Perspectives and challenges of tropical medicinal herbs and modern drug discovery in the current scenario

Tropical diseases such as malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for a large number of deaths annually. Herbs are an excellent source of tropical medicines. Many advancements and discoveries have taken place in the field of drug discovery but still, a major population of tropical diseases relies on herbal traditional medicine. There are some challenges related to policy implementation, efficacy, resistance and toxicity of tropical medicines. There are many tropical diseases such as such as schistosomiasis, leishmaniasis, African sleeping sickness, filariasis and chagas disease which are neglected because very few pharmaceutical companies have shown their interest in developing therapeutics against these diseases of poor people. There are many benefits associated with herbal medicine such as the cost of production, patient tolerance, large scale availability, efficacy, safety, potency, recyclability, and environment friendly. A large number of natural extracts such as curcumin, artemisinin, morphine, reserpine, and hypericin, are in use for treatment of different tropical diseases for a long time. The current review is to discuss the overview of tropical medicinal herbs, its scope and limitations in the modern drug discovery process.

Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors

In this paper we investigate the effectiveness of ensemble-based learners for web robot session identification from web server logs. We also perform multi fold robot session labeling to improve the performance of learner. We conduct a comparative study for various ensemble methods (Bagging, Boosting, and Voting) with simple classifiers in perspective of classification. We also evaluate the effectiveness of these classifiers (both ensemble and simple) on five different data sets of varying session length. Presently the results of web server log analyzers are not very much reliable because the input log files are highly inflated by sessions of automated web traverse software’s, known as web robots. Presence of web robots access traffic entries in web server log repositories imposes a great challenge to extract any actionable and usable knowledge about browsing behavior of actual visitors. So web robots sessions need accurate and fast detection from web server log repositories to extract knowledge about genuine visitors and to produce correct results of log analyzers.

Antimicrobial Activity of Acidified Sodium Chlorite and Cell Free Culture Supernatent of Lactic Acid Bacteria against <i>Salmonella</i>Typhimurium

Most methods used by food industries to decontaminate eggs involve washing of egg surface with various chemicals. In this study, the effectiveness of two organic decontaminants viz., acidified sodium chlorite (ASC) and cell free culture supernatant (CFCS) of two lactic acid bacteria (Lactobacillus plantarum and Pediococcus cerevisiae) was evaluated for the decontamination of spiked Salmonella Typhimurium on chicken egg shell surface. Acidified sodium chlorite at 100 μl/L concentration with the contact time of 20 min completely inhibited S. Typhimurium on egg shell surface while at 50 μl/L concentration 1 - 2 log10 units reduction was observed in counts of S. Typhimurium as compared to control group. Likewise, CFCS of P. cerevisiae completely inhibited the growth of S. Typhimurium on 30 min contact, whereas L. plantarum and combination of both were revealed significant reduction in the counts of S. Typhimurium counts.

Overreaction and Availability Bias: Analysis of Real Estate Sector’s Stock Prices and Investors’ Reaction during Demonetisation in India

The stock market is full of events that affect the sensitivity reaction of investors at a large scale. Individual investor sentiment is just like his/her personal feeling depending upon their nature, risk appetite, and market scenario. This research study investigates the investors’ reaction in the stock market for the real estate segment during the massive market crisis in developing countries. Demonetisation of 2016 in India has been taken with the purpose of implementing a pilot study to analyse the overreaction and availability bias. The primary focus was on analysing how the investors react on the information of demonetisation and their pattern of investment in the stock market with a special emphasis on real estate sector where the effect of the event had dramatically changed the stock prices. Therefore, a pre- and post- analysis had been conducted to gauge the prices, sensitivity, and reaction of investors in the stock market. The reaction of the citizens after these events was found to be drastically affected. Five real estate companies had been focused upon in this study to examine the impact of investors’ overreaction owing to the demonetisation and their investment pattern for stocks during pre- and post- demonetisation period at that timeframe. The analysis was done on a shorter period of time so that the impact of overreaction and availability bias can be critically analysed. The paper thus exhibits how investor sentiments and reaction for stock preference had changed over time through statistical study.

