Potential therapeutic applications of microbial surface-activecompounds

Numerous investigations of microbial surface-active compounds or biosurfactants over the past two decades have led to the discovery of many interesting physicochemical and biological properties including antimicrobial, anti-biofilm and therapeutic among many other pharmaceutical and medical applications. Microbial control and inhibition strategies involving the use of antibiotics are becoming continually challenged due to the emergence of resistant strains mostly embedded within biofilm formations that are difficult to eradicate. Different aspects of antimicrobial and anti-biofilm control are becoming issues of increasing importance in clinical, hygiene, therapeutic and other applications. Biosurfactants research has resulted in increasing interest into their ability to inhibit microbial activity and disperse microbial biofilms in addition to being mostly nontoxic and stable at extremes conditions. Some biosurfactants are now in use in clinical, food and environmental fields, whilst others remain under investigation and development. The dispersal properties of biosurfactants have been shown to rival that of conventional inhibitory agents against bacterial, fungal and yeast biofilms as well as viral membrane structures. This presents them as potential candidates for future uses in new generations of antimicrobial agents or as adjuvants to other antibiotics and use as preservatives for microbial suppression and eradication strategies

Efficacy of Major Plant Extracts/Molecules on Field Insect Pests

Insect pests are considered the major hurdle in enhancing the production and productivity of any farming system. The use of conventional synthetic pesticides has led to the emergence of pesticide-resistant insects, environmental pollution, and negative effects on natural enemies, which have caused an ecological imbalance of the predator-prey ratio and human health hazards; therefore, eco-friendly alternative strategies are required. The plant kingdom, a rich repertoire of secondary metabolites, can be tapped as an alternative for insect pest management strategies. A number of plants have been documented to have insecticidal properties against various orders of insects in vitro by acting as antifeedants, repellents, sterilant and oviposition deterrents, etc. However, only a few plant compounds are applicable at the field level or presently commercialised. Here, we have provided an overview of the broad-spectrum insecticidal activity of plant compounds from neem, Annona, Pongamia, and Jatropha. Additionally, the impact of medicinal plants, herbs, spices, and essential oils has been reviewed briefl

An efficient deep learning-based scheme for web spam detection in IoT environment

From the last few years, Internet of Things has revolutionized the entire world. In this, various smart objects perform the tasks of sensing and computing to provide uninterrupted services to the end users in different applications such as smart transportation, e-healthcare to name a few. With the inherent capabilities of these objects to take adaptive intelligent decisions, Cognitive Internet of Things is another paradigm of Internet of Things which emerges during this era. However, while accessing data from the Internet, web spam is one of the challenges to be handled. It has been observed from the literature review that for accessing data, search engines are preferred mostly by an individual. The search engine’s effective ranking can decrease the computational cost of objects during the data access. The current solutions to this issue are aimed to discover the spam in the search engine after its occurrence. So, in this proposal, we present a cognitive spammer framework that removes spam pages when search engines calculate the web page rank score. The framework detects web spam with the support of Long Short-Term Memory network by training the link features. This training resulted with an accuracy of 95.25, as more than 1,11,000 hosts are being correctly classified. However, the content features are trained by neural network. The proposed scheme has been validated with the WEBSPAM-UK 2007 dataset. Prior to processing, the dataset is pre-processed using a new technique called ‘Split by Over-sampling and Train by Under-fitting’. The ensemble and cross validation approach has been used for optimization of results with an accuracy of 96.96%. So, the proposed scheme outperforms the existing techniques

User behavior analysis-based smart energy management for webpage ranking: Learning automata-based solution

Search engines are widely used for surfing the Internet. Different search engines vary with respect to their accuracy and time to fetch the information from the distributed/centralized database repository across the globe. However, it has been found in the literature that webpage ranking helps in saving the user's surfing time which in turn saves considerable energy consumption during computation and transmission across the network. Most of the earlier solutions reported in the literature uses the hyperlink structure of graph which consume a lot of energy during the computation. It may lead to the link leakage problem with the occurrence of spam pages more often. Nowadays, hyperlink structure alone is inadequate for predicting webpage importance keeping in view of the energy consumption of various smart devices. User browsing behavior depicts its real importance. It is essential to demote the spam pages to increase the search engine accuracy and speed. Hence, user behavior analysis along with demotion of spam pages can improve Search Engine Result Pages (SERP) which in turn saves the energy consumption. In the proposed approach, web page importance score is computed by analyzing user surfing behavior attributes, dwell time, and click count. After computing the webpage importance score, the ranks are revised by implementing it in Learning Automata (LA) environment. Learning automaton is the stochastic system which learns from the environment and responds either with a reward or a penalty. With every response from the environment, the probability of visiting the webpage is updated. Probability computation is done using Normal and Gamma distribution functions. In the proposal, we have considered only the dangling pages for experiments. Inactive webpages are punished and degraded from the system. We have validated proposed approach with Microsoft Learning to Rank dataset. It has been found in the experiments performed that 3403 dangling pages out of 12211 dangling pages have been degraded using the proposed scheme. The objective of the proposed scheme is achieved by saving web energy and computational cost. It takes 100 iterations to convergence which executed in 21.88 ms. However, the user behavior analysis helped in improving PageRank score of the webpages

Cognitive spammer: A Framework for PageRank analysis with Split by Over-sampling and Train by Under-fitting

From the past few years, there is an exponential increase in one of the most popular technologies of the modern era called as Internet of Things (IoT). In IoT, various objects perform the tasks of sensing, communication, and computation for providing uninterrupted services (e.g., e-health, e-transportation, security access, etc.) to the end users. In this era, Cognitive Internet of Things (CIoT) is an another paradigm of IoT developed to enhance the capabilities of intelligence in IoT objects where these objects can take independent decisions in any environment. IoT follows the service oriented architecture (SOA), in which the application layer is the topmost layer. It enables the IoT objects to interact with the other objects located across the globe. The power of learning, thinking, and understanding by these objects, can make the information access more accurate and reliable but Web spam is one of the challenges while accessing information from the web. It has been observed from the literature review that search engines are preferred mostly by the people for accessing information. The efficient ranking by the search engines can reduce the computational cost of information exchange by IoT objects. Search engines should be able to prevent the spam from being injected into the web. But, the existing techniques for this problem target in finding the spam after its occurrence in search engine result pages. So, in this proposal, we present an intelligent cognitive spammer framework, Cognitive spammer, which eliminates the spam pages during the web page rank score calculation by search engines. The framework update the Google's ranking algorithm, PageRank in such a way that it automatically prevents link spam by considering the link structure of web for rank score computation. The updated PageRank algorithm provided the better ranking of web pages. The proposed framework is validated with the WEBSPAM-UK2007 dataset. Before processing, the dataset is preprocessed with a new technique, called as ‘Split by Over-sampling and Train by Under-fitting’ to remove the trade off between imbalanced instances of target class. After data cleaning, we applied machine learning techniques (Bagged model, Boosted linear model, etc) with the web page features to make accurate predictions. The detection classifiers only consider the link features of the web page irrespective of the page content. Out of the fifteen classifiers, best three are ensemble, which results in better performance with overall accuracy improvement. Ten-fold cross validation has also been applied with the resulted ensemble model, which results in getting the accuracy of 99.6% in the proposed scheme

PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention

Image spam is a spamming technique that integrates spam text content into graphical images in order to bypass conventional text-based spam filters. In order to detect image spam efficiently, it is important to analyze the image data. The existing image spam detection techniques in literature focus on textual or graphic features of the image. None of the existing techniques considered the link information of the image which results in low accuracy and performance degradation. So, to fill these gaps, in this paper, we analyzed the link properties of image, for image spam detection and prevention. We propose an optimized framework called as PROTECTOR. In PROTECTOR, the rank score is generated by using the linkage information of the image, textual information of the image, and metadata information of the image. The computed rank score indicates the relevance of an image. This rank score is then used to train a deep neural network design of deep learning, which yields the accuracy of 96% with respect to various performance evaluation metrics. Also, the optimization algorithm, i.e., genetic algorithm is fitted in the results according to the defined fitting function. The proposed framework is validated with standard dataset of Image spam Hunter

FS2RNN: Feature Selection Scheme for Web Spam Detection Using Recurrent Neural Networks

In modern era, Internet plays a key role in accessing and fetching web information and web resources from World Wide Web (WWW). The websites act as a medium for retrieving information from the web. Although it increases the data retrieval and users interactions, it also opens the gate for various types of attacks. For example, spams in the websites attract various Internet users. It has been observed from the literature that many authors attempted to detect the web spam using various machine learning techniques. However, none of these techniques used deep learning architecture for detection of hidden patterns. Hence, in this paper, a deep learning algorithm, i.e., Recurrent Neural Networks (RNN), has been used for the classification of spam nodes. We devise here a framework called FS2RNN: Feature Selection Scheme using Recurrent Neural Networks. In this framework, the dataset is preprocessed before applying RNN in which principal component analysis (PCA) is used for dimension reduction on the data set and recursive feature elimination (RFE) is used for feature selection. The accuracy of the proposed framework, when compared before and after preprocessing, is improved by 24.2 %, which is excellent result

The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection

In the modern era, Internet of Things(IoT) plays an important role in connecting the people across the globe. The IoT objects enable the communication and data exchange among each other irrespective of their geographical locations. In such an environment, the Web of Things (WoT) provides the Internet service to the IoT objects. The Internet is mostly accessed by the search engines. The success of search engine depends upon the ranking algorithm. Although, Google is preferred by the maximum Internet users, but still the Google's ranking algorithm, PageRank experiences the occurrence of spam web pages. In this paper, the webpage filtering algorithm is proposed which automatically detects the spam web pages. The spam webpages are detected before these are processed by the ranking module of search engines. The machine learning model, i.e., decision tree is used for the validation of the proposed scheme. The ten fold cross validation approach is used to improve the accuracy of model, i.e., 98.2%. The results obtained demonstrate that the proposed scheme has the power of preventing the spam web pages in Cognitive Internet of Things (CIoT) environment

SPAMI: A cognitive spam protector for advertisement malicious images

In modern era, the graphical information is presented in the form of web images. As the dependency of human beings on web information is increasing day-by-day, so the spammers are injecting spam by adopting new spamming techniques. Image spam is a spamming technique that integrates spam text contents into graphical images in order to bypass conventional text-based spam filters. The spam images are of various categories, such as redirection spam, advertisement spam, fake review, and content spam. In order to detect image spam efficiently, it is important to analyze the features of the image data. However, the existing image spam detection techniques in literature focused on textual or graphic features of the image. Moreover, to extract the relevant features from the images is also a challenging task. So, to fill these gaps, in this paper, we propose a Spam Protector for Advertisement of Malicious Images (SPAMI) framework using features extraction by browsing different websites and webpages. SPAMI is a cognitive spam protector which labels the spam advertisement images by using deep learning models. Three deep learning models are used for the same, i.e., CNN, RNN, and LSTM. The regress analysis of output from these models is done in the proposed SPAMI framework. Finally, we analysed the labels (Advertisement, Suspicious, Normal) for all the 600 images collected. The accuracy obtained from these models is 95% with real-time collected images, which improved up to 97% when tested with ”Image Spam Hunter” dataset