Smishing is an attack targeted to mobile devices in which the attacker sends text messages containing malicious links, phone numbers or E-Mail IDs to the victim and the attacker aims to steal sensitive user data like bank account details, passwords, user credentials, credit card details, etc through this message. Through this message, the attacker prompts the user to click on the link or contact the phone number or E-mail ID provided in the SMS. In this paper, we have discussed various mobile smishing attacks. We have also suggested various best policies which can be followed by the user to mitigate the smishing attacks. In addition to this, various techniques that are useful in mitigating and identifying smishing messages are also discussed. Finally, we gave an overview of methodologies proposed by other researchers in this field to differentiate smishing messages from legitimate ones. This paper would enable users to be more careful while clicking on the links provided in the SMS for downloading and installing apps and filling up fake user interfaces. Also, this paper would be a great contribution to the study on how to identify mobile smishing attacks.

Around the world, most of the proposed techniques for the identification of sarcasm either take the utterance in isolation or these methods only perform the categorization of the textual data. Very limited work has been done on how to train or manipulate the various parameters related to textual data so that to improve on the accuracy of the classification method. In this article, we are trying to identify the sarcasm in the textual data using neural networks. We have tried to classify the data using convolutional neural networks (CNN), recurrent neural networks (RNN) and a blend of these techniques to improve accuracy. Our work is not limited to the classification of the sarcastic text, we have also tried to measure the impact of the training data, number of epochs and amount of dropout in the network. The paper also discusses the impact of various embedding on the dataset when converting the same dataset into vectors via different word embeddings. We measured the influence of various parameters on the very large-scale Reddit1 corpus.

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians’ detection rate was 74.60 percent lower than deep learning’s detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High‐intensity energy‐spectral CT images are more difficult to segment than low‐intensity energy‐spectral CT images.

With the ever increasing acceptance of Online Social Networks (OSNs), a new dimension has evolved for communication amongst humans. People now share continuous streams of messages to contribute their interests, indications, discuss activities, status, latest trends and much more. This gives an opportunity to monitor and mine the opinions of a large number of online active population in real time. On one side where researchers can find out a pattern to judge the mood of the user, a serious problem of detection of irony and sarcasm in textual data poses threat to the accuracy of the techniques evolved till date. In this paper, we are proposing a deep learning based convolutional neural network method that focuses on the skip gram technique to convert words to vectors and further to detect and identify the sarcasm and irony from the normal polarized text taken from Twitter. We try to discover patterns from the textual datasets based on the identifiable features. Our approach is giving an overall accuracy of 89.9%.