An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt recently.They still have been pioneering collections of massive volumes of health data.Duplicate detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts yield.Record linkage specifies the issue of finding identical records across various data sources.The similarity existing between two records is characterized based on domain-based similarity functions over different features.De-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining programmes.The objective is to match all the records associated with the same entity.Various measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been introduced.An outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is presented.This article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of patients.The first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)classifier.Using the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective RANFIS.Febrl,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty

With the abundance of exceptionally High Dimensional data, feature selection has become an essential element in the Data Mining process. In this paper, we investigate the problem of efficient feature selection for classification on High Dimensional datasets. We present a novel filter based approach for feature selection that sorts out the features based on a score and then we measure the performance of four different Data Mining classification algorithms on the resulting data. In the proposed approach, we partition the sorted feature and search the important feature in forward manner as well as in reversed manner, while starting from first and last feature simultaneously in the sorted list. The proposed approach is highly scalable and effective as it parallelizes over both attribute and tuples simultaneously allowing us to evaluate many of potential features for High Dimensional datasets. The newly proposed framework for feature selection is experimentally shown to be very valuable with real and synthetic High Dimensional datasets which improve the precision of selected features. We have also tested it to measure classification accuracy against various feature selection process.

Structural and electrical properties of ferroelectric BiFeO_(3)/HfO_(2) gate stack for nonvolatile memory applications

Difficulties in the fabrication of direct interface of ferroelectric BiFeO_(3)/HfO_(2) on the gate of ferroelectric field effect transistor(FeFET)is well known.This paper reports the optimization and fabrication of ferroelectric/dielectric(BiFeO_(3)/HfO_(2))gate stack for the FeFET applications.RF magnetron sputtering has been used for the deposition of BiFeO_(3),HfO_(2) films and their stack.X-Ray diffraction(XRD)analysis of BiFeO_(3) shows the dominant perovskite phase of(104),(110)orientation at 2θ=32°at the annealing temperature of 500℃.XRD analysis also confirms the amorphous nature of the HfO2 film at annealing temperature of 400℃,500℃ and 600℃.Multiple angle analysis shows the variation ion the refractive index between 2.98–3.0214 for BiFeO_(3) and 2.74–2.9 for the HfO2 film with the annealing temperature.Metal/Ferroelectric/Silicon(MFS),Metal/Ferroelectric/Metal(MFM),Metal/Insulator/Silicon(MIS),and Metal/Ferroelectric/Insulator/Silicon(MFIS)structures have been fabricated to obtain the electric characteristic of the ferroelectric,dielectric and their stacks.Electrical characteristics of the MFIS structure show the memory improvement from 2.7 V for MFS structure to 4.65 V for MFIS structure with 8 nm of buffer dielectric layer.This structure also shows the breakdown voltage of 40 V with data retention capacity greater than 9×10^(9)iteration cycles.

A study of the COVID-19 epidemic in India using the SEIRD model

Background:The coronavirus pandemic(COVID-19)is causing a havoc globally,exacerbated by the newly discovered SARS-CoV-2 virus.Due to its high population density,India is one of the most badly effected countries from the first wave of COVID-19.Therefore,it is extremely necessary to accurately predict the state-wise and overall dynamics of COVID-19 to get the effective and efficient organization of resources across India.Methods:In this study,the dynamics of COVID-19 in India and several of its selected states with different demographic structures were analyzed using the SEIRD epidemiological model.The basic reproductive ratio Ra was systemically estimated to predict the dynamics of the temporal progression of COVID-19 in India and eight of its states,Andhra Pradesh,Chhattisgarh,Delhi,Gujarat,Madhya Pradesh,Maharashtra,Tamil Nadu,and Uttar Pradesh.Results:For India,the SEIRD model calculations show that the peak of infection is expected to appear around the middle of October,2020.Furthermore,we compared the model scenario to a Gaussian fit of the daily infected cases and obtained similar results.The early imposition of a nation-wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak significantly.Conclusion:After comparing our calculations using India’s data to the real life dynamics observed in Italy and Russia,we can conclude that the SEIRD model can predict the dynamics of COVID-19 with sufficient accuracy.

User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user

Research-based on user behavior analysis for authentication is the motivation for this research.We move ahead using a behavioral approach to identify malicious users and legitimate users.In this paper,we have explained how we have applied big data analytics to application-layer logs and predicted malicious users by employing a Machine Learning algorithm based on certain metrics explained later in the paper.Machine Learning would present a list of IP addresses or user identification tokens(UIT),deduced from live data which would be performing a malicious activity or are suspected of malicious activity based on their browsing behavior.We have created an e-commerce web application and induced vulnerabilities intentionally for this purpose.We have hosted our setup on LAMP[1]stack based on AWS cloud[2].This method has a huge potential as any organization can imply this to monitor probable attackers thus narrowing down on their efforts to safeguard their infrastructure.The idea is based on the fact that the browsing pattern,as well as the access pattern of a genuine user,varies widely with that of a hacker.These patterns would be used to sort out the incoming traffic from and list out IP addresses and UIT that are the most probable cases of hack attempts.