Vanshika Rastogi
@epcet.ac.in
Asst. Professor
East Point College of Engineering & Technology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Vanshika Rastogi and Ajit Kumar Jain
IEEE
Nowadays, Corona Virus Disease-19 (COVID-19) disease is considered one of the main health issues, which is spreading in the worldwide environment, and due to this, infected patients are isolated to avoid spreading the disease. So, instant recognition is required for classifying the disease in a precise manner. Hence, the SWHO deep convolutional neural network (CNN) classification is developed to analyze the disease and the process of rising the COVID-19 classification scheme commences with collecting the blood samples and proceeds through preprocessing, feature extraction, as well as classification. Thus, the accuracy, sensitivity, as well as specificity of the developed method attained the value of 89.797%, 92.351%, and 88.252%, at 80% of training.
N Gagan, S Sai Kumar, M Keerthana, Samarth S Vaidya, and Vanshika Rastogi
IEEE
Since android smartphones are so popular, malware writers have found them to be lucrative targets, which poses a serious risk to user security and privacy. To address this problem, several malware detection strategies have been put out recently. In this review paper, we examine an efficient method for detecting malware on Android that makes use of machine learning techniques. Our method divides Android applications into benign and harmful categories using a combination of feature extraction methods and machine learning algorithms. Overall, our suggested method offers a dependable and efficient alternative for detecting Android malware, giving consumers a more dependable and safe environment for mobile computing.
Vanshika Rastogi and Ajit Kumar Jain
Informa UK Limited
Vanshika Rastogi and Ajit Kumar Jain
IEEE
COVID-19 is the transmittable disease that emerged as a recent epidemic and threatened the lives of various people. The emerged pandemic initiated a change in the people’s routine and impacted a serious financial crisis. This initiated a necessity for developing a deeper insight of the COVID-19 disease and multiple researches are performed based on the COVID-19 epidemic, which possess the challenges of basic analysis of information about the disease, lack of data, lack of knowledge about the parameters that cause disease and to overcome this a deep COVID-19 analysis epidemic via the deep CNN classifier is accomplished in the research. The impact of the disease is examined based on the gender, age group, symptoms and outbreak of the disease. This analysis provides comprehensive information about the disease and helps in making the preventive measures, which will greatly reduce the impacts of the disease. The accomplishment of deep CNN instinctively analyzes the essential features needed for the classification that helps in reducing the effort and time of the individuals. The performance is analyzed with the metrics specificity, accuracy and sensitivity, which obtained values of 0.48 %, 0.27 %, 2.82 % corresponding to and 2.88 %, 1.5 %, 0.36% considering training percentage, which is more efficient.
Ishika Verma, Astha Rai, Himani Yadav, Vanshika Rastogi, and Sugandha Satija
IEEE
Visual disability is a global issue. The number of visually impaired people is estimated to be around 285 million in the world. Out of which 246 million people have poor vision and 39 million are completely blind. The statistics of visually impaired people. Ninety percent of this population lives in developing countries. The difficulties faced by them in using the latest technologies and how they dealt with these are largely unknown and under-explored, especially in the developing world. Availability of video to Braille transcription anytime, anywhere, provides a potentially life-changing opportunity for the visually challenged to improve their communication capability. We are introducing software for hearing and visually impaired people to transcribe video to printed Braille scripts. It is evident that implementation of such a system for visually handicapped people with a productive method of Real-time speech to Braille transcription, text to speech, text to Braille, video to Braille conversion, work on commands with flexibility will be of great use