The rise of Vehicular Ad-hoc Networks (VANETs) has led to the growing significance in intelligent transportation systems. This research suggests a deep learning model for anomaly detection based on GRU over VANET network traffic to address this challenge. Consumer electronics technologies can be successfully introduced to the market in one of two ways: either there is a clear benefit for the customer from using this technology, or it is required by a regulatory order that prevents the use of alternatives. It is possible to detect unknown assaults and DoS floods using traffic anomalies. Users can keep track of the security features of multimedia services by using Traffic Anomaly Detection, which provides an overview of traffic anomaly detection analysis. Anomaly detection methods fall into three categories: unsupervised, semi-supervised, and supervised. The right anomaly detection technique basically depends on the labels that are present in the dataset. To further improve the accuracy of proposed model, a new semi-supervised technique for detecting VANET network activity anomalies called SEMI-GRU has been proposed. The results demonstrate that proposed GRU-based deep learning model outperforms existing methods in detecting network anomalies with low false positive rates.

Efficient and reliable medical image analysis is indispensable in modern healthcare settings. The conventional approaches in diagnostics and evaluations from a mere picture are complex. It often leads to subjectivity due to experts' various experiences and expertise. Using convolutional neural networks, we proposed an end-to-end pipeline for automatic Cobb angle measurement to pinpoint scoliosis severity. Our results show that the Residual U-Net architecture provides vertebrae average segmentation accuracy of 92.95% based on Dice and Jaccard similarity coefficients. Furthermore, a comparative benchmark between physician's measurement and our machine-driven approach produces an acceptable mean deviation of 1.57 degrees and a T-test p-value of 0.9028, indicating no significant difference. This study has the potential to help doctors in prompt scoliosis magnitude assessments.

Automated medical image analysis driven by artificial intelligence can revolutionize modern healthcare in producing swift and precise diagnostics. Due to doctors’ varying breadths of training and expertise, traditional leukemia screening methods frequently involve considerable subjectivity. Using a 3-stage transfer learning approach and stacks of convolutional neural networks, we constructed an efficient pathway for automatic leukemia identification and classification through various phases. Experimental findings disclosed that our pipeline powered by InceptionResNetV2 architecture decisively affects the accuracy with 99.60% (normal vs. leukemia) and 94.67% (normal to L3). Moreover, it reduces error rates by 1.65% and 6.05%, respectively. A consistent result via the T-test confirms our proposed framework robustness with a significant positive difference of 4.71% over the standard transfer learning mechanism (p-value = 0.0001 & t = 0.85310). This research could aid and support oncologists in early yet reliable prognoses of acute lymphoblastic leukemia types.

Artificial intelligence-based medical image analysis promises an efficient and reliable diagnosis in today's healthcare. Traditional approaches for cataract screening by medical practitioners often results in subjectivity due to their varying levels of knowledge and expertise. Using transfer learning, ensembles of pre-trained convolutional neural networks, and stacked long short-term memory networks, we developed a non-invasive and streamlined pipeline for automatic cataract severity classification. Empirical results show that our proposed combined models of AlexNet, InceptionV3, Xception, and InceptionResNetV2 using a weighted average algorithm produces 99.20% (normal vs. cataract) and 97.76% (normal to severe) accuracies compared to standalone models. Furthermore, the ensemble model reduces classification error rates by an average of 2.17%. This study has the potential to help doctors to specify the magnitude of cataract stages with highly acceptable precision.