors are a common communication tool used in our day-to-day life. The detection and generation of metaphors in textual form have been studied extensively but metaphors in other forms have been under-explored. Rec...
详细信息
Wind energy is a resource that is environmentally sustainable, crucial component needed for wind energy production. Accurate estimation of wind speed is essential for many applications. This paper proposes the Hybrid ...
详细信息
India depends heavily on agriculture for its survival. Rainfall is crucial to agriculture. Predicting rainfall has become a significant issue recently. People are made aware of the possibility of rain and are better p...
详细信息
The goal of the proposed system is to identify whether a video has endured software manipulation or not. This system specially deals with identifying deepfake videos from real ones. As new techniques emerged to make d...
详细信息
In today's interconnected digital ecosystem, protecting cyber-physical systems is critical. STPA-Sec is a systematic method that allows to analyze system designs and identify vulnerabilities in those designs from ...
详细信息
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehic...
详细信息
With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of
The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the...
详细信息
The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe
Deepfake videos, generated by sophisticated AI algorithms, pose significant challenges to the authenticity and trustworthiness of multimedia content on the internet. In this study, we propose a deepfake video detectio...
详细信息
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of th...
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.
暂无评论