Fundamentally, phishing is a common cybercrime that is indulged by the intruders or hackers on naive and credible individuals and make them to reveal their unique and sensitive information through fictitious websites. The primary intension of this kind of cybercrime is to gain access to the ad hominem or classified information from the recipients. The obtained data comprises of information that can very well utilized to recognize an individual. The purloined personal or sensitive information is commonly marketed in the online dark market and subsequently these information will be bought by the personal identity brigands. Depending upon the sensitivity and the importance of the stolen information, the price of a single piece of purloined information would vary from few dollars to thousands of dollars. Machine learning (ML) as well as Deep Learning (DL) are powerful methods to analyse and endeavour against these phishing attacks. A machine learning based phishing detection system is proposed to protect the website and users from such attacks. In order to optimize the results in a better way, the TF-IDF (Term Frequency-Inverse Document Frequency) value of webpages is employed within the system. ML methods such as LR (Logistic Regression), RF (Random Forest), SVM (Support Vector Machine), NB (Naive Bayes) and SGD (Stochastic Gradient Descent) are applied for training and testing the obtained dataset. Henceforth, a robust phishing website detection system is developed with 90.68% accuracy.

The brain tumour classification and its early identification are significant for treating the tumour efficiently. The 3D visualisation and detection of the brain tumours from MRI is an error-prone task and computationally time-consuming one. This paper offers a 3D reconstruction scheme for reconstruction of brain tumour. Initially, the preprocessing is performed by the process of average filter and histogram equalisation. Subsequently, the K-means clustering-based segmentation is carried out. As a novelty, to detect the brain tumour, an optimised convolutional neural network (CNN) is employed, in which the training process is carried out by a new Levy-adopted Tunicate Swarm Algorithm (L-TSA) through the optimal weight tuning. At last, the 3D reconstruction of brain tumours is performed by the improved marching cube (MC) algorithm for accurate 3D construction of the tumour region. At last, the performance of developed approach is carried out in MATLAB, and the results are verified for the extant schemes concerning certain metrics like sensitivity, FNR, specificity, FPR, accuracy, FNR, precision, MCC, NPV and F1-score. Accordingly, the accuracy of the adopted CNN+L-TSA algorithm attains a higher value of ~0.934 to extant schemes, including CNN+GWO, CNN+EHO, CNN+TSA and CNN+CUGWA, correspondingly.

Today medical field has provided us enormous facilities that has been never thought before. There has been done many improvements in the field of surgery, medicine, X-Rays and many more. But some areas still want some improvements so that patients don't need to face any type of difficulty or pain. This paper is trying to highlight the difficulties and in the treatments that are based on the ultrasound images. So in paper this our main focus is to improve the treatments based on ultrasound scans which is used widely in medical field due to vast area of application and cost effectiveness. These ultrasound scans are very important to detect any kind of injury of disease in human body because it used to scan the internal tissues of the body. One major disadvantage of these images is that they include huge amount of noise so doctors face difficulty in finding the exact location of the nerve where they have to inject the medicine to operate. These pictures are not clear enough to find the neve at once so they have to inject needle very times. With this application they can find the nerve very easily because it includes the segmentation of these nerves in ultrasound images. This application is further extended to train the system with this data so that it can be used worldwide.

Agile development methodology has become popular among software developers. Some non-agile developers integrate certain agile and design thinking practices in their development. Among these practices include identifying personas, creating empathy and journey maps and writing user stories. This research adopts the Design Science Research Methodology, focusing on integrating agile and design thinking practices in generating quality user stories in the design of augmented reality (AR) applications. An integrated development process is modelled and applied in an example application, an AR application for student welfare services events. The study goes through the process of eliciting user requirements by identifying user personas, creating empathy maps and customer journeys and writing user stories as part of the discovery stage of the AR application development. The user stories are evaluated by fifteen agile practitioners based on fourteen criteria adapted from Quality User Story Framework. The user stories are scored and assessed to determine the level of quality. Request access from your librarian to read this article's full text.