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One of the many methods for identifying malware is to disassemble the malware files and obtain the opcodes from them. Since malware have predominantly been found to contain specific opcode sequences in them, the presence of the same sequences in any incoming file or network content can be taken up as a possible malware identification scheme. Malware detection systems help us to understand more about ways on how malware attack a system and how it can be prevented. The proposed method analyses malware executable files with the help of opcode information by converting the incoming executable files to assembly language thereby extracting opcode information (opcode count) from the same. The opcode count is then converted into opcode frequency which is stored in a CSV file format. The CSV file is passed to various machine learning algorithms like Decision Tree Classifier, Random Forest Classifier and Naive Bayes Classifier. Random Forest Classifier produced the highest accuracy and hence the same model was used to predict whether an incoming file contains a potential malware or not.

Cyclomatic complexity is a metric for evaluating the complexity of a software program. This paper is based on an algorithm to compute cyclomatic complexity and the insights one can derive from the cyclomatic complexity of an executable file to see how it can play a major role in determining benignity or malignity of the file. The data derived from the complexity of a program is used to train various machine learning classification algorithms and see which performs the best in classifying files as either benign or malware.

This study examines gastrointestinal parasites in the endangered lion-tailed macaque, which is sympatric with the bonnet macaque that has relocated from nearby towns or agriculture landscapes dominated by humans and livestock. One hundred and ninety-four fresh fecal samples from lion-tailed macaques were collected from a group located at Chiksuli in the central Western Ghats. Of these, 48.5% had at least one endoparasite taxon. The prevalence of endoparasites varied from 0 to 75.0%, and observed endoparasite taxa varied between 0 and 10 across different months. The prevalence of endoparasites decreased with increasing rainfall and with increasing average maximum temperature across months. Of the 17 endoparasite taxa, 11 were nematodes, two were cestodes, and four were protozoans. The prevalence of Ascaris sp. and Entamoeba coli was higher than the other taxa. The overall load, helminth load, and protozoan load did not differ between months. The overall endoparasite load was greater in immature macaques in all seasons. Helminth load was higher in adult males, especially in the summer. Comparing our findings with those from sympatric relocated bonnet macaques of Chiksuli (Kumar et al. in PLoS ONE 13(11):e0207495, 2018) and lion-tailed macaques of Anamalai Hills (Hussain et al. in PLoS ONE 8(5):e63685, 2013) revealed: (a) a much higher prevalence of endoparasites in lion-tailed macaques from fragments of Anamalai Hills than in lion-tailed and bonnet macaques of Chiksuli; (b) higher richness of endoparasites in both macaque species of Chiksuli than in Anamalai lion-tailed macaques; and (c) more similar composition of endoparasite taxa between the Chiksuli lion-tailed and bonnet macaques than with the Anamalai Hills lion-tailed macaques. We suggest a complete cessation of relocation of commensal animals to the wild habitat. If relocation is necessary, then individuals to be relocated should be thoroughly screened and treated to prevent transferring endoparasite infections to wild populations.

With the advent of the novel coronavirus, human and environmental impacts have become a vital topic of study. The sudden outburst of the virus not only brought normal life to a halt but also made us more aware of health impacts and hygiene guidelines that should be strictly maintained. There is a direct human-to-human transmission of the virus, which happens with a lack of adequate infection controls and improper hygiene precautions. In severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the concept of sewage epidemiology or water-based epidemiology holds significance to detect the virus’s presence in untreated sewage through fecal matter. In this context, this chapter is focused on a detailed discussion of municipal and health care wastewater characterization, methods of disinfection of viruses in wastewater treatment plants, and survival of SARS-CoV-2 in wastewater, urine, and biosolids. Usage of personal protective equipment (PPE), appropriate disinfection agents, training for the sanitary and health care workers as guided by the World Health Organization (WHO), and scientific safe disposal of biosolids could alleviate the indirect transmission of SARS-CoV-2. However, there is also a dire need for reorientation of conventional wastewater treatment operations since there is a possibility of viral load in municipal wastewater from the household origin that has to be monitored by pollution control boards.

Tobacco addiction is one of the leading causes of premature mortality. Early and effective intervention in primary care, though possible and feasible, is seldom done in India. This case series describes the diagnosis and management of three patients of tobacco addiction by a primary care doctor (PCD) who is being trained in digitally driven four modules based “Primary Care Psychiatry Program” of National Institute of Mental Health and Neurosciences, Bengaluru, India. This article discusses about the way in which two modules (telepsychiatric on-consultation training and collaborative video consultation) helped a primary care doctor (M.B.P.) working approximately 1,500 miles away from an academic institute to treat tobacco dependence at a rural primary health center of India.

Video Shot Detection is utilized to detect change of scene in the video. Video Summarization gives most useful frames to create the conceptual information of the given video. In this paper, we have coordinated both the ideas of shot detection and summarization for the better result of the outcomes. The SIFT features are extracted from the frames and compared with consecutive frames for Video Shot detection and the Video Summarization is done using Expected-Maximization. The shot frames of Video shot detection are utilized as contribution for the Video Summarization. The quality metric parameters are utilized to assess the strength of the key frame extraction.