The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan. Thus, planning treatments is a crucial step in improving a patient's quality of life. In general, several image techniques such as CT, MRI, and ultrasound have been used for assessing tumors in the prostate, breast, lung, brain, etc. Primarily, MRI images are applied to detect tumors in the brain during this work. The enormous amount of data produced by the MRI scan thwarts tumor vs. non-tumor manual classification at a particular time. Unfortunately, with a small number of images, it has certain limitations (i.e., precise quantitative measurements). Therefore, an automated classification system is necessary to avoid human mortality. The automatic categorization of brain tumors in the surrounding tumor region is a challenging task concerning space and structural variability. Four deep learning models: AlexNet, VGG16, GoogleNet, and RestNet50, are used in this comparative study to classify brain tumors. Based on accuracy, the results showed that RestNet50 is the best model with an accuracy of 95.8%, while AlexNet has the fast performance with a processing time of 1.2 seconds. In addition, a hardware parallel processing unit (GPU) is employed for real-time purposes, where AlexNet (the fastest model) has a processing time of only 8.3 msec.
<span>Internet of Things technology allows many devices to connect with each other. The interaction could be between humans and devices or between devices itself. In fact, the data are traveling between the devices through the media within the boundary, and it could be traveling outside the boundary when it required to be analyzed or stored in the cloud through the internet. Due the transmission media and internet, the data are vulnerable to attacks. Thus, the data need to be encrypted strongly for the purpose of protection. Usually, most of the encryption techniques will consume computer resources. In this work, we divide the data that are used in the IoT environment into three levels of sensitivity which are low, medium and high sensitive data to leverage the computer resources such as time of encryption and decryption, battery usage and so on. A framework is proposed in this work to encrypt the data depends on the level of sensitivity using the machine learning K nearest neighbors (K-NN).</span>
In the era of digital communication, the information sharing is rapidly increased. All the data which being sent or received are vulnerable to many active and passive attacks. Therefore, secure the data during communication is the most important concern. Cryptography performs an essential role to secure the communication in network and it comes with an amazing solution to supply the needed protection against the intruders of data. Over a considerable time, the techniques of data encryption took a huge leap from very easy methods to very difficult mathematical calculations in an effort to generate a strong security for the communication. However still along with its difficulty, the algorithms of cryptographic are prone to many attacks. this paper is explains many techniques of symmetric key encryption, its comparison and their vulnerabilities to attacks.
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