Enriched Model of Case Based Reasoning and Neutrosophic Intelligent System for DDoS Attack Defence in Software Defined Network based Cloud

Software Defined Networking in Cloud paradigm is most suitable for dynamic functionality and reduces the computation complexity. The routers and switches located at the network's boundaries are managed by software-defined netwrking (SDN) using open protocols and specialised open programmable interfaces. But the security threats often degrade the performance of SDN due to its constraints of resource usage. The most sensitive components which are vulnerable to DDoS attacks are controller and control plane bandwidth. The existing conventional classification algorithms lacks in detection of new or unknown traffic packets which are malicious and results in degradation of SDN performance in cloud resources. Hence, in this paper double filtering methodology is devised to detect both known and unknown pattern of malicious packets which affects the bandwidth of the control panel and the controller. The case-based reasoning is adapted for determining the known incoming traffic patterns before entering the SDN system. It classifies the packets are normal or abnormal based on the previous information gathered. The traffic patterns which is not matched from the previous patterns is treated as indeterministic packet and it is defined more precisely using the triplet representation of Neutrosophic intelligent system. The grade of belongingness, non-belongingness and indeterminacyis used as the main factors to detect the new pattern of attacking packets more effectively. From the experimental outcomes it is proved that DDoS attack detection in SDN based cloud environment is improved by adopting CBR-NIS compared to the existing classification model

Clinical Profile and ECG Changes in Scorpion Envenomation

1. In this study, the incidence of scorpion sting in males(56.6%) was found to be higher than females. 2. The incidence of scorpion envenomation was found to be maximum in the age group of 31-40 years (33%) and 21-30 years (29%). This indicates the risk of exposure to the scorpion sting at work place and during household chores. 3. More patients presented to the Poison center in the night (53%) than in the morning. This is in line with the predatory pattern of the scorpions and the risk of disturbing scorpion homes in the darkness of the night. 4. Fifty –nine percent of the patients presented with Grade 1 envenomation. Seven percent with Grade 2 and thirty four percent with Grade 3 envenomation. 5. Hand was the commonest site of sting in this study. Most of the scorpion stings were accidental and occurred indoors. 6. Pain (83%) and Tachycardia (19%) were the commonest presenting symptom and sign respectively. 7. Sinus tachycardia (6%) was the commonest ECG abnormality seen in the study. 8. There was no significant difference in clinical presentation with respect to age group and gender.(P value=0.20447). 9. The patients who presented late to the emergency room after scorpion sting were found to have greater morbidity.(P value<0.001). 10.There was statistical significance in the relationship between ECG change and biochemical markers CPK and CPK-MB. 11.Patients with Grade 1 envenomation treated with local infiltration of lidocaine at the pain site, anxiolytics , antibiotics and observed. Those with Grade 3 envenomation required oxygen and managed with intravenous fluids and inotropic agents for shock. One of the three patients with pulmonary edema required mechanical ventilation. 12. The transient hyperglycemia and hypertension observed in patients with severe envenomation resolved in 48 hours

Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images Using Convolutional Neural Networks

Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have the potential to identify structural changes that can be used as biomarkers to predict growth and neurodevelopmental outcomes. For this purpose, high throughput, accurate, and unbiased measurements are necessary to replace existing manual, semi-automatic, and automated approaches which are tedious and lack reproducibility and accuracy. In this study, we propose a new deep learning algorithm for automated segmentation of the fetal cerebellum from 2-dimensional (2D) US images. We propose ResU-Net-c a semantic segmentation model optimized for fetal cerebellum structure. We leverage U-Net as a base model with the integration of residual blocks (Res) and introduce dilation convolution in the last two layers to segment the cerebellum (c) from noisy US images. Our experiments used a 5-fold cross-validation with 588 images for training and 146 for testing. Our ResU-Net-c achieved a mean Dice Score Coefficient, Hausdorff Distance, Recall, and Precision of 87.00&#x0025;, 28.15, 86.00&#x0025;, and 90.00&#x0025;, respectively. The superiority of the proposed method over the other U-Net based methods is statistically significant (p &#x003C; 0.001). Our proposed method can be leveraged to enable high throughput image analysis in clinical research fetal US images and can be employed in the biometric assessment in fetal US images on a larger scale