Nowadays network growing rampant manner and uses as transfer medium like data, money transaction, information etc. Even though internet plays a vital role still there is some vulnerability. Ex: virus, spam, hacking, DOS, DDos, etc.We are focusing Distributed Denial of Service; there is plenty of Denial of Service mechanism existed in that we took SYN Flood attacks.With this view my proposed work is, an efficient method to detecting and mitigation against TCP SYN flooding attacks using Three Counters Algorithm, which detects spoofed IP packets up to 80%.
Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful to detect and classify the different grades of ICH. Because of the latest advancement of deep learning (DL) models on image processing applications, several medical imaging techniques utilize it. This study develops a new densely connected convolutional network (DenseNet) with extreme learning machine (ELM)) for ICH diagnosis and classification, called DN-ELM. The presented DL-ELM model utilizes Tsallis entropy with a grasshopper optimization algorithm (GOA), named TEGOA, for image segmentation and DenseNet for feature extraction. Finally, an extreme learning machine (ELM) is exploited for image classification purposes. To examine the effective classification outcome of the proposed method, a wide range of experiments were performed, and the results are determined using several performance measures. The simulation results ensured that the DL-ELM model has reached a proficient diagnostic performance with the maximum accuracy of 96.34%.
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