In the modern world where digitization is everywhere, SMS has become one of the most vital forms of communications, unlike other chatting-based messaging systems like Facebook, WhatsApp etc, SMS does not require active inter n et conne ct ion a t all. As we all know that Hackers / Spammer tries to intrude in Mobile Computing Device, and SMS support for mobile devices had become vulnerable, as attacker tries to intrude to the system by sending unwanted link, with which on clicking those link the attacker can gain remote access over the mobile computing device. So, to identify those messages Authors have developed a system which will identify such malicious messages and will identify whether or not the message is SPAM or HAM (malicious or not malicious). Authors have created a dictionary using the TF-IDF Vectorizer algorithm, which will include all the features of words a SPAM SMS possess, based on content of message and referring to this dictionary the system will be classifying the SMS as spam or ham.

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Mobile Ad-hoc Networks (MANETs) have gained a lot of importance in the recent years due to their flexible ways of operation, where mobile nodes form the network to communicate with each other, without the aid of any fixed infrastructure. Routing in MANETs is a challenging task mostly for its rapidly changing topology. Several routing protocols have already been proposed and implemented for ad-hoc networks. This paper proposes a new approach to evolve a suitable algorithm named Mobile Ad-hoc Network Routing Protocol (MOADRP). This algorithm is used to find routes for message transmission between mobile nodes in MANETs. The main objective of this newly implemented routing protocol is to reduce the overhead of maintaining a large routing table and also reduce the time delay for finding the route.978-1-4244-5875-2/09/$26.00 ©2009 IEEE

Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren’t always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way.