Computer Tomography (CT) images have become quite important to diagnose diseases. CT scan slice contains a vast amount of data that may not be properly examined with the requisite precision and speed using normal visual inspection. A computer-assisted skull fracture classification expert system is needed to assist physicians. Convolutional Neural Networks (CNNs) are the most extensively used deep learning models for image categorization since most often time they outperform other models in terms of accuracy and results. The CNN models were then developed and tested, and several convolutional neural network (CNN) architectures were compared. ResNet50, which was used for feature extraction combined with a gradient boosted decision tree machine learning algorithm to act as a classifier for the categorization of skull fractures from brain CT scans into three fracture categories, had the best overall F1-score of 96%, Hamming Score of 95%, Balanced accuracy Score of 94% & ROC AUC curve of 96% for the classification of skull fractures.

Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury types. So it is vital to detect and classify the fracture very early. In real world, often fractures occur at multiple sites. This makes it harder to detect the fracture type where many fracture types might summarize a skull fracture. Unfortunately, manual detection of skull fracture and the classification process is time-consuming, threatening a patient's life. Because of the emergence of deep learning, this process could be automated. Convolutional Neural Networks (CNNs) are the most widely used deep learning models for image categorization because they deliver high accuracy and outstanding outcomes compared to other models. We propose a new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN for feature extraction and lazy learning approach which acts as a classifier for classification of skull fractures from brain CT images to classify five fracture types. Our suggested model achieved a subset accuracy of 88%, an F1 score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled classification.

Some of the most popular robots on the market are'dangerously easy' to hack, experts are warning. Seattle-based cybersecurity firm IOActive Inc. discovered several consumer and industrial robots can easily be turned into bugging devices or even weapons with just a little hacking, with one even able to cause a skull fracture if taken over by someone with malicious intention. The machines studied include robots from Softbank Robotics, UBTECH Robotics, Universal Robotics, Asratec Corp, ROBOTIS, and Rethink Robotics. Seattle-based cybersecurity firm IOActive Inc. discovered several consumer and industrial robots can easily be turned into bugging devices or even weapons with just a little hacking Overall, these vulnerabilities lead to a plethora of dangers, including the possibility they could be hijacked and used as secretive listening devices or even weapons. For example, Universal Robots's industrial devices are designed to work directly alongside humans, but IOActive was able to remotely disable the robot's key safety features in a way that could result in someone programming it to injure nearby humans.