Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach

GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment

Three Dimensional (3D) interaction is the plausible human interaction inside a Virtual Environment (VE). The rise of the Virtual Reality (VR) applications in various domains demands for a feasible 3D interface. Ensuring immersivity in a virtual space, this paper presents an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers; thumb and index. The two fingertip-thimbles made of paper are used to trace states and positions of the fingers by an ordinary camera. Based on the positions of the fingers, the basic interaction tasks; selection, scaling, rotation, translation and navigation are performed by intuitive gestures of the fingers. Without keeping a gestural database, the features-free detection of the fingers guarantees speedier interactions. Moreover, the system is user-independent and depends neither on the size nor on the color of the users’ hand. With a case-study project; Interactions by the Gestures of Fingers (IGF) the technique is implemented for evaluation. The IGF application traces gestures of the fingers using the libraries of OpenCV at the back-end. At the front-end, the objects of the VE are rendered accordingly using the Open Graphics Library; OpenGL. The system is assessed in a moderate lighting condition by a group of 15 users. Furthermore, usability of the technique is investigated in games. Outcomes of the evaluations revealed that the approach is suitable for VR applications both in terms of cost and accuracy

EVEN-VE: Eyes Visibility Based Egocentric Navigation for Virtual Environments

Navigation is one of the 3D interactions often needed to interact with a synthetic world. The latest advancements in image processing have made possible gesture based interaction with a virtual world. However, the speed with which a 3D virtual world responds to a user’s gesture is far greater than posing of the gesture itself. To incorporate faster and natural postures in the realm of Virtual Environment (VE), this paper presents a novel eyes-based interaction technique for navigation and panning. Dynamic wavering and positioning of eyes are deemed as interaction instructions by the system. The opening of eyes preceded by closing for a distinct time-threshold, activates forward or backward navigation. Supporting 2-Degree of Freedom head’s gestures (Rolling and Pitching) panning is performed over the xy-plane. The proposed technique was implemented in a case-study project; EWI (Eyes Wavering based Interaction). With EWI, real time detection and tracking of eyes are performed by the libraries of OpenCV at the backend. To interactively follow trajectory of both the eyes, dynamic mapping is performed in OpenGL. The technique was evaluated in two separate sessions by a total of 28 users to assess accuracy, speed and suitability of the system in Virtual Reality (VR). Using an ordinary camera, an average accuracy of 91% was achieved. However, assessment made by using a high quality camera testified that accuracy of the system could be raised to a higher level besides increase in navigation speed. Results of the unbiased statistical evaluations suggest/demonstrate applicability of the system in the emerging domains of virtual and augmented realities

Risk Factors of Diarrhoea in Malnourished Children Under Age of 5 Years

Background: Acute infectious enteritis remains one of the commonest causes of death among infants and children in developing countries. Acute enteritis is defined as a loss of stool consistency with pasty or liquid stools, and/or an increase in stool frequency to more than three stools in 24 hours with or without fever or vomiting. Human survival depends on the secretion and reabsorption of fluid and electrolytes in the intestinal tract. The objective of the study is to evaluate the risk factors of diarrhoea in children under age of 5 years. Methodology: It was an observational study. Study was completed in about six months. Non-probability purposive sampling technique was used. In this study, 270 samples were taken from Diarrheal ward of The Children Hospital Lahore, Pakistan. Results: In this study, out of 270 patients, 58.52% were males and 41.48% were females. 90.37% patients were vaccinated. 54.81% had weaning history. 91.85% patients had feeding history. 29.26% had blood in stool. 96.67% patients were dehydrated. 95.56% patients had loose watery diarrhoea. 62.96% patients used boiled water. 58.52% patients consumed less than half litre of water, 30.00% patients consumed 1 litre of water and 11.48% patients consumed > 1 litre of water. 49.18% patients had proper hygiene. 38.15% mothers of patients were well educated. 40.37% patients had model household condition. 57.41% patients lived in rural area and 42.59% patients lived in urban area. Conclusion: The variation in the level of diarrheal morbidity was well explained by maternal education, income, personal hygiene, refuse disposal system and the effect of health extension programme

Analysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm

Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutionary operators are parent selection, crossover, and mutation. Each operator has broad implementations with its pros and cons. A successful GA is highly dependent on genetic diversity which is the main driving force that steers a GA towards an optimal solution. Mutation operator implements the idea of exploration to search for uncharted areas and introduces diversity in a population. Thus, increasing the probability of GA to converge to a globally optimum solution. In this paper, a new variant of mutation operator is proposed, and its functions are studied and compared with the existing operators. The proposed mutation operator as well as others such as m-mutation, shuffle, swap, and inverse are tested for their ability to introduce diversity in population and hence, their effects on the performance of GA. All these operators are applied to Max one problem. The results concluded that the proposed variant is far more superior to the existing operators in terms of introducing diversity and hence early convergence to an optimum solution