The recent surge in the abundance of fake news appearing on social media and news websites poses a potential threat to high-quality journalism. Misinformation hurts people, society, science, and democracy. This reason has led many researchers to develop techniques to identify fake news. In this paper, we discuss a stance prediction technique using the Deep Learning approach, which can be used as a factor to determine the authenticity of news articles. The Fake News Stance Prediction is the process of automatically classifying the stance of a news article towards a target into one of the following classes: Agree, Disagree, Discuss, Unrelated. The stance prediction task’s input is the news articles containing a pair: a headline as the target and a body as a claim. This paper proposes a deep learning architecture using Bi-directional Long Short Term Memory and Autoencoder for stance prediction. We illustrate, through empirical studies, that the method is reasonably accurate at predicting stance, achieving a classification accuracy as high as 94%. The proposed stance detection method would be useful for assessing the credibility of news articles.
With the advent of computer vision, there has been significant growth in the research and development of facial recognition based automated attendance systems. Although current systems have been successful in alleviating human interaction and manual efforts, there still exist several challenges such as severe misclassifications, undetectable face angles, and different lighting conditions which result in a drastic drop in the accuracy. The system introduced in this paper has achieved an overall accuracy of 93.33%. A concept termed the “two-tier authentication” method has been developed to improve the overall accuracy of the system and to integrate a mechanism of time allowance for students. This method facilitates granting attendance to students based on the number of recognized faces as well as the probability of each prediction allowing for a more robust method of marking attendance to students. The novelty of this approach is to introduce an accurate statistical sequence for the execution of a proxy-free automated attendance system that employs state-of-the-art algorithms. Composed of 3 distinct parts, every sub-system performs a specific task namely, face detection, generation of face embeddings (FaceNet), and face classification. A comparative study was carried out to select the most appropriate detection and classification algorithms where Faster R-CNN and Support Vector Classifier outperformed their corresponding competitors respectively.
Cancer develops when cells in any part of the body start to grow out of control. It can spread to other parts of the body. Melanoma is a type of skin cancer that is developed when melanocytes i.e. cells which produce melanin (the pigment which is responsible for the perceived color of skin) begin to grow out of control. Melanoma is dangerous as it has a high tendency to spread to other parts of the body, if not detected early and left untreated. In this paper, we use deep learning techniques to build a classification system to categorise a skin lesion into malignant and benign. This system relies on a dataset which consists of skin lesion images from various sites on the body. We augment the dataset using appropriate transformations and evaluate the classification system using various metrics. The different models used in this implementation are compared based on the metrics to find the superior performing model. ResNet-50 as per the results of sensitivity, specificity and accuracy has the best results among the other three with values 99.7%, 55.67%, 93.96% respectively.
Most software employees are facing challenges on integrating programs written in different languages for implementing the techniques for their software development. This can be achieved by automating the conversion of programs using natural language programming techniques. This research presents a novel ‘Semantic Rule-based Automatic Code conversion System (SRACS)’ that uses semantic layering, keyword identification, and a semantic rule-based constructor. The code snippets for ‘Hello World’, ‘For Loop’, ‘While Loop’, ‘If else’, ‘Factorial’ and ‘Travelling Salesman Program’ are converted from Java to Python and vice versa, and the accuracies are presented. An average accuracy of 71.57% is achieved for the conversion of the code snippets from Java to Python, and a 77.07% is achieved for Python to Java. The accuracy is based on the ‘accuracy in the conversion of the variables’, ‘accuracy in the conversion of the attributes’ and on the ‘proper indentation of the code in the target code’.
In Software Development Life-cycle, Verification and Validation plays a very important role, especially in the case of Safety-Critical Industries like Aerospace. Display dashboard consists of multiple static and dynamic objects having affine transformation, graphics overlap, shadows and less inter symbol discriminative features compared to natural images. Manual Software graphics verification is an error-prone and time-consuming activity. In this paper, we propose a novel software graphics verification pipeline to verify graphics symbols and alphanumeric objects as per Software requirements. To the best of our knowledge, our proposed approach is the first study on deep learning-based graphics symbol detection from complex synthetic background which requires high model accuracy. We experiment using Single-shot Multibox Detector (SSD) and You Only Look Once (YOLO v2) to detect different Graphical symbols from display simulator real-time captured video frames. These detected objects are further classified based on their nature. Objects containing alphanumeric digits can be recognized using Optical Character Recognition and dynamic symbols are detected using object detection to infer other properties. Finally, all the extracted properties can be compared with test expectations to verify their correctness. The result shows superior accuracy of the SSD algorithm over other state-of-the-art object detection algorithms for detecting real-time graphics symbols.
