Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.

As the complexity of medical computing increases the use of intelligent methods based on methods of soft computing also increases. During current decade this intelligent computing involves various meta-heuristic algorithms for Optimization. Many new meta-heuristic algorithms are proposed in last few years. The dimension of this data has also wide. Feature selection processes play an important role in these types of wide data. In intelligent computation feature selection is important phase after the pre-processing phase. The success of any model depends on how better optimization algorithms is used. Sometime single optimization algorithms are not enough in order to produce better result. In this paper meta-heuristic algorithm like butterfly optimization algorithm and enhanced lion optimization algorithm are used to show better accuracy in feature selection. The study focuses on nature based integrated meta-heuristic algorithm like Butterfly Optimization and lion-based optimization. Also, in this paper various other Optimization algorithms are analyzed. The study shows how integrated methods are useful to enhance the accuracy of any computing model to solve Complex problems. Here experimental result has shown by proposing and hybrid model for two major psychiatric disorders one is known as autism spectrum and second one is Parkinson's disease.

No abstract

No abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Autism spectrum disorder (ASD) is a neurodevelopment disorder. Clinical decision-making process is complex. Due to complex nature of disease sign and its symptoms clinical decision making may lead to misclassification. To deal with such complex medical problems methods or approaches of soft computing play an important role. This paper will focus on presenting an integrated Neuro-fuzzy model. This integrated model has the learning strength of neural network and knowledge representation ability of fuzzy logic. Modified Adaptive Neuro –Fuzzy inference system (M-ANFIS) is used here for classification and predication. Here Fuzzy C-mean (FCM) Clustering is used first to make classes of data before presenting in to ANFIS. This FCM based class will reduce the classifier computational overhead. Precision error and recall, F-measure and accuracy matrices are used to compare the experimental results with other classic methods